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Martins M, Dos Santos AM, da Costa CHS, Canner SW, Chungyoun M, Gray JJ, Skaf MS, Ostermeier M, Goldbeck R. Thermostability Enhancement of GH 62 α-l-Arabinofuranosidase by Directed Evolution and Rational Design. J Agric Food Chem 2024; 72:4225-4236. [PMID: 38354215 DOI: 10.1021/acs.jafc.3c08019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
GH 62 arabinofuranosidases are known for their excellent specificity for arabinoxylan of agroindustrial residues and their synergism with endoxylanases and other hemicellulases. However, the low thermostability of some GH enzymes hampers potential industrial applications. Protein engineering research highly desires mutations that can enhance thermostability. Therefore, we employed directed evolution using one round of error-prone PCR and site-saturation mutagenesis for thermostability enhancement of GH 62 arabinofuranosidase from Aspergillus fumigatus. Single mutants with enhanced thermostability showed significant ΔΔG changes (<-2.5 kcal/mol) and improvements in perplexity scores from evolutionary scale modeling inverse folding. The best mutant, G205K, increased the melting temperature by 5 °C and the energy of denaturation by 41.3%. We discussed the functional mechanisms for improved stability. Analyzing the adjustments in α-helices, β-sheets, and loops resulting from point mutations, we have obtained significant knowledge regarding the potential impacts on protein stability, folding, and overall structural integrity.
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Affiliation(s)
- Manoela Martins
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, 3400 N Charles Street, Baltimore, Maryland 21218, United States
- Department of Food Engineering, State University of Campinas, Monteiro Lobato, 80, Cidade Universitária, Campinas, São Paulo 13083-862, Brazil
| | - Alberto M Dos Santos
- Department of Chemistry, State University of Campinas, 336, R. Josué de Castro, 126-Cidade Universitária, Campinas, São Paulo 13083-861, Brazil
| | - Clauber H S da Costa
- Department of Chemistry, State University of Campinas, 336, R. Josué de Castro, 126-Cidade Universitária, Campinas, São Paulo 13083-861, Brazil
| | - Samuel W Canner
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, 3400 N Charles Street, Baltimore, Maryland 21218, United States
| | - Michael Chungyoun
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, 3400 N Charles Street, Baltimore, Maryland 21218, United States
| | - Jeffrey J Gray
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, 3400 N Charles Street, Baltimore, Maryland 21218, United States
| | - Munir S Skaf
- Department of Chemistry, State University of Campinas, 336, R. Josué de Castro, 126-Cidade Universitária, Campinas, São Paulo 13083-861, Brazil
| | - Marc Ostermeier
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, 3400 N Charles Street, Baltimore, Maryland 21218, United States
| | - Rosana Goldbeck
- Department of Food Engineering, State University of Campinas, Monteiro Lobato, 80, Cidade Universitária, Campinas, São Paulo 13083-862, Brazil
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2
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Chu L, Ruffolo JA, Harmalkar A, Gray JJ. Flexible protein-protein docking with a multitrack iterative transformer. Protein Sci 2024; 33:e4862. [PMID: 38148272 PMCID: PMC10804679 DOI: 10.1002/pro.4862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 11/17/2023] [Accepted: 12/06/2023] [Indexed: 12/28/2023]
Abstract
Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multitrack iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments, GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. On the Database of Interacting Protein Structures (DIPS) test set, GeoDock achieves a 43% top-1 success rate, outperforming all other tested methods. However, in the standard DIPS train/test splits, we discovered contamination of close homologs in the training set. After decontaminating the training set, the success rate is 31%. On the DB5.5 test set and a benchmark dataset of antibody-antigen complexes, GeoDock outperforms the deep learning models trained using the same dataset but falls behind most of the conventional methods and AlphaFold-Multimer. GeoDock attains an average inference speed of under 1 s on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.
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Affiliation(s)
- Lee‐Shin Chu
- Department of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jeffrey A. Ruffolo
- Program in Molecular BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
- Program in Molecular BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
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3
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Almendros A, Choi YR, Leung TL, Tam WYJ, Hernandez Muguiro D, Woodhouse FM, Gray JJ, Beatty JA, Barrs VR. Low prevalence of Babesia hongkongensis infection in community and privately-owned cats in Hong Kong. Ticks Tick Borne Dis 2024; 15:102278. [PMID: 37979475 DOI: 10.1016/j.ttbdis.2023.102278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/05/2023] [Accepted: 10/26/2023] [Indexed: 11/20/2023]
Abstract
Domestic cats are susceptible to infection with at least 11 species of Babesia. In Hong Kong, where dogs are commonly infected with B. gibsoni, a single infection in a cat by a novel species, B. hongkongensis, was reported previously. The aim of this study was to investigate the frequency of Babesia spp. detection in cats in Hong Kong. Residual blood-derived DNA from healthy free-roaming community cats (n = 239), and privately-owned cats with and without anaemia undergoing diagnostic investigations (n = 125) was tested for Babesia spp. DNA using a pan-Babesia PCR targeting mitochondrial Cytochrome B, and a B. hongkongensis specific PCR targeting 18S rRNA. Positive samples were confirmed by sequencing and comparative sequence analysis against the GenBank nucleotide database. Babesia hongkongensis was detected in 4/239 (1.7 %) community cats, and 0/125 (0.0 %) privately-owned cats. Babesia gibsoni was detected in 0/239 community cats and 1/125 (0.8 %) privately-owned cats. Cats infected with B. hongkongensis were clinically healthy at the time of sampling. The B. gibsoni-infected cat was anaemic and thrombocytopenic. Cats in Hong Kong can be infected with B. hongkongensis and B. gibsoni, albeit at low frequency. The tick vector for B. hongkongensis is yet to be identified.
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Affiliation(s)
- A Almendros
- Department of Veterinary Clinical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong Special Administrative Region of China.
| | - Y R Choi
- Department of Veterinary Clinical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong Special Administrative Region of China; Centre for Animal Health and Welfare, City University of Hong Kong, Kowloon Tong, Hong Kong Special Administrative Region of China
| | - T L Leung
- Department of Veterinary Clinical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong Special Administrative Region of China
| | - W Y J Tam
- Department of Veterinary Clinical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong Special Administrative Region of China
| | - D Hernandez Muguiro
- Veterinary Diagnostic Laboratory, City University of Hong Kong, Kowloon Tong, Hong Kong Special Administrative Region of China
| | - F M Woodhouse
- The Society for the Prevention of Cruelty to Animals, Wan Chai, Hong Kong Special Administrative Region of China
| | - J J Gray
- The Society for the Prevention of Cruelty to Animals, Wan Chai, Hong Kong Special Administrative Region of China
| | - J A Beatty
- Department of Veterinary Clinical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong Special Administrative Region of China; Centre for Animal Health and Welfare, City University of Hong Kong, Kowloon Tong, Hong Kong Special Administrative Region of China
| | - V R Barrs
- Department of Veterinary Clinical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong Special Administrative Region of China; Centre for Animal Health and Welfare, City University of Hong Kong, Kowloon Tong, Hong Kong Special Administrative Region of China
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4
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Samanta R, Gray JJ. Implicit model to capture electrostatic features of membrane environment. PLoS Comput Biol 2024; 20:e1011296. [PMID: 38252688 PMCID: PMC10833867 DOI: 10.1371/journal.pcbi.1011296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 02/01/2024] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.
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Affiliation(s)
- Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States of America
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
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5
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Lensink MF, Brysbaert G, Raouraoua N, Bates PA, Giulini M, Honorato RV, van Noort C, Teixeira JMC, Bonvin AMJJ, Kong R, Shi H, Lu X, Chang S, Liu J, Guo Z, Chen X, Morehead A, Roy RS, Wu T, Giri N, Quadir F, Chen C, Cheng J, Del Carpio CA, Ichiishi E, Rodriguez‐Lumbreras LA, Fernandez‐Recio J, Harmalkar A, Chu L, Canner S, Smanta R, Gray JJ, Li H, Lin P, He J, Tao H, Huang S, Roel‐Touris J, Jimenez‐Garcia B, Christoffer CW, Jain AJ, Kagaya Y, Kannan H, Nakamura T, Terashi G, Verburgt JC, Zhang Y, Zhang Z, Fujuta H, Sekijima M, Kihara D, Khan O, Kotelnikov S, Ghani U, Padhorny D, Beglov D, Vajda S, Kozakov D, Negi SS, Ricciardelli T, Barradas‐Bautista D, Cao Z, Chawla M, Cavallo L, Oliva R, Yin R, Cheung M, Guest JD, Lee J, Pierce BG, Shor B, Cohen T, Halfon M, Schneidman‐Duhovny D, Zhu S, Yin R, Sun Y, Shen Y, Maszota‐Zieleniak M, Bojarski KK, Lubecka EA, Marcisz M, Danielsson A, Dziadek L, Gaardlos M, Gieldon A, Liwo A, Samsonov SA, Slusarz R, Zieba K, Sieradzan AK, Czaplewski C, Kobayashi S, Miyakawa Y, Kiyota Y, Takeda‐Shitaka M, Olechnovic K, Valancauskas L, Dapkunas J, Venclovas C, Wallner B, Yang L, Hou C, He X, Guo S, Jiang S, Ma X, Duan R, Qui L, Xu X, Zou X, Velankar S, Wodak SJ. Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment. Proteins 2023; 91:1658-1683. [PMID: 37905971 PMCID: PMC10841881 DOI: 10.1002/prot.26609] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 11/02/2023]
Abstract
We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.
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Affiliation(s)
- Marc F. Lensink
- Univ. Lille, CNRS, UMR8576 – UGSF – Unité de Glycobiologie Structurale et FonctionnelleLilleFrance
| | - Guillaume Brysbaert
- Univ. Lille, CNRS, UMR8576 – UGSF – Unité de Glycobiologie Structurale et FonctionnelleLilleFrance
| | - Nessim Raouraoua
- Univ. Lille, CNRS, UMR8576 – UGSF – Unité de Glycobiologie Structurale et FonctionnelleLilleFrance
| | - Paul A. Bates
- Biomolecular Modeling LaboratoryThe Francis Crick InstituteLondonUK
| | - Marco Giulini
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Rodrigo V. Honorato
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Charlotte van Noort
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Joao M. C. Teixeira
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science – ChemistryUtrecht UniversityUtrechtThe Netherlands
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Xufeng Lu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
| | - Jian Liu
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Zhiye Guo
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Xiao Chen
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Alex Morehead
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Raj S. Roy
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Tianqi Wu
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Nabin Giri
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Farhan Quadir
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Chen Chen
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | - Jianlin Cheng
- Dept. of Electrical Engineering and Computer ScienceUniversity of MissouriColumbiaMissouriUSA
| | | | - Eichiro Ichiishi
- International University of Health and Welfare (IUHV Hospital)Nasushiobara‐CityJapan
| | - Luis A. Rodriguez‐Lumbreras
- Instituto de Ciencias de la Vida y del Vino (ICVV)CSIC ‐ Universidad de La Rioja ‐ Gobierno de La RiojaLogronoSpain
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
| | - Juan Fernandez‐Recio
- Instituto de Ciencias de la Vida y del Vino (ICVV)CSIC ‐ Universidad de La Rioja ‐ Gobierno de La RiojaLogronoSpain
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
| | - Ameya Harmalkar
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Lee‐Shin Chu
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Sam Canner
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Rituparna Smanta
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jeffrey J. Gray
- Dept. of Chemical and Biomolecular EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
- Program in Molecular BiophysicsJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Hao Li
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Peicong Lin
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Jiahua He
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Huanyu Tao
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Sheng‐You Huang
- School of PhysicsHuazhong University of Science and TechnologyWuhanChina
| | - Jorge Roel‐Touris
- Protein Design and Modeling Lab, Dept. of Structural BiologyMolecular Biology Institute of Barcelona (IBMB‐CSIC)BarcelonaSpain
| | | | | | - Anika J. Jain
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Yuki Kagaya
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Harini Kannan
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
- Dept. of Biotechnology, Bhupat and Jyoti Mehta School of BiosciencesIndian Institute of Technology MadrasChennaiIndia
| | - Tsukasa Nakamura
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Genki Terashi
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Jacob C. Verburgt
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | - Yuanyuan Zhang
- Dept. of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
| | - Zicong Zhang
- Dept. of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
| | - Hayato Fujuta
- Dept. of Biotechnology, Bhupat and Jyoti Mehta School of BiosciencesIndian Institute of Technology MadrasChennaiIndia
| | | | - Daisuke Kihara
- Dept. of Computer SciencePurdue UniversityWest LafayetteIndianaUSA
- Dept. of Biological SciencesPurdue UniversityWest LafayetteIndianaUSA
| | | | | | | | | | | | | | | | - Surendra S. Negi
- Sealy Center for Structural Biology and Molecular BiophysicsUniversity of Texas Medical BranchGalvestonTexasUSA
| | | | | | - Zhen Cao
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Mohit Chawla
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
- Department of Chemistry and BiologyUniversity of SalernoFiscianoItaly
| | | | - Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Melyssa Cheung
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Chemistry and BiochemistryUniversity of MarylandCollege ParkMarylandUSA
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Jessica Lee
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Dept. of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Ben Shor
- School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael
| | - Tomer Cohen
- School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael
| | - Matan Halfon
- School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael
| | | | - Shaowen Zhu
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
| | - Rujie Yin
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
| | - Yuanfei Sun
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
| | - Yang Shen
- Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationTexasUSA
- Department of Computer Science and EngineeringTexas A&M UniversityCollege StationTexasUSA
- Institute of Biosciences and Technology and Department of Translational Medical SciencesTexas A&M UniversityHoustonTexasUSA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Yuta Miyakawa
- School of PharmacyKitasato UniversityMinato‐kuTokyoJapan
| | - Yasuomi Kiyota
- School of PharmacyKitasato UniversityMinato‐kuTokyoJapan
| | | | - Kliment Olechnovic
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Lukas Valancauskas
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Justas Dapkunas
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Ceslovas Venclovas
- Institute of Biotechnology, Life Sciences CenterVilnius UniversityVilniusLithuania
| | - Bjorn Wallner
- Bioinformatics Division, Department of Physics, Chemistry, and BiologyLinkoping UniversityLinköpingSweden
| | - Lin Yang
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
- School of Aerospace, Mechanical and Mechatronic EngineeringThe University of SydneyNew South WalesAustralia
| | - Chengyu Hou
- School of Electronics and Information EngineeringHarbin Institute of TechnologyHarbinChina
| | - Xiaodong He
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
- Shenzhen STRONG Advanced Materials Research Institute Col, LtdShenzhenPeople's Republic of China
| | - Shuai Guo
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
| | - Shenda Jiang
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
| | - Xiaoliang Ma
- National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Center for Composite Materials and StructuresHarbin Institute of TechnologyHarbinChina
| | - Rui Duan
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
| | - Liming Qui
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
| | - Xianjin Xu
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
| | - Xiaoqin Zou
- Dalton Cardiovascular Research CenterUniversity of MissouriColumbiaMissouriUSA
- Dept. of Physics and AstronomyUniversity of MissouriColumbiaMissouriUSA
- Dept. of BiochemistryUniversity of MissouriColumbiaMissouriUSA
- Institute for Data Science and InformaticsUniversity of MissouriColumbiaMissouriUSA
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonCambridgeUK
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6
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Abstract
Therapeutic antibody engineering seeks to identify antibody sequences with specific binding to a target and optimized drug-like properties. When guided by deep learning, antibody generation methods can draw on prior knowledge and experimental efforts to improve this process. By leveraging the increasing quantity and quality of predicted structures of antibodies and target antigens, powerful structure-based generative models are emerging. In this review, we tie the advancements in deep learning-based protein structure prediction and design to the study of antibody therapeutics.
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Affiliation(s)
- Michael Chungyoun
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21287, USA
- Program in Molecular Biophysics, institute for Nanobiotechnology, and Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21287, USA
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7
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Abstract
Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases. In this work, we combine AlphaFold as a structural template generator with a physics-based replica exchange docking algorithm. Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AlphaFold confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol to complete a robust in-silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 66% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (19% success rate), AlphaRED demonstrates a success rate of 51%. This new strategy demonstrates the success possible by integrating deep-learning based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at github.com/Graylab/AlphaRED.
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8
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Shuai RW, Ruffolo JA, Gray JJ. IgLM: Infilling language modeling for antibody sequence design. Cell Syst 2023; 14:979-989.e4. [PMID: 37909045 PMCID: PMC11018345 DOI: 10.1016/j.cels.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 06/14/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023]
Abstract
Discovery and optimization of monoclonal antibodies for therapeutic applications relies on large sequence libraries but is hindered by developability issues such as low solubility, high aggregation, and high immunogenicity. Generative language models, trained on millions of protein sequences, are a powerful tool for the on-demand generation of realistic, diverse sequences. We present the Immunoglobulin Language Model (IgLM), a deep generative language model for creating synthetic antibody libraries. Compared with prior methods that leverage unidirectional context for sequence generation, IgLM formulates antibody design based on text-infilling in natural language, allowing it to re-design variable-length spans within antibody sequences using bidirectional context. We trained IgLM on 558 million (M) antibody heavy- and light-chain variable sequences, conditioning on each sequence's chain type and species of origin. We demonstrate that IgLM can generate full-length antibody sequences from a variety of species and its infilling formulation allows it to generate infilled complementarity-determining region (CDR) loop libraries with improved in silico developability profiles. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Richard W Shuai
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | - Jeffrey A Ruffolo
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, USA; Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA.
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9
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Hernandez NE, Jankowski W, Frick R, Kelow SP, Lubin JH, Simhadri V, Adolf-Bryfogle J, Khare SD, Dunbrack RL, Gray JJ, Sauna ZE. Corrigendum to "Computational design of nanomolar-binding antibodies specific to multiple SARS-CoV-2 variants by engineering a specificity switch of antibody 80R using RosettaAntibodyDesign (RAbD) results in potential generalizable therapeutic antibodies for novel SARS-CoV-2 virus" [Heliyon 9(4) (April 2023) e15032]. Heliyon 2023; 9:e17901. [PMID: 37701412 PMCID: PMC10493423 DOI: 10.1016/j.heliyon.2023.e17901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 09/14/2023] Open
Abstract
[This corrects the article DOI: 10.1016/j.heliyon.2023.e15032.].
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Affiliation(s)
- Nancy E. Hernandez
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation and Research U.S. FDA, Silver Spring, MD, USA
| | - Wojciech Jankowski
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation and Research U.S. FDA, Silver Spring, MD, USA
| | - Rahel Frick
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Simon P. Kelow
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
- Dept. of Biochemistry and Molecular Biophysics, University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph H. Lubin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Vijaya Simhadri
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation and Research U.S. FDA, Silver Spring, MD, USA
| | | | - Sagar D. Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Roland L. Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Jeffrey J. Gray
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
- Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD, USA
| | - Zuben E. Sauna
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation and Research U.S. FDA, Silver Spring, MD, USA
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10
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Mahajan SP, Ruffolo JA, Gray JJ. Contextual protein and antibody encodings from equivariant graph transformers. bioRxiv 2023:2023.07.15.549154. [PMID: 37503113 PMCID: PMC10370091 DOI: 10.1101/2023.07.15.549154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The optimal residue identity at each position in a protein is determined by its structural, evolutionary, and functional context. We seek to learn the representation space of the optimal amino-acid residue in different structural contexts in proteins. Inspired by masked language modeling (MLM), our training aims to transduce learning of amino-acid labels from non-masked residues to masked residues in their structural environments and from general (e.g., a residue in a protein) to specific contexts (e.g., a residue at the interface of a protein or antibody complex). Our results on native sequence recovery and forward folding with AlphaFold2 suggest that the amino acid label for a protein residue may be determined from its structural context alone (i.e., without knowledge of the sequence labels of surrounding residues). We further find that the sequence space sampled from our masked models recapitulate the evolutionary sequence neighborhood of the wildtype sequence. Remarkably, the sequences conditioned on highly plastic structures recapitulate the conformational flexibility encoded in the structures. Furthermore, maximum-likelihood interfaces designed with masked models recapitulate wildtype binding energies for a wide range of protein interfaces and binding strengths. We also propose and compare fine-tuning strategies to train models for designing CDR loops of antibodies in the structural context of the antibody-antigen interface by leveraging structural databases for proteins, antibodies (synthetic and experimental) and protein-protein complexes. We show that pretraining on more general contexts improves native sequence recovery for antibody CDR loops, especially for the hypervariable CDR H3, while fine-tuning helps to preserve patterns observed in special contexts.
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Affiliation(s)
- Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Jeffrey A Ruffolo
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland, United States
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11
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Moleirinho S, Kitamura Y, Borges PSGN, Auduong S, Kilic S, Deng D, Kanaya N, Kozono D, Zhou J, Gray JJ, Revai-Lechtich E, Zhu Y, Shah K. Fate and Efficacy of Engineered Allogeneic Stem Cells Targeting Cell Death and Proliferation Pathways in Primary and Brain Metastatic Lung Cancer. Stem Cells Transl Med 2023; 12:444-458. [PMID: 37311043 PMCID: PMC10346421 DOI: 10.1093/stcltm/szad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 04/07/2023] [Indexed: 06/15/2023] Open
Abstract
Primary and metastatic lung cancer is a leading cause of cancer-related death and novel therapies are urgently needed. Epidermal growth factor receptor (EGFR) and death receptor (DR) 4/5 are both highly expressed in primary and metastatic non-small cell lung cancer (NSCLC); however, targeting these receptors individually has demonstrated limited therapeutic benefit in patients. In this study, we created and characterized diagnostic and therapeutic stem cells (SC), expressing EGFR-targeted nanobody (EV) fused to the extracellular domain of death DR4/5 ligand (DRL) (EVDRL) that simultaneously targets EGFR and DR4/5, in primary and metastatic NSCLC tumor models. We show that EVDRL targets both cell surface receptors, and induces caspase-mediated apoptosis in a broad spectrum of NSCLC cell lines. Utilizing real-time dual imaging and correlative immunohistochemistry, we show that allogeneic SCs home to tumors and when engineered to express EVDRL, alleviate tumor burden and significantly increase survival in primary and brain metastatic NSCLC. This study reports mechanistic insights into simultaneous targeting of EGFR- and DR4/5 in lung tumors and presents a promising approach for translation into the clinical setting.
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Affiliation(s)
- Susana Moleirinho
- Center for Stem Cell and Translational Immunotherapy (CSTI), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Yohei Kitamura
- Center for Stem Cell and Translational Immunotherapy (CSTI), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Paulo S G N Borges
- Center for Stem Cell and Translational Immunotherapy (CSTI), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Sophia Auduong
- Center for Stem Cell and Translational Immunotherapy (CSTI), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Seyda Kilic
- Center for Stem Cell and Translational Immunotherapy (CSTI), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - David Deng
- Center for Stem Cell and Translational Immunotherapy (CSTI), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Nobuhiko Kanaya
- Center for Stem Cell and Translational Immunotherapy (CSTI), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - David Kozono
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Jing Zhou
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MA, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MA, USA
| | - Esther Revai-Lechtich
- Center for Stem Cell and Translational Immunotherapy (CSTI), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Yanni Zhu
- Center for Stem Cell and Translational Immunotherapy (CSTI), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Khalid Shah
- Center for Stem Cell and Translational Immunotherapy (CSTI), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA
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12
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Chu LS, Ruffolo JA, Harmalkar A, Gray JJ. Flexible Protein-Protein Docking with a Multi-Track Iterative Transformer. bioRxiv 2023:2023.06.29.547134. [PMID: 37425754 PMCID: PMC10327054 DOI: 10.1101/2023.06.29.547134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and re-ranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, e.g., structure-based virtual screening. Existing deep learning methods for protein-protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding-induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multi-track iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments (MSAs), GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. For a benchmark set of rigid targets, GeoDock obtains a 41% success rate, outperforming all the other tested methods. For a more challenging benchmark set of flexible targets, GeoDock achieves a similar number of top-model successes as the traditional method ClusPro [1], but fewer than ReplicaDock2 [2]. GeoDock attains an average inference speed of under one second on a single GPU, enabling its application in large-scale structure screening. Although binding-induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.
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Affiliation(s)
- Lee-Shin Chu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey A Ruffolo
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
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13
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Samanta R, Gray JJ. Implicit model to capture electrostatic features of membrane environment. bioRxiv 2023:2023.06.26.546486. [PMID: 37425950 PMCID: PMC10327106 DOI: 10.1101/2023.06.26.546486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.
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Affiliation(s)
- Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland, United States
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14
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Canner SW, Shanker S, Gray JJ. Structure-based neural network protein-carbohydrate interaction predictions at the residue level. Front Bioinform 2023; 3:1186531. [PMID: 37409346 PMCID: PMC10318439 DOI: 10.3389/fbinf.2023.1186531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/31/2023] [Indexed: 07/07/2023] Open
Abstract
Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.
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Affiliation(s)
- Samuel W. Canner
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States
| | - Sudhanshu Shanker
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Jeffrey J. Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
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15
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Hess RA, Erickson OA, Cole RB, Isaacs JM, Alvarez-Clare S, Arnold J, Augustus-Wallace A, Ayoob JC, Berkowitz A, Branchaw J, Burgio KR, Cannon CH, Ceballos RM, Cohen CS, Coller H, Disney J, Doze VA, Eggers MJ, Ferguson EL, Gray JJ, Greenberg JT, Hoffmann A, Jensen-Ryan D, Kao RM, Keene AC, Kowalko JE, Lopez SA, Mathis C, Minkara M, Murren CJ, Ondrechen MJ, Ordoñez P, Osano A, Padilla-Crespo E, Palchoudhury S, Qin H, Ramírez-Lugo J, Reithel J, Shaw CA, Smith A, Smith RJ, Tsien F, Dolan EL. Virtually the Same? Evaluating the Effectiveness of Remote Undergraduate Research Experiences. CBE Life Sci Educ 2023; 22:ar25. [PMID: 37058442 PMCID: PMC10228262 DOI: 10.1187/cbe.22-01-0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/27/2023] [Accepted: 03/17/2023] [Indexed: 06/02/2023]
Abstract
In-person undergraduate research experiences (UREs) promote students' integration into careers in life science research. In 2020, the COVID-19 pandemic prompted institutions hosting summer URE programs to offer them remotely, raising questions about whether undergraduates who participate in remote research can experience scientific integration and whether they might perceive doing research less favorably (i.e., not beneficial or too costly). To address these questions, we examined indicators of scientific integration and perceptions of the benefits and costs of doing research among students who participated in remote life science URE programs in Summer 2020. We found that students experienced gains in scientific self-efficacy pre- to post-URE, similar to results reported for in-person UREs. We also found that students experienced gains in scientific identity, graduate and career intentions, and perceptions of the benefits of doing research only if they started their remote UREs at lower levels on these variables. Collectively, students did not change in their perceptions of the costs of doing research despite the challenges of working remotely. Yet students who started with low cost perceptions increased in these perceptions. These findings indicate that remote UREs can support students' self-efficacy development, but may otherwise be limited in their potential to promote scientific integration.
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Affiliation(s)
- Riley A. Hess
- Department of Psychology, University of Georgia, Athens, GA 30602
| | - Olivia A. Erickson
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602
| | - Rebecca B. Cole
- Department of Psychology, University of Georgia, Athens, GA 30602
| | - Jared M. Isaacs
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602
| | | | - Jonathan Arnold
- Department of Genetics, University of Georgia, Athens, GA 30602
| | | | - Joseph C. Ayoob
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260
| | - Alan Berkowitz
- Education Department, Cary Institute for Ecosystem Studies, Millbrook, NY 12545
| | - Janet Branchaw
- WISCIENCE and Department of Kinesiology, University of Wisconsin–Madison, Madison, WI 53706
| | - Kevin R. Burgio
- Education Department, Cary Institute for Ecosystem Studies, Millbrook, NY 12545
| | | | | | - C. Sarah Cohen
- Department of Biology, Estuary and Ocean Science Center, San Francisco State University, San Francisco, CA 94132
| | - Hilary Coller
- Department of Molecular, Cell and Developmental Biology and, University of California Los Angeles, Los Angeles, CA 90095
| | - Jane Disney
- Community Environmental Health Laboratory, Mt. Desert Island Biological Laboratory, Salisbury Cove, ME 04672
| | - Van A. Doze
- Department of Biomedical Sciences, University of North Dakota, Grand Forks, ND 58202
| | - Margaret J. Eggers
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT 59717
| | - Edwin L. Ferguson
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 606307
| | - Jeffrey J. Gray
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Jean T. Greenberg
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 606307
| | - Alexander Hoffmann
- Department of Microbiology, Immunology, and Molecular Genetics and Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA 90095
| | - Danielle Jensen-Ryan
- Department of Math and Sciences, Laramie County Community College, Cheyenne, WY 82007
| | - Robert M. Kao
- Science Department, College of Arts and Sciences, Heritage University, Toppenish, WA 98948
| | - Alex C. Keene
- Department of Biology, Texas A&M University, College Station, TX 77840
| | - Johanna E. Kowalko
- Department of Biological Sciences, Lehigh University, Bethlehem, PA 18015
| | - Steven A. Lopez
- Department of Chemistry & Chemical Biology, Northeastern University, Boston, MA 02115
| | | | - Mona Minkara
- Department of Bioengineering, Northeastern University, Boston, MA 02115
| | | | - Mary Jo Ondrechen
- Department of Chemistry & Chemical Biology, Northeastern University, Boston, MA 02115
| | - Patricia Ordoñez
- Department of Computer Science, University of Puerto Rico–Río Piedras, San Juan, PR 00925
| | - Anne Osano
- Department of Natural Sciences, Bowie State University, Bowie, MD 20715
| | - Elizabeth Padilla-Crespo
- Department of Science and Technology, Inter American University of Puerto Rico–Aguadilla, Aguadilla, PR 00605
| | | | - Hong Qin
- Department of Computer Science and Engineering and Department of Biology, Geology, and Environmental Science, University of Tennessee at Chattanooga, Chattanooga, TN 37403
| | - Juan Ramírez-Lugo
- Department of Biology, University of Puerto Rico–Río Piedras, San Juan, PR 00925
| | - Jennifer Reithel
- Rocky Mountain Biological Laboratory, PO Box 519, Crested Butte, CO 81224
| | - Colin A. Shaw
- Undergraduate Scholars Program and Department of Earth Sciences, Montana State University, Bozeman, MT 59717
| | - Amber Smith
- Wisconsin Institute for Science Education and Community Engagement, University of Wisconsin–Madison, Madison, WI 53706
| | - Rosemary J. Smith
- Rocky Mountain Biological Laboratory, PO Box 519, Crested Butte, CO 81224
- Department of Biological Sciences, Idaho State University, Pocatello, ID 83209
| | - Fern Tsien
- Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, LA 70112
| | - Erin L. Dolan
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602
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16
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Ruffolo JA, Chu LS, Mahajan SP, Gray JJ. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nat Commun 2023; 14:2389. [PMID: 37185622 PMCID: PMC10129313 DOI: 10.1038/s41467-023-38063-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge. Here, we present IgFold, a fast deep learning method for antibody structure prediction. IgFold consists of a pre-trained language model trained on 558 million natural antibody sequences followed by graph networks that directly predict backbone atom coordinates. IgFold predicts structures of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s). Accurate structure prediction on this timescale makes possible avenues of investigation that were previously infeasible. As a demonstration of IgFold's capabilities, we predicted structures for 1.4 million paired antibody sequences, providing structural insights to 500-fold more antibodies than have experimentally determined structures.
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Affiliation(s)
- Jeffrey A Ruffolo
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Lee-Shin Chu
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeffrey J Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, 21218, USA.
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA.
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17
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Hernandez NE, Jankowski W, Frick R, Kelow SP, Lubin JH, Simhadri V, Adolf-Bryfogle J, Khare SD, Dunbrack RL, Gray JJ, Sauna ZE. Computational design of nanomolar-binding antibodies specific to multiple SARS-CoV-2 variants by engineering a specificity switch of antibody 80R using RosettaAntibodyDesign (RAbD) results in potential generalizable therapeutic antibodies for novel SARS-CoV-2 virus. Heliyon 2023; 9:e15032. [PMID: 37035348 PMCID: PMC10069166 DOI: 10.1016/j.heliyon.2023.e15032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/22/2023] [Accepted: 03/24/2023] [Indexed: 04/05/2023] Open
Abstract
The human infectious disease COVID-19 caused by the SARS-CoV-2 virus has become a major threat to global public health. Developing a vaccine is the preferred prophylactic response to epidemics and pandemics. However, for individuals who have contracted the disease, the rapid design of antibodies that can target the SARS-CoV-2 virus fulfils a critical need. Further, discovering antibodies that bind multiple variants of SARS-CoV-2 can aid in the development of rapid antigen tests (RATs) which are critical for the identification and isolation of individuals currently carrying COVID-19. Here we provide a proof-of-concept study for the computational design of high-affinity antibodies that bind to multiple variants of the SARS-CoV-2 spike protein using RosettaAntibodyDesign (RAbD). Well characterized antibodies that bind with high affinity to the SARS-CoV-1 (but not SARS-CoV-2) spike protein were used as templates and re-designed to bind the SARS-CoV-2 spike protein with high affinity, resulting in a specificity switch. A panel of designed antibodies were experimentally validated. One design bound to a broad range of variants of concern including the Omicron, Delta, Wuhan, and South African spike protein variants.
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Affiliation(s)
- Nancy E. Hernandez
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation and Research U.S. FDA, Silver Spring, MD, USA
| | - Wojciech Jankowski
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation and Research U.S. FDA, Silver Spring, MD, USA
| | - Rahel Frick
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Simon P. Kelow
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
- Dept. of Biochemistry and Molecular Biophysics, University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph H. Lubin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ USA
| | - Vijaya Simhadri
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation and Research U.S. FDA, Silver Spring, MD, USA
| | | | - Sagar D. Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Roland L. Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Jeffrey J. Gray
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
- Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD, USA
| | - Zuben E. Sauna
- Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics, Office of Therapeutic Products, Center for Biologics Evaluation and Research U.S. FDA, Silver Spring, MD, USA
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18
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Canner SW, Shanker S, Gray JJ. Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level. bioRxiv 2023:2023.03.14.531382. [PMID: 36993750 PMCID: PMC10054975 DOI: 10.1101/2023.03.14.531382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate binding sites on any given protein. Here, we present two deep learning models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predict carbohydrate binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2 predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.
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Affiliation(s)
- Samuel W. Canner
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Sudhanshu Shanker
- Dept. of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jeffrey J. Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States of America
- Dept. of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Correspondence: Jeffrey J. Gray,
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19
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Boorla VS, Chowdhury R, Ramasubramanian R, Ameglio B, Frick R, Gray JJ, Maranas CD. De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD). Proteins 2023; 91:196-208. [PMID: 36111441 PMCID: PMC9538105 DOI: 10.1002/prot.26422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/17/2022] [Accepted: 09/06/2022] [Indexed: 01/11/2023]
Abstract
The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.
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Affiliation(s)
- Veda Sheersh Boorla
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802
| | - Ratul Chowdhury
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802
| | | | - Brandon Ameglio
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Rahel Frick
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802,Corresponding author:
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20
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Harmalkar A, Rao R, Richard Xie Y, Honer J, Deisting W, Anlahr J, Hoenig A, Czwikla J, Sienz-Widmann E, Rau D, Rice AJ, Riley TP, Li D, Catterall HB, Tinberg CE, Gray JJ, Wei KY. Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features. MAbs 2023; 15:2163584. [PMID: 36683173 PMCID: PMC9872953 DOI: 10.1080/19420862.2022.2163584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/14/2022] [Accepted: 12/26/2022] [Indexed: 01/24/2023] Open
Abstract
Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches - one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features - to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, ρ , of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task (ρ = 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.
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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Roshan Rao
- Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - Yuxuan Richard Xie
- Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jonas Honer
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Wibke Deisting
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Jonas Anlahr
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Anja Hoenig
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Julia Czwikla
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Eva Sienz-Widmann
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Doris Rau
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Austin J. Rice
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Timothy P. Riley
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Danqing Li
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | | | | | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Kathy Y. Wei
- Therapeutic Discovery, Amgen Research, Amgen Inc, South San Francisco, CA, USA
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21
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Mahajan SP, Ruffolo JA, Frick R, Gray JJ. Hallucinating structure-conditioned antibody libraries for target-specific binders. Front Immunol 2022; 13:999034. [PMID: 36341416 PMCID: PMC9635398 DOI: 10.3389/fimmu.2022.999034] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/22/2022] [Indexed: 11/29/2022] Open
Abstract
Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space. Deep learning (DL) models are transforming the field of protein engineering and design. While several DL-based protein design methods have shown promise, the antibody design problem is distinct, and specialized models for antibody design are desirable. Inspired by hallucination frameworks that leverage accurate structure prediction DL models, we propose the FvHallucinator for designing antibody sequences, especially the CDR loops, conditioned on an antibody structure. Such a strategy generates targeted CDR libraries that retain the conformation of the binder and thereby the mode of binding to the epitope on the antigen. On a benchmark set of 60 antibodies, FvHallucinator generates sequences resembling natural CDRs and recapitulates perplexity of canonical CDR clusters. Furthermore, the FvHallucinator designs amino acid substitutions at the VH-VL interface that are enriched in human antibody repertoires and therapeutic antibodies. We propose a pipeline that screens FvHallucinator designs to obtain a library enriched in binders for an antigen of interest. We apply this pipeline to the CDR H3 of the Trastuzumab-HER2 complex to generate in silico designs predicted to improve upon the binding affinity and interfacial properties of the original antibody. Thus, the FvHallucinator pipeline enables generation of inexpensive, diverse, and targeted antibody libraries enriched in binders for antibody affinity maturation.
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Affiliation(s)
- Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Jeffrey A. Ruffolo
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, United States
| | - Rahel Frick
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, United States
- *Correspondence: Jeffrey J. Gray,
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22
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Alford RF, Leaver-Fay A, Jeliazkov JR, O'Meara MJ, DiMaio FP, Park H, Shapovalov MV, Renfrew PD, Mulligan VK, Kappel K, Labonte JW, Pacella MS, Bonneau R, Bradley P, Dunbrack RL, Das R, Baker D, Kuhlman B, Kortemme T, Gray JJ. Correction to "The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design". J Chem Theory Comput 2022; 18:4594. [PMID: 35667008 DOI: 10.1021/acs.jctc.2c00500] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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23
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Akpinaroglu D, Ruffolo JA, Mahajan SP, Gray JJ. Simultaneous prediction of antibody backbone and side-chain conformations with deep learning. PLoS One 2022; 17:e0258173. [PMID: 35704640 PMCID: PMC9200299 DOI: 10.1371/journal.pone.0258173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 05/24/2022] [Indexed: 11/20/2022] Open
Abstract
Antibody engineering is becoming increasingly popular in medicine for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side-chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side-chain dihedrals of the antibody variable fragment. The network requires only sequence as input, rendering it particularly useful for antibodies without any known backbone conformations. Rotamer predictions use an interpretable self-attention layer, which learns to identify structurally conserved anchor positions across several species. We evaluate the performance of the model for discriminating near-native structures from sets of decoys and find that DeepSCAb outperforms similar methods lacking side-chain context. When compared to alternative rotamer repacking methods, which require an input backbone structure, DeepSCAb predicts side-chain conformations competitively. Our findings suggest that DeepSCAb improves antibody structure prediction with accurate side-chain modeling and is adaptable to applications in docking of antibody-antigen complexes and design of new therapeutic antibody sequences.
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Affiliation(s)
- Deniz Akpinaroglu
- Department of Bioengineering, University of California, Merced, CA, United States of America
| | - Jeffrey A. Ruffolo
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Jeffrey J. Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States of America
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
- * E-mail:
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24
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Krivacic C, Kundert K, Pan X, Pache RA, Liu L, Conchúir SO, Jeliazkov JR, Gray JJ, Thompson MC, Fraser JS, Kortemme T. Accurate positioning of functional residues with robotics-inspired computational protein design. Proc Natl Acad Sci U S A 2022; 119:e2115480119. [PMID: 35254891 PMCID: PMC8931229 DOI: 10.1073/pnas.2115480119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/27/2022] [Indexed: 11/18/2022] Open
Abstract
SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.
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Affiliation(s)
- Cody Krivacic
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Kale Kundert
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
| | - Xingjie Pan
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Roland A. Pache
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Lin Liu
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - Shane O Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | | | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Michael C. Thompson
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
| | - James S. Fraser
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
| | - Tanja Kortemme
- UC Berkeley–UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA 94158
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158
- Biophysics Graduate Program, University of California, San Francisco, CA 94158
- Quantitative Biosciences Institute, University of California, San Francisco, CA 94158
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25
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Erickson OA, Cole RB, Isaacs JM, Alvarez-Clare S, Arnold J, Augustus-Wallace A, Ayoob JC, Berkowitz A, Branchaw J, Burgio KR, Cannon CH, Ceballos RM, Cohen CS, Coller H, Disney J, Doze VA, Eggers MJ, Farina S, Ferguson EL, Gray JJ, Greenberg JT, Hoffmann A, Jensen-Ryan D, Kao RM, Keene AC, Kowalko JE, Lopez SA, Mathis C, Minkara M, Murren CJ, Ondrechen MJ, Ordoñez P, Osano A, Padilla-Crespo E, Palchoudhury S, Qin H, Ramírez-Lugo J, Reithel J, Shaw CA, Smith A, Smith R, Summers AP, Tsien F, Dolan EL. "How Do We Do This at a Distance?!" A Descriptive Study of Remote Undergraduate Research Programs during COVID-19. CBE Life Sci Educ 2022; 21:ar1. [PMID: 34978923 PMCID: PMC9250374 DOI: 10.1187/cbe.21-05-0125] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The COVID-19 pandemic shut down undergraduate research programs across the United States. A group of 23 colleges, universities, and research institutes hosted remote undergraduate research programs in the life sciences during Summer 2020. Given the unprecedented offering of remote programs, we carried out a study to describe and evaluate them. Using structured templates, we documented how programs were designed and implemented, including who participated. Through focus groups and surveys, we identified programmatic strengths and shortcomings as well as recommendations for improvements from students' perspectives. Strengths included the quality of mentorship, opportunities for learning and professional development, and a feeling of connection with a larger community. Weaknesses included limited cohort building, challenges with insufficient structure, and issues with technology. Although all programs had one or more activities related to diversity, equity, inclusion, and justice, these topics were largely absent from student reports even though programs coincided with a peak in national consciousness about racial inequities and structural racism. Our results provide evidence for designing remote Research Experiences for Undergraduates (REUs) that are experienced favorably by students. Our results also indicate that remote REUs are sufficiently positive to further investigate their affordances and constraints, including the potential to scale up offerings, with minimal concern about disenfranchising students.
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Affiliation(s)
- Olivia A. Erickson
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602
| | - Rebecca B. Cole
- Department of Psychology, University of Georgia, Athens, GA 30602
| | - Jared M. Isaacs
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602
| | | | - Jonathan Arnold
- Department of Genetics, University of Georgia, Athens, GA 30602
| | - Allison Augustus-Wallace
- Department of Medicine & Office of Diversity & Community Engagement, Louisiana State University Health Sciences Center, New Orleans, LA 70112
| | - Joseph C. Ayoob
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260
| | - Alan Berkowitz
- Education Department, Cary Institute for Ecosystem Studies, Millbrook, NY 12545
| | - Janet Branchaw
- WISCIENCE and the Department of Kinesiology, University of Wisconsin–Madison, Madison, WI 53706
| | - Kevin R. Burgio
- Education Department, Cary Institute for Ecosystem Studies, Millbrook, NY 12545
- New York City Audubon Society, New York, NY 10010; and Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269
| | | | | | - C. Sarah Cohen
- Department of Biology, Estuary and Ocean Science Center, San Francisco State University, San Francisco, CA 94132
| | - Hilary Coller
- Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA 90095
| | - Jane Disney
- Community Environmental Health Laboratory, Mt. Desert Island Biological Laboratory, Salisbury Cove, ME 04672
| | - Van A. Doze
- Department of Biomedical Sciences, University of North Dakota, Grand Forks, ND 58202
| | - Margaret J. Eggers
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT 59717
| | - Stacy Farina
- Department of Biology, Howard University, Washington, DC 20059
| | - Edwin L. Ferguson
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 60637
| | - Jeffrey J. Gray
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Jean T. Greenberg
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 60637
| | - Alexander Hoffmann
- Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA, 90095
| | - Danielle Jensen-Ryan
- Department of Math and Sciences, Laramie County Community College, Cheyenne, WY 82007
| | - Robert M. Kao
- Science Department, College of Arts and Sciences, Heritage University, Toppenish, WA 98948
| | - Alex C. Keene
- Department of Biological Sciences, Florida Atlantic University, Jupiter, FL 33458
| | | | - Steven A. Lopez
- Department of Chemistry & Chemical Biology, Northeastern University, Boston, MA 02115
| | - Camille Mathis
- Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD 21218
| | - Mona Minkara
- Department of Bioengineering, Northeastern University, Boston, MA 02115
| | | | - Mary Jo Ondrechen
- Department of Chemistry & Chemical Biology, Northeastern University, Boston, MA 02115
| | - Patricia Ordoñez
- Department of Computer Science, University of Puerto Rico Río Piedras, San Juan, PR 00925
| | - Anne Osano
- Department of Natural Sciences, Bowie State University, Bowie, MD 20715
| | - Elizabeth Padilla-Crespo
- Department of Science and Technology, Inter American University of Puerto Rico–Aguadilla, Aguadilla, PR 00605
| | - Soubantika Palchoudhury
- Civil and Chemical Engineering Department, University of Tennessee at Chattanooga, Chattanooga, TN 37403-2598
| | - Hong Qin
- Department of Computer Science and Engineering, Department of Biology, Geology, and Environmental Science, University of Tennessee at Chattanooga, Chattanooga, TN 37403
| | - Juan Ramírez-Lugo
- Department of Biology, University of Puerto Rico Río Piedras, San Juan, PR 00925
| | - Jennifer Reithel
- Rocky Mountain Biological Laboratory, PO Box 519, Crested Butte, CO 81224
| | - Colin A. Shaw
- Undergraduate Scholars Program and Department of Earth Sciences, Montana State University, Bozeman, MT 59717
| | - Amber Smith
- Wisconsin Institute for Science Education and Community Engagement, University of Wisconsin–Madison, Madison, WI 53706
| | - Rosemary Smith
- Rocky Mountain Biological Laboratory, PO Box 519, Crested Butte, CO 81224
- Department of Biological Sciences, Idaho State University, Pocatello, ID 83209
| | - Adam P. Summers
- Friday Harbor Laboratories, Bio/SAFS, University of Washington, Friday Harbor, WA 98250
| | - Fern Tsien
- Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, LA 70112
| | - Erin L. Dolan
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602
- *Address correspondence to: Erin L. Dolan, ()
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Frick R, Høydahl LS, Hodnebrug I, Vik ES, Dalhus B, Sollid LM, Gray JJ, Sandlie I, Løset GÅ. Affinity maturation of TCR-like antibodies using phage display guided by structural modeling. Protein Eng Des Sel 2022; 35:6649134. [PMID: 35871543 PMCID: PMC9536190 DOI: 10.1093/protein/gzac005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 12/01/2022] Open
Abstract
TCR-like antibodies represent a unique type of engineered antibodies with specificity toward pHLA, a ligand normally restricted to the sensitive recognition by T cells. Here, we report a phage display-based sequential development path of such antibodies. The strategy goes from initial lead identification through in silico informed CDR engineering in combination with framework engineering for affinity and thermostability optimization, respectively. The strategy allowed the identification of HLA-DQ2.5 gluten peptide-specific TCR-like antibodies with low picomolar affinity. Our method outlines an efficient and general method for development of this promising class of antibodies, which should facilitate their utility including translation to human therapy.
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Affiliation(s)
- Rahel Frick
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- Centre for Immune Regulation and Department of Biosciences, University of Oslo , Blindernveien 31, 0371 Oslo, Norway
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University , 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Lene S Høydahl
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- Centre for Immune Regulation and Department of Biosciences, University of Oslo , Blindernveien 31, 0371 Oslo, Norway
- KG Jebsen Coeliac Disease Research Centre, University of Oslo , Sognsvannsveien 20, 0372 Oslo, Norway
| | - Ina Hodnebrug
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- Centre for Immune Regulation and Department of Biosciences, University of Oslo , Blindernveien 31, 0371 Oslo, Norway
| | - Erik S Vik
- Nextera AS , Gaustadalléen 21, 0349 Oslo, Norway
| | - Bjørn Dalhus
- Department for Medical Biochemistry , Institute for Clinical Medicine, , Sognsvannsveien 20, 0372 Oslo, Norway
- University of Oslo , Institute for Clinical Medicine, , Sognsvannsveien 20, 0372 Oslo, Norway
- Department for Microbiology , Clinic for Laboratory Medicine, , Sognsvannsveien 20, 0372 Oslo, Norway
- Oslo University Hospital , Clinic for Laboratory Medicine, , Sognsvannsveien 20, 0372 Oslo, Norway
| | - Ludvig M Sollid
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- KG Jebsen Coeliac Disease Research Centre, University of Oslo , Sognsvannsveien 20, 0372 Oslo, Norway
| | - Jeffrey J Gray
- Program in Molecular Biophysics, Johns Hopkins University , 3400 N. Charles Street, Baltimore, MD 21218, USA
- Department of Chemical and Biomolecular Engineering and Institute of NanoBioTechnology, Johns Hopkins University , 3400 N. Charles Street, Baltimore, MD 21218, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine , 733 N Broadway, Baltimore, MD 21205, USA
| | - Inger Sandlie
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- Centre for Immune Regulation and Department of Biosciences, University of Oslo , Blindernveien 31, 0371 Oslo, Norway
| | - Geir Åge Løset
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- Centre for Immune Regulation and Department of Biosciences, University of Oslo , Blindernveien 31, 0371 Oslo, Norway
- Nextera AS , Gaustadalléen 21, 0349 Oslo, Norway
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27
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Pooja Mahajan S, Ruffolo J, Frick R, Gray JJ. Towards deep learning models for target-specific antibody design. Biophys J 2022. [DOI: 10.1016/j.bpj.2021.11.2783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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29
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Lensink MF, Brysbaert G, Mauri T, Nadzirin N, Velankar S, Chaleil RAG, Clarence T, Bates PA, Kong R, Liu B, Yang G, Liu M, Shi H, Lu X, Chang S, Roy RS, Quadir F, Liu J, Cheng J, Antoniak A, Czaplewski C, Giełdoń A, Kogut M, Lipska AG, Liwo A, Lubecka EA, Maszota-Zieleniak M, Sieradzan AK, Ślusarz R, Wesołowski PA, Zięba K, Del Carpio Muñoz CA, Ichiishi E, Harmalkar A, Gray JJ, Bonvin AMJJ, Ambrosetti F, Vargas Honorato R, Jandova Z, Jiménez-García B, Koukos PI, Van Keulen S, Van Noort CW, Réau M, Roel-Touris J, Kotelnikov S, Padhorny D, Porter KA, Alekseenko A, Ignatov M, Desta I, Ashizawa R, Sun Z, Ghani U, Hashemi N, Vajda S, Kozakov D, Rosell M, Rodríguez-Lumbreras LA, Fernandez-Recio J, Karczynska A, Grudinin S, Yan Y, Li H, Lin P, Huang SY, Christoffer C, Terashi G, Verburgt J, Sarkar D, Aderinwale T, Wang X, Kihara D, Nakamura T, Hanazono Y, Gowthaman R, Guest JD, Yin R, Taherzadeh G, Pierce BG, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Sun Y, Zhu S, Shen Y, Park T, Woo H, Yang J, Kwon S, Won J, Seok C, Kiyota Y, Kobayashi S, Harada Y, Takeda-Shitaka M, Kundrotas PJ, Singh A, Vakser IA, Dapkūnas J, Olechnovič K, Venclovas Č, Duan R, Qiu L, Xu X, Zhang S, Zou X, Wodak SJ. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment. Proteins 2021; 89:1800-1823. [PMID: 34453465 PMCID: PMC8616814 DOI: 10.1002/prot.26222] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/24/2021] [Accepted: 08/05/2021] [Indexed: 12/19/2022]
Abstract
We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.
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Affiliation(s)
- Marc F Lensink
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Guillaume Brysbaert
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Théo Mauri
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Nurul Nadzirin
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | | | - Tereza Clarence
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Bin Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Guangbo Yang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ming Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xufeng Lu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Farhan Quadir
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Anna Antoniak
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Artur Giełdoń
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Mateusz Kogut
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Emilia A Lubecka
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland
| | | | | | - Rafał Ślusarz
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Patryk A Wesołowski
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
- Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, Gdansk, Poland
| | - Karolina Zięba
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Eiichiro Ichiishi
- International University of Health and Welfare Hospital (IUHW Hospital), Nasushiobara City, Japan
| | - Ameya Harmalkar
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey J Gray
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo Vargas Honorato
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Zuzana Jandova
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Siri Van Keulen
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W Van Noort
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Manon Réau
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
- Innopolis University, Russia
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Andrey Alekseenko
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
- Institute of Computer-Aided Design of the Russian Academy of Sciences, Moscow, Russia
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Ryota Ashizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Nasser Hashemi
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Mireia Rosell
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Luis A Rodríguez-Lumbreras
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Juan Fernandez-Recio
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | | | - Sergei Grudinin
- Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tsukasa Nakamura
- Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan
| | - Yuya Hanazono
- Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Tokai, Ibaraki, Japan
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Ghazaleh Taherzadeh
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | | | - Zhen Cao
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Romina Oliva
- University of Naples "Parthenope", Napoli, Italy
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Shaowen Zhu
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jinsol Yang
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jonghun Won
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Yasuomi Kiyota
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Yoshiki Harada
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Amar Singh
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Shuang Zhang
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xiaoqin Zou
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, USA
- Department of Biochemistry, University of Missouri, Columbia, Missouri, USA
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30
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Koehler Leman J, Lyskov S, Lewis SM, Adolf-Bryfogle J, Alford RF, Barlow K, Ben-Aharon Z, Farrell D, Fell J, Hansen WA, Harmalkar A, Jeliazkov J, Kuenze G, Krys JD, Ljubetič A, Loshbaugh AL, Maguire J, Moretti R, Mulligan VK, Nance ML, Nguyen PT, Ó Conchúir S, Roy Burman SS, Samanta R, Smith ST, Teets F, Tiemann JKS, Watkins A, Woods H, Yachnin BJ, Bahl CD, Bailey-Kellogg C, Baker D, Das R, DiMaio F, Khare SD, Kortemme T, Labonte JW, Lindorff-Larsen K, Meiler J, Schief W, Schueler-Furman O, Siegel JB, Stein A, Yarov-Yarovoy V, Kuhlman B, Leaver-Fay A, Gront D, Gray JJ, Bonneau R. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nat Commun 2021; 12:6947. [PMID: 34845212 PMCID: PMC8630030 DOI: 10.1038/s41467-021-27222-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 11/02/2021] [Indexed: 01/14/2023] Open
Abstract
Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Steven M Lewis
- Cyrus Biotechnology, 1201 Second Ave, Suite 900, Seattle, WA, 98101, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kyle Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ziv Ben-Aharon
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Daniel Farrell
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Jason Fell
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - William A Hansen
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeliazko Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - Justyna D Krys
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Ajasja Ljubetič
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Phuong T Nguyen
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Shane Ó Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Shannon T Smith
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Frank Teets
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Johanna K S Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Hope Woods
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Brahm J Yachnin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | | | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - William Schief
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Justin B Siegel
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Brian Kuhlman
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Andrew Leaver-Fay
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
- Department of Computer Science, New York University, New York, NY, 10003, USA.
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31
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Frick R, Høydahl LS, Petersen J, du Pré MF, Kumari S, Berntsen G, Dewan AE, Jeliazkov JR, Gunnarsen KS, Frigstad T, Vik ES, Llerena C, Lundin KEA, Yaqub S, Jahnsen J, Gray JJ, Rossjohn J, Sollid LM, Sandlie I, Løset GÅ. A high-affinity human TCR-like antibody detects celiac disease gluten peptide-MHC complexes and inhibits T cell activation. Sci Immunol 2021; 6:6/62/eabg4925. [PMID: 34417258 DOI: 10.1126/sciimmunol.abg4925] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 07/22/2021] [Indexed: 12/12/2022]
Abstract
Antibodies specific for peptides bound to human leukocyte antigen (HLA) molecules are valuable tools for studies of antigen presentation and may have therapeutic potential. Here, we generated human T cell receptor (TCR)-like antibodies toward the immunodominant signature gluten epitope DQ2.5-glia-α2 in celiac disease (CeD). Phage display selection combined with secondary targeted engineering was used to obtain highly specific antibodies with picomolar affinity. The crystal structure of a Fab fragment of the lead antibody 3.C11 in complex with HLA-DQ2.5:DQ2.5-glia-α2 revealed a binding geometry and interaction mode highly similar to prototypic TCRs specific for the same complex. Assessment of CeD biopsy material confirmed disease specificity and reinforced the notion that abundant plasma cells present antigen in the inflamed CeD gut. Furthermore, 3.C11 specifically inhibited activation and proliferation of gluten-specific CD4+ T cells in vitro and in HLA-DQ2.5 humanized mice, suggesting a potential for targeted intervention without compromising systemic immunity.
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Affiliation(s)
- Rahel Frick
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | - Lene S Høydahl
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | - Jan Petersen
- Infection and Immunity Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia.,Australian Research Council Centre of Excellence for Advanced Molecular Imaging, Monash University, Clayton, Victoria, Australia
| | - M Fleur du Pré
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | | | | | - Alisa E Dewan
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | | | - Kristin S Gunnarsen
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | | | | | - Carmen Llerena
- Infection and Immunity Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Knut E A Lundin
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway.,Department of Gastroenterology, Oslo University Hospital-Rikshospitalet, Oslo, Norway
| | - Sheraz Yaqub
- Department of Gastrointestinal Surgery, Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jørgen Jahnsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Gastroenterology, Akershus University Hospital, Lørenskog, Norway
| | - Jeffrey J Gray
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA.,Department of Chemical and Biomolecular Engineering and Institute of NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jamie Rossjohn
- Infection and Immunity Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia.,Australian Research Council Centre of Excellence for Advanced Molecular Imaging, Monash University, Clayton, Victoria, Australia.,Institute of Infection and Immunity, Cardiff University School of Medicine, Heath Park, Cardiff, UK
| | - Ludvig M Sollid
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | - Inger Sandlie
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | - Geir Åge Løset
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway. .,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway.,Nextera AS, Oslo, Norway
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32
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Abstract
Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane proteins lag behind due to sparse and low-quality data, leading to overfit tools. To overcome this challenge, we assembled a suite of 12 tests on independent data sets varying in size, diversity, and resolution. The tests probe an energy function's ability to capture membrane protein orientation, stability, sequence, and structure. Here, we present the tests and use the franklin2019 energy function to demonstrate them. We then identify areas for energy function improvement and discuss potential future integration with machine-learning-based optimization methods. The tests are available through the Rosetta Benchmark Server (https://benchmark.graylab.jhu.edu/) and GitHub (https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark).
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Affiliation(s)
- Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
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33
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Nance ML, Labonte JW, Adolf-Bryfogle J, Gray JJ. Development and Evaluation of GlycanDock: A Protein-Glycoligand Docking Refinement Algorithm in Rosetta. J Phys Chem B 2021; 125:10.1021/acs.jpcb.1c00910. [PMID: 34133179 PMCID: PMC8742512 DOI: 10.1021/acs.jpcb.1c00910] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Carbohydrate chains are ubiquitous in the complex molecular processes of life. These highly diverse chains are recognized by a variety of protein receptors, enabling glycans to regulate many biological functions. High-resolution structures of protein-glycoligand complexes reveal the atomic details necessary to understand this level of molecular recognition and inform application-focused scientific and engineering pursuits. When experimental challenges hinder high-throughput determination of quality structures, computational tools can, in principle, fill the gap. In this work, we introduce GlycanDock, a residue-centric protein-glycoligand docking refinement algorithm developed within the Rosetta macromolecular modeling and design software suite. We performed a benchmark docking assessment using a set of 109 experimentally determined protein-glycoligand complexes as well as 62 unbound protein structures. The GlycanDock algorithm can sample and discriminate among protein-glycoligand models of native-like structural accuracy with statistical reliability from starting structures of up to 7 Å root-mean-square deviation in the glycoligand ring atoms. We show that GlycanDock-refined models qualitatively replicated the known binding specificity of a bacterial carbohydrate-binding module. Finally, we present a protein-glycoligand docking pipeline for generating putative protein-glycoligand complexes when only the glycoligand sequence and unbound protein structure are known. In combination with other carbohydrate modeling tools, the GlycanDock docking refinement algorithm will accelerate research in the glycosciences.
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Affiliation(s)
- Morgan L. Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Chemistry, Franklin & Marshall College, Lancaster, Pennsylvania 17603, United States
- Department of Chemistry, Gettysburg College, Gettysburg, Pennsylvania 17325, United States
| | - Jared Adolf-Bryfogle
- Protein Design Lab, Institute for Protein Innovation, Boston, Massachusetts 02115, United States
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, Massachusetts 02115, United States
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Jeffrey J. Gray
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
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34
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Le KH, Adolf-Bryfogle J, Klima JC, Lyskov S, Labonte J, Bertolani S, Burman SSR, Leaver-Fay A, Weitzner B, Maguire J, Rangan R, Adrianowycz MA, Alford RF, Adal A, Nance ML, Wu Y, Willis J, Kulp DW, Das R, Dunbrack RL, Schief W, Kuhlman B, Siegel JB, Gray JJ. PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design. Biophysicist (Rockv) 2021; 2:108-122. [PMID: 35128343 DOI: 10.35459/tbp.2019.000147] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.
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Affiliation(s)
- Kathy H Le
- T. C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States
| | - Jason C Klima
- Institute for Protein Design, University of Washington, Seattle, Washington, United States.,Department of Biochemistry, University of Washington, Seattle, Washington, United States.,Lyell Immunopharma, Inc., Seattle, Washington, United States
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jason Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Department of Chemistry, Franklin & Marshall College, Lancaster, Pennsylvania, United States
| | - Steven Bertolani
- Department of Chemistry, Department of Biochemistry and Molecular Medicine, Genome Center, University of California, Davis, Davis, California, United States
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Andrew Leaver-Fay
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Brian Weitzner
- Institute for Protein Design, University of Washington, Seattle, Washington, United States.,Department of Biochemistry, University of Washington, Seattle, Washington, United States.,Lyell Immunopharma, Inc., Seattle, Washington, United States
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Ramya Rangan
- Program in Biophysics, Stanford University, Stanford, California, United States
| | - Matt A Adrianowycz
- Program in Biophysics, Stanford University, Stanford, California, United States
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aleexsan Adal
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
| | - Yuanhan Wu
- Vaccine and Immunotherapy Center, Wistar Institute, Philadelphia, Pennsylvania, United States
| | - Jordan Willis
- RubrYc Therapeutics, San Ramon, California, United States
| | - Daniel W Kulp
- Vaccine and Immunotherapy Center, Wistar Institute, Philadelphia, Pennsylvania, United States
| | - Rhiju Das
- Program in Biophysics, Stanford University, Stanford, California, United States
| | | | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, California, United States
| | - Brian Kuhlman
- Department of Biochemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States.,Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Justin B Siegel
- Department of Chemistry, Department of Biochemistry and Molecular Medicine, Genome Center, University of California, Davis, Davis, California, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
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35
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Abstract
Motivation Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody and predict new CDR H3 loop structures de novo. Results When evaluated on the Rosetta antibody benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same and worse structures [measured by root-mean-squared distance (RMSD) from the experimental CDR H3 loop structure] than the standard Rosetta energy function for 33, 6 and 10 targets, respectively, and improved the average RMSD of predictions by 32.1% (1.4 Å). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loops. When applied to de novo prediction of CDR H3 loop structures, DeepH3 achieves an average RMSD of 2.2 ± 1.1 Å on the Rosetta antibody benchmark. Availability and Implementation DeepH3 source code and pre-trained model parameters are freely available at https://github.com/Graylab/deepH3-distances-orientations. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jeffrey A Ruffolo
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Carlos Guerra
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeremias Sulam
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.,Mathematical Institute for Data Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD 21218, USA.,Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
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36
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Mahajan SP, Srinivasan Y, Labonte JW, DeLisa MP, Gray JJ. Structural basis for peptide substrate specificities of glycosyltransferase GalNAc-T2. ACS Catal 2021; 11:2977-2991. [PMID: 34322281 DOI: 10.1021/acscatal.0c04609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The polypeptide N-acetylgalactosaminyl transferase (GalNAc-T) enzyme family initiates O-linked mucin-type glycosylation. The family constitutes 20 isoenzymes in humans. GalNAc-Ts exhibit both redundancy and finely tuned specificity for a wide range of peptide substrates. In this work, we deciphered the sequence and structural motifs that determine the peptide substrate preferences for the GalNAc-T2 isoform. Our approach involved sampling and characterization of peptide-enzyme conformations obtained from Rosetta Monte Carlo-minimization-based flexible docking. We computationally scanned 19 amino acid residues at positions -1 and +1 of an eight-residue peptide substrate, which comprised a dataset of 361 (19x19) peptides with previously characterized experimental GalNAc-T2 glycosylation efficiencies. The calculations recapitulated experimental specificity data, successfully discriminating between glycosylatable and non-glycosylatable peptides with a probability of 96.5% (ROC-AUC score), a balanced accuracy of 85.5% and a false positive rate of 7.3%. The glycosylatable peptide substrates viz. peptides with proline, serine, threonine, and alanine at the -1 position of the peptide preferentially exhibited cognate sequon-like conformations. The preference for specific residues at the -1 position of the peptide was regulated by enzyme residues R362, K363, Q364, H365 and W331, which modulate the pocket size and specific enzyme-peptide interactions. For the +1 position of the peptide, enzyme residues K281 and K363 formed gating interactions with aromatics and glutamines at the +1 position of the peptide, leading to modes of peptide-binding sub-optimal for catalysis. Overall, our work revealed enzyme features that lead to the finely tuned specificity observed for a broad range of peptide substrates for the GalNAc-T2 enzyme. We anticipate that the key sequence and structural motifs can be extended to analyze specificities of other isoforms of the GalNAc-T family and can be used to guide design of variants with tailored specificity.
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Affiliation(s)
- Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Yashes Srinivasan
- Department of Bioengineering, University of California—Los Angeles, Los Angeles, California 90095, United States
| | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Chemistry, Franklin & Marshall College, Lancaster, Pennsylvania 17604, United States
| | - Matthew P. DeLisa
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Department of Microbiology, and Nancy E. and Peter C. Meinig School of Biomedical Engineering, Biochemistry, Molecular and Cell Biology, Cornell University, Ithaca, New York 14853, United States
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland 21224, United States
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37
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Guest JD, Vreven T, Zhou J, Moal I, Jeliazkov JR, Gray JJ, Weng Z, Pierce BG. An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants. Structure 2021; 29:606-621.e5. [PMID: 33539768 DOI: 10.1016/j.str.2021.01.005] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 11/15/2020] [Accepted: 01/11/2021] [Indexed: 01/04/2023]
Abstract
Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.
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Affiliation(s)
- Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Jing Zhou
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Iain Moal
- Computational Sciences, GlaxoSmithKline Research and Development, Stevenage SG1 2NY, UK
| | - Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
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38
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Light TP, Wang Z, Karl K, Zapata-Mercado EA, Pogorelov TV, Gray JJ, Hristova K. The L920F EphA4 Oncogenic Mutation Alters the SAM Domain Fold and Induces EphA4 Oligomerization. Biophys J 2021. [DOI: 10.1016/j.bpj.2020.11.2075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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39
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Abstract
Protein engineering can yield new molecular tools for nanotechnology and therapeutic applications through modulating physiochemical and biological properties. Engineering membrane proteins is especially attractive because they perform key cellular processes including transport, nutrient uptake, removal of toxins, respiration, motility, and signaling. In this chapter, we describe two protocols for membrane protein engineering with the Rosetta software: (1) ΔΔG calculations for single point mutations and (2) sequence optimization in different membrane lipid compositions. These modular protocols are easily adaptable for more complex problems and serve as a foundation for efficient membrane protein engineering calculations.
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Affiliation(s)
- Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA. .,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA.
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40
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Harmalkar A, Gray JJ. Advances to tackle backbone flexibility in protein docking. Curr Opin Struct Biol 2020; 67:178-186. [PMID: 33360497 DOI: 10.1016/j.sbi.2020.11.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/18/2020] [Accepted: 11/25/2020] [Indexed: 12/11/2022]
Abstract
Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the 'difficult' targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change.
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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA; Program in Molecular Biophysics, Institute for Nanobiotechnology, and Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
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41
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Abstract
Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the "sequence → structure → function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.
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Affiliation(s)
- Wenhao Gao
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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42
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Burman SSR, Nance ML, Jeliazkov JR, Labonte JW, Lubin JH, Biswas N, Gray JJ. Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI rounds 37-45. Proteins 2020; 88:973-985. [PMID: 31742764 PMCID: PMC8589291 DOI: 10.1002/prot.25855] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 11/04/2019] [Accepted: 11/13/2019] [Indexed: 02/06/2023]
Abstract
Critical Assessment of PRediction of Interactions (CAPRI) rounds 37 through 45 introduced larger complexes, new macromolecules, and multistage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pregenerated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in round 41. Although the results were broadly encouraging, they also highlighted the pressing need to invest in (a) flexible docking algorithms with the ability to model loop and linker motions and in (b) new sampling and scoring methods for oligosaccharide-protein interactions.
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Affiliation(s)
- Shourya S. Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Morgan L. Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
| | | | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Joseph H. Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Naireeta Biswas
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
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43
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 373] [Impact Index Per Article: 93.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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44
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Alford RF, Smolin N, Young HS, Gray JJ, Robia SL. Protein docking and steered molecular dynamics suggest alternative phospholamban-binding sites on the SERCA calcium transporter. J Biol Chem 2020; 295:11262-11274. [PMID: 32554805 DOI: 10.1074/jbc.ra120.012948] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 06/16/2020] [Indexed: 01/27/2023] Open
Abstract
The transport activity of the sarco(endo)plasmic reticulum calcium ATPase (SERCA) in cardiac myocytes is modulated by an inhibitory interaction with a transmembrane peptide, phospholamban (PLB). Previous biochemical studies have revealed that PLB interacts with a specific inhibitory site on SERCA, and low-resolution structural evidence suggests that PLB interacts with distinct alternative sites on SERCA. High-resolution details of the structural determinants of SERCA regulation have been elusive because of the dynamic nature of the regulatory complex. In this study, we used computational approaches to develop a structural model of SERCA-PLB interactions to gain a mechanistic understanding of PLB-mediated SERCA transport regulation. We combined steered molecular dynamics and membrane protein-protein docking experiments to achieve both a global search and all-atom force calculations to determine the relative affinities of PLB for candidate sites on SERCA. We modeled the binding of PLB to several SERCA conformations, representing different enzymatic states sampled during the calcium transport catalytic cycle. The results of the steered molecular dynamics and docking experiments indicated that the canonical PLB-binding site (comprising transmembrane helices M2, M4, and M9) is the preferred site. This preference was even more stringent for a superinhibitory PLB variant. Interestingly, PLB-binding specificity became more ambivalent for other SERCA conformers. These results provide evidence for polymorphic PLB interactions with novel sites on M3 and with the outside of the SERCA helix M9. Our findings are compatible with previous physical measurements that suggest that PLB interacts with multiple binding sites, conferring dynamic responsiveness to changing physiological conditions.
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Affiliation(s)
- Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Nikolai Smolin
- Department of Cell and Molecular Physiology, Stritch School of Medicine, Cardiovascular Research Institute, Loyola University Chicago, Maywood, Illinois, USA
| | - Howard S Young
- Department of Biochemistry, University of Alberta, Edmonton, Alberta, Canada
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Seth L Robia
- Department of Cell and Molecular Physiology, Stritch School of Medicine, Cardiovascular Research Institute, Loyola University Chicago, Maywood, Illinois, USA
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45
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Koehler Leman J, Weitzner BD, Renfrew PD, Lewis SM, Moretti R, Watkins AM, Mulligan VK, Lyskov S, Adolf-Bryfogle J, Labonte JW, Krys J, Bystroff C, Schief W, Gront D, Schueler-Furman O, Baker D, Bradley P, Dunbrack R, Kortemme T, Leaver-Fay A, Strauss CEM, Meiler J, Kuhlman B, Gray JJ, Bonneau R. Better together: Elements of successful scientific software development in a distributed collaborative community. PLoS Comput Biol 2020; 16:e1007507. [PMID: 32365137 PMCID: PMC7197760 DOI: 10.1371/journal.pcbi.1007507] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
- Dept of Biology, New York University, New York, NY, United States of America
| | - Brian D. Weitzner
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Dept of Biochemistry, University of Washington, Seattle, WA, United States of America
- Institute for Protein Design, University of Washington, Seattle, WA, United States of America
- Lyell Immunopharma, Seattle, WA, United States of America
| | - P. Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
| | - Steven M. Lewis
- Dept of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- Dept of Biochemistry, Duke University, Durham, NC, United States of America
- Cyrus Biotechnology, Seattle, WA United States of America
| | - Rocco Moretti
- Dept of Chemistry, Vanderbilt University, Nashville, TN, United States of America
| | - Andrew M. Watkins
- Dept of Biochemistry, Stanford University School of Medicine, Stanford CA, United States of America
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
- Dept of Biochemistry, University of Washington, Seattle, WA, United States of America
- Institute for Protein Design, University of Washington, Seattle, WA, United States of America
| | - Sergey Lyskov
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jared Adolf-Bryfogle
- Dept of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Jason W. Labonte
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Dept of Chemistry, Franklin & Marshall College, Lancaster, PA, United States of America
| | - Justyna Krys
- Dept of Chemistry, University of Warsaw, Warsaw, Poland
| | | | - Christopher Bystroff
- Dept of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States of America
| | - William Schief
- Dept of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Dominik Gront
- Dept of Chemistry, University of Warsaw, Warsaw, Poland
| | - Ora Schueler-Furman
- Dept of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - David Baker
- Dept of Biochemistry, University of Washington, Seattle, WA, United States of America
- Institute for Protein Design, University of Washington, Seattle, WA, United States of America
| | - Philip Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia PA, United States of America
| | - Tanja Kortemme
- Dept of Bioengineering and Therapeutic Sciences, University of California San Francisco, CA, United States of America
| | - Andrew Leaver-Fay
- Dept of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Charlie E. M. Strauss
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States of America
| | - Jens Meiler
- Depts of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States of America
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, United States of America
- Institute for Drug Discovery, Leipzig University, Leipzig, Germany
| | - Brian Kuhlman
- Dept of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Jeffrey J. Gray
- Dept of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, United States of America
- Dept of Biology, New York University, New York, NY, United States of America
- Dept of Computer Science, New York University, New York, NY, United States of America
- Center for Data Science, New York University, New York, NY, United States of America
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46
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Lee J, Der BS, Karamitros CS, Li W, Marshall NM, Lungu OI, Miklos AE, Xu J, Kang TH, Lee CH, Tan B, Hughes RA, Jung ST, Ippolito GC, Gray JJ, Zhang Y, Kuhlman B, Georgiou G, Ellington AD. Computer-based Engineering of Thermostabilized Antibody Fragments. AIChE J 2020; 66:e16864. [PMID: 32336757 PMCID: PMC7181397 DOI: 10.1002/aic.16864] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
We used the molecular modeling program Rosetta to identify clusters of amino acid substitutions in antibody fragments (scFvs and scAbs) that improve global protein stability and resistance to thermal deactivation. Using this methodology, we increased the melting temperature (Tm) and resistance to heat treatment of an antibody fragment that binds to the Clostridium botulinum hemagglutinin protein (anti-HA33). Two designed antibody fragment variants with two amino acid replacement clusters, designed to stabilize local regions, were shown to have both higher Tm compared to the parental scFv and importantly, to retain full antigen binding activity after 2 hours of incubation at 70 °C. The crystal structure of one thermostabilized scFv variants was solved at 1.6 Å and shown to be in close agreement with the RosettaAntibody model prediction.
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Affiliation(s)
- Jiwon Lee
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755
| | - Bryan S. Der
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC 27599
| | | | - Wenzong Li
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712
| | - Nicholas M. Marshall
- Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Oana I. Lungu
- Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Aleksandr E. Miklos
- U.S. Army Combat Capabilities Development Command Chemical Biological Center, APGEA, MD 21010
| | - Jianqing Xu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MA 21218
| | - Tae Hyun Kang
- Biopharmaceutical Chemistry Major, School of Applied Chemistry, Kookmin University, Seongbuk-gu, Seoul 02707, Republic of Korea
| | - Chang-Han Lee
- Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Bing Tan
- Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712
| | - Randall A. Hughes
- US Army Research Laboratory, Austin, TX 78712,Applied Research Laboratories, The University of Texas at Austin, Austin, TX 78712
| | - Sang Taek Jung
- Department of Biomedical Science, Graduate School of Medicine, Korea University, Seoul 02841, Republic of Korea
| | - Gregory C. Ippolito
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MA 21218
| | - Yan Zhang
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC 27599
| | - George Georgiou
- Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712,Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712,Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712,Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712,To whom correspondence should be addressed: George Georgiou () and Andrew D. Ellington ()
| | - Andrew D. Ellington
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712,Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, TX 78712,Department of Chemistry, The University of Texas at Austin, Austin, TX 78712,To whom correspondence should be addressed: George Georgiou () and Andrew D. Ellington ()
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47
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Alford RF, Gray JJ. Big Data from Sparse Data: Diverse Scientific Benchmarks Reveal Optimization Imperatives for Implicit Membrane Energy Functions. Biophys J 2020. [DOI: 10.1016/j.bpj.2019.11.2078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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48
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Jeliazkov JR, Robinson AC, García-Moreno E B, Berger JM, Gray JJ. Toward the computational design of protein crystals with improved resolution. Acta Crystallogr D Struct Biol 2019; 75:1015-1027. [PMID: 31692475 DOI: 10.1107/s2059798319013226] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 09/26/2019] [Indexed: 11/10/2022]
Abstract
Substantial advances have been made in the computational design of protein interfaces over the last 20 years. However, the interfaces targeted by design have typically been stable and high-affinity. Here, we report the development of a generic computational design method to stabilize the weak interactions at crystallographic interfaces. Initially, we analyzed structures reported in the Protein Data Bank to determine whether crystals with more stable interfaces result in higher resolution structures. We found that for 22 variants of a single protein crystallized by a single individual, the Rosetta-calculated `crystal score' correlates with the reported diffraction resolution. We next developed and tested a computational design protocol, seeking to identify point mutations that would improve resolution in a highly stable variant of staphylococcal nuclease (SNase). Using a protocol based on fixed protein backbones, only one of the 11 initial designs crystallized, indicating modeling inaccuracies and forcing us to re-evaluate our strategy. To compensate for slight changes in the local backbone and side-chain environment, we subsequently designed on an ensemble of minimally perturbed protein backbones. Using this strategy, four of the seven designed proteins crystallized. By collecting diffraction data from multiple crystals per design and solving crystal structures, we found that the designed crystals improved the resolution modestly and in unpredictable ways, including altering the crystal space group. Post hoc, in silico analysis of the three observed space groups for SNase showed that the native space group was the lowest scoring for four of six variants (including the wild type), but that resolution did not correlate with crystal score, as it did in the preliminary results. Collectively, our results show that calculated crystal scores can correlate with reported resolution, but that the correlation is absent when the problem is inverted. This outcome suggests that more comprehensive modeling of the crystallographic state is necessary to design high-resolution protein crystals from poorly diffracting crystals.
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Affiliation(s)
- Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Aaron C Robinson
- T. C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - James M Berger
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey J Gray
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, USA
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49
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Marze NA, Roy Burman SS, Sheffler W, Gray JJ. Efficient flexible backbone protein-protein docking for challenging targets. Bioinformatics 2019; 34:3461-3469. [PMID: 29718115 DOI: 10.1093/bioinformatics/bty355] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 04/27/2018] [Indexed: 11/15/2022] Open
Abstract
Motivation Binding-induced conformational changes challenge current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased flexibility, these methods struggle with efficiency and high false positive rates. Results Here, we develop and benchmark RosettaDock 4.0, which efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of near-native candidate structures in the coarse-grained phase compared to RosettaDock 3.2. It adaptively samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20-80%. In local docking of a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate (defined as cases with ≥50% chance of achieving 3 near-native structures in the 5 top-ranked ones) for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, the method successfully docks the complex. Availability and implementation As a part of the Rosetta software suite, RosettaDock 4.0 is available at https://www.rosettacommons.org to all non-commercial users for free and to commercial users for a fee. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nicholas A Marze
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Shourya S Roy Burman
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - William Sheffler
- Department of Biochemistry, University of Washington, Seattle, WA, USA.,Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jeffrey J Gray
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA.,Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
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50
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Fernandez AJ, Daniel EJP, Mahajan SP, Gray JJ, Gerken TA, Tabak LA, Samara NL. The structure of the colorectal cancer-associated enzyme GalNAc-T12 reveals how nonconserved residues dictate its function. Proc Natl Acad Sci U S A 2019; 116:20404-20410. [PMID: 31548401 PMCID: PMC6789641 DOI: 10.1073/pnas.1902211116] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Polypeptide N-acetylgalactosaminyl transferases (GalNAc-Ts) initiate mucin type O-glycosylation by catalyzing the transfer of N-acetylgalactosamine (GalNAc) to Ser or Thr on a protein substrate. Inactive and partially active variants of the isoenzyme GalNAc-T12 are present in subsets of patients with colorectal cancer, and several of these variants alter nonconserved residues with unknown functions. While previous biochemical studies have demonstrated that GalNAc-T12 selects for peptide and glycopeptide substrates through unique interactions with its catalytic and lectin domains, the molecular basis for this distinct substrate selectivity remains elusive. Here we examine the molecular basis of the activity and substrate selectivity of GalNAc-T12. The X-ray crystal structure of GalNAc-T12 in complex with a di-glycosylated peptide substrate reveals how a nonconserved GalNAc binding pocket in the GalNAc-T12 catalytic domain dictates its unique substrate selectivity. In addition, the structure provides insight into how colorectal cancer mutations disrupt the activity of GalNAc-T12 and illustrates how the rules dictating GalNAc-T12 function are distinct from those for other GalNAc-Ts.
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Affiliation(s)
- Amy J Fernandez
- Section on Biological Chemistry, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892
| | | | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD 21218
| | - Thomas A Gerken
- Department of Biochemistry, Case Western Reserve University, Cleveland, OH 44106
- Department of Chemistry, Case Western Reserve University, Cleveland, OH 44106
| | - Lawrence A Tabak
- Section on Biological Chemistry, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892
| | - Nadine L Samara
- Structural Biochemistry Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, 20892
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