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He J, Wu W, Wang X. DIProT: A deep learning based interactive toolkit for efficient and effective Protein design. Synth Syst Biotechnol 2024; 9:217-222. [PMID: 38385151 PMCID: PMC10876589 DOI: 10.1016/j.synbio.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/02/2024] [Accepted: 01/30/2024] [Indexed: 02/23/2024] Open
Abstract
The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets, with promising sequence recovery and inference time. Case studies further illustrate how DIProT can facilitate user-guided protein design.
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Affiliation(s)
| | | | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China
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2
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Guo Z, Liu J, Wang Y, Chen M, Wang D, Xu D, Cheng J. Diffusion models in bioinformatics and computational biology. NATURE REVIEWS BIOENGINEERING 2024; 2:136-154. [PMID: 38576453 PMCID: PMC10994218 DOI: 10.1038/s44222-023-00114-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/25/2023] [Indexed: 04/06/2024]
Abstract
Denoising diffusion models embody a type of generative artificial intelligence that can be applied in computer vision, natural language processing and bioinformatics. In this Review, we introduce the key concepts and theoretical foundations of three diffusion modelling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks and score stochastic differential equations). We then explore their applications in bioinformatics and computational biology, including protein design and generation, drug and small-molecule design, protein-ligand interaction modelling, cryo-electron microscopy image data analysis and single-cell data analysis. Finally, we highlight open-source diffusion model tools and consider the future applications of diffusion models in bioinformatics.
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Affiliation(s)
- Zhiye Guo
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Yanli Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Mengrui Chen
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Duolin Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
- NextGen Precision Health, University of Missouri, Columbia, MO, USA
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3
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Shah SM, Demidova EV, Ringenbach S, Faezov B, Andrake M, Gandhi A, Mur P, Viana-Errasti J, Xiu J, Swensen J, Valle L, Dunbrack RL, Hall MJ, Arora S. Exploring Co-occurring POLE Exonuclease and Non-exonuclease Domain Mutations and Their Impact on Tumor Mutagenicity. CANCER RESEARCH COMMUNICATIONS 2024; 4:213-225. [PMID: 38282550 PMCID: PMC10812383 DOI: 10.1158/2767-9764.crc-23-0312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/05/2023] [Accepted: 01/02/2024] [Indexed: 01/13/2024]
Abstract
POLE driver mutations in the exonuclease domain (ExoD driver) are prevalent in several cancers, including colorectal cancer and endometrial cancer, leading to dramatically ultra-high tumor mutation burden (TMB). To understand whether POLE mutations that are not classified as drivers (POLE Variant) contribute to mutagenesis, we assessed TMB in 447 POLE-mutated colorectal cancers, endometrial cancers, and ovarian cancers classified as TMB-high ≥10 mutations/Mb (mut/Mb) or TMB-low <10 mut/Mb. TMB was significantly highest in tumors with "POLE ExoD driver plus POLE Variant" (colorectal cancer and endometrial cancer, P < 0.001; ovarian cancer, P < 0.05). TMB increased with additional POLE variants (P < 0.001), but plateaued at 2, suggesting an association between the presence of these variants and TMB. Integrated analysis of AlphaFold2 POLE models and quantitative stability estimates predicted the impact of multiple POLE variants on POLE functionality. The prevalence of immunogenic neoepitopes was notably higher in the "POLE ExoD driver plus POLE Variant" tumors. Overall, this study reveals a novel correlation between POLE variants in POLE ExoD-driven tumors, and ultra-high TMB. Currently, only select pathogenic ExoD mutations with a reliable association with ultra-high TMB inform clinical practice. Thus, these findings are hypothesis-generating, require functional validation, and could potentially inform tumor classification, treatment responses, and clinical outcomes. SIGNIFICANCE Somatic POLE ExoD driver mutations cause proofreading deficiency that induces high TMB. This study suggests a novel modifier role for POLE variants in POLE ExoD-driven tumors, associated with ultra-high TMB. These data, in addition to future functional studies, may inform tumor classification, therapeutic response, and patient outcomes.
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Affiliation(s)
- Shreya M. Shah
- Cancer Prevention and Control Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania
- Science Scholars Program, Temple University, Philadelphia, Pennsylvania
| | - Elena V. Demidova
- Cancer Prevention and Control Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russian Federation
| | - Salena Ringenbach
- Cancer Prevention and Control Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania
- Lewis Katz School of Medicine, Temple University, Bethlehem, Pennsylvania
| | - Bulat Faezov
- Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russian Federation
- Program in Cancer Signaling and Microenvironment, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Mark Andrake
- Program in Cancer Signaling and Microenvironment, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Arjun Gandhi
- Cancer Prevention and Control Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania
- University College Dublin School of Medicine and Medical Science, Dublin, Ireland
| | - Pilar Mur
- Hereditary Cancer Program, Catalan Institute of Oncology, IDIBELL, Hospitalet de Llobregat, Barcelona, Spain
| | - Julen Viana-Errasti
- Hereditary Cancer Program, Catalan Institute of Oncology, IDIBELL, Hospitalet de Llobregat, Barcelona, Spain
| | | | | | - Laura Valle
- Hereditary Cancer Program, Catalan Institute of Oncology, IDIBELL, Hospitalet de Llobregat, Barcelona, Spain
| | - Roland L. Dunbrack
- Program in Cancer Signaling and Microenvironment, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Michael J. Hall
- Cancer Prevention and Control Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Sanjeevani Arora
- Cancer Prevention and Control Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania
- Department of Radiation Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania
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4
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Samanta R, Gray JJ. Implicit model to capture electrostatic features of membrane environment. PLoS Comput Biol 2024; 20:e1011296. [PMID: 38252688 PMCID: PMC10833867 DOI: 10.1371/journal.pcbi.1011296] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 02/01/2024] [Accepted: 12/13/2023] [Indexed: 01/24/2024] Open
Abstract
Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.
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Affiliation(s)
- Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States of America
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
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5
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Greisen PJ, Yi L, Zhou R, Zhou J, Johansson E, Dong T, Liu H, Johnsen LB, Lund S, Svensson LA, Zhu H, Thomas N, Yang Z, Østergaard H. Computational design of N-linked glycans for high throughput epitope profiling. Protein Sci 2023; 32:e4726. [PMID: 37421602 PMCID: PMC10521239 DOI: 10.1002/pro.4726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 06/12/2023] [Accepted: 07/06/2023] [Indexed: 07/10/2023]
Abstract
Efficient identification of epitopes is crucial for drug discovery and design as it enables the selection of optimal epitopes, expansion of lead antibody diversity, and verification of binding interface. Although high-resolution low throughput methods like x-ray crystallography can determine epitopes or protein-protein interactions accurately, they are time-consuming and can only be applied to a limited number of complexes. To overcome these limitations, we have developed a rapid computational method that incorporates N-linked glycans to mask epitopes or protein interaction surfaces, thereby providing a mapping of these regions. Using human coagulation factor IXa (fIXa) as a model system, we computationally screened 158 positions and expressed 98 variants to test experimentally for epitope mapping. We were able to delineate epitopes rapidly and reliably through the insertion of N-linked glycans that efficiently disrupted binding in a site-selective manner. To validate the efficacy of our method, we conducted ELISA experiments and high-throughput yeast surface display assays. Furthermore, x-ray crystallography was employed to verify the results, thereby recapitulating through the method of N-linked glycans a coarse-grained mapping of the epitope.
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Affiliation(s)
| | - Li Yi
- Global Research TechnologiesNovo Nordisk A/SMaaloevDenmark
| | - Rong Zhou
- Discovery Technology China, Novo Nordisk Research CentreNovo Nordisk A/SBeijingChina
| | - Jian Zhou
- Discovery Technology China, Novo Nordisk Research CentreNovo Nordisk A/SBeijingChina
| | - Eva Johansson
- Global Research TechnologiesNovo Nordisk A/SMaaloevDenmark
| | - Tiantang Dong
- Discovery Technology China, Novo Nordisk Research CentreNovo Nordisk A/SBeijingChina
| | - Haimo Liu
- Discovery Technology China, Novo Nordisk Research CentreNovo Nordisk A/SBeijingChina
| | | | - Søren Lund
- Global Research TechnologiesNovo Nordisk A/SMaaloevDenmark
| | | | - Haisun Zhu
- Discovery Technology China, Novo Nordisk Research CentreNovo Nordisk A/SBeijingChina
| | - Nidhin Thomas
- Digital Science and InnovationNovo Nordisk A/SSeattleUSA
| | - Zhiru Yang
- Discovery Technology China, Novo Nordisk Research CentreNovo Nordisk A/SBeijingChina
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6
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Samanta R, Gray JJ. Implicit model to capture electrostatic features of membrane environment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.26.546486. [PMID: 37425950 PMCID: PMC10327106 DOI: 10.1101/2023.06.26.546486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.
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Affiliation(s)
- Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland, United States
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7
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Stalke A, Behrendt A, Hennig F, Gohlke H, Buhl N, Reinkens T, Baumann U, Schlegelberger B, Illig T, Pfister ED, Skawran B. Functional characterization of novel or yet uncharacterized ATP7B missense variants detected in patients with clinical Wilson's disease. Clin Genet 2023. [PMID: 37157876 DOI: 10.1111/cge.14352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/14/2023] [Accepted: 04/20/2023] [Indexed: 05/10/2023]
Abstract
Wilson's disease (WD, MIM#277900) is an autosomal recessive disorder resulting in copper excess caused by biallelic variants in the ATP7B gene (MIM#606882) encoding a copper transporting P-type ATPase. ATP7B variants of unknown significance (VUS) are detected frequently, sometimes impeding a clear diagnosis. Functional analyses can help to classify these variants as benign or pathogenic. Additionally, variants already classified as (likely) pathogenic benefit from functional analyses to understand their pathomechanism, thus contribute to the development of personalized treatment approaches in the future. We described clinical features of six WD patients and functionally characterized five ATP7B missense variants (two VUS, three yet uncharacterized likely pathogenic variants), detected in these patients. We determined the protein level, copper export capacity, and cellular localization in an in vitro model and potential structural consequences using an ATP7B protein model based on AlphaFold. Our analyses give insight into the pathomechanism and allowed reclassification for the two VUS to likely pathogenic and for two of the three likely pathogenic variants to pathogenic.
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Affiliation(s)
- Amelie Stalke
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
- Department of Pediatric Gastroenterology and Hepatology, Division of Kidney, Liver, and Metabolic Diseases, Hannover Medical School, Hannover, Germany
| | - Annika Behrendt
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Finja Hennig
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Nicole Buhl
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
- Department of Pediatric Gastroenterology and Hepatology, Division of Kidney, Liver, and Metabolic Diseases, Hannover Medical School, Hannover, Germany
| | - Thea Reinkens
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Ulrich Baumann
- Department of Pediatric Gastroenterology and Hepatology, Division of Kidney, Liver, and Metabolic Diseases, Hannover Medical School, Hannover, Germany
| | | | - Thomas Illig
- Hannover Unified Bank, Hannover Medical School, Hannover, Germany
| | - Eva-Doreen Pfister
- Department of Pediatric Gastroenterology and Hepatology, Division of Kidney, Liver, and Metabolic Diseases, Hannover Medical School, Hannover, Germany
| | - Britta Skawran
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
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8
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Bondarenko V, Chen Q, Singewald K, Haloi N, Tillman TS, Howard RJ, Lindahl E, Xu Y, Tang P. Structural Elucidation of Ivermectin Binding to α7nAChR and the Induced Channel Desensitization. ACS Chem Neurosci 2023; 14:1156-1165. [PMID: 36821490 PMCID: PMC10020961 DOI: 10.1021/acschemneuro.2c00783] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
The α7 nicotinic acetylcholine receptor (α7nAChR) mediates signaling in the central nervous system and cholinergic anti-inflammatory pathways. Ivermectin is a positive allosteric modulator of a full-length α7nAChR and an agonist of the α7nAChR construct containing transmembrane (TMD) and intracellular (ICD) domains, but structural insights of the binding have not previously been determined. Here, combining nuclear magnetic resonance as a primary experimental tool with Rosetta comparative modeling and molecular dynamics simulations, we have revealed details of ivermectin binding to the α7nAChR TMD + ICD and corresponding structural changes in an ivermectin-induced desensitized state. Ivermectin binding was stabilized predominantly by hydrophobic interactions from interfacial residues between adjacent subunits near the extracellular end of the TMD, where the inter-subunit gap was substantially expanded in comparison to the apo structure. The ion-permeation pathway showed a profile distinctly different from the resting-state profile but similar to profiles of desensitized α7nAChR. The ICD also exhibited structural changes, including reorientation of the MX and h3 helices relative to the channel axis. The resulting structures of the α7nAChR TMD + ICD in complex with ivermectin provide opportunities for discovering new modulators of therapeutic potential and exploring the structural basis of cytoplasmic signaling under different α7nAChR functional states.
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Affiliation(s)
- Vasyl Bondarenko
- Department
of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Qiang Chen
- Department
of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Kevin Singewald
- Department
of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Nandan Haloi
- Department
of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, PO Box 1031, SE-17121 Solna, Sweden
- Department
of Applied Physics, Swedish e-Science Research Center, KTH Royal Institute of Technology, PO Box 1031, SE-17121 Solna, Sweden
| | - Tommy S. Tillman
- Department
of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Rebecca J. Howard
- Department
of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, PO Box 1031, SE-17121 Solna, Sweden
- Department
of Applied Physics, Swedish e-Science Research Center, KTH Royal Institute of Technology, PO Box 1031, SE-17121 Solna, Sweden
| | - Erik Lindahl
- Department
of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, PO Box 1031, SE-17121 Solna, Sweden
- Department
of Applied Physics, Swedish e-Science Research Center, KTH Royal Institute of Technology, PO Box 1031, SE-17121 Solna, Sweden
| | - Yan Xu
- Department
of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Pharmacology and Chemical Biology, University
of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Structural Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Physics and Astronomy, University of
Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Pei Tang
- Department
of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Pharmacology and Chemical Biology, University
of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Department
of Computational and Systems Biology, University
of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
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9
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Harmalkar A, Rao R, Richard Xie Y, Honer J, Deisting W, Anlahr J, Hoenig A, Czwikla J, Sienz-Widmann E, Rau D, Rice AJ, Riley TP, Li D, Catterall HB, Tinberg CE, Gray JJ, Wei KY. Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features. MAbs 2023; 15:2163584. [PMID: 36683173 PMCID: PMC9872953 DOI: 10.1080/19420862.2022.2163584] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/14/2022] [Accepted: 12/26/2022] [Indexed: 01/24/2023] Open
Abstract
Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches - one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features - to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, ρ , of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task (ρ = 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.
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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Roshan Rao
- Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA
| | - Yuxuan Richard Xie
- Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jonas Honer
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Wibke Deisting
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Jonas Anlahr
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Anja Hoenig
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Julia Czwikla
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Eva Sienz-Widmann
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Doris Rau
- Therapeutic Discovery, Amgen Research (Munich) GmbH, Munich, Germany
| | - Austin J. Rice
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Timothy P. Riley
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | - Danqing Li
- Therapeutic Discovery, Amgen Research, Amgen Inc, Thousand Oaks, CA, USA
| | | | | | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Kathy Y. Wei
- Therapeutic Discovery, Amgen Research, Amgen Inc, South San Francisco, CA, USA
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10
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Mortensen JC, Damjanovic J, Miao J, Hui T, Lin Y. A backbone-dependent rotamer library with high (ϕ, ψ) coverage using metadynamics simulations. Protein Sci 2022; 31:e4491. [PMID: 36327064 PMCID: PMC9679973 DOI: 10.1002/pro.4491] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 12/06/2023]
Abstract
Backbone-dependent rotamer libraries are commonly used to assign the side chain dihedral angles of amino acids when modeling protein structures. Most rotamer libraries are created by curating protein crystal structure data and using various methods to extrapolate the existing data to cover all possible backbone conformations. However, these rotamer libraries may not be suitable for modeling the structures of cyclic peptides and other constrained peptides because these molecules frequently sample backbone conformations rarely seen in the crystal structures of linear proteins. To provide backbone-dependent side chain information beyond the α-helix, β-sheet, and PPII regions, we used explicit-solvent metadynamics simulations of model dipeptides to create a new rotamer library that has high coverage in the (ϕ, ψ) space. Furthermore, this approach can be applied to build high-coverage rotamer libraries for noncanonical amino acids. The resulting Metadynamics of Dipeptides for Rotamer Distribution (MEDFORD) rotamer library predicts the side chain conformations of high-resolution protein crystal structures with similar accuracy (~80%) to a state-of-the-art rotamer library. Our ability to test the accuracy of MEDFORD at predicting the side chain dihedral angles of amino acids in noncanonical backbone conformation is restricted by the limited structural data available for cyclic peptides. For the cyclic peptide data that are currently available, MEDFORD and the state-of-the-art rotamer library perform comparably. However, the two rotamer libraries indeed make different rotamer predictions in noncanonical (ϕ, ψ) regions. For noncanonical amino acids, the MEDFORD rotamer library predicts the χ1 values with approximately 75% accuracy.
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Affiliation(s)
| | | | - Jiayuan Miao
- Department of ChemistryTufts UniversityMedfordMassachusettsUSA
| | - Tiffani Hui
- Department of ChemistryTufts UniversityMedfordMassachusettsUSA
| | - Yu‐Shan Lin
- Department of ChemistryTufts UniversityMedfordMassachusettsUSA
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11
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Dauparas J, Anishchenko I, Bennett N, Bai H, Ragotte RJ, Milles LF, Wicky BIM, Courbet A, de Haas RJ, Bethel N, Leung PJY, Huddy TF, Pellock S, Tischer D, Chan F, Koepnick B, Nguyen H, Kang A, Sankaran B, Bera AK, King NP, Baker D. Robust deep learning-based protein sequence design using ProteinMPNN. Science 2022; 378:49-56. [PMID: 36108050 PMCID: PMC9997061 DOI: 10.1126/science.add2187] [Citation(s) in RCA: 297] [Impact Index Per Article: 148.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo-electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.
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Affiliation(s)
- J. Dauparas
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - I. Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - N. Bennett
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
| | - H. Bai
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - R. J. Ragotte
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - L. F. Milles
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - B. I. M. Wicky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - A. Courbet
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - R. J. de Haas
- Department of Physical Chemistry and Soft Matter, Wageningen University and Research, Wageningen, The Netherlands
| | - N. Bethel
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - P. J. Y. Leung
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
| | - T. F. Huddy
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - S. Pellock
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - D. Tischer
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - F. Chan
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - B. Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - H. Nguyen
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - A. Kang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - B. Sankaran
- Berkeley Center for Structural Biology, Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - A. K. Bera
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - N. P. King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - D. Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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12
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Skokowa J, Hernandez Alvarez B, Coles M, Ritter M, Nasri M, Haaf J, Aghaallaei N, Xu Y, Mir P, Krahl AC, Rogers KW, Maksymenko K, Bajoghli B, Welte K, Lupas AN, Müller P, ElGamacy M. A topological refactoring design strategy yields highly stable granulopoietic proteins. Nat Commun 2022; 13:2948. [PMID: 35618709 PMCID: PMC9135769 DOI: 10.1038/s41467-022-30157-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 04/19/2022] [Indexed: 11/09/2022] Open
Abstract
Protein therapeutics frequently face major challenges, including complicated production, instability, poor solubility, and aggregation. De novo protein design can readily address these challenges. Here, we demonstrate the utility of a topological refactoring strategy to design novel granulopoietic proteins starting from the granulocyte-colony stimulating factor (G-CSF) structure. We change a protein fold by rearranging the sequence and optimising it towards the new fold. Testing four designs, we obtain two that possess nanomolar activity, the most active of which is highly thermostable and protease-resistant, and matches its designed structure to atomic accuracy. While the designs possess starkly different sequence and structure from the native G-CSF, they show specific activity in differentiating primary human haematopoietic stem cells into mature neutrophils. The designs also show significant and specific activity in vivo. Our topological refactoring approach is largely independent of sequence or structural context, and is therefore applicable to a wide range of protein targets. Skokowa et al. reconstruct the fold of a granulopoietic cytokine, resulting in de novo, hyperstable, highly active proteins with therapeutic potential for treating several neutropenia disorders.
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Affiliation(s)
- Julia Skokowa
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany.
| | | | - Murray Coles
- Max Planck Institute for Biology, 72076, Tübingen, Germany
| | - Malte Ritter
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Masoud Nasri
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Jérémy Haaf
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Narges Aghaallaei
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Yun Xu
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Perihan Mir
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Ann-Christin Krahl
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Katherine W Rogers
- Friedrich Miescher Laboratory of the Max Planck Society, 72076, Tübingen, Germany.,Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kateryna Maksymenko
- Max Planck Institute for Biology, 72076, Tübingen, Germany.,Friedrich Miescher Laboratory of the Max Planck Society, 72076, Tübingen, Germany
| | - Baubak Bajoghli
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Karl Welte
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany
| | - Andrei N Lupas
- Max Planck Institute for Biology, 72076, Tübingen, Germany
| | - Patrick Müller
- Friedrich Miescher Laboratory of the Max Planck Society, 72076, Tübingen, Germany.,Department of Biology, University of Konstanz, 78464, Konstanz, Germany
| | - Mohammad ElGamacy
- Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, 72076, Tübingen, Germany. .,Friedrich Miescher Laboratory of the Max Planck Society, 72076, Tübingen, Germany. .,Heliopolis Biotechnology Ltd, Cambridge, CB24 9RX, UK. .,Max Planck Institute for Biology, 72076, Tübingen, Germany.
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13
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Wu X, Xia T, Shin WJ, Yu KM, Jung W, Herrmann A, Foo SS, Chen W, Zhang P, Lee JS, Poo H, Comhair SAA, Jehi L, Choi YK, Ensser A, Jung JU. Viral Mimicry of Interleukin-17A by SARS-CoV-2 ORF8. mBio 2022; 13:e0040222. [PMID: 35343786 PMCID: PMC9040823 DOI: 10.1128/mbio.00402-22] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 02/25/2022] [Indexed: 01/04/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection triggers cytokine-mediated inflammation, leading to a myriad of clinical presentations in COVID-19. The SARS-CoV-2 open reading frame 8 (ORF8) is a secreted and rapidly evolving glycoprotein. Patients infected with SARS-CoV-2 variants with ORF8 deleted are associated with mild disease outcomes, but the molecular mechanism behind this is unknown. Here, we report that SARS-CoV-2 ORF8 is a viral cytokine that is similar to but distinct from interleukin 17A (IL-17A) as it induces stronger and broader human IL-17 receptor (hIL-17R) signaling than IL-17A. ORF8 primarily targeted blood monocytes and induced the heterodimerization of hIL-17RA and hIL-17RC, triggering a robust inflammatory response. Transcriptome analysis revealed that besides its activation of the hIL-17R pathway, ORF8 upregulated gene expression for fibrosis signaling and coagulation dysregulation. A naturally occurring ORF8 L84S variant that was highly associated with mild COVID-19 showed reduced hIL-17RA binding and attenuated inflammatory responses. This study reveals how SARS-CoV-2 ORF8 by a viral mimicry of the IL-17 cytokine contributes to COVID-19 severe inflammation. IMPORTANCE Patients infected with SARS-CoV-2 variants lacking open reading frame 8 (ORF8) have been associated with milder infection and disease outcome, but the molecular mechanism behind how this viral accessory protein mediates disease pathogenesis is not yet known. In our study, we revealed that secreted ORF8 protein mimics host IL-17 to activate IL-17 receptors A and C (IL-17RA/C) and induces a significantly stronger inflammatory response than host IL-17A, providing molecular insights into the role of ORF8 in COVID-19 pathogenesis and serving as a potential therapeutic target.
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Affiliation(s)
- Xin Wu
- Cancer Biology Department, Infection Biology Program, and Global Center for Pathogen and Human Health Research, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Tian Xia
- Cancer Biology Department, Infection Biology Program, and Global Center for Pathogen and Human Health Research, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Woo-Jin Shin
- Cancer Biology Department, Infection Biology Program, and Global Center for Pathogen and Human Health Research, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Cleveland Clinic Florida Research & Innovation Center, Port St. Lucie, Florida, USA
| | - Kwang-Min Yu
- Center for Study of Emerging and Re-emerging Viruses, Korea Virus Research Institute, Institute for Basic Science (IBS), Daejeon, Republic of Korea
| | - Wooram Jung
- Cancer Biology Department, Infection Biology Program, and Global Center for Pathogen and Human Health Research, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Alexandra Herrmann
- Institute for Clinical and Molecular Virology, University Hospital Erlangen, Friedrich Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Suan-Sin Foo
- Cancer Biology Department, Infection Biology Program, and Global Center for Pathogen and Human Health Research, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Weiqiang Chen
- Cancer Biology Department, Infection Biology Program, and Global Center for Pathogen and Human Health Research, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Pengfei Zhang
- Cancer Biology Department, Infection Biology Program, and Global Center for Pathogen and Human Health Research, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Jong-Soo Lee
- College of Veterinary Medicine, Chungnam National University, Daejeon, Republic of Korea
| | - Haryoung Poo
- Infectious Disease Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea
| | - Suzy A. A. Comhair
- Respiratory Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Lara Jehi
- Department of Neurology, Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
| | - Young Ki Choi
- Center for Study of Emerging and Re-emerging Viruses, Korea Virus Research Institute, Institute for Basic Science (IBS), Daejeon, Republic of Korea
| | - Armin Ensser
- Institute for Clinical and Molecular Virology, University Hospital Erlangen, Friedrich Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Jae U. Jung
- Cancer Biology Department, Infection Biology Program, and Global Center for Pathogen and Human Health Research, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
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14
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Sorensen AB, Greisen PJ, Madsen JJ, Lund J, Andersen G, Wulff-Larsen PG, Pedersen AA, Gandhi PS, Overgaard MT, Østergaard H, Olsen OH. A systematic approach for evaluating the role of surface-exposed loops in trypsin-like serine proteases applied to the 170 loop in coagulation factor VIIa. Sci Rep 2022; 12:3747. [PMID: 35260627 PMCID: PMC8904457 DOI: 10.1038/s41598-022-07620-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/14/2022] [Indexed: 12/27/2022] Open
Abstract
Proteases play a major role in many vital physiological processes. Trypsin-like serine proteases (TLPs), in particular, are paramount in proteolytic cascade systems such as blood coagulation and complement activation. The structural topology of TLPs is highly conserved, with the trypsin fold comprising two β-barrels connected by a number of variable surface-exposed loops that provide a surprising capacity for functional diversity and substrate specificity. To expand our understanding of the roles these loops play in substrate and co-factor interactions, we employ a systematic methodology akin to the natural truncations and insertions observed through evolution of TLPs. The approach explores a larger deletion space than classical random or directed mutagenesis. Using FVIIa as a model system, deletions of 1–7 amino acids through the surface exposed 170 loop, a vital allosteric regulator, was introduced. All variants were extensively evaluated by established functional assays and computational loop modelling with Rosetta. The approach revealed detailed structural and functional insights recapitulation and expanding on the main findings in relation to 170 loop functions elucidated over several decades using more cumbersome crystallization and single deletion/mutation methodologies. The larger deletion space was key in capturing the most active variant, which unexpectedly had a six-amino acid truncation. This variant would have remained undiscovered if only 2–3 deletions were considered, supporting the usefulness of the methodology in general protease engineering approaches. Our findings shed further light on the complex role that surface-exposed loops play in TLP function and supports the important role of loop length in the regulation and fine-tunning of enzymatic function throughout evolution.
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Affiliation(s)
- Anders B Sorensen
- Global Research, Novo Nordisk A/S, 2760, Måløv, Denmark.,Department of Chemistry and Bioscience, Aalborg University, 9220, Ålborg, Denmark
| | | | - Jesper J Madsen
- Global and Planetary Health, College of Public Health, University of South Florida, Tampa, FL, 33612, USA.,Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, 33612, USA
| | - Jacob Lund
- Global Research, Novo Nordisk A/S, 2760, Måløv, Denmark
| | - Gorm Andersen
- Global Research, Novo Nordisk A/S, 2760, Måløv, Denmark
| | | | | | | | - Michael T Overgaard
- Department of Chemistry and Bioscience, Aalborg University, 9220, Ålborg, Denmark
| | | | - Ole H Olsen
- Global Research, Novo Nordisk A/S, 2760, Måløv, Denmark. .,Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology, University of Copenhagen, Blegdamsvej 3b, 2200, Copenhagen, Denmark.
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15
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Malik A, Banerjee A, Pal A, Mitra P. A sequence space search engine for computational protein design to modulate molecular functionality. J Biomol Struct Dyn 2022; 41:2937-2946. [PMID: 35220920 DOI: 10.1080/07391102.2022.2042386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
De-novo protein design explores the untapped sequence space that is otherwise less discovered during the evolutionary process. This necessitates an efficient sequence space search engine for effective convergence in computational protein design. We propose a greedy simulated annealing-based Monte-Carlo parallel search algorithm for better sequence-structure compatibility probing in protein design. The guidance provided by the evolutionary profile, the greedy approach, and the cooling schedule adopted in the Monte Carlo simulation ensures sufficient exploration and exploitation of the search space leading to faster convergence. On evaluating the proposed algorithm, we find that a dataset of 76 target scaffolds report an average root-mean-square-deviation (RMSD) of 1.07 Å and an average TM-Score of 0.93 with the modeled designed protein sequences. High sequence recapitulation of 48.7% (59.4%) observed in the design sequences for all (hydrophobic) solvent-inaccessible residues again establish the goodness of the proposed algorithm. A high (93.4%) intra-group recapitulation of hydrophobic residues in the solvent-inaccessible region indicates that the proposed protein design algorithm preserves the core residues in the protein and provides alternative residue combinations in the solvent-accessible regions of the target protein. Furthermore, a COFACTOR-based protein functional analysis shows that the design sequences exhibit altered molecular functionality and introduce new molecular functions compared to the target scaffolds.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ayush Malik
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Anupam Banerjee
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Abantika Pal
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
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16
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Bondarenko V, Wells MM, Chen Q, Tillman TS, Singewald K, Lawless MJ, Caporoso J, Brandon N, Coleman JA, Saxena S, Lindahl E, Xu Y, Tang P. Structures of highly flexible intracellular domain of human α7 nicotinic acetylcholine receptor. Nat Commun 2022; 13:793. [PMID: 35145092 PMCID: PMC8831596 DOI: 10.1038/s41467-022-28400-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/10/2022] [Indexed: 11/24/2022] Open
Abstract
The intracellular domain (ICD) of Cys-loop receptors mediates diverse functions. To date, no structure of a full-length ICD is available due to challenges stemming from its dynamic nature. Here, combining nuclear magnetic resonance (NMR) and electron spin resonance experiments with Rosetta computations, we determine full-length ICD structures of the human α7 nicotinic acetylcholine receptor in a resting state. We show that ~57% of the ICD residues are in highly flexible regions, primarily in a large loop (loop L) with the most mobile segment spanning ~50 Å from the central channel axis. Loop L is anchored onto the MA helix and virtually forms two smaller loops, thereby increasing its stability. Previously known motifs for cytoplasmic binding, regulation, and signaling are found in both the helices and disordered flexible regions, supporting the essential role of the ICD conformational plasticity in orchestrating a broad range of biological processes. The intracellular domain (ICD) of Cys-loop receptors mediates many of their functions, but no complete structure of a Cys-loop receptor ICD is available to date. Here, the authors combine NMR and ESR spectroscopy to determine the full-length ICD structures of the human α7 nicotinic acetylcholine receptor (α7nAChR).
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Affiliation(s)
- Vasyl Bondarenko
- Depatment of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Marta M Wells
- Depatment of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Qiang Chen
- Depatment of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Tommy S Tillman
- Depatment of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Kevin Singewald
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Matthew J Lawless
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Joel Caporoso
- Depatment of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Nicole Brandon
- Depatment of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Jonathan A Coleman
- Department of Structural Biology, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Sunil Saxena
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Erik Lindahl
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden.,Department of Applied Physics, Swedish e-Science Research Center, KTH Royal Institute of Technology, Solna, Sweden
| | - Yan Xu
- Depatment of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Structural Biology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Pei Tang
- Depatment of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA. .,Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, 15260, USA. .,Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
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17
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Anand N, Eguchi R, Mathews II, Perez CP, Derry A, Altman RB, Huang PS. Protein sequence design with a learned potential. Nat Commun 2022; 13:746. [PMID: 35136054 PMCID: PMC8826426 DOI: 10.1038/s41467-022-28313-9] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 01/08/2022] [Indexed: 11/08/2022] Open
Abstract
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.
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Affiliation(s)
- Namrata Anand
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Raphael Eguchi
- Department of Biochemistry, Stanford University, Stanford, CA, USA
| | - Irimpan I Mathews
- Stanford Synchrotron Radiation Lightsource, Menlo Park, CA, 94025, USA
| | - Carla P Perez
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Alexander Derry
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Departments of Genetics and Medicine, Stanford University, Stanford, CA, USA
| | - Po-Ssu Huang
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
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18
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Williams CJ, Richardson DC, Richardson JS. The importance of residue-level filtering and the Top2018 best-parts dataset of high-quality protein residues. Protein Sci 2022; 31:290-300. [PMID: 34779043 PMCID: PMC8740842 DOI: 10.1002/pro.4239] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 01/03/2023]
Abstract
We have curated a high-quality, "best-parts" reference dataset of about 3 million protein residues in about 15,000 PDB-format coordinate files, each containing only residues with good electron density support for a physically acceptable model conformation. The resulting prefiltered data typically contain the entire core of each chain, in quite long continuous fragments. Each reference file is a single protein chain, and the total set of files were selected for low redundancy, high resolution, good MolProbity score, and other chain-level criteria. Then each residue was critically tested for adequate local map quality to firmly support its conformation, which must also be free of serious clashes or covalent-geometry outliers. The resulting Top2018 prefiltered datasets have been released on the Zenodo online web service and are freely available for all uses under a Creative Commons license. Currently, one dataset is residue filtered on main chain plus Cβ atoms, and a second dataset is full-residue filtered; each is available at four different sequence-identity levels. Here, we illustrate both statistics and examples that show the beneficial consequences of residue-level filtering. That process is necessary because even the best of structures contain a few highly disordered local regions with poor density and low-confidence conformations that should not be included in reference data. Therefore, the open distribution of these very large, prefiltered reference datasets constitutes a notable advance for structural bioinformatics and the fields that depend upon it.
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19
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Koehler Leman J, Lyskov S, Lewis SM, Adolf-Bryfogle J, Alford RF, Barlow K, Ben-Aharon Z, Farrell D, Fell J, Hansen WA, Harmalkar A, Jeliazkov J, Kuenze G, Krys JD, Ljubetič A, Loshbaugh AL, Maguire J, Moretti R, Mulligan VK, Nance ML, Nguyen PT, Ó Conchúir S, Roy Burman SS, Samanta R, Smith ST, Teets F, Tiemann JKS, Watkins A, Woods H, Yachnin BJ, Bahl CD, Bailey-Kellogg C, Baker D, Das R, DiMaio F, Khare SD, Kortemme T, Labonte JW, Lindorff-Larsen K, Meiler J, Schief W, Schueler-Furman O, Siegel JB, Stein A, Yarov-Yarovoy V, Kuhlman B, Leaver-Fay A, Gront D, Gray JJ, Bonneau R. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nat Commun 2021; 12:6947. [PMID: 34845212 PMCID: PMC8630030 DOI: 10.1038/s41467-021-27222-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 11/02/2021] [Indexed: 01/14/2023] Open
Abstract
Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Steven M Lewis
- Cyrus Biotechnology, 1201 Second Ave, Suite 900, Seattle, WA, 98101, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kyle Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ziv Ben-Aharon
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Daniel Farrell
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Jason Fell
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - William A Hansen
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeliazko Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - Justyna D Krys
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Ajasja Ljubetič
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Phuong T Nguyen
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Shane Ó Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Shannon T Smith
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Frank Teets
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Johanna K S Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Hope Woods
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Brahm J Yachnin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | | | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - William Schief
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Justin B Siegel
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Brian Kuhlman
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Andrew Leaver-Fay
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
- Department of Computer Science, New York University, New York, NY, 10003, USA.
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20
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Epistasis shapes the fitness landscape of an allosteric specificity switch. Nat Commun 2021; 12:5562. [PMID: 34548494 PMCID: PMC8455584 DOI: 10.1038/s41467-021-25826-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 09/03/2021] [Indexed: 11/08/2022] Open
Abstract
Epistasis is a major determinant in the emergence of novel protein function. In allosteric proteins, direct interactions between inducer-binding mutations propagate through the allosteric network, manifesting as epistasis at the level of biological function. Elucidating this relationship between local interactions and their global effects is essential to understanding evolution of allosteric proteins. We integrate computational design, structural and biophysical analysis to characterize the emergence of novel inducer specificity in an allosteric transcription factor. Adaptive landscapes of different inducers of the designed mutant show that a few strong epistatic interactions constrain the number of viable sequence pathways, revealing ridges in the fitness landscape leading to new specificity. The structure of the designed mutant shows that a striking change in inducer orientation still retains allosteric function. Comparing biophysical and functional properties suggests a nonlinear relationship between inducer binding affinity and allostery. Our results highlight the functional and evolutionary complexity of allosteric proteins. Epistasis plays an important role in the evolution of novel protein functions because it determines the mutational path a protein takes. Here, the authors combine functional, structural and biophysical analyses to characterize epistasis in a computationally redesigned ligand-inducible allosteric transcription factor and found that epistasis creates distinct biophysical and biological functional landscapes.
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21
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Gulsevin A, Papke RL, Stokes C, Tran HNT, Jin AH, Vetter I, Meiler J. The Allosteric Activation of α7 nAChR by α-Conotoxin MrIC Is Modified by Mutations at the Vestibular Site. Toxins (Basel) 2021; 13:toxins13080555. [PMID: 34437426 PMCID: PMC8402416 DOI: 10.3390/toxins13080555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 08/02/2021] [Accepted: 08/09/2021] [Indexed: 12/18/2022] Open
Abstract
α-conotoxins are 13–19 amino acid toxin peptides that bind various nicotinic acetylcholine receptor (nAChR) subtypes. α-conotoxin Mr1.7c (MrIC) is a 17 amino acid peptide that targets α7 nAChR. Although MrIC has no activating effect on α7 nAChR when applied by itself, it evokes a large response when co-applied with the type II positive allosteric modulator PNU-120596, which potentiates the α7 nAChR response by recovering it from a desensitized state. A lack of standalone activity, despite activation upon co-application with a positive allosteric modulator, was previously observed for molecules that bind to an extracellular domain allosteric activation (AA) site at the vestibule of the receptor. We hypothesized that MrIC may activate α7 nAChR allosterically through this site. We ran voltage-clamp electrophysiology experiments and in silico peptide docking calculations in order to gather evidence in support of α7 nAChR activation by MrIC through the AA site. The experiments with the wild-type α7 nAChR supported an allosteric mode of action, which was confirmed by the significantly increased MrIC + PNU-120596 responses of three α7 nAChR AA site mutants that were designed in silico to improve MrIC binding. Overall, our results shed light on the allosteric activation of α7 nAChR by MrIC and suggest the involvement of the AA site.
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Affiliation(s)
- Alican Gulsevin
- Center for Structural Biology, Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA;
- Correspondence:
| | - Roger L. Papke
- Department of Pharmacology and Therapeutics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (R.L.P.); (C.S.)
| | - Clare Stokes
- Department of Pharmacology and Therapeutics, College of Medicine, University of Florida, Gainesville, FL 32610, USA; (R.L.P.); (C.S.)
| | - Hue N. T. Tran
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD 4072, Australia; (H.N.T.T.); (A.H.J.); (I.V.)
| | - Aihua H. Jin
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD 4072, Australia; (H.N.T.T.); (A.H.J.); (I.V.)
| | - Irina Vetter
- Institute for Molecular Bioscience, The University of Queensland, St Lucia, QLD 4072, Australia; (H.N.T.T.); (A.H.J.); (I.V.)
- School of Pharmacy, The University of Queensland, Woolloongabba, QLD 4102, Australia
| | - Jens Meiler
- Center for Structural Biology, Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA;
- Institute for Drug Discovery, Leipzig University Medical School, 04103 Leipzig, Germany
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22
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Pereira JM, Vieira M, Santos SM. Step-by-step design of proteins for small molecule interaction: A review on recent milestones. Protein Sci 2021; 30:1502-1520. [PMID: 33934427 PMCID: PMC8284594 DOI: 10.1002/pro.4098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/21/2021] [Accepted: 04/23/2021] [Indexed: 01/01/2023]
Abstract
Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.
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Affiliation(s)
- José M. Pereira
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
| | - Maria Vieira
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
| | - Sérgio M. Santos
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
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23
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Alford RF, Samanta R, Gray JJ. Diverse Scientific Benchmarks for Implicit Membrane Energy Functions. J Chem Theory Comput 2021; 17:5248-5261. [PMID: 34310137 DOI: 10.1021/acs.jctc.0c00646] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane proteins lag behind due to sparse and low-quality data, leading to overfit tools. To overcome this challenge, we assembled a suite of 12 tests on independent data sets varying in size, diversity, and resolution. The tests probe an energy function's ability to capture membrane protein orientation, stability, sequence, and structure. Here, we present the tests and use the franklin2019 energy function to demonstrate them. We then identify areas for energy function improvement and discuss potential future integration with machine-learning-based optimization methods. The tests are available through the Rosetta Benchmark Server (https://benchmark.graylab.jhu.edu/) and GitHub (https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark).
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Affiliation(s)
- Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
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24
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Riahi S, Lee JH, Wei S, Cost R, Masiero A, Prades C, Olfati-Saber R, Wendt M, Park A, Qiu Y, Zhou Y. Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers. Antib Ther 2021; 4:109-122. [PMID: 34396040 PMCID: PMC8344454 DOI: 10.1093/abt/tbab011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 01/07/2023] Open
Abstract
As the COVID-19 pandemic continues to spread, hundreds of new initiatives including
studies on existing medicines are running to fight the disease. To deliver a potentially
immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new
collaborations and ways of sharing are required to create as many paths forward as
possible. Here, we leverage our expertise in computational antibody engineering to
rationally design/engineer three previously reported SARS-CoV neutralizing antibodies and
share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing
antibodies, m396, 80R and CR-3022 were chosen as templates due to their diversified
epitopes and confirmed neutralization potency against SARS-CoV (but not SARS-CoV-2 except
for CR3022). Structures of variable fragment (Fv) in complex with receptor binding domain
(RBD) from SARS-CoV or SARS-CoV-2 were subjected to our established in silico antibody
engineering platform to improve their binding affinity to SARS-CoV-2 and developability
profiles. The selected top mutations were ensembled into a focused library for each
antibody for further screening. In addition, we convert the selected binders with
different epitopes into the trispecific format, aiming to increase potency and to prevent
mutational escape. Lastly, to avoid antibody-induced virus activation or enhancement, we
suggest application of NNAS and DQ mutations to the Fc region to eliminate effector
functions and extend half-life.
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Affiliation(s)
- Saleh Riahi
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Jae Hyeon Lee
- Data & Data Science, Sanofi, Cambridge, MA, United States
| | - Shuai Wei
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Robert Cost
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | | | | | | | - Maria Wendt
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Anna Park
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Yu Qiu
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Yanfeng Zhou
- Large Molecule Research, Sanofi, Framingham, MA, United States
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25
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Zhang Y, He X, Zhai J, Ji B, Man VH, Wang J. In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor. Brief Bioinform 2021; 22:6278153. [PMID: 34013346 PMCID: PMC8194596 DOI: 10.1093/bib/bbab188] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 03/30/2021] [Accepted: 04/22/2021] [Indexed: 11/20/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel coronavirus, has brought an unprecedented pandemic to the world and affected over 64 million people. The virus infects human using its spike glycoprotein mediated by a crucial area, receptor-binding domain (RBD), to bind to the human ACE2 (hACE2) receptor. Mutations on RBD have been observed in different countries and classified into nine types: A435S, D364Y, G476S, N354D/D364Y, R408I, V341I, V367F, V483A and W436R. Employing molecular dynamics (MD) simulation, we investigated dynamics and structures of the complexes of the prototype and mutant types of SARS-CoV-2 spike RBDs and hACE2. We then probed binding free energies of the prototype and mutant types of RBD with hACE2 protein by using an end-point molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. According to the result of MM-PBSA binding free energy calculations, we found that V367F and N354D/D364Y mutant types showed enhanced binding affinities with hACE2 compared to the prototype. Our computational protocols were validated by the successful prediction of relative binding free energies between prototype and three mutants: N354D/D364Y, V367F and W436R. Thus, this study provides a reliable computational protocol to fast assess the existing and emerging RBD mutations. More importantly, the binding hotspots identified by using the molecular mechanics generalized Born surface area (MM-GBSA) free energy decomposition approach can guide the rational design of small molecule drugs or vaccines free of drug resistance, to interfere with or eradicate spike-hACE2 binding.
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Affiliation(s)
- Yuzhao Zhang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xibing He
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Jingchen Zhai
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Beihong Ji
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Viet Hoang Man
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Junmei Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
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26
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Meinen BA, Bahl CD. Breakthroughs in computational design methods open up new frontiers for de novo protein engineering. Protein Eng Des Sel 2021; 34:6243354. [DOI: 10.1093/protein/gzab007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/16/2021] [Accepted: 03/08/2021] [Indexed: 02/03/2023] Open
Abstract
Abstract
Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.
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Affiliation(s)
- Ben A Meinen
- Institute for Protein Innovation, Harvard Institutes of Medicine 4 Blackfan Circle, Room 941 Boston, MA 02115-5701 Boston, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Harvard Institutes of Medicine 4 Blackfan Circle, Room 941 Boston, MA 02115-5701 Boston, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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27
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Abstract
While native proteins cover diverse structural spaces and achieve various biological events, not many of them can directly serve human needs. One reason is that the native proteins usually contain idiosyncrasies evolved for their native functions but disfavoring engineering requirements. To overcome this issue, one strategy is to create de novo proteins which are designed to possess improved stability, high environmental tolerance, and enhanced engineering potential. Compared to other protein engineering strategies, in silico design of de novo proteins has significantly expanded the protein structural and sequence spaces, reduced wet lab workload, and incorporated engineered features in a guided and efficient manner. In the Baker laboratory we have been applying a design pipeline that uses the blueprint builder to design different folds of de novo proteins, and have successfully obtained libraries of de novo proteins with improved stability and engineering potential. In this article, we will use the design of de novo β-barrel proteins as an example to describe the principles and basic procedures of the blueprint builder-based design pipeline. © 2020 Wiley Periodicals LLC. Basic Protocol 1: The construction of blueprints Alternate Protocol: Build blueprints based on existing protein .pdb files Basic Protocol 2: De novo protein design pipeline using the blueprint builder.
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Affiliation(s)
- Linna An
- Institute for Protein Design, University of Washington, Seattle, Washington
| | - Gyu Rie Lee
- Institute for Protein Design, University of Washington, Seattle, Washington
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28
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Abstract
Protein engineering can yield new molecular tools for nanotechnology and therapeutic applications through modulating physiochemical and biological properties. Engineering membrane proteins is especially attractive because they perform key cellular processes including transport, nutrient uptake, removal of toxins, respiration, motility, and signaling. In this chapter, we describe two protocols for membrane protein engineering with the Rosetta software: (1) ΔΔG calculations for single point mutations and (2) sequence optimization in different membrane lipid compositions. These modular protocols are easily adaptable for more complex problems and serve as a foundation for efficient membrane protein engineering calculations.
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Affiliation(s)
- Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA. .,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA.
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29
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Pan X, Kortemme T. Recent advances in de novo protein design: Principles, methods, and applications. J Biol Chem 2021; 296:100558. [PMID: 33744284 PMCID: PMC8065224 DOI: 10.1016/j.jbc.2021.100558] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.
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Affiliation(s)
- Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA.
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, California, USA.
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30
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Hernandez Alvarez B, Skokowa J, Coles M, Mir P, Nasri M, Maksymenko K, Weidmann L, Rogers KW, Welte K, Lupas AN, Müller P, ElGamacy M. Design of novel granulopoietic proteins by topological rescaffolding. PLoS Biol 2020; 18:e3000919. [PMID: 33351791 PMCID: PMC7755208 DOI: 10.1371/journal.pbio.3000919] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 11/24/2020] [Indexed: 11/18/2022] Open
Abstract
Computational protein design is rapidly becoming more powerful, and improving the accuracy of computational methods would greatly streamline protein engineering by eliminating the need for empirical optimization in the laboratory. In this work, we set out to design novel granulopoietic agents using a rescaffolding strategy with the goal of achieving simpler and more stable proteins. All of the 4 experimentally tested designs were folded, monomeric, and stable, while the 2 determined structures agreed with the design models within less than 2.5 Å. Despite the lack of significant topological or sequence similarity to their natural granulopoietic counterpart, 2 designs bound to the granulocyte colony-stimulating factor (G-CSF) receptor and exhibited potent, but delayed, in vitro proliferative activity in a G-CSF-dependent cell line. Interestingly, the designs also induced proliferation and differentiation of primary human hematopoietic stem cells into mature granulocytes, highlighting the utility of our approach to develop highly active therapeutic leads purely based on computational design. De novo designed cytokines that activate the G-CSF receptor show that the receptor-binding information can be encoded onto stable, miniaturised protein scaffolds that possess potent granulopoietic activity; such novel proteins provide for ideal candidates for protein-based therapeutics.
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Affiliation(s)
| | - Julia Skokowa
- University Hospital Tübingen, Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Germany
- * E-mail: (JS); (ME)
| | - Murray Coles
- Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Perihan Mir
- University Hospital Tübingen, Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Germany
| | - Masoud Nasri
- University Hospital Tübingen, Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Germany
| | | | - Laura Weidmann
- Max Planck Institute for Developmental Biology, Tübingen, Germany
| | | | - Karl Welte
- University Hospital Tübingen, Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Germany
| | - Andrei N. Lupas
- Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Patrick Müller
- University Hospital Tübingen, Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Germany
- Friedrich Miescher Laboratory of the Max Planck Society Tübingen, Germany
| | - Mohammad ElGamacy
- Max Planck Institute for Developmental Biology, Tübingen, Germany
- University Hospital Tübingen, Division of Translational Oncology, Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Germany
- Friedrich Miescher Laboratory of the Max Planck Society Tübingen, Germany
- Heliopolis Biotechnology Ltd., London, United Kingdom
- * E-mail: (JS); (ME)
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31
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Maguire JB, Haddox HK, Strickland D, Halabiya SF, Coventry B, Griffin JR, Pulavarti SVSRK, Cummins M, Thieker DF, Klavins E, Szyperski T, DiMaio F, Baker D, Kuhlman B. Perturbing the energy landscape for improved packing during computational protein design. Proteins 2020; 89:436-449. [PMID: 33249652 DOI: 10.1002/prot.26030] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 10/04/2020] [Accepted: 11/21/2020] [Indexed: 01/04/2023]
Abstract
The FastDesign protocol in the molecular modeling program Rosetta iterates between sequence optimization and structure refinement to stabilize de novo designed protein structures and complexes. FastDesign has been used previously to design novel protein folds and assemblies with important applications in research and medicine. To promote sampling of alternative conformations and sequences, FastDesign includes stages where the energy landscape is smoothened by reducing repulsive forces. Here, we discover that this process disfavors larger amino acids in the protein core because the protein compresses in the early stages of refinement. By testing alternative ramping strategies for the repulsive weight, we arrive at a scheme that produces lower energy designs with more native-like sequence composition in the protein core. We further validate the protocol by designing and experimentally characterizing over 4000 proteins and show that the new protocol produces higher stability proteins.
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Affiliation(s)
- Jack B Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Hugh K Haddox
- Department of Biochemistry, University of Washington, Seattle, Washington, USA.,Institute for Protein Design, University of Washington, Seattle, Washington, USA
| | - Devin Strickland
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Samer F Halabiya
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Brian Coventry
- Institute for Protein Design, University of Washington, Seattle, Washington, USA.,Molecular Engineering PhD Program, University of Washington, Seattle, Washington, USA
| | - Jermel R Griffin
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York, USA
| | | | - Matthew Cummins
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - David F Thieker
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Eric Klavins
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Thomas Szyperski
- Department of Chemistry, State University of New York at Buffalo, Buffalo, New York, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, Washington, USA.,Institute for Protein Design, University of Washington, Seattle, Washington, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington, USA.,Institute for Protein Design, University of Washington, Seattle, Washington, USA.,Howard Hughes Medical Institute, University of Washington, Seattle, Washington, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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32
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Abstract
For two decades, Rosetta has consistently been at the forefront of protein structure
prediction. While it has become a very large package comprising programs, scripts, and tools, for
different types of macromolecular modelling such as ligand docking, protein-protein docking,
protein design, and loop modelling, it started as the implementation of an algorithm for ab initio
protein structure prediction. The term ’Rosetta’ appeared for the first time twenty years ago in the
literature to describe that algorithm and its contribution to the third edition of the community wide
Critical Assessment of techniques for protein Structure Prediction (CASP3). Similar to the Rosetta
stone that allowed deciphering the ancient Egyptian civilisation, David Baker and his co-workers
have been contributing to deciphering ’the second half of the genetic code’. Although the focus of
Baker’s team has expended to de novo protein design in the past few years, Rosetta’s ‘fame’ is
associated with its fragment-assembly protein structure prediction approach. Following a
presentation of the main concepts underpinning its foundation, especially sequence-structure
correlation and usage of fragments, we review the main stages of its developments and highlight
the milestones it has achieved in terms of protein structure prediction, particularly in CASP.
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Affiliation(s)
- Jad Abbass
- Department of Computer Science, Lebanese International University, Bekaa, Lebanon
| | - Jean-Christophe Nebel
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE, United Kingdom
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33
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Predicting antibody affinity changes upon mutations by combining multiple predictors. Sci Rep 2020; 10:19533. [PMID: 33177627 PMCID: PMC7658247 DOI: 10.1038/s41598-020-76369-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023] Open
Abstract
Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biotherapeutic and bioengineering applications. The prediction of antibody affinity changes upon mutations (\documentclass[12pt]{minimal}
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\begin{document}$${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$\end{document}ΔΔGbinding) is important for antibody engineering. Numerous computational methods have been proposed based on different approaches including molecular mechanics and machine learning. However, the accuracy by each individual predictor is not enough for efficient antibody development. In this study, we develop a new prediction method by combining multiple predictors based on machine learning. Our method was tested on the SiPMAB database, evaluating the Pearson’s correlation coefficient between predicted and experimental \documentclass[12pt]{minimal}
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\begin{document}$${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$\end{document}ΔΔGbinding. Our method achieved higher accuracy (R = 0.69) than previous molecular mechanics or machine-learning based methods (R = 0.59) and the previous method using the average of multiple predictors (R = 0.64). Feature importance analysis indicated that the improved accuracy was obtained by combining predictors with different importance, which have different protocols for calculating energies and for generating mutant and unbound state structures. This study demonstrates that machine learning is a powerful framework for combining different approaches to predict antibody affinity changes.
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34
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Lee CH, Salio M, Napolitani G, Ogg G, Simmons A, Koohy H. Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors. Front Immunol 2020; 11:565096. [PMID: 33193332 PMCID: PMC7642207 DOI: 10.3389/fimmu.2020.565096] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 09/07/2020] [Indexed: 12/13/2022] Open
Abstract
Adaptive immune recognition is mediated by specific interactions between heterodimeric T cell receptors (TCRs) and their cognate peptide-MHC (pMHC) ligands, and the methods to accurately predict TCR:pMHC interaction would have profound clinical, therapeutic and pharmaceutical applications. Herein, we review recent developments in predicting cross-reactivity and antigen specificity of TCR recognition. We discuss current experimental and computational approaches to investigate cross-reactivity and antigen-specificity of TCRs and highlight how integrating kinetic, biophysical and structural features may offer valuable insights in modeling immunogenicity. We further underscore the close inter-relationship of these two interconnected notions and the need to investigate each in the light of the other for a better understanding of T cell responsiveness for the effective clinical applications.
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Affiliation(s)
- Chloe H. Lee
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Mariolina Salio
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Giorgio Napolitani
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Graham Ogg
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Alison Simmons
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, United Kingdom
| | - Hashem Koohy
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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35
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Smith ST, Meiler J. Assessing multiple score functions in Rosetta for drug discovery. PLoS One 2020; 15:e0240450. [PMID: 33044994 PMCID: PMC7549810 DOI: 10.1371/journal.pone.0240450] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/27/2020] [Indexed: 12/25/2022] Open
Abstract
Rosetta is a computational software suite containing algorithms for a wide variety of macromolecular structure prediction and design tasks including small molecule protocols commonly used in drug discovery or enzyme design. Here, we benchmark RosettaLigand score functions and protocols in comparison to results of other software recently published in the Comparative Assessment of Score Functions (CASF-2016). The CASF-2016 benchmark covers a wide variety of tests including scoring and ranking multiple compounds against a target, ligand docking of a small molecule to a target, and virtual screening to extract binders from a compound library. Direct comparison to the score functions provided by CASF-2016 results shows that the original RosettaLigand score function ranks among the top software for scoring, ranking, docking and screening tests. Most notably, the RosettaLigand score function ranked 2/34 among other report score functions in CASF-2016. We additionally perform a ligand docking test with full sampling to mimic typical use cases. Despite improved performance of newer score functions in canonical protein structure prediction and design, we demonstrate here that more recent Rosetta score functions have reduced performance across all small molecule benchmarks. The tests described here have also been uploaded to the Rosetta scientific benchmarking server and will be run weekly to track performance as the code is continually being developed.
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Affiliation(s)
- Shannon T. Smith
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Departments of Chemistry, Pharmacology, and Biomedical Informatics, Center for Structural Biology and Institute of Chemical Biology, Nashville, Tennessee, United States of America
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Städelschule, Germany
- * E-mail:
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36
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Huang X, Pearce R, Zhang Y. EvoEF2: accurate and fast energy function for computational protein design. Bioinformatics 2020; 36:1135-1142. [PMID: 31588495 DOI: 10.1093/bioinformatics/btz740] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 09/19/2019] [Accepted: 09/25/2019] [Indexed: 01/26/2023] Open
Abstract
MOTIVATION The accuracy and success rate of de novo protein design remain limited, mainly due to the parameter over-fitting of current energy functions and their inability to discriminate incorrect designs from correct designs. RESULTS We developed an extended energy function, EvoEF2, for efficient de novo protein sequence design, based on a previously proposed physical energy function, EvoEF. Remarkably, EvoEF2 recovered 32.5%, 47.9% and 22.3% of all, core and surface residues for 148 test monomers, and was generally applicable to protein-protein interaction design, as it recapitulated 30.9%, 42.4%, 31.3% and 21.4% of all, core, interface and surface residues for 88 test dimers, significantly outperforming EvoEF on the native sequence recapitulation. We further used I-TASSER to evaluate the foldability of the 148 designed monomer sequences, where all of them were predicted to fold into structures with high fold- and atomic-level similarity to their corresponding native structures, as demonstrated by the fact that 87.8% of the predicted structures shared a root-mean-square-deviation less than 2 Å to their native counterparts. The study also demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than EvoEF, which was optimized on thermodynamic mutation data. AVAILABILITY AND IMPLEMENTATION The source code of EvoEF2 and the benchmark datasets are freely available at https://zhanglab.ccmb.med.umich.edu/EvoEF. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaoqiang Huang
- Department of Computational Medicine and Bioinformatics, MI 48109, USA
| | - Robin Pearce
- Department of Computational Medicine and Bioinformatics, MI 48109, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, MI 48109, USA.,Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
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37
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Adeshina YO, Deeds EJ, Karanicolas J. Machine learning classification can reduce false positives in structure-based virtual screening. Proc Natl Acad Sci U S A 2020; 117:18477-18488. [PMID: 32669436 PMCID: PMC7414157 DOI: 10.1073/pnas.2000585117] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that aims to generate highly compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework. In retrospective benchmarks, our classifier shows outstanding performance relative to other scoring functions. In a prospective context, nearly all candidate inhibitors from a screen against acetylcholinesterase show detectable activity; beyond this, 10 of 23 compounds have IC50 better than 50 μM. Without any medicinal chemistry optimization, the most potent hit has IC50 280 nM, corresponding to Ki of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.
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Affiliation(s)
- Yusuf O Adeshina
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111
- Center for Computational Biology, University of Kansas, Lawrence, KS 66045
| | - Eric J Deeds
- Center for Computational Biology, University of Kansas, Lawrence, KS 66045
- Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66045
| | - John Karanicolas
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111;
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38
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Citation(s) in RCA: 417] [Impact Index Per Article: 104.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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39
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Sevy AM, Gilchuk IM, Brown BP, Bozhanova NG, Nargi R, Jensen M, Meiler J, Crowe JE. Computationally Designed Cyclic Peptides Derived from an Antibody Loop Increase Breadth of Binding for Influenza Variants. Structure 2020; 28:1114-1123.e4. [PMID: 32610044 DOI: 10.1016/j.str.2020.04.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/12/2020] [Accepted: 04/06/2020] [Indexed: 01/12/2023]
Abstract
The influenza hemagglutinin (HA) glycoprotein is the target of many broadly neutralizing antibodies. However, influenza viruses can rapidly escape antibody recognition by mutation of hypervariable regions of HA that overlap with the binding epitope. We hypothesized that by designing peptides to mimic antibody loops, we could enhance breadth of binding to HA antigenic variants by reducing contact with hypervariable residues on HA that mediate escape. We designed cyclic peptides that mimic the heavy-chain complementarity-determining region 3 (CDRH3) of anti-influenza broadly neutralizing antibody C05 and show that these peptides bound to HA molecules with <100 nM affinity, comparable with that of the full-length parental C05 IgG. In addition, these peptides exhibited increased breadth of recognition to influenza H4 and H7 subtypes by eliminating clashes between the hypervariable antigenic regions and the antibody CDRH1 loop. This approach can be used to generate antibody-derived peptides against a wide variety of targets.
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Affiliation(s)
- Alexander M Sevy
- Chemical & Physical Biology Program, Vanderbilt University, Nashville, TN 37235, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA; Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Iuliia M Gilchuk
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Benjamin P Brown
- Chemical & Physical Biology Program, Vanderbilt University, Nashville, TN 37235, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Nina G Bozhanova
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Rachel Nargi
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Mattie Jensen
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jens Meiler
- Chemical & Physical Biology Program, Vanderbilt University, Nashville, TN 37235, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37235, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA.
| | - James E Crowe
- Chemical & Physical Biology Program, Vanderbilt University, Nashville, TN 37235, USA; Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
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40
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Badaczewska-Dawid AE, Kmiecik S, Koliński M. Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement. Brief Bioinform 2020; 22:5855394. [PMID: 32520310 PMCID: PMC8138832 DOI: 10.1093/bib/bbaa109] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 05/04/2020] [Accepted: 05/06/2020] [Indexed: 12/19/2022] Open
Abstract
The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein–peptide complex structures and model scoring. We evaluated the proposed protocol on the set of seven different GPCR–peptide complexes (including one containing a cyclic peptide), for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface root-mean-square-deviation measure.
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Affiliation(s)
| | | | - Michał Koliński
- Corresponding author: Michał Koliński, Bioinformatics Laboratory, Mossakowski Medical Research Centre, Polish Academy of Sciences, 5 Pawińskiego St, 02-106 Warsaw, Poland. Tel: (+48) 22 849 93 58; Fax: (+48) 22 668 55 32; E-mail:
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41
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Froning K, Maguire J, Sereno A, Huang F, Chang S, Weichert K, Frommelt AJ, Dong J, Wu X, Austin H, Conner EM, Fitchett JR, Heng AR, Balasubramaniam D, Hilgers MT, Kuhlman B, Demarest SJ. Computational stabilization of T cell receptors allows pairing with antibodies to form bispecifics. Nat Commun 2020; 11:2330. [PMID: 32393818 PMCID: PMC7214467 DOI: 10.1038/s41467-020-16231-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 04/20/2020] [Indexed: 01/29/2023] Open
Abstract
Recombinant T cell receptors (TCRs) can be used to redirect naïve T cells to eliminate virally infected or cancerous cells; however, they are plagued by low stability and uneven expression. Here, we use molecular modeling to identify mutations in the TCR constant domains (Cα/Cβ) that increase the unfolding temperature of Cα/Cβ by 20 °C, improve the expression of four separate α/β TCRs by 3- to 10-fold, and improve the assembly and stability of TCRs with poor intrinsic stability. The stabilizing mutations rescue the expression of TCRs destabilized through variable domain mutation. The improved stability and folding of the TCRs reduces glycosylation, perhaps through conformational stabilization that restricts access to N-linked glycosylation enzymes. The Cα/Cβ mutations enables antibody-like expression and assembly of well-behaved bispecific molecules that combine an anti-CD3 antibody with the stabilized TCR. These TCR/CD3 bispecifics can redirect T cells to kill tumor cells with target HLA/peptide on their surfaces in vitro.
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Affiliation(s)
- Karen Froning
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Arlene Sereno
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Flora Huang
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Shawn Chang
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Kenneth Weichert
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Anton J Frommelt
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Jessica Dong
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Xiufeng Wu
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Heather Austin
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Elaine M Conner
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Jonathan R Fitchett
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Aik Roy Heng
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | | | - Mark T Hilgers
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Stephen J Demarest
- Eli Lilly Biotechnology Center, 10300 Campus Point Drive, San Diego, CA, 92121, USA.
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42
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Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane. Biophys J 2020; 118:2042-2055. [PMID: 32224301 DOI: 10.1016/j.bpj.2020.03.006] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 02/04/2020] [Accepted: 03/09/2020] [Indexed: 11/19/2022] Open
Abstract
Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. Although soluble protein design has advanced, membrane protein design remains challenging because of difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational benchmarks against experimental targets, including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure discrimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Furthermore, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.
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43
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Awosanya EO, Lapin J, Nevzorov AA. NMR "Crystallography" for Uniformly ( 13 C, 15 N)-Labeled Oriented Membrane Proteins. Angew Chem Int Ed Engl 2020; 59:3554-3557. [PMID: 31887238 DOI: 10.1002/anie.201915110] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 12/29/2019] [Indexed: 01/01/2023]
Abstract
In oriented-sample (OS) solid-state NMR of membrane proteins, the angular-dependent dipolar couplings and chemical shifts provide a direct input for structure calculations. However, so far only 1 H-15 N dipolar couplings and 15 N chemical shifts have been routinely assessed in oriented 15 N-labeled samples. The main obstacle for extending this technique to membrane proteins of arbitrary topology has remained in the lack of additional experimental restraints. We have developed a new experimental triple-resonance NMR technique, which was applied to uniformly doubly (15 N, 13 C)-labeled Pf1 coat protein in magnetically aligned DMPC/DHPC bicelles. The previously inaccessible 1 Hα -13 Cα dipolar couplings have been measured, which make it possible to determine the torsion angles between the peptide planes without assuming α-helical structure a priori. The fitting of three angular restraints per peptide plane and filtering by Rosetta scoring functions has yielded a consensus α-helical transmembrane structure for Pf1 protein.
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Affiliation(s)
- Emmanuel O Awosanya
- Department of Chemistry, North Carolina State University, 2620 Yarbrough Drive, Raleigh, NC, 27695-8204, USA
| | - Joel Lapin
- Department of Chemistry, North Carolina State University, 2620 Yarbrough Drive, Raleigh, NC, 27695-8204, USA
| | - Alexander A Nevzorov
- Department of Chemistry, North Carolina State University, 2620 Yarbrough Drive, Raleigh, NC, 27695-8204, USA
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44
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Awosanya EO, Lapin J, Nevzorov AA. NMR “Crystallography” for Uniformly (
13
C,
15
N)‐Labeled Oriented Membrane Proteins. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201915110] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Emmanuel O. Awosanya
- Department of Chemistry North Carolina State University 2620 Yarbrough Drive Raleigh NC 27695-8204 USA
| | - Joel Lapin
- Department of Chemistry North Carolina State University 2620 Yarbrough Drive Raleigh NC 27695-8204 USA
| | - Alexander A. Nevzorov
- Department of Chemistry North Carolina State University 2620 Yarbrough Drive Raleigh NC 27695-8204 USA
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45
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Zhou J, Panaitiu AE, Grigoryan G. A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures. Proc Natl Acad Sci U S A 2020; 117:1059-1068. [PMID: 31892539 PMCID: PMC6969538 DOI: 10.1073/pnas.1908723117] [Citation(s) in RCA: 59] [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] [Indexed: 12/23/2022] Open
Abstract
Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.
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Affiliation(s)
- Jianfu Zhou
- Department of Computer Science, Dartmouth College, Hanover, NH 03755
| | | | - Gevorg Grigoryan
- Department of Computer Science, Dartmouth College, Hanover, NH 03755;
- Department of Biological Sciences, Dartmouth College, Hanover, NH 03755
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46
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Huang X, Pearce R, Zhang Y. Toward the Accuracy and Speed of Protein Side-Chain Packing: A Systematic Study on Rotamer Libraries. J Chem Inf Model 2019; 60:410-420. [PMID: 31851497 DOI: 10.1021/acs.jcim.9b00812] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein rotamers refer to the conformational isomers taken by the side-chains of amino acids to accommodate specific structural folding environments. Since accurate modeling of atomic interactions is difficult, rotamer information collected from experimentally solved protein structures is often used to guide side-chain packing in protein folding and sequence design studies. Many rotamer libraries have been built in the literature but there is little quantitative guidance on which libraries should be chosen for different structural modeling studies. Here, we performed a comparative study of six widely used rotamer libraries and systematically examined their suitability for protein folding and sequence design in four aspects: (1) side-chain match accuracy, (2) side-chain conformation prediction, (3) de novo protein sequence design, and (4) computational time cost. We demonstrated that, compared to the backbone-dependent rotamer libraries (BBDRLs), the backbone-independent rotamer libraries (BBIRLs) generated conformations that more closely matched the native conformations due to the larger number of rotamers in the local rotamer search spaces. However, more practically, using an optimized physical energy function incorporated into a simulated annealing Monte Carlo searching scheme, we showed that utilization of the BBDRLs could result in higher accuracies in side-chain prediction and higher sequence recapitulation rates in protein design experiments. Detailed data analyses showed that the major advantage of BBDRLs lies in the energy term derived from the rotamer probabilities that are associated with the individual backbone torsion angle subspaces. This term is important for distinguishing between amino acid identities as well as the rotamer conformations of an amino acid. Meanwhile, the backbone torsion angle subspace-specific rotamer search drastically speeds up the searching time, despite the significantly larger number of total rotamers in the BBDRLs. These results should provide important guidance for the development and selection of rotamer libraries for practical protein design and structure prediction studies.
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47
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Kuhlman B. Designing protein structures and complexes with the molecular modeling program Rosetta. J Biol Chem 2019; 294:19436-19443. [PMID: 31699898 DOI: 10.1074/jbc.aw119.008144] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Proteins perform an amazingly diverse set of functions in all aspects of life. Critical to the function of many proteins are the highly specific three-dimensional structures they adopt. For this reason, there is strong interest in learning how to rationally design proteins that adopt user-defined structures. Over the last 25 years, there has been significant progress in the field of computational protein design as rotamer-based sequence optimization protocols have enabled accurate design of protein tertiary and quaternary structure. In this award article, I will summarize how the molecular modeling program Rosetta is used to design new protein structures and describe how we have taken advantage of this capability to create proteins that have important applications in research and medicine. I will highlight three protein design stories: the use of protein interface design to create therapeutic bispecific antibodies, the engineering of light-inducible proteins that can be used to recruit proteins to specific locations in the cell, and the de novo design of new protein structures from pieces of naturally occurring proteins.
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Affiliation(s)
- Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina 27599-7260 .,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599
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48
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Kanjanatanin P, Pichyangkura R, Sitthiyotha T, Charoenwongpaiboon T, Wangpaiboon K, Chunsrivirot S. Computational design of Bacillus licheniformis RN-01 levansucrase for control of the chain length of levan-type fructooligosaccharides. Int J Biol Macromol 2019; 140:1239-1248. [PMID: 31437510 DOI: 10.1016/j.ijbiomac.2019.08.151] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 07/31/2019] [Accepted: 08/17/2019] [Indexed: 10/26/2022]
Abstract
Levansucrase (LS) from Gram-positive bacteria generally produces a large quantity of levan polymer, a polyfructose with glucose at the end (GFn) but a small quantity of levan-type fructooligosaccharides (LFOs). The properties of levan and LFOs depend on their chain lengths, thereby determining their potential applications in food and pharmaceutical industries such as prebiotics and anti-tumor agents. Therefore, an ability to redesign and engineer the active site of levansucrase for synthesis of products with desired degree of polymerization (DP) is very beneficial. We employed computational protein design, docking and molecular dynamics to redesign and engineer the active site of Bacillus licheniformis RN-01 levansucrase for production of LFOs with DP up to five (GF4), using two approaches: 1) blocking oligosaccharide binding track of GF3-LS complex with large aromatic residues and 2) eliminating hydrogen bond interactions between terminal glucose of GF4 and side chains of binding residues of GF4-LS complex. The designed enzymes and their product patterns from these two approaches were experimentally characterized. The experimental results show that the first approach was successful in creating N251W and N251W/K372Y mutants that synthesized LFOs with DP up to five. This work illustrates how computer-aided approaches can offer novel opportunities to engineer enzymes for desired products.
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Affiliation(s)
- Pongsakorn Kanjanatanin
- Department of Biochemistry, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand; Structural and Computational Biology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand
| | - Rath Pichyangkura
- Department of Biochemistry, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand
| | - Thassanai Sitthiyotha
- Department of Biochemistry, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand; Structural and Computational Biology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand
| | - Thanapon Charoenwongpaiboon
- Department of Biochemistry, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand
| | - Karan Wangpaiboon
- Department of Biochemistry, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand
| | - Surasak Chunsrivirot
- Department of Biochemistry, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand; Structural and Computational Biology Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand.
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Wang D, Geng L, Zhao YJ, Yang Y, Huang Y, Zhang Y, Shen HB. Artificial intelligence-based multi-objective optimization protocol for protein structure refinement. Bioinformatics 2019; 36:437-448. [PMID: 31274151 PMCID: PMC7999140 DOI: 10.1093/bioinformatics/btz544] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 06/06/2019] [Accepted: 07/04/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics algorithm. The one-dimension optimization of a single energy function may take the structure too far away without a constraint. The basic motivation of our study is to reduce the bias problem caused by minimizing only a single energy function due to the very diversity of different protein structures. RESULTS We report a new Artificial Intelligence-based protein structure Refinement method called AIR. Its fundamental idea is to use multiple energy functions as multi-objectives in an effort to correct the potential inaccuracy from a single function. A multi-objective particle swarm optimization algorithm-based structure refinement is designed, where each structure is considered as a particle in the protocol. With the refinement iterations, the particles move around. The quality of particles in each iteration is evaluated by three energy functions, and the non-dominated particles are put into a set called Pareto set. After enough iteration times, particles from the Pareto set are screened and part of the top solutions are outputted as the final refined structures. The multi-objective energy function optimization strategy designed in the AIR protocol provides a different constraint view of the structure, by extending the one-dimension optimization to a new three-dimension space optimization driven by the multi-objective particle swarm optimization engine. Experimental results on CASP11, CASP12 refinement targets and blind tests in CASP 13 turn to be promising. AVAILABILITY AND IMPLEMENTATION The AIR is available online at: www.csbio.sjtu.edu.cn/bioinf/AIR/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Yu-Jun Zhao
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Yang Yang
- Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yan Huang
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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Wink LH, Baker DL, Cole JA, Parrill AL. A benchmark study of loop modeling methods applied to G protein-coupled receptors. J Comput Aided Mol Des 2019; 33:573-595. [PMID: 31123958 PMCID: PMC6628340 DOI: 10.1007/s10822-019-00196-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/12/2019] [Indexed: 11/25/2022]
Abstract
G protein-coupled receptors (GPCR) are important drug discovery targets. Despite progress, many GPCR structures have not yet been solved. For these targets, comparative modeling is used in virtual ligand screening to prioritize experimental efforts. However, the structure of extracellular loop 2 (ECL2) is often poorly predicted. This is significant due to involvement of ECL2 in ligand binding for many Class A GPCR. Here we examine the performance of loop modeling protocols available in the Rosetta (cyclic coordinate descent [CCD], KIC with fragments [KICF] and next generation KIC [NGK]) and Molecular Operating Environment (MOE) software suites (de novo search). ECL2 from GPCR crystal structures served as the structure prediction targets and were divided into four sets depending on loop length. Results suggest that KICF and NGK sampled and scored more loop models with sub-angstrom and near-atomic accuracy than CCD or de novo search for loops of 24 or fewer residues. None of the methods were able to sample loop conformations with near-atomic accuracy for the longest targets ranging from 25 to 32 residues based on 1000 models generated. For these long loop targets, increased conformational sampling is necessary. The strongly conserved disulfide bond between Cys3.25 and Cys45.50 in ECL2 proved an effective filter. Setting an upper limit of 5.1 Å on the S-S distance improved the lowest RMSD model included in the top 10 scored structures in Groups 1-4 on average between 0.33 and 1.27 Å. Disulfide bond formation and geometry optimization of ECL2 provided an additional incremental benefit in structure quality.
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Affiliation(s)
- Lee H Wink
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA
| | - Daniel L Baker
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA
| | - Judith A Cole
- Department of Biological Sciences, The University of Memphis, Memphis, TN, 38152, USA
| | - Abby L Parrill
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA.
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