1
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Meador K, Castells-Graells R, Aguirre R, Sawaya MR, Arbing MA, Sherman T, Senarathne C, Yeates TO. A suite of designed protein cages using machine learning and protein fragment-based protocols. Structure 2024; 32:751-765.e11. [PMID: 38513658 PMCID: PMC11162342 DOI: 10.1016/j.str.2024.02.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/22/2024] [Accepted: 02/23/2024] [Indexed: 03/23/2024]
Abstract
Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, self-assembling protein cages. For the generation of docked conformations, we emphasize a protein fragment-based approach, while for sequence design of the de novo interface, a comparison of knowledge-based and machine learning protocols highlights the power and increased experimental success achieved using ProteinMPNN. An analysis of design outcomes provides insights for improving interface design protocols, including prioritizing fragment-based motifs, balancing interface hydrophobicity and polarity, and identifying preferred polar contact patterns. In all, we report five structures for seven protein cages, along with two structures of intermediate assemblies, with the highest resolution reaching 2.0 Å using cryo-EM. This set of designed cages adds substantially to the body of available protein nanoparticles, and to methodologies for their creation.
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Affiliation(s)
- Kyle Meador
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA
| | | | - Roman Aguirre
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA
| | - Michael R Sawaya
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA
| | - Mark A Arbing
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA
| | - Trent Sherman
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA
| | - Chethaka Senarathne
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA
| | - Todd O Yeates
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA; UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA.
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2
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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: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [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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3
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Meador K, Castells-Graells R, Aguirre R, Sawaya MR, Arbing MA, Sherman T, Senarathne C, Yeates TO. A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.09.561468. [PMID: 37873110 PMCID: PMC10592684 DOI: 10.1101/2023.10.09.561468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, while methods for their creation remain challenging and unpredictable. In the present study, we apply new computational approaches to design a suite of new tetrahedrally symmetric, self-assembling protein cages. For the generation of docked poses, we emphasize a protein fragment-based approach, while for de novo interface design, a comparison of computational protocols highlights the power and increased experimental success achieved using the machine learning program ProteinMPNN. In relating information from docking and design, we observe that agreement between fragment-based sequence preferences and ProteinMPNN sequence inference correlates with experimental success. Additional insights for designing polar interactions are highlighted by experimentally testing larger and more polar interfaces. In all, using X-ray crystallography and cryo-EM, we report five structures for seven protein cages, with atomic resolution in the best case reaching 2.0 Å. We also report structures of two incompletely assembled protein cages, providing unique insights into one type of assembly failure. The new set of designed cages and their structures add substantially to the body of available protein nanoparticles, and to methodologies for their creation.
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Affiliation(s)
- Kyle Meador
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA 90095
| | | | - Roman Aguirre
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA 90095
| | - Michael R. Sawaya
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA, USA 90095
| | - Mark A. Arbing
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA, USA 90095
| | - Trent Sherman
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA 90095
| | - Chethaka Senarathne
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA 90095
| | - Todd O. Yeates
- Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA 90095
- UCLA-DOE Institute for Genomics and Proteomics, Los Angeles, CA, USA 90095
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4
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Wodak SJ, Vajda S, Lensink MF, Kozakov D, Bates PA. Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes. Annu Rev Biophys 2023; 52:183-206. [PMID: 36626764 PMCID: PMC10885158 DOI: 10.1146/annurev-biophys-102622-084607] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.
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Affiliation(s)
- Shoshana J Wodak
- VIB-VUB Center for Structural Biology, Vrije Universiteit Brussel, Brussels, Belgium;
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA;
- Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Marc F Lensink
- Univ. Lille, CNRS, UMR 8576-UGSF-Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France;
| | - 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
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, United Kingdom;
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5
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Ferraz MVF, Neto JCS, Lins RD, Teixeira ES. An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties. Phys Chem Chem Phys 2023; 25:7257-7267. [PMID: 36810523 DOI: 10.1039/d2cp05644e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol-1 to 2.45 kcal mol-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.
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Affiliation(s)
- Matheus V F Ferraz
- Department of Virology, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, FIOCRUZ, Recife, PE, Brazil.,Department of Fundamental Chemistry, Federal University of Pernambuco, UFPE, Recife, PE, Brazil.,Heidelberg Institute for Theoretical Studies, HITS, Heidelberg, Germany
| | - José C S Neto
- Recife Center for Advanced Studies and Systems, CESAR, Recife, PE, Brazil.
| | - Roberto D Lins
- Department of Virology, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, FIOCRUZ, Recife, PE, Brazil.,Department of Fundamental Chemistry, Federal University of Pernambuco, UFPE, Recife, PE, Brazil
| | - Erico S Teixeira
- Recife Center for Advanced Studies and Systems, CESAR, Recife, PE, Brazil.
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6
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Tsuchiya Y, Yamamori Y, Tomii K. Protein-protein interaction prediction methods: from docking-based to AI-based approaches. Biophys Rev 2022; 14:1341-1348. [PMID: 36570321 PMCID: PMC9759050 DOI: 10.1007/s12551-022-01032-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Protein-protein interactions (PPIs), such as protein-protein inhibitor, antibody-antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody-antigen interactions, offering both historical and forward-looking perspectives.
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Affiliation(s)
- Yuko Tsuchiya
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Yu Yamamori
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Kentaro Tomii
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
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7
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Listov D, Lipsh‐Sokolik R, Rosset S, Yang C, Correia BE, Fleishman SJ. Assessing and enhancing foldability in designed proteins. Protein Sci 2022; 31:e4400. [PMID: 36040259 PMCID: PMC9375437 DOI: 10.1002/pro.4400] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/09/2022] [Accepted: 07/11/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Dina Listov
- Department of Biomolecular Sciences Weizmann Institute of Science Rehovot Israel
| | | | - Stéphane Rosset
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
| | - Che Yang
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
| | - Bruno E. Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
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8
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Desantis F, Miotto M, Di Rienzo L, Milanetti E, Ruocco G. Spatial organization of hydrophobic and charged residues affects protein thermal stability and binding affinity. Sci Rep 2022; 12:12087. [PMID: 35840609 PMCID: PMC9287411 DOI: 10.1038/s41598-022-16338-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022] Open
Abstract
What are the molecular determinants of protein–protein binding affinity and whether they are similar to those regulating fold stability are two major questions of molecular biology, whose answers bring important implications both from a theoretical and applicative point of view. Here, we analyze chemical and physical features on a large dataset of protein–protein complexes with reliable experimental binding affinity data and compare them with a set of monomeric proteins for which melting temperature data was available. In particular, we probed the spatial organization of protein (1) intramolecular and intermolecular interaction energies among residues, (2) amino acidic composition, and (3) their hydropathy features. Analyzing the interaction energies, we found that strong Coulombic interactions are preferentially associated with a high protein thermal stability, while strong intermolecular van der Waals energies correlate with stronger protein–protein binding affinity. Statistical analysis of amino acids abundances, exposed to the molecular surface and/or in interaction with the molecular partner, confirmed that hydrophobic residues present on the protein surfaces are preferentially located in the binding regions, while charged residues behave oppositely. Leveraging on the important role of van der Waals interface interactions in binding affinity, we focused on the molecular surfaces in the binding regions and evaluated their shape complementarity, decomposing the molecular patches in the 2D Zernike basis. For the first time, we quantified the correlation between local shape complementarity and binding affinity via the Zernike formalism. In addition, considering the solvent interactions via the residue hydropathy, we found that the hydrophobicity of the binding regions dictates their shape complementary as much as the correlation between van der Waals energy and binding affinity. In turn, these relationships pave the way to the fast and accurate prediction and design of optimal binding regions as the 2D Zernike formalism allows a rapid and superposition-free comparison between possible binding surfaces.
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Affiliation(s)
- Fausta Desantis
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161, Rome, Italy.,The Open University Affiliated Research Centre at Istituto Italiano di Tecnologia, Via Morego, 30, 16163, Genoa, Italy
| | - Mattia Miotto
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161, Rome, Italy.
| | - Lorenzo Di Rienzo
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161, Rome, Italy
| | - Edoardo Milanetti
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161, Rome, Italy.,Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy
| | - Giancarlo Ruocco
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161, Rome, Italy.,Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy
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9
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Swanson S, Sivaraman V, Grigoryan G, Keating AE. Tertiary motifs as building blocks for the design of protein‐binding peptides. Protein Sci 2022; 31:e4322. [PMID: 35634780 PMCID: PMC9088223 DOI: 10.1002/pro.4322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 11/07/2022]
Affiliation(s)
- Sebastian Swanson
- Department of Biology Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Venkatesh Sivaraman
- Department of Biology Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Gevorg Grigoryan
- Department of Computer Science Dartmouth College Hanover New Hampshire USA
| | - Amy E. Keating
- Department of Biology Massachusetts Institute of Technology Cambridge Massachusetts USA
- Department of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
- Koch Center for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts USA
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10
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Ghalandari B, Yu Y, Ghorbani F, Warden AR, Ahmad KZ, Sang X, Huang S, Zhang Y, Su W, Divsalar A, Ding X. Polydopamine nanospheres coated with bovine serum albumin permit enhanced cell differentiation: fundamental mechanism and practical application for protein coating formation. NANOSCALE 2021; 13:20098-20110. [PMID: 34846416 DOI: 10.1039/d1nr07469e] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Protein coating is a strategy for modifying and improving the surface functional properties of nanomaterials. However, the underlying mechanism behind protein coating formation, which is essential for its practical applications, remains largely unknown. Herein, we investigate the fundamental molecular mechanism of protein coating formation. Polydopamine nanospheres (PDANS) coated with bovine serum albumin (BSA) are examined in this study due to their wide biomedical potential. Our results demonstrate that BSAs can flexibly bind to PDANS and maintain their structural dynamicity. Our findings unveil that regular structure formation arises from BSAs lateral interactions via electrostatic forces. Notably, the protein coating modified PDANS surface enhances cell adhesion and proliferation as well as osteogenic differentiation. Such an enhancement is attributed to complementary surface properties provided by the dynamic PDANS-BSA complex and regular structure caused by BSA-BSA interactions in protein coating formation. This study provides a fundamental understanding of the molecular mechanism of protein coating formation, which facilitates the further development of functional protein-coated nanomaterials and guides the bioengineering decision making for biomedical applications, especially in bone tissue engineering.
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Affiliation(s)
- Behafarid Ghalandari
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Youyi Yu
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Farnaz Ghorbani
- Department of Orthopedics, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, 2800 Gongwei Road, Pudong, Shanghai 201399, China
| | - Antony R Warden
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Khan Zara Ahmad
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Xiao Sang
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Shiyi Huang
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Yu Zhang
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Wenqiong Su
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Adeleh Divsalar
- Department of Cell and Molecular Sciences, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Xianting Ding
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
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11
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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: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [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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12
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Abstract
The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.
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13
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Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction. J Comput Aided Mol Des 2021; 35:1095-1123. [PMID: 34708263 DOI: 10.1007/s10822-021-00423-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 10/11/2021] [Indexed: 10/20/2022]
Abstract
The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics. However, problems such as predicting the bound structure of a protein-ligand complex along with its affinity have proven to be an enormous challenge. In recent years, machine learning-based methods have proven to be more accurate than older methods, many based on simple linear regression. Nonetheless, there remains room for improvement, as these methods are often trained on a small set of features, with a single functional form for any given physical effect, and often with little mention of the rationale behind choosing one functional form over another. Moreover, it is not entirely clear why one machine learning method is favored over another. In this work, we endeavor to undertake a comprehensive effort towards developing high-accuracy, machine-learned scoring functions, systematically investigating the effects of machine learning method and choice of features, and, when possible, providing insights into the relevant physics using methods that assess feature importance. Here, we show synergism among disparate features, yielding adjusted R2 with experimental binding affinities of up to 0.871 on an independent test set and enrichment for native bound structures of up to 0.913. When purely physical terms that model enthalpic and entropic effects are used in the training, we use feature importance assessments to probe the relevant physics and hopefully guide future investigators working on this and other computational chemistry problems.
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14
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Peleg Y, Vincentelli R, Collins BM, Chen KE, Livingstone EK, Weeratunga S, Leneva N, Guo Q, Remans K, Perez K, Bjerga GEK, Larsen Ø, Vaněk O, Skořepa O, Jacquemin S, Poterszman A, Kjær S, Christodoulou E, Albeck S, Dym O, Ainbinder E, Unger T, Schuetz A, Matthes S, Bader M, de Marco A, Storici P, Semrau MS, Stolt-Bergner P, Aigner C, Suppmann S, Goldenzweig A, Fleishman SJ. Community-Wide Experimental Evaluation of the PROSS Stability-Design Method. J Mol Biol 2021; 433:166964. [PMID: 33781758 DOI: 10.1016/j.jmb.2021.166964] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 03/08/2021] [Accepted: 03/22/2021] [Indexed: 10/21/2022]
Abstract
Recent years have seen a dramatic improvement in protein-design methodology. Nevertheless, most methods demand expert intervention, limiting their widespread adoption. By contrast, the PROSS algorithm for improving protein stability and heterologous expression levels has been successfully applied to a range of challenging enzymes and binding proteins. Here, we benchmark the application of PROSS as a stand-alone tool for protein scientists with no or limited experience in modeling. Twelve laboratories from the Protein Production and Purification Partnership in Europe (P4EU) challenged the PROSS algorithm with 14 unrelated protein targets without support from the PROSS developers. For each target, up to six designs were evaluated for expression levels and in some cases, for thermal stability and activity. In nine targets, designs exhibited increased heterologous expression levels either in prokaryotic and/or eukaryotic expression systems under experimental conditions that were tailored for each target protein. Furthermore, we observed increased thermal stability in nine of ten tested targets. In two prime examples, the human Stem Cell Factor (hSCF) and human Cadherin-Like Domain (CLD12) from the RET receptor, the wild type proteins were not expressible as soluble proteins in E. coli, yet the PROSS designs exhibited high expression levels in E. coli and HEK293 cells, respectively, and improved thermal stability. We conclude that PROSS may improve stability and expressibility in diverse cases, and that improvement typically requires target-specific expression conditions. This study demonstrates the strengths of community-wide efforts to probe the generality of new methods and recommends areas for future research to advance practically useful algorithms for protein science.
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Affiliation(s)
- Yoav Peleg
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Renaud Vincentelli
- Unité Mixte de Recherche (UMR) 7257, Centre National de la Recherche Scientifique (CNRS) Aix-Marseille Université, Architecture et Fonction des Macromolécules Biologiques (AFMB), Marseille, France
| | - Brett M Collins
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Kai-En Chen
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Emma K Livingstone
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Saroja Weeratunga
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Natalya Leneva
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Qian Guo
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Queensland 4072, Australia
| | - Kim Remans
- European Molecular Biology Laboratory (EMBL), Protein Expression and Purification Core Facility, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Kathryn Perez
- European Molecular Biology Laboratory (EMBL), Protein Expression and Purification Core Facility, Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Gro E K Bjerga
- NORCE Norwegian Research Centre, Postboks 22 Nygårdstangen, 5038 Bergen, Norway
| | - Øivind Larsen
- NORCE Norwegian Research Centre, Postboks 22 Nygårdstangen, 5038 Bergen, Norway
| | - Ondřej Vaněk
- Department of Biochemistry, Faculty of Science, Charles University, Hlavova 2030/8, 12840 Prague, Czech Republic
| | - Ondřej Skořepa
- Department of Biochemistry, Faculty of Science, Charles University, Hlavova 2030/8, 12840 Prague, Czech Republic
| | - Sophie Jacquemin
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Centre National de la Recherche Scientifique (CNRS), UMR 7104, Institut National de la Santé et de la Recherche Médicale (INSERM), U1258, Université de Strasbourg, France
| | - Arnaud Poterszman
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Centre National de la Recherche Scientifique (CNRS), UMR 7104, Institut National de la Santé et de la Recherche Médicale (INSERM), U1258, Université de Strasbourg, France
| | - Svend Kjær
- Structural Biology Science Technology Platform, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Evangelos Christodoulou
- Structural Biology Science Technology Platform, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Shira Albeck
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Orly Dym
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Elena Ainbinder
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tamar Unger
- Department of Life Sciences Core Facilities (LSCF), Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Anja Schuetz
- Max-Delbrück Center for Molecular Medicine (MDC), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany
| | - Susann Matthes
- Max-Delbrück Center for Molecular Medicine (MDC), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany
| | - Michael Bader
- Max-Delbrück Center for Molecular Medicine (MDC), Robert-Rössle-Straße 10, 13125 Berlin-Buch, Germany; University of Lübeck, Institute for Biology, Ratzeburger Allee 160, 23562 Lübeck, Germany; Charité University Medicine, Charitéplatz 1, 10117 Berlin, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | - Ario de Marco
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Slovenia
| | - Paola Storici
- Elettra Sincrotrone Trieste - SS 14 - km 163, 5 in Area Science Park, 34149 Basovizza, Trieste, Italy
| | - Marta S Semrau
- Elettra Sincrotrone Trieste - SS 14 - km 163, 5 in Area Science Park, 34149 Basovizza, Trieste, Italy
| | - Peggy Stolt-Bergner
- Vienna Biocenter Core Facilities GmbH, Dr. Bohr-gasse 3, 1030 Vienna, Austria
| | - Christian Aigner
- Vienna Biocenter Core Facilities GmbH, Dr. Bohr-gasse 3, 1030 Vienna, Austria
| | - Sabine Suppmann
- Max-Planck Institute of Biochemistry, Biochemistry Core Facility, Am Klopferspitz 18, 82152 Martinsried, Germany
| | - Adi Goldenzweig
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
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15
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Lopez-Hernandez AE, Xie Y, Guo W, Li L. The Electrostatic Features of Dengue Virus Capsid Assembly. JOURNAL OF COMPUTATIONAL BIOPHYSICS AND CHEMISTRY 2021. [DOI: 10.1142/s2737416520420089] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Dengue virus causes serious diseases and deaths in the world. Understanding the fundamental mechanisms of dengue virus is highly demanded to develop treatments for dengue virus caused diseases. Here, we present a computational work which focused on the stability of dengue viral capsid. The interactions among E proteins on the dengue viral capsid were studied using several computational approaches. It was found that the electrostatic distribution on a single E protein monomer is highly inhomogeneous, which makes an E protein strongly binding with another E protein. This is the reason why all the E proteins form homodimers as the basic units on the whole dengue viral capsids. The pKa calculations of E proteins demonstrated that the folding energy of an E protein is low and stable in the range of pH 6–10, which is different from many other proteins that are stable at certain pH. The pH dependence of binding energy of E protein homodimer shows that the binding energy is low and independent from pH when the pH is also in the range of 6–10. This finding implies that the dengue virus can survive in a wide range of pH, which can explain why the dengue virus is so widely distributed in the world and spreads fast.
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Affiliation(s)
| | - Yixin Xie
- Computational Science Program, University of Texas at El Paso, El Paso, TX, USA
| | - Wenhan Guo
- Computational Science Program, University of Texas at El Paso, El Paso, TX, USA
| | - Lin Li
- Computational Science Program, University of Texas at El Paso, El Paso, TX, USA
- Department of Physics, University of Texas at El Paso, El Paso, TX, USA
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16
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Residue-based pharmacophore approaches to study protein-protein interactions. Curr Opin Struct Biol 2021; 67:205-211. [PMID: 33486430 DOI: 10.1016/j.sbi.2020.12.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/04/2020] [Accepted: 12/28/2020] [Indexed: 01/22/2023]
Abstract
This review focuses on pharmacophore approaches in researching protein interfaces that bind protein ligands. Pharmacophore descriptions of binding interfaces that employ molecular dynamics simulation can account for effects of solvation and conformational flexibility. In addition, these calculations provide an approximation to entropic considerations and as such, a better approximation of the free energy of binding. Residue-based pharmacophore approaches can facilitate a variety of drug discovery tasks such as the identification of receptor-ligand partners, identifying their binding poses, designing protein interfaces for selectivity, or defining a reduced mutational combinatorial exploration for subsequent experimental engineering techniques by orders of magnitudes.
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17
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Park J, Gill KS, Aghajani AA, Heredia JD, Choi H, Oberstein A, Procko E. Engineered receptors for human cytomegalovirus that are orthogonal to normal human biology. PLoS Pathog 2020; 16:e1008647. [PMID: 32559251 PMCID: PMC7329128 DOI: 10.1371/journal.ppat.1008647] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 07/01/2020] [Accepted: 05/20/2020] [Indexed: 12/12/2022] Open
Abstract
A trimeric glycoprotein complex on the surface of human cytomegalovirus (HCMV) binds to platelet-derived growth factor (PDGF) receptor α (PDGFRα) to mediate host cell recognition and fusion of the viral and cellular membranes. Soluble PDGFRα potently neutralizes HCMV in tissue culture, and its potential use as an antiviral therapeutic has the benefit that any escape mutants will likely be attenuated. However, PDGFRα binds multiple PDGF ligands in the human body as part of developmental programs in embryogenesis and continuing through adulthood. Any therapies with soluble receptor therefore come with serious efficacy and safety concerns, especially for the treatment of congenital HCMV. Soluble virus receptors that are orthogonal to human biology might resolve these concerns. This engineering problem is solved by deep mutational scanning on the D2-D3 domains of PDGFRα to identify variants that maintain interactions with the HCMV glycoprotein trimer in the presence of competing PDGF ligands. Competition by PDGFs is conformation-dependent, whereas HCMV trimer binding is independent of proper D2-D3 conformation, and many mutations at the receptor-PDGF interface are suitable for functionally separating trimer from PDGF interactions. Purified soluble PDGFRα carrying a targeted mutation succeeded in displaying wild type affinity for HCMV trimer with a simultaneous loss of PDGF binding, and neutralizes trimer-only and trimer/pentamer-expressing HCMV strains infecting fibroblasts or epithelial cells. Overall, this work makes important progress in the realization of soluble HCMV receptors for clinical application. Human cytomegalovirus (HCMV) causes severe disease in transplant recipients and immunocompromised patients, and infections in a fetus or neonate are responsible for life-long neurological defects. Cell entry is in part mediated by a trimeric glycoprotein complex on the viral surface, which binds tightly to the host receptor PDGFRα. The soluble extracellular region of PDGFRα can be used as an antiviral agent to potently neutralize the virus in vitro. However, PDGFRα ordinarily binds growth factors in the human body to regulate developmental programs, which will limit the in vivo efficacy and safety of soluble PDGFRα. Using saturation mutagenesis and selections in human cell culture, mutations in PDGFRα are identified that eliminate off-target growth factor interactions while preserving HCMV binding and neutralization.
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Affiliation(s)
- Jihye Park
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
| | - Kevin Sean Gill
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
| | - Ali Asghar Aghajani
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
| | - Jeremiah Dallas Heredia
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
| | - Hannah Choi
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
| | - Adam Oberstein
- Department of Microbiology and Immunology, University of Illinois College of Medicine, Chicago, Illinois, United States of America
| | - Erik Procko
- Department of Biochemistry, University of Illinois, Urbana, Illinois, United States of America
- Cancer Center at Illinois (CCIL), University of Illinois, Urbana, Illinois, United States of America
- * E-mail:
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18
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Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets. Biochim Biophys Acta Gen Subj 2020; 1864:129535. [DOI: 10.1016/j.bbagen.2020.129535] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 01/09/2020] [Accepted: 01/14/2020] [Indexed: 11/18/2022]
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19
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Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization. Nat Commun 2020; 11:297. [PMID: 31941882 PMCID: PMC6962383 DOI: 10.1038/s41467-019-13895-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 11/28/2019] [Indexed: 11/09/2022] Open
Abstract
Quantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔGbind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed an attractive approach that allows us to quantify ΔΔGbind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, deep sequencing, and a few experimental ΔΔGbind data points on purified proteins to generate ΔΔGbind values for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin (BT). We show that ΔΔGbind for this interaction could be quantified with high accuracy over the range of 12 kcal mol−1 displayed by various BPTI single mutants. Quantifying the effect of mutations on binding free energy is important to understand protein-protein interaction (PPI). Here the authors develop a method based on yeast display and next-generation sequencing to generate quantitative binding landscapes for any PPI regardless of their Kd value.
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20
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Lensink MF, Nadzirin N, Velankar S, Wodak SJ. Modeling protein‐protein, protein‐peptide, and protein‐oligosaccharide complexes: CAPRI 7th edition. Proteins 2020; 88:916-938. [DOI: 10.1002/prot.25870] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/19/2019] [Accepted: 12/26/2019] [Indexed: 12/19/2022]
Affiliation(s)
- Marc F. Lensink
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle F‐59000 Lille France
| | - Nurul Nadzirin
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome Campus Cambridge UK
| | - Sameer Velankar
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome Campus Cambridge UK
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21
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Lensink MF, Brysbaert G, Nadzirin N, Velankar S, Chaleil RAG, Gerguri T, Bates PA, Laine E, Carbone A, Grudinin S, Kong R, Liu RR, Xu XM, Shi H, Chang S, Eisenstein M, Karczynska A, Czaplewski C, Lubecka E, Lipska A, Krupa P, Mozolewska M, Golon Ł, Samsonov S, Liwo A, Crivelli S, Pagès G, Karasikov M, Kadukova M, Yan Y, Huang SY, Rosell M, Rodríguez-Lumbreras LA, Romero-Durana M, Díaz-Bueno L, Fernandez-Recio J, Christoffer C, Terashi G, Shin WH, Aderinwale T, Subraman SRMV, Kihara D, Kozakov D, Vajda S, Porter K, Padhorny D, Desta I, Beglov D, Ignatov M, Kotelnikov S, Moal IH, Ritchie DW, de Beauchêne IC, Maigret B, Devignes MD, Echartea MER, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Cao Y, Shen Y, Baek M, Park T, Woo H, Seok C, Braitbard M, Bitton L, Scheidman-Duhovny D, Dapkūnas J, Olechnovič K, Venclovas Č, Kundrotas PJ, Belkin S, Chakravarty D, Badal VD, Vakser IA, Vreven T, Vangaveti S, Borrman T, Weng Z, Guest JD, Gowthaman R, Pierce BG, Xu X, Duan R, Qiu L, Hou J, Merideth BR, Ma Z, Cheng J, Zou X, Koukos PI, Roel-Touris J, Ambrosetti F, Geng C, Schaarschmidt J, Trellet ME, Melquiond ASJ, Xue L, Jiménez-García B, van Noort CW, Honorato RV, Bonvin AMJJ, Wodak SJ. Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment. Proteins 2019; 87:1200-1221. [PMID: 31612567 PMCID: PMC7274794 DOI: 10.1002/prot.25838] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 12/28/2022]
Abstract
We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.
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Affiliation(s)
- Marc F. Lensink
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Guillaume Brysbaert
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Nurul Nadzirin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Tereza Gerguri
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Elodie Laine
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
| | - Alessandra Carbone
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
- Institut Universitaire de France (IUF), Paris, France
| | - Sergei Grudinin
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ran-Ran Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xi-Ming Xu
- 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
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Miriam Eisenstein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | | | | | - Emilia Lubecka
- Institute of Informatics, Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Gdańsk, Poland
| | | | - Paweł Krupa
- Polish Academy of Sciences, Institute of Physics, Warsaw, Poland
| | | | - Łukasz Golon
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | | | - Guillaume Pagès
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | | | - Maria Kadukova
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mireia Rosell
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | - Luis A. Rodríguez-Lumbreras
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | | | | | - Juan Fernandez-Recio
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
- Instituto de Biología Molecular de Barcelona (IBMB-CSIC), Barcelona, Spain
| | | | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | | | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
- Department of Chemistry, Boston University, Boston, Massachusetts
| | - Kathryn Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sergey Kotelnikov
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Iain H. Moal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | | | | | | | | | - Didier Barradas-Bautista
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Zhen Cao
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Luigi Cavallo
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Romina Oliva
- Department of Sciences and Technologies, University of Naples “Parthenope”, Napoli, Italy
| | - Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Merav Braitbard
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lirane Bitton
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Scheidman-Duhovny
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - 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
| | - Petras J. Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Saveliy Belkin
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Devlina Chakravarty
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Varsha D. Badal
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Ilya A. Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Thom Vreven
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Sweta Vangaveti
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Tyler Borrman
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Zhiping Weng
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Jie Hou
- Department of Computer Science, University of Missouri, Columbia, Missouri
| | - Benjamin Ryan Merideth
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
- Department of Biochemistry, University of Missouri, Columbia, Missouri
| | - Panagiotis I. Koukos
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Cunliang Geng
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Mikael E. Trellet
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Adrien S. J. Melquiond
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Li Xue
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W. van Noort
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo V. Honorato
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M. J. J. Bonvin
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
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22
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Treado JD, Mei Z, Regan L, O’Hern CS. Void distributions reveal structural link between jammed packings and protein cores. Phys Rev E 2019; 99:022416. [PMID: 30934238 PMCID: PMC6902428 DOI: 10.1103/physreve.99.022416] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Indexed: 11/07/2022]
Abstract
Dense packing of hydrophobic residues in the cores of globular proteins determines their stability. Recently, we have shown that protein cores possess packing fraction ϕ≈0.56, which is the same as dense, random packing of amino-acid-shaped particles. In this article, we compare the structural properties of protein cores and jammed packings of amino-acid-shaped particles in much greater depth by measuring their local and connected void regions. We find that the distributions of surface Voronoi cell volumes and local porosities obey similar statistics in both systems. We also measure the probability that accessible, connected void regions percolate as a function of the size of a spherical probe particle and show that both systems possess the same critical probe size. We measure the critical exponent τ that characterizes the size distribution of connected void clusters at the onset of percolation. We find that the cluster size statistics are similar for void percolation in packings of amino-acid-shaped particles and randomly placed spheres, but different from that for void percolation in jammed sphere packings. We propose that the connected void regions are a defining structural feature of proteins and can be used to differentiate experimentally observed proteins from decoy structures that are generated using computational protein design software. This work emphasizes that jammed packings of amino-acid-shaped particles can serve as structural and mechanical analogs of protein cores, and could therefore be useful in modeling the response of protein cores to cavity-expanding and -reducing mutations.
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Affiliation(s)
- John D. Treado
- Department of Mechanical Engineering & Materials Science, Yale University, New Haven, Connecticut 06520, USA
- Integrated Graduate Program in Physical & Engineering Biology, Yale University, New Haven, Connecticut 06520, USA
| | - Zhe Mei
- Integrated Graduate Program in Physical & Engineering Biology, Yale University, New Haven, Connecticut 06520, USA
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, USA
| | - Lynne Regan
- Integrated Graduate Program in Physical & Engineering Biology, Yale University, New Haven, Connecticut 06520, USA
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, USA
- Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, Connecticut 06520, USA
| | - Corey S. O’Hern
- Department of Mechanical Engineering & Materials Science, Yale University, New Haven, Connecticut 06520, USA
- Integrated Graduate Program in Physical & Engineering Biology, Yale University, New Haven, Connecticut 06520, USA
- Department of Physics, Yale University, New Haven, Connecticut 06520, USA
- Department of Applied Physics, Yale University, New Haven, Connecticut 06520, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA
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23
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León I, Alonso ER, Cabezas C, Mata S, Alonso JL. Unveiling the n→π* interactions in dipeptides. Commun Chem 2019. [DOI: 10.1038/s42004-018-0103-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
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24
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Sormanni P, Aprile FA, Vendruscolo M. Third generation antibody discovery methods: in silico rational design. Chem Soc Rev 2018; 47:9137-9157. [PMID: 30298157 DOI: 10.1039/c8cs00523k] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.
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Affiliation(s)
- Pietro Sormanni
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK.
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25
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Netzer R, Listov D, Lipsh R, Dym O, Albeck S, Knop O, Kleanthous C, Fleishman SJ. Ultrahigh specificity in a network of computationally designed protein-interaction pairs. Nat Commun 2018; 9:5286. [PMID: 30538236 PMCID: PMC6290019 DOI: 10.1038/s41467-018-07722-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 11/21/2018] [Indexed: 01/21/2023] Open
Abstract
Protein networks in all organisms comprise homologous interacting pairs. In these networks, some proteins are specific, interacting with one or a few binding partners, whereas others are multispecific and bind a range of targets. We describe an algorithm that starts from an interacting pair and designs dozens of new pairs with diverse backbone conformations at the binding site as well as new binding orientations and sequences. Applied to a high-affinity bacterial pair, the algorithm results in 18 new ones, with cognate affinities from pico- to micromolar. Three pairs exhibit 3-5 orders of magnitude switch in specificity relative to the wild type, whereas others are multispecific, collectively forming a protein-interaction network. Crystallographic analysis confirms design accuracy, including in new backbones and polar interactions. Preorganized polar interaction networks are responsible for high specificity, thus defining design principles that can be applied to program synthetic cellular interaction networks of desired affinity and specificity. The molecular basis of ultrahigh specificity in protein-protein interactions remains obscure. The authors present a computational method to design atomically accurate new pairs exhibiting >100,000-fold specificity switches, generating a large and complex interaction network.
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Affiliation(s)
- Ravit Netzer
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Dina Listov
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Rosalie Lipsh
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Orly Dym
- Structural Proteomics Unit, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Shira Albeck
- Structural Proteomics Unit, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Orli Knop
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Colin Kleanthous
- Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, 7610001, Rehovot, Israel.
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26
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Raucci R, Laine E, Carbone A. Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity. Structure 2018; 26:905-915.e4. [PMID: 29779789 DOI: 10.1016/j.str.2018.04.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 02/06/2018] [Accepted: 04/10/2018] [Indexed: 12/27/2022]
Abstract
Several models estimating the strength of the interaction between proteins in a complex have been proposed. By exploring the geometry of contact distribution at protein-protein interfaces, we provide an improved model of binding energy. Local interaction signal analysis (LISA) is a radial function based on terms describing favorable and non-favorable contacts obtained by density functional theory, the support-core-rim interface residue distribution, non-interacting charged residues and secondary structures contribution. The three-dimensional organization of the contacts and their contribution on localized hot-sites over the entire interaction surface were numerically evaluated. LISA achieves a correlation of 0.81 (and a root-mean-square error of 2.35 ± 0.38 kcal/mol) when tested on 125 complexes for which experimental measurements were realized. LISA's performance is stable for subsets defined by functional composition and extent of conformational changes upon complex formation. A large-scale comparison with 17 other functions demonstrated the power of the geometrical model in the understanding of complex binding.
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Affiliation(s)
- Raffaele Raucci
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France; Sorbonne Université, Institut des Sciences du Calcul et des Données (ISCD), 75005 Paris, France
| | - Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France
| | - Alessandra Carbone
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 4 Place Jussieu, 75005 Paris, France; Institut Universitaire de France, 75005 Paris, France.
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27
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Gaines JC, Acebes S, Virrueta A, Butler M, Regan L, O'Hern CS. Comparing side chain packing in soluble proteins, protein-protein interfaces, and transmembrane proteins. Proteins 2018; 86:581-591. [PMID: 29427530 PMCID: PMC5912992 DOI: 10.1002/prot.25479] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 01/23/2018] [Accepted: 02/06/2018] [Indexed: 12/26/2022]
Abstract
We compare side chain prediction and packing of core and non-core regions of soluble proteins, protein-protein interfaces, and transmembrane proteins. We first identified or created comparable databases of high-resolution crystal structures of these 3 protein classes. We show that the solvent-inaccessible cores of the 3 classes of proteins are equally densely packed. As a result, the side chains of core residues at protein-protein interfaces and in the membrane-exposed regions of transmembrane proteins can be predicted by the hard-sphere plus stereochemical constraint model with the same high prediction accuracies (>90%) as core residues in soluble proteins. We also find that for all 3 classes of proteins, as one moves away from the solvent-inaccessible core, the packing fraction decreases as the solvent accessibility increases. However, the side chain predictability remains high (80% within 30°) up to a relative solvent accessibility, rSASA≲0.3, for all 3 protein classes. Our results show that ≈40% of the interface regions in protein complexes are "core", that is, densely packed with side chain conformations that can be accurately predicted using the hard-sphere model. We propose packing fraction as a metric that can be used to distinguish real protein-protein interactions from designed, non-binding, decoys. Our results also show that cores of membrane proteins are the same as cores of soluble proteins. Thus, the computational methods we are developing for the analysis of the effect of hydrophobic core mutations in soluble proteins will be equally applicable to analyses of mutations in membrane proteins.
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Affiliation(s)
- J C Gaines
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, 06520
- Integrated Graduate Program in Physical and Engineering Biology (IGPPEB), Yale University, New Haven, Connecticut, 06520
| | - S Acebes
- Department of Mechanical Engineering and Materials Science, Yale University, New Haven, Connecticut, 06520
| | - A Virrueta
- Integrated Graduate Program in Physical and Engineering Biology (IGPPEB), Yale University, New Haven, Connecticut, 06520
- Department of Mechanical Engineering and Materials Science, Yale University, New Haven, Connecticut, 06520
| | - M Butler
- Department of Physics and Astronomy, University of Southern California, Los Angeles, California, 90007
| | - L Regan
- Integrated Graduate Program in Physical and Engineering Biology (IGPPEB), Yale University, New Haven, Connecticut, 06520
- Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, Connecticut, 06520
- Department of Chemistry, Yale University, New Haven, Connecticut, 06520
| | - C S O'Hern
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, 06520
- Integrated Graduate Program in Physical and Engineering Biology (IGPPEB), Yale University, New Haven, Connecticut, 06520
- Department of Mechanical Engineering and Materials Science, Yale University, New Haven, Connecticut, 06520
- Department of Physics, Yale University, New Haven, Connecticut, 06520
- Department of Applied Physics, Yale University, New Haven, Connecticut, 06520
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28
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Abstract
The immune systems protect our bodies from foreign molecules or antigens, where antibodies play important roles. Antibodies evolve over time upon antigen encounter by somatically mutating their genome sequences. The end result is a series of antibodies that display higher affinities and specificities to specific antigens. This process is called affinity maturation. Recent improvements in computer hardware and modeling algorithms now enable the rational design of protein structures and functions, and several works on computer-aided antibody design have been published. In this chapter, we briefly describe computational methods for antibody affinity maturation, focusing on methods for sampling antibody conformations and for scoring designed antibody variants. We also discuss lessons learned from the successful computer-aided design of antibodies.
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Affiliation(s)
- Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
- Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
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29
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Lensink MF, Velankar S, Baek M, Heo L, Seok C, Wodak SJ. The challenge of modeling protein assemblies: the CASP12-CAPRI experiment. Proteins 2017; 86 Suppl 1:257-273. [PMID: 29127686 DOI: 10.1002/prot.25419] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 10/31/2017] [Accepted: 11/07/2017] [Indexed: 12/18/2022]
Abstract
We present the quality assessment of 5613 models submitted by predictor groups from both CAPRI and CASP for the total of 15 most tractable targets from the second joint CASP-CAPRI protein assembly prediction experiment. These targets comprised 12 homo-oligomers and 3 hetero-complexes. The bulk of the analysis focuses on 10 targets (of CAPRI Round 37), which included all 3 hetero-complexes, and whose protein chains or the full assembly could be readily modeled from structural templates in the PDB. On average, 28 CAPRI groups and 10 CASP groups (including automatic servers), submitted models for each of these 10 targets. Additionally, about 16 groups participated in the CAPRI scoring experiments. A range of acceptable to high quality models were obtained for 6 of the 10 Round 37 targets, for which templates were available for the full assembly. Poorer results were achieved for the remaining targets due to the lower quality of the templates available for the full complex or the individual protein chains, highlighting the unmet challenge of modeling the structural adjustments of the protein components that occur upon binding or which must be accounted for in template-based modeling. On the other hand, our analysis indicated that residues in binding interfaces were correctly predicted in a sizable fraction of otherwise poorly modeled assemblies and this with higher accuracy than published methods that do not use information on the binding partner. Lastly, the strengths and weaknesses of the assessment methods are evaluated and improvements suggested.
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Affiliation(s)
- Marc F Lensink
- University Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, Korea
| | - Lim Heo
- Department of Chemistry, Seoul National University, Seoul, Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Korea
| | - Shoshana J Wodak
- VIB Structural Biology Research Center, VUB, Pleinlaan 2, Brussels, Belgium
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30
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Tramontano A. The computational prediction of protein assemblies. Curr Opin Struct Biol 2017; 46:170-175. [PMID: 29102305 DOI: 10.1016/j.sbi.2017.10.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Revised: 10/04/2017] [Accepted: 10/05/2017] [Indexed: 10/18/2022]
Abstract
The function of proteins in the cell is almost always mediated by their interaction with different partners, including other proteins, nucleic acids or small organic molecules. The ability of identifying all of them is an essential step in our quest for understanding life at the molecular level. The inference of the protein complex composition and of its molecular details can also provide relevant clues for the development and the design of drugs. In this short review, I will discuss the computational aspects of the analysis and prediction of protein-protein assemblies and discuss some of the most recent developments as seen in the last Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment.
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Affiliation(s)
- Anna Tramontano
- Physics Department, Sapienza University of Rome, Piazzale Aldo Moro, 5 I-00185 Roma, Italy; Istituto Pasteur - Fondazione Cenci Bolognetti, Sapienza University of Rome, Piazzale Aldo Moro, 5 I-00185 Roma, Italy
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31
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Structure-based cross-docking analysis of antibody-antigen interactions. Sci Rep 2017; 7:8145. [PMID: 28811664 PMCID: PMC5557897 DOI: 10.1038/s41598-017-08414-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 07/10/2017] [Indexed: 12/02/2022] Open
Abstract
Antibody–antigen interactions are critical to our immune response, and understanding the structure-based biophysical determinants for their binding specificity and affinity is of fundamental importance. We present a computational structure-based cross-docking study to test the identification of native antibody–antigen interaction pairs among cognate and non-cognate complexes. We picked a dataset of 17 antibody–antigen complexes of which 11 have both bound and unbound structures available, and we generated a representative ensemble of cognate and non-cognate complexes. Using the Rosetta interface score as a classifier, the cognate pair was the top-ranked model in 80% (14/17) of the antigen targets using bound monomer structures in docking, 35% (6/17) when using unbound, and 12% (2/17) when using the homology-modeled backbones to generate the complexes. Increasing rigid-body diversity of the models using RosettaDock’s local dock routine lowers the discrimination accuracy with the cognate antibody–antigen pair ranking in bound and unbound models but recovers additional top-ranked cognate complexes when using homology models. The study is the first structure-based cross-docking attempt aimed at distinguishing antibody–antigen binders from non-binders and demonstrates the challenges to address for the methods to be widely applicable to supplement high-throughput experimental antibody sequencing workflows.
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32
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Marcu O, Dodson EJ, Alam N, Sperber M, Kozakov D, Lensink MF, Schueler-Furman O. FlexPepDock lessons from CAPRI peptide-protein rounds and suggested new criteria for assessment of model quality and utility. Proteins 2017; 85:445-462. [PMID: 28002624 PMCID: PMC6618814 DOI: 10.1002/prot.25230] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 11/15/2016] [Accepted: 11/23/2016] [Indexed: 12/21/2022]
Abstract
CAPRI rounds 28 and 29 included, for the first time, peptide-receptor targets of three different systems, reflecting increased appreciation of the importance of peptide-protein interactions. The CAPRI rounds allowed us to objectively assess the performance of Rosetta FlexPepDock, one of the first protocols to explicitly include peptide flexibility in docking, accounting for peptide conformational changes upon binding. We discuss here successes and challenges in modeling these targets: we obtain top-performing, high-resolution models of the peptide motif for cases with known binding sites but there is a need for better modeling of flanking regions, as well as better selection criteria, in particular for unknown binding sites. These rounds have also provided us the opportunity to reassess the success criteria, to better reflect the quality of a peptide-protein complex model. Using all models submitted to CAPRI, we analyze the correlation between current classification criteria and the ability to retrieve critical interface features, such as hydrogen bonds and hotspots. We find that loosening the backbone (and ligand) RMSD threshold, together with a restriction on the side chain RMSD measure, allows us to improve the selection of high-accuracy models. We also suggest a new measure to assess interface hydrogen bond recovery, which is not assessed by the current CAPRI criteria. Finally, we find that surprisingly much can be learned from rather inaccurate models about binding hotspots, suggesting that the current status of peptide-protein docking methods, as reflected by the submitted CAPRI models, can already have a significant impact on our understanding of protein interactions. Proteins 2017; 85:445-462. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Orly Marcu
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, the Hebrew University of Jerusalem, Israel
| | - Emma-Joy Dodson
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, the Hebrew University of Jerusalem, Israel
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, the Hebrew University of Jerusalem, Israel
| | - Michal Sperber
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, the Hebrew University of Jerusalem, Israel
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brooks University, Stony Brook, New York, 11794
| | - Marc F Lensink
- University of Lille, CNRS UMR8576 UGSF, Lille, 59000, France
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, the Hebrew University of Jerusalem, Israel
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33
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Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning. Proc Natl Acad Sci U S A 2017; 114:2265-2270. [PMID: 28196882 DOI: 10.1073/pnas.1614437114] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Proteins are marginally stable, and an understanding of the sequence determinants for improved protein solubility is highly desired. For enzymes, it is well known that many mutations that increase protein solubility decrease catalytic activity. These competing effects frustrate efforts to design and engineer stable, active enzymes without laborious high-throughput activity screens. To address the trade-off between enzyme solubility and activity, we performed deep mutational scanning using two different screens/selections that purport to gauge protein solubility for two full-length enzymes. We assayed a TEM-1 beta-lactamase variant and levoglucosan kinase (LGK) using yeast surface display (YSD) screening and a twin-arginine translocation pathway selection. We then compared these scans with published experimental fitness landscapes. Results from the YSD screen could explain 37% of the variance in the fitness landscapes for one enzyme. Five percent to 10% of all single missense mutations improve solubility, matching theoretical predictions of global protein stability. For a given solubility-enhancing mutation, the probability that it would retain wild-type fitness was correlated with evolutionary conservation and distance to active site, and anticorrelated with contact number. Hybrid classification models were developed that could predict solubility-enhancing mutations that maintain wild-type fitness with an accuracy of 90%. The downside of using such classification models is the removal of rare mutations that improve both fitness and solubility. To reveal the biophysical basis of enhanced protein solubility and function, we determined the crystallographic structure of one such LGK mutant. Beyond fundamental insights into trade-offs between stability and activity, these results have potential biotechnological applications.
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34
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Wei Q, La D, Kihara D. BindML/BindML+: Detecting Protein-Protein Interaction Interface Propensity from Amino Acid Substitution Patterns. Methods Mol Biol 2017; 1529:279-289. [PMID: 27914057 DOI: 10.1007/978-1-4939-6637-0_14] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Prediction of protein-protein interaction sites in a protein structure provides important information for elucidating the mechanism of protein function and can also be useful in guiding a modeling or design procedures of protein complex structures. Since prediction methods essentially assess the propensity of amino acids that are likely to be part of a protein docking interface, they can help in designing protein-protein interactions. Here, we introduce BindML and BindML+ protein-protein interaction sites prediction methods. BindML predicts protein-protein interaction sites by identifying mutation patterns found in known protein-protein complexes using phylogenetic substitution models. BindML+ is an extension of BindML for distinguishing permanent and transient types of protein-protein interaction sites. We developed an interactive web-server that provides a convenient interface to assist in structural visualization of protein-protein interactions site predictions. The input data for the web-server are a tertiary structure of interest. BindML and BindML+ are available at http://kiharalab.org/bindml/ and http://kiharalab.org/bindml/plus/ .
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Affiliation(s)
- Qing Wei
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - David La
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
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35
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Elucidating Mechanisms of Molecular Recognition Between Human Argonaute and miRNA Using Computational Approaches. Methods Mol Biol 2016. [PMID: 27924488 DOI: 10.1007/978-1-4939-6563-2_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
MicroRNA (miRNA) and Argonaute (AGO) protein together form the RNA-induced silencing complex (RISC) that plays an essential role in the regulation of gene expression. Elucidating the underlying mechanism of AGO-miRNA recognition is thus of great importance not only for the in-depth understanding of miRNA function but also for inspiring new drugs targeting miRNAs. In this chapter we introduce a combined computational approach of molecular dynamics (MD) simulations, Markov state models (MSMs), and protein-RNA docking to investigate AGO-miRNA recognition. Constructed from MD simulations, MSMs can elucidate the conformational dynamics of AGO at biologically relevant timescales. Protein-RNA docking can then efficiently identify the AGO conformations that are geometrically accessible to miRNA. Using our recent work on human AGO2 as an example, we explain the rationale and the workflow of our method in details. This combined approach holds great promise to complement experiments in unraveling the mechanisms of molecular recognition between large, flexible, and complex biomolecules.
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36
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Yap EH, Fiser A. ProtLID, a Residue-Based Pharmacophore Approach to Identify Cognate Protein Ligands in the Immunoglobulin Superfamily. Structure 2016; 24:2217-2226. [PMID: 27889206 PMCID: PMC5444293 DOI: 10.1016/j.str.2016.10.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 07/26/2016] [Accepted: 10/25/2016] [Indexed: 10/20/2022]
Abstract
Members of the extracellular immunoglobulin superfamily (IgSF) play a key role in immune regulation through the control of the co-stimulatory pathway, and have emerged as potent drug targets in cancers, infectious diseases, and autoimmunity. Despite the difficult experimental access to this class of proteins, single structures of ectodomains of IgSF proteins are solved with regularity. However, the most biologically critical challenge for this class of proteins is the identification of their cognate ligands that communicate intercellular signals. We describe a conceptually novel method, protein-ligand interface design (ProtLID), to identify cognate ligands from a subproteome for a given target receptor protein. ProtLID designs an optimal protein interface for a given receptor by running extensive molecular dynamics simulations of single-residue probes. The type and location of residue preferences establish a residue-based pharmacophore, which is subsequently used to find potential matches among candidate ligands within a subproteome.
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Affiliation(s)
- Eng-Hui Yap
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA; Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Andras Fiser
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA; Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
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37
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Lensink MF, Velankar S, Wodak SJ. Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition. Proteins 2016; 85:359-377. [PMID: 27865038 DOI: 10.1002/prot.25215] [Citation(s) in RCA: 156] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 10/07/2016] [Accepted: 10/10/2016] [Indexed: 12/19/2022]
Abstract
We present the sixth report evaluating the performance of methods for predicting the atomic resolution structures of protein complexes offered as targets to the community-wide initiative on the Critical Assessment of Predicted Interactions (CAPRI). The evaluation is based on a total of 20,670 predicted models for 8 protein-peptide complexes, a novel category of targets in CAPRI, and 12 protein-protein targets in CAPRI prediction Rounds held during the years 2013-2016. For two of the protein-protein targets, the focus was on the prediction of side-chain conformation and positions of interfacial water molecules. Seven of the protein-protein targets were particularly challenging owing to their multicomponent nature, to conformational changes at the binding site, or to a combination of both. Encouragingly, the very large multiprotein complex with the nucleosome was correctly predicted, and correct models were submitted for the protein-peptide targets, but not for some of the challenging protein-protein targets. Models of acceptable quality or better were obtained for 14 of the 20 targets, including medium quality models for 13 targets and high quality models for 8 targets, indicating tangible progress of present-day computational methods in modeling protein complexes with increased accuracy. Our evaluation suggests that the progress stems from better integration of different modeling tools with docking procedures, as well as the use of more sophisticated evolutionary information to score models. Nonetheless, adequate modeling of conformational flexibility in interacting proteins remains an important area with a crucial need for improvement. Proteins 2017; 85:359-377. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Marc F Lensink
- University of Lille, CNRS UMR8576 UGSF, Lille, 59000, France
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Shoshana J Wodak
- VIB Structural Biology Research Center, VUB Pleinlaan 2, Brussels, 1050, Belgium
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38
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Riley TP, Ayres CM, Hellman LM, Singh NK, Cosiano M, Cimons JM, Anderson MJ, Piepenbrink KH, Pierce BG, Weng Z, Baker BM. A generalized framework for computational design and mutational scanning of T-cell receptor binding interfaces. Protein Eng Des Sel 2016; 29:595-606. [PMID: 27624308 PMCID: PMC5181382 DOI: 10.1093/protein/gzw050] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 08/19/2016] [Accepted: 08/23/2016] [Indexed: 11/13/2022] Open
Abstract
T-cell receptors (TCRs) have emerged as a new class of therapeutics, most prominently for cancer where they are the key components of new cellular therapies as well as soluble biologics. Many studies have generated high affinity TCRs in order to enhance sensitivity. Recent outcomes, however, have suggested that fine manipulation of TCR binding, with an emphasis on specificity may be more valuable than large affinity increments. Structure-guided design is ideally suited for this role, and here we studied the generality of structure-guided design as applied to TCRs. We found that a previous approach, which successfully optimized the binding of a therapeutic TCR, had poor accuracy when applied to a broader set of TCR interfaces. We thus sought to develop a more general purpose TCR design framework. After assembling a large dataset of experimental data spanning multiple interfaces, we trained a new scoring function that accounted for unique features of each interface. Together with other improvements, such as explicit inclusion of molecular flexibility, this permitted the design new affinity-enhancing mutations in multiple TCRs, including those not used in training. Our approach also captured the impacts of mutations and substitutions in the peptide/MHC ligand, and recapitulated recent findings regarding TCR specificity, indicating utility in more general mutational scanning of TCR-pMHC interfaces.
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Affiliation(s)
- Timothy P Riley
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Cory M Ayres
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Lance M Hellman
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Nishant K Singh
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Michael Cosiano
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Jennifer M Cimons
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Michael J Anderson
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Kurt H Piepenbrink
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
| | - Brian G Pierce
- Institute for Bioscience and Biotechnology Research, University of Maryland, 9600 Gudelsky Drive, Rockville, MD 20850, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Brian M Baker
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA
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39
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Laine E, Carbone A. Protein social behavior makes a stronger signal for partner identification than surface geometry. Proteins 2016; 85:137-154. [PMID: 27802579 PMCID: PMC5242317 DOI: 10.1002/prot.25206] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Revised: 10/10/2016] [Accepted: 10/20/2016] [Indexed: 01/26/2023]
Abstract
Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico‐chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross‐docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S‐index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface‐based (ranking) score to discriminate partners from non‐interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137–154. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Elodie Laine
- Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Paris, 75005, France
| | - Alessandra Carbone
- Sorbonne Universités, UPMC-Univ P6, CNRS, Laboratoire de Biologie Computationnelle et Quantitative - UMR 7238, Paris, 75005, France.,Institut Universitaire de France, Paris, 75005, France
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40
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Dygut J, Kalinowska B, Banach M, Piwowar M, Konieczny L, Roterman I. Structural Interface Forms and Their Involvement in Stabilization of Multidomain Proteins or Protein Complexes. Int J Mol Sci 2016; 17:ijms17101741. [PMID: 27763556 PMCID: PMC5085769 DOI: 10.3390/ijms17101741] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2016] [Revised: 09/30/2016] [Accepted: 10/11/2016] [Indexed: 12/20/2022] Open
Abstract
The presented analysis concerns the inter-domain and inter-protein interface in protein complexes. We propose extending the traditional understanding of the protein domain as a function of local compactness with an additional criterion which refers to the presence of a well-defined hydrophobic core. Interface areas in selected homodimers vary with respect to their contribution to share as well as individual (domain-specific) hydrophobic cores. The basic definition of a protein domain, i.e., a structural unit characterized by tighter packing than its immediate environment, is extended in order to acknowledge the role of a structured hydrophobic core, which includes the interface area. The hydrophobic properties of interfaces vary depending on the status of interacting domains—In this context we can distinguish: (1) Shared hydrophobic cores (spanning the whole dimer); (2) Individual hydrophobic cores present in each monomer irrespective of whether the dimer contains a shared core. Analysis of interfaces in dystrophin and utrophin indicates the presence of an additional quasi-domain with a prominent hydrophobic core, consisting of fragments contributed by both monomers. In addition, we have also attempted to determine the relationship between the type of interface (as categorized above) and the biological function of each complex. This analysis is entirely based on the fuzzy oil drop model.
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Affiliation(s)
- Jacek Dygut
- Department of Rehabilitation, Hospital in Przemyśl, Monte Cassino 18, 37-700 Przemyśl, Poland.
| | - Barbara Kalinowska
- Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, 30-348 Krakow, Poland.
| | - Mateusz Banach
- Department of Bioinformatics and Telemedicine, Jagiellonian University-Medical College, Łazarza 16, 31-530 Krakow, Poland.
| | - Monika Piwowar
- Department of Bioinformatics and Telemedicine, Jagiellonian University-Medical College, Łazarza 16, 31-530 Krakow, Poland.
| | - Leszek Konieczny
- Chair of Medical Biochemistry, Jagiellonian University-Medical College, Kopernika 7, 31-034 Krakow, Poland.
| | - Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University-Medical College, Łazarza 16, 31-530 Krakow, Poland.
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41
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Harris DT, Wang N, Riley TP, Anderson SD, Singh NK, Procko E, Baker BM, Kranz DM. Deep Mutational Scans as a Guide to Engineering High Affinity T Cell Receptor Interactions with Peptide-bound Major Histocompatibility Complex. J Biol Chem 2016; 291:24566-24578. [PMID: 27681597 DOI: 10.1074/jbc.m116.748681] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 09/15/2016] [Indexed: 11/06/2022] Open
Abstract
Proteins are often engineered to have higher affinity for their ligands to achieve therapeutic benefit. For example, many studies have used phage or yeast display libraries of mutants within complementarity-determining regions to affinity mature antibodies and T cell receptors (TCRs). However, these approaches do not allow rapid assessment or evolution across the entire interface. By combining directed evolution with deep sequencing, it is now possible to generate sequence fitness landscapes that survey the impact of every amino acid substitution across the entire protein-protein interface. Here we used the results of deep mutational scans of a TCR-peptide-MHC interaction to guide mutational strategies. The approach yielded stable TCRs with affinity increases of >200-fold. The substitutions with the greatest enrichments based on the deep sequencing were validated to have higher affinity and could be combined to yield additional improvements. We also conducted in silico binding analyses for every substitution to compare them with the fitness landscape. Computational modeling did not effectively predict the impacts of mutations distal to the interface and did not account for yeast display results that depended on combinations of affinity and protein stability. However, computation accurately predicted affinity changes for mutations within or near the interface, highlighting the complementary strengths of computational modeling and yeast surface display coupled with deep mutational scanning for engineering high affinity TCRs.
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Affiliation(s)
- Daniel T Harris
- From the Department of Biochemistry, University of Illinois, Urbana, Illinois 61801 and
| | - Ningyan Wang
- From the Department of Biochemistry, University of Illinois, Urbana, Illinois 61801 and
| | - Timothy P Riley
- the Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, South Bend, Indiana 46557
| | - Scott D Anderson
- From the Department of Biochemistry, University of Illinois, Urbana, Illinois 61801 and
| | - Nishant K Singh
- the Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, South Bend, Indiana 46557
| | - Erik Procko
- From the Department of Biochemistry, University of Illinois, Urbana, Illinois 61801 and
| | - Brian M Baker
- the Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, South Bend, Indiana 46557
| | - David M Kranz
- From the Department of Biochemistry, University of Illinois, Urbana, Illinois 61801 and.
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42
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Shimba N, Kamiya N, Nakamura H. Model Building of Antibody–Antigen Complex Structures Using GBSA Scores. J Chem Inf Model 2016; 56:2005-2012. [DOI: 10.1021/acs.jcim.6b00066] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Noriko Shimba
- Device
Research Laboratory, Advanced Research Division, Panasonic Corporation, 3-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan
| | - Narutoshi Kamiya
- Advanced
Institute for Computational Science, RIKEN, QBiC Building B, 6-2-4 Furuedai, Suita, Osaka 565-0874, Japan
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Haruki Nakamura
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
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43
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Lensink MF, Velankar S, Kryshtafovych A, Huang SY, Schneidman-Duhovny D, Sali A, Segura J, Fernandez-Fuentes N, Viswanath S, Elber R, Grudinin S, Popov P, Neveu E, Lee H, Baek M, Park S, Heo L, Rie Lee G, Seok C, Qin S, Zhou HX, Ritchie DW, Maigret B, Devignes MD, Ghoorah A, Torchala M, Chaleil RAG, Bates PA, Ben-Zeev E, Eisenstein M, Negi SS, Weng Z, Vreven T, Pierce BG, Borrman TM, Yu J, Ochsenbein F, Guerois R, Vangone A, Rodrigues JPGLM, van Zundert G, Nellen M, Xue L, Karaca E, Melquiond ASJ, Visscher K, Kastritis PL, Bonvin AMJJ, Xu X, Qiu L, Yan C, Li J, Ma Z, Cheng J, Zou X, Shen Y, Peterson LX, Kim HR, Roy A, Han X, Esquivel-Rodriguez J, Kihara D, Yu X, Bruce NJ, Fuller JC, Wade RC, Anishchenko I, Kundrotas PJ, Vakser IA, Imai K, Yamada K, Oda T, Nakamura T, Tomii K, Pallara C, Romero-Durana M, Jiménez-García B, Moal IH, Férnandez-Recio J, Joung JY, Kim JY, Joo K, Lee J, Kozakov D, Vajda S, Mottarella S, Hall DR, Beglov D, Mamonov A, Xia B, Bohnuud T, Del Carpio CA, Ichiishi E, Marze N, Kuroda D, Roy Burman SS, Gray JJ, Chermak E, Cavallo L, Oliva R, Tovchigrechko A, Wodak SJ. Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment. Proteins 2016; 84 Suppl 1:323-48. [PMID: 27122118 PMCID: PMC5030136 DOI: 10.1002/prot.25007] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Revised: 12/30/2015] [Accepted: 02/02/2016] [Indexed: 12/26/2022]
Abstract
We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Marc F Lensink
- University Lille, CNRS UMR8576 UGSF, Lille, F-59000, France.
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | | | - Shen-You Huang
- Research Support Computing, University of Missouri Bioinformatics Consortium, and Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Dina Schneidman-Duhovny
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, 94158
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94158
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, 94158
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94158
- California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California, 94158
| | - Joan Segura
- GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC), Madrid, 28049, Spain
| | - Narcis Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, SY233FG, United Kingdom
| | - Shruthi Viswanath
- Department of Computer Science, University of Texas at Austin, Austin, Texas, 78712
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, 78712
| | - Ron Elber
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, 78712
- Department of Chemistry, University of Texas at Austin, Austin, Texas, 78712
| | - Sergei Grudinin
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
| | - Petr Popov
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Emilie Neveu
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
| | - Hasup Lee
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Sangwoo Park
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Lim Heo
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Gyu Rie Lee
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida, 32306, USA
| | - Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida, 32306, USA
| | | | - Bernard Maigret
- CNRS, LORIA, Campus Scientifique, BP 239, Vandœuvre-lès-Nancy, 54506, France
| | | | - Anisah Ghoorah
- Department of Computer Science and Engineering, University of Mauritius, Reduit, Mauritius
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Raphaël A G Chaleil
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Paul A Bates
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Efrat Ben-Zeev
- G-INCPM, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Miriam Eisenstein
- Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Surendra S Negi
- Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, 301 University Boulevard, Galveston, Texas, 77555-0857
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Brian G Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Tyler M Borrman
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Jinchao Yu
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Françoise Ochsenbein
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Raphaël Guerois
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - João P G L M Rodrigues
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Gydo van Zundert
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Mehdi Nellen
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Li Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Ezgi Karaca
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Adrien S J Melquiond
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Koen Visscher
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
| | - Chengfei Yan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
| | - Jilong Li
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
- Informatics Institute, University of Missouri, Columbia, Missouri, 65211
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
- Informatics Institute, University of Missouri, Columbia, Missouri, 65211
- Department of Biochemistry, University of Missouri, Columbia, Missouri, 65211
| | - Yang Shen
- Toyota Technological Institute at Chicago, 6045 S Kenwood Avenue, Chicago, Illinois, 60637
| | - Lenna X Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | - Hyung-Rae Kim
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | - Amit Roy
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
- Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, National Institutes of Health, Hamilton, Montano 59840
| | - Xusi Han
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | | | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
- Department of Computer Science, Purdue University, West Lafayette, IN, USA, 47907
| | - Xiaofeng Yu
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Neil J Bruce
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Jonathan C Fuller
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Ivan Anishchenko
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Petras J Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Ilya A Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
- Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047
| | - Kenichiro Imai
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Kazunori Yamada
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Toshiyuki Oda
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Tsukasa Nakamura
- Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan
| | - Kentaro Tomii
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
- Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan
| | - Chiara Pallara
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Miguel Romero-Durana
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Brian Jiménez-García
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Iain H Moal
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Juan Férnandez-Recio
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Jong Young Joung
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Jong Yun Kim
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Keehyoung Joo
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Jooyoung Lee
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
- School of Computational Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Dima Kozakov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
- Department of Chemistry, Boston University, Boston, Massachusetts
| | - Scott Mottarella
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - David R Hall
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Artem Mamonov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Bing Xia
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Tanggis Bohnuud
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Carlos A Del Carpio
- Institute of Biological Diversity, International Pacific Institute of Indiana, Bloomington, Indiana, 47401
- Drosophila Genetic Resource Center, Kyoto Institute of Technology, Ukyo-Ku, 616-8354, Japan
| | - Eichiro Ichiishi
- International University of Health and Welfare Hospital (IUHW Hospital), Asushiobara-City, Tochigi Prefecture, 329-2763, Japan
| | - Nicholas Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Edrisse Chermak
- King Abdullah University of Science and Technology, Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology, Saudi Arabia
| | - Romina Oliva
- University of Naples "Parthenope", Napoli, Italy
| | - Andrey Tovchigrechko
- J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, Maryland, 20850
| | - Shoshana J Wodak
- Departments of Biochemistry and Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
- VIB Structural Biology Research Center, VUB Pleinlaan 2, Brussels, 1050, Belgium.
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44
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Bale JB, Gonen S, Liu Y, Sheffler W, Ellis D, Thomas C, Cascio D, Yeates TO, Gonen T, King NP, Baker D. Accurate design of megadalton-scale two-component icosahedral protein complexes. Science 2016; 353:389-94. [PMID: 27463675 PMCID: PMC5485857 DOI: 10.1126/science.aaf8818] [Citation(s) in RCA: 388] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 06/21/2016] [Indexed: 12/24/2022]
Abstract
Nature provides many examples of self- and co-assembling protein-based molecular machines, including icosahedral protein cages that serve as scaffolds, enzymes, and compartments for essential biochemical reactions and icosahedral virus capsids, which encapsidate and protect viral genomes and mediate entry into host cells. Inspired by these natural materials, we report the computational design and experimental characterization of co-assembling, two-component, 120-subunit icosahedral protein nanostructures with molecular weights (1.8 to 2.8 megadaltons) and dimensions (24 to 40 nanometers in diameter) comparable to those of small viral capsids. Electron microscopy, small-angle x-ray scattering, and x-ray crystallography show that 10 designs spanning three distinct icosahedral architectures form materials closely matching the design models. In vitro assembly of icosahedral complexes from independently purified components occurs rapidly, at rates comparable to those of viral capsids, and enables controlled packaging of molecular cargo through charge complementarity. The ability to design megadalton-scale materials with atomic-level accuracy and controllable assembly opens the door to a new generation of genetically programmable protein-based molecular machines.
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Affiliation(s)
- Jacob B Bale
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA. Graduate Program in Molecular and Cellular Biology, University of Washington, Seattle, WA 98195, USA
| | - Shane Gonen
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Yuxi Liu
- Department of Chemistry and Biochemistry, University of California-Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - William Sheffler
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Daniel Ellis
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Chantz Thomas
- Department of Chemistry, University of Washington, Seattle, WA 98195, USA
| | - Duilio Cascio
- Department of Chemistry and Biochemistry, University of California-Los Angeles (UCLA), Los Angeles, CA 90095, USA. UCLA-Department of Energy (DOE) Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA. Department of Biological Chemistry and Molecular Biology Institute, UCLA, Los Angeles, CA 90095, USA
| | - Todd O Yeates
- Department of Chemistry and Biochemistry, University of California-Los Angeles (UCLA), Los Angeles, CA 90095, USA. UCLA-Department of Energy (DOE) Institute for Genomics and Proteomics, Los Angeles, CA 90095, USA
| | - Tamir Gonen
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Neil P King
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA. Institute for Protein Design, University of Washington, Seattle, WA 98195, USA.
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA. Institute for Protein Design, University of Washington, Seattle, WA 98195, USA. Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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45
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Regan L, Caballero D, Hinrichsen MR, Virrueta A, Williams DM, O'Hern CS. Protein design: Past, present, and future. Biopolymers 2016; 104:334-50. [PMID: 25784145 DOI: 10.1002/bip.22639] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Revised: 03/05/2015] [Accepted: 03/07/2015] [Indexed: 01/16/2023]
Abstract
Building on the pioneering work of Ho and DeGrado (J Am Chem Soc 1987, 109, 6751-6758) in the late 1980s, protein design approaches have revealed many fundamental features of protein structure and stability. We are now in the era that the early work presaged - the design of new proteins with practical applications and uses. Here we briefly survey some past milestones in protein design, in addition to highlighting recent progress and future aspirations.
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Affiliation(s)
- Lynne Regan
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT.,Department of Chemistry, Yale University, New Haven, CT.,Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT
| | - Diego Caballero
- Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT.,Department of Physics, Yale University, New Haven, CT
| | - Michael R Hinrichsen
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT
| | - Alejandro Virrueta
- Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT.,Department of Mechanical Engineering and Materials Science, Yale University, New Haven, CT
| | - Danielle M Williams
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT
| | - Corey S O'Hern
- Integrated Graduate Program in Physical and Engineering Biology, Yale University, New Haven, CT.,Department of Physics, Yale University, New Haven, CT.,Department of Mechanical Engineering and Materials Science, Yale University, New Haven, CT.,Department of Applied Physics, Yale University, New Haven, CT
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46
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de Ruyck J, Brysbaert G, Blossey R, Lensink MF. Molecular docking as a popular tool in drug design, an in silico travel. Adv Appl Bioinform Chem 2016; 9:1-11. [PMID: 27390530 PMCID: PMC4930227 DOI: 10.2147/aabc.s105289] [Citation(s) in RCA: 97] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
New molecular modeling approaches, driven by rapidly improving computational platforms, have allowed many success stories for the use of computer-assisted drug design in the discovery of new mechanism-or structure-based drugs. In this overview, we highlight three aspects of the use of molecular docking. First, we discuss the combination of molecular and quantum mechanics to investigate an unusual enzymatic mechanism of a flavoprotein. Second, we present recent advances in anti-infectious agents' synthesis driven by structural insights. At the end, we focus on larger biological complexes made by protein-protein interactions and discuss their relevance in drug design. This review provides information on how these large systems, even in the presence of the solvent, can be investigated with the outlook of drug discovery.
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Affiliation(s)
| | | | - Ralf Blossey
- University Lille, CNRS UMR8576 UGSF, Lille, France
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47
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Hosseinzadeh P, Lu Y. Design and fine-tuning redox potentials of metalloproteins involved in electron transfer in bioenergetics. BIOCHIMICA ET BIOPHYSICA ACTA 2016; 1857:557-581. [PMID: 26301482 PMCID: PMC4761536 DOI: 10.1016/j.bbabio.2015.08.006] [Citation(s) in RCA: 114] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2015] [Accepted: 08/20/2015] [Indexed: 12/25/2022]
Abstract
Redox potentials are a major contributor in controlling the electron transfer (ET) rates and thus regulating the ET processes in the bioenergetics. To maximize the efficiency of the ET process, one needs to master the art of tuning the redox potential, especially in metalloproteins, as they represent major classes of ET proteins. In this review, we first describe the importance of tuning the redox potential of ET centers and its role in regulating the ET in bioenergetic processes including photosynthesis and respiration. The main focus of this review is to summarize recent work in designing the ET centers, namely cupredoxins, cytochromes, and iron-sulfur proteins, and examples in design of protein networks involved these ET centers. We then discuss the factors that affect redox potentials of these ET centers including metal ion, the ligands to metal center and interactions beyond the primary ligand, especially non-covalent secondary coordination sphere interactions. We provide examples of strategies to fine-tune the redox potential using both natural and unnatural amino acids and native and nonnative cofactors. Several case studies are used to illustrate recent successes in this area. Outlooks for future endeavors are also provided. This article is part of a Special Issue entitled Biodesign for Bioenergetics--the design and engineering of electronic transfer cofactors, proteins and protein networks, edited by Ronald L. Koder and J.L. Ross Anderson.
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Affiliation(s)
- Parisa Hosseinzadeh
- Department of Chemistry and Department of Biochemistry, University of Illinois at Urbana-Champaign, 600 S. Mathews St., Urbana, IL, 61801, USA
| | - Yi Lu
- Department of Chemistry and Department of Biochemistry, University of Illinois at Urbana-Champaign, 600 S. Mathews St., Urbana, IL, 61801, USA.
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48
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Maximova T, Moffatt R, Ma B, Nussinov R, Shehu A. Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics. PLoS Comput Biol 2016; 12:e1004619. [PMID: 27124275 PMCID: PMC4849799 DOI: 10.1371/journal.pcbi.1004619] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.
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Affiliation(s)
- Tatiana Maximova
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Ryan Moffatt
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
| | - Buyong Ma
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
| | - Ruth Nussinov
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
- Department of Biongineering, George Mason University, Fairfax, Virginia, United States of America
- School of Systems Biology, George Mason University, Manassas, Virginia, United States of America
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49
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Hoque MT, Yang Y, Mishra A, Zhou Y. s
DFIRE
: Sequence‐specific statistical energy function for protein structure prediction by decoy selections. J Comput Chem 2016; 37:1119-24. [DOI: 10.1002/jcc.24298] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Revised: 12/06/2015] [Accepted: 12/13/2015] [Indexed: 12/15/2022]
Affiliation(s)
- Md Tamjidul Hoque
- Computer Science, University of New Orleans, New OrleansLouisiana70148
| | - Yuedong Yang
- Institute for Glycomics and School of Informatics and Communication Technology, Griffith UniversityQueensland4222 Australia
| | - Avdesh Mishra
- Computer Science, University of New Orleans, New OrleansLouisiana70148
| | - Yaoqi Zhou
- Institute for Glycomics and School of Informatics and Communication Technology, Griffith UniversityQueensland4222 Australia
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50
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Sirin S, Apgar JR, Bennett EM, Keating AE. AB-Bind: Antibody binding mutational database for computational affinity predictions. Protein Sci 2016; 25:393-409. [PMID: 26473627 PMCID: PMC4815335 DOI: 10.1002/pro.2829] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2015] [Revised: 10/09/2015] [Accepted: 10/12/2015] [Indexed: 12/26/2022]
Abstract
Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < -1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.
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Affiliation(s)
- Sarah Sirin
- Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusetts02139
| | - James R. Apgar
- Global Biotherapeutics Technologies, Pfizer Inc610 Main StreetCambridgeMassachusetts02139
| | - Eric M. Bennett
- Global Biotherapeutics Technologies, Pfizer Inc610 Main StreetCambridgeMassachusetts02139
| | - Amy E. Keating
- Department of BiologyMassachusetts Institute of TechnologyCambridgeMassachusetts02139
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMassachusetts02139
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