1
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Zhang G, Kuang X, Zhang Y, Liu Y, Su Z, Zhang T, Wu Y. Machine-learning-based structural analysis of interactions between antibodies and antigens. Biosystems 2024; 243:105264. [PMID: 38964652 DOI: 10.1016/j.biosystems.2024.105264] [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: 12/13/2023] [Revised: 06/21/2024] [Accepted: 07/01/2024] [Indexed: 07/06/2024]
Abstract
Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.
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Affiliation(s)
- Grace Zhang
- Staples High School, 70 North Avenue, Westport, CT, 06880, USA
| | - Xiaohan Kuang
- Data Science Institute, Vanderbilt University, 1001 19th Ave S, Nashville, TN, 37212, USA
| | - Yuhao Zhang
- Data Science Institute, Vanderbilt University, 1001 19th Ave S, Nashville, TN, 37212, USA
| | - Yunchao Liu
- Department of Computer Science, Vanderbilt University, 1400 18th Ave S, Nashville, TN, 37212, USA
| | - Zhaoqian Su
- Data Science Institute, Vanderbilt University, 1001 19th Ave S, Nashville, TN, 37212, USA
| | - Tom Zhang
- California Institute of Technology, 1200 East California Boulevard, Pasadena, CA, 91125, USA.
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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2
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Bisarad P, Kelbauskas L, Singh A, Taguchi AT, Trenchevska O, Woodbury NW. Predicting monoclonal antibody binding sequences from a sparse sampling of all possible sequences. Commun Biol 2024; 7:979. [PMID: 39134636 PMCID: PMC11319732 DOI: 10.1038/s42003-024-06650-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 07/29/2024] [Indexed: 08/15/2024] Open
Abstract
Previous work has shown that binding of target proteins to a sparse, unbiased sample of all possible peptide sequences is sufficient to train a machine learning model that can then predict, with statistically high accuracy, target binding to any possible peptide sequence of similar length. Here, highly sequence-specific molecular recognition is explored by measuring binding of 8 monoclonal antibodies (mAbs) with specific linear cognate epitopes to an array containing 121,715 near-random sequences about 10 residues in length. Network models trained on resulting sequence-binding values are used to predict the binding of each mAb to its cognate sequence and to an in silico generated one million random sequences. The model always ranks the binding of the cognate sequence in the top 100 sequences, and for 6 of the 8 mAbs, the cognate sequence ranks in the top ten. Practically, this approach has potential utility in selecting highly specific mAbs for therapeutics or diagnostics. More fundamentally, this demonstrates that very sparse random sampling of a large amino acid sequence spaces is sufficient to generate comprehensive models predictive of highly specific molecular recognition.
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Affiliation(s)
- Pritha Bisarad
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ, USA
- Pediatric Movement Disorders Program, Division of Pediatric Neurology, Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, USA
- Department of Child Health, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA
| | - Laimonas Kelbauskas
- Center for Molecular Design and Biomimetics, Biodesign Institute, Arizona State University, Tempe, AZ, USA
- Biomorph Technologies, Chandler, AZ, USA
| | - Akanksha Singh
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ, USA
- Prellis Biologics Inc., Berkeley, CA, USA
| | | | | | - Neal W Woodbury
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA.
- Center for Innovations in Medicine, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
- Center for Molecular Design and Biomimetics, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
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3
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Zhang H, Lan J, Wang H, Lu R, Zhang N, He X, Yang J, Chen L. AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease. Front Mol Biosci 2024; 11:1414916. [PMID: 39139810 PMCID: PMC11319189 DOI: 10.3389/fmolb.2024.1414916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/15/2024] [Indexed: 08/15/2024] Open
Abstract
Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.
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Affiliation(s)
- Hong Zhang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Jiajing Lan
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Huijie Wang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Ruijie Lu
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Nanqi Zhang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Xiaobai He
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
- Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang Province, Hangzhou, China
| | - Jun Yang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Linjie Chen
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
- Zhejiang Engineering Research Centre for Key Technology of Diagnostic Testing, Hangzhou, China
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4
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DeRoo J, Terry JS, Zhao N, Stasevich TJ, Snow CD, Geiss BJ. PAbFold: Linear Antibody Epitope Prediction using AlphaFold2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.19.590298. [PMID: 38659833 PMCID: PMC11042291 DOI: 10.1101/2024.04.19.590298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform their molecular functions. However, while determining linear epitopes of monoclonal antibodies can be accomplished utilizing well-established empirical procedures, these approaches are generally labor- and time-intensive and costly. To take advantage of the recent advances in protein structure prediction algorithms available to the scientific community, we developed a calculation pipeline based on the localColabFold implementation of AlphaFold2 that can predict linear antibody epitopes by predicting the structure of the complex between antibody heavy and light chains and target peptide sequences derived from antigens. We found that this AlphaFold2 pipeline, which we call PAbFold, was able to accurately flag known epitope sequences for several well-known antibody targets (HA / Myc) when the target sequence was broken into small overlapping linear peptides and antibody complementarity determining regions (CDRs) were grafted onto several different antibody framework regions in the single-chain antibody fragment (scFv) format. To determine if this pipeline was able to identify the epitope of a novel antibody with no structural information publicly available, we determined the epitope of a novel anti-SARS-CoV-2 nucleocapsid targeted antibody using our method and then experimentally validated our computational results using peptide competition ELISA assays. These results indicate that the AlphaFold2-based PAbFold pipeline we developed is capable of accurately identifying linear antibody epitopes in a short time using just antibody and target protein sequences. This emergent capability of the method is sensitive to methodological details such as peptide length, AlphaFold2 neural network versions, and multiple-sequence alignment database. PAbFold is available at https://github.com/jbderoo/PAbFold.
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Affiliation(s)
- Jacob DeRoo
- School of Biomedical Engineering, Colorado State University, Fort Collins CO USA
| | - James S. Terry
- Department of Microbiology, Immunology, & Pathology, Colorado State University, Fort Collins CO USA
| | - Ning Zhao
- Department of Biochemistry and Molecular Genetics, University of Colorado-Anschutz Medical Campus, Aurora, CO USA
| | - Timothy J. Stasevich
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins CO USA
| | - Christopher D. Snow
- School of Biomedical Engineering, Colorado State University, Fort Collins CO USA
- Department of Chemical & Biological Engineering, Colorado State University, Fort Collins CO USA
| | - Brian J. Geiss
- School of Biomedical Engineering, Colorado State University, Fort Collins CO USA
- Department of Microbiology, Immunology, & Pathology, Colorado State University, Fort Collins CO USA
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Li R, Wilderotter S, Stoddard M, Van Egeren D, Chakravarty A, Joseph-McCarthy D. Computational identification of antibody-binding epitopes from mimotope datasets. FRONTIERS IN BIOINFORMATICS 2024; 4:1295972. [PMID: 38463209 PMCID: PMC10920257 DOI: 10.3389/fbinf.2024.1295972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 01/24/2024] [Indexed: 03/12/2024] Open
Abstract
Introduction: A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Methods: Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest ("mimotopes") usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Results and Discussion: Herein we describe two novel methods for mapping mimotopes to epitopes. The first is a novel algorithm named MimoTree that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody, and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes. The second is an ensemble approach, which combines the prediction results from MimoTree and two existing methods.
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Affiliation(s)
- Rang Li
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Sabrina Wilderotter
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | | | - Debra Van Egeren
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, United States
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Acar DD, Witkowski W, Wejda M, Wei R, Desmet T, Schepens B, De Cae S, Sedeyn K, Eeckhaut H, Fijalkowska D, Roose K, Vanmarcke S, Poupon A, Jochmans D, Zhang X, Abdelnabi R, Foo CS, Weynand B, Reiter D, Callewaert N, Remaut H, Neyts J, Saelens X, Gerlo S, Vandekerckhove L. Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warranted. EBioMedicine 2024; 100:104960. [PMID: 38232633 PMCID: PMC10803917 DOI: 10.1016/j.ebiom.2023.104960] [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: 05/01/2023] [Revised: 12/22/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND SARS-CoV-2-neutralizing antibodies (nABs) showed great promise in the early phases of the COVID-19 pandemic. The emergence of resistant strains, however, quickly rendered the majority of clinically approved nABs ineffective. This underscored the imperative to develop nAB cocktails targeting non-overlapping epitopes. METHODS Undertaking a nAB discovery program, we employed a classical workflow, while integrating artificial intelligence (AI)-based prediction to select non-competing nABs very early in the pipeline. We identified and in vivo validated (in female Syrian hamsters) two highly potent nABs. FINDINGS Despite the promising results, in depth cryo-EM structural analysis demonstrated that the AI-based prediction employed with the intention to ensure non-overlapping epitopes was inaccurate. The two nABs in fact bound to the same receptor-binding epitope in a remarkably similar manner. INTERPRETATION Our findings indicate that, even in the Alphafold era, AI-based predictions of paratope-epitope interactions are rough and experimental validation of epitopes remains an essential cornerstone of a successful nAB lead selection. FUNDING Full list of funders is provided at the end of the manuscript.
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Affiliation(s)
- Delphine Diana Acar
- HIV Cure Research Center, Department of Internal Medicine and Pediatrics, Ghent University Hospital, Ghent University, Ghent 9000, Belgium
| | - Wojciech Witkowski
- HIV Cure Research Center, Department of Internal Medicine and Pediatrics, Ghent University Hospital, Ghent University, Ghent 9000, Belgium
| | - Magdalena Wejda
- HIV Cure Research Center, Department of Internal Medicine and Pediatrics, Ghent University Hospital, Ghent University, Ghent 9000, Belgium
| | - Ruifang Wei
- HIV Cure Research Center, Department of Internal Medicine and Pediatrics, Ghent University Hospital, Ghent University, Ghent 9000, Belgium
| | - Tim Desmet
- Department of Basic and Applied Medical Sciences, Ghent University, Ghent 9000, Belgium
| | - Bert Schepens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Ghent 9052, Belgium
| | - Sieglinde De Cae
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Ghent 9052, Belgium
| | - Koen Sedeyn
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Ghent 9052, Belgium
| | - Hannah Eeckhaut
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Ghent 9052, Belgium
| | - Daria Fijalkowska
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Ghent 9052, Belgium
| | - Kenny Roose
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Ghent 9052, Belgium
| | - Sandrine Vanmarcke
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Ghent 9052, Belgium
| | | | - Dirk Jochmans
- Laboratory of Virology and Chemotherapy, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven 3000, Belgium
| | - Xin Zhang
- Laboratory of Virology and Chemotherapy, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven 3000, Belgium
| | - Rana Abdelnabi
- Laboratory of Virology and Chemotherapy, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven 3000, Belgium
| | - Caroline S Foo
- Laboratory of Virology and Chemotherapy, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven 3000, Belgium
| | - Birgit Weynand
- Department of Imaging and Pathology, Translational Cell and Tissue Research, KU Leuven, Leuven 3000, Belgium
| | - Dirk Reiter
- Department of Bioengineering Sciences, Vrije Universiteit Brussel, Brussels 1050, Belgium
| | - Nico Callewaert
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Ghent 9052, Belgium
| | - Han Remaut
- Department of Bioengineering Sciences, Vrije Universiteit Brussel, Brussels 1050, Belgium; VIB-VUB Center for Structural Biology, VIB, Brussels 1050, Belgium
| | - Johan Neyts
- Laboratory of Virology and Chemotherapy, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, Leuven 3000, Belgium
| | - Xavier Saelens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium; Department of Biochemistry and Microbiology, Ghent University, Ghent 9052, Belgium
| | - Sarah Gerlo
- HIV Cure Research Center, Department of Internal Medicine and Pediatrics, Ghent University Hospital, Ghent University, Ghent 9000, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent 9000, Belgium
| | - Linos Vandekerckhove
- HIV Cure Research Center, Department of Internal Medicine and Pediatrics, Ghent University Hospital, Ghent University, Ghent 9000, Belgium.
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7
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Zhang G, Su Z, Zhang T, Wu Y. Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570397. [PMID: 38106177 PMCID: PMC10723427 DOI: 10.1101/2023.12.06.570397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.
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Affiliation(s)
- Grace Zhang
- Staples High School, 70 North Avenue, Westport, CT 06880
| | - Zhaoqian Su
- Data Science Institute, Vanderbilt University, 1001 19th Ave S, Nashville, TN, 37212
| | - Tom Zhang
- California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461
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Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, allosteric, and orthosteric drug discovery: Ways forward. Drug Discov Today 2023; 28:103551. [PMID: 36907321 PMCID: PMC10238671 DOI: 10.1016/j.drudis.2023.103551] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/27/2023] [Accepted: 03/07/2023] [Indexed: 03/13/2023]
Abstract
Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
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9
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Desta IT, Kotelnikov S, Jones G, Ghani U, Abyzov M, Kholodov Y, Standley DM, Beglov D, Vajda S, Kozakov D. The ClusPro AbEMap web server for the prediction of antibody epitopes. Nat Protoc 2023; 18:1814-1840. [PMID: 37188806 PMCID: PMC10898366 DOI: 10.1038/s41596-023-00826-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/19/2023] [Indexed: 05/17/2023]
Abstract
Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein-protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody-antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server's capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45-90 min depending on the size of the proteins.
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Affiliation(s)
- Israel T Desta
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | | | - Daron M Standley
- Department of Genome Informatics, Osaka University, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
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