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Wang X, Gao X, Fan X, Huai Z, Zhang G, Yao M, Wang T, Huang X, Lai L. WUREN: Whole-modal union representation for epitope prediction. Comput Struct Biotechnol J 2024; 23:2122-2131. [PMID: 38817963 PMCID: PMC11137340 DOI: 10.1016/j.csbj.2024.05.023] [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: 01/26/2024] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
B-cell epitope identification plays a vital role in the development of vaccines, therapies, and diagnostic tools. Currently, molecular docking tools in B-cell epitope prediction are heavily influenced by empirical parameters and require significant computational resources, rendering a great challenge to meet large-scale prediction demands. When predicting epitopes from antigen-antibody complex, current artificial intelligence algorithms cannot accurately implement the prediction due to insufficient protein feature representations, indicating novel algorithm is desperately needed for efficient protein information extraction. In this paper, we introduce a multimodal model called WUREN (Whole-modal Union Representation for Epitope predictioN), which effectively combines sequence, graph, and structural features. It achieved AUC-PR scores of 0.213 and 0.193 on the solved structures and AlphaFold-generated structures, respectively, for the independent test proteins selected from DiscoTope3 benchmark. Our findings indicate that WUREN is an efficient feature extraction model for protein complexes, with the generalizable application potential in the development of protein-based drugs. Moreover, the streamlined framework of WUREN could be readily extended to model similar biomolecules, such as nucleic acids, carbohydrates, and lipids.
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Affiliation(s)
| | | | - Xuezhe Fan
- XtalPi Innovation Center, Beijing, China
| | - Zhe Huai
- XtalPi Innovation Center, Beijing, China
| | | | | | | | | | - Lipeng Lai
- XtalPi Innovation Center, Beijing, China
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2
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Viswanathan R, Carroll M, Roffe A, Fajardo JE, Fiser A. Computational prediction of multiple antigen epitopes. Bioinformatics 2024; 40:btae556. [PMID: 39271143 PMCID: PMC11453099 DOI: 10.1093/bioinformatics/btae556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 08/08/2024] [Accepted: 09/11/2024] [Indexed: 09/15/2024] Open
Abstract
MOTIVATION Identifying antigen epitopes is essential in medical applications, such as immunodiagnostic reagent discovery, vaccine design, and drug development. Computational approaches can complement low-throughput, time-consuming, and costly experimental determination of epitopes. Currently available prediction methods, however, have moderate success predicting epitopes, which limits their applicability. Epitope prediction is further complicated by the fact that multiple epitopes may be located on the same antigen and complete experimental data is often unavailable. RESULTS Here, we introduce the antigen epitope prediction program ISPIPab that combines information from two feature-based methods and a docking-based method. We demonstrate that ISPIPab outperforms each of its individual classifiers as well as other state-of-the-art methods, including those designed specifically for epitope prediction. By combining the prediction algorithm with hierarchical clustering, we show that we can effectively capture epitopes that align with available experimental data while also revealing additional novel targets for future experimental investigations.
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Affiliation(s)
- Rajalakshmi Viswanathan
- Department of Chemistry and Biochemistry, Yeshiva College, New York, NY 10033, United States
| | - Moshe Carroll
- Department of Chemistry and Biochemistry, Yeshiva College, New York, NY 10033, United States
| | - Alexandra Roffe
- Department of Chemistry and Biochemistry, Stern College for Women, New York, NY 10016, United States
| | - Jorge E Fajardo
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, United States
| | - Andras Fiser
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, United States
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3
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Wang C, Wang J, Song W, Luo G, Jiang T. EpiScan: accurate high-throughput mapping of antibody-specific epitopes using sequence information. NPJ Syst Biol Appl 2024; 10:101. [PMID: 39251627 PMCID: PMC11383971 DOI: 10.1038/s41540-024-00432-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 08/27/2024] [Indexed: 09/11/2024] Open
Abstract
The identification of antibody-specific epitopes on virus proteins is crucial for vaccine development and drug design. Nonetheless, traditional wet-lab approaches for the identification of epitopes are both costly and labor-intensive, underscoring the need for the development of efficient and cost-effective computational tools. Here, EpiScan, an attention-based deep learning framework for predicting antibody-specific epitopes, is presented. EpiScan adopts a multi-input and single-output strategy by designing independent blocks for different parts of antibodies, including variable heavy chain (VH), variable light chain (VL), complementary determining regions (CDRs), and framework regions (FRs). The block predictions are weighted and integrated for the prediction of potential epitopes. Using multiple experimental data samples, we show that EpiScan, which only uses antibody sequence information, can accurately map epitopes on specific antigen structures. The antibody-specific epitopes on the receptor binding domain (RBD) of SARS coronavirus 2 (SARS-CoV-2) were located by EpiScan, and the potentially valuable vaccine epitope was identified. EpiScan can expedite the epitope mapping process for high-throughput antibody sequencing data, supporting vaccine design and drug development. Availability: For the convenience of related wet-experimental researchers, the source code and web server of EpiScan are publicly available at https://github.com/gzBiomedical/EpiScan .
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Affiliation(s)
- Chuan Wang
- School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Guangzhou National Laboratory, Guangzhou, China
| | | | - Wenjun Song
- Guangzhou National Laboratory, Guangzhou, China
- Institute of Integration of Traditional and Western Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Guanzheng Luo
- School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
| | - Taijiao Jiang
- Guangzhou National Laboratory, Guangzhou, China.
- State Key Laboratory of Respiratory Disease, The Key laboratory of Advanced Interdisciplinary Studies Center, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
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4
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Carroll M, Rosenbaum E, Viswanathan R. Computational Methods to Predict Conformational B-Cell Epitopes. Biomolecules 2024; 14:983. [PMID: 39199371 PMCID: PMC11352882 DOI: 10.3390/biom14080983] [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: 07/09/2024] [Revised: 08/04/2024] [Accepted: 08/08/2024] [Indexed: 09/01/2024] Open
Abstract
Accurate computational prediction of B-cell epitopes can greatly enhance biomedical research and rapidly advance efforts to develop therapeutics, monoclonal antibodies, vaccines, and immunodiagnostic reagents. Previous research efforts have primarily focused on the development of computational methods to predict linear epitopes rather than conformational epitopes; however, the latter is much more biologically predominant. Several conformational B-cell epitope prediction methods have recently been published, but their predictive performances are weak. Here, we present a review of the latest computational methods and assess their performances on a diverse test set of 29 non-redundant unbound antigen structures. Our results demonstrate that ISPIPab performs better than most methods and compares favorably with other recent antigen-specific methods. Finally, we suggest new strategies and opportunities to improve computational predictions of conformational B-cell epitopes.
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Affiliation(s)
| | | | - R. Viswanathan
- Department of Chemistry and Biochemistry, Yeshiva College, Yeshiva University, New York, NY 10033, USA; (M.C.); (E.R.)
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5
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Viswanathan R, Carroll M, Roffe A, Fajardo JE, Fiser A. Computational Prediction of Multiple Antigen Epitopes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.08.607232. [PMID: 39211281 PMCID: PMC11360938 DOI: 10.1101/2024.08.08.607232] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Motivation Identifying antigen epitopes is essential in medical applications, such as immunodiagnostic reagent discovery, vaccine design, and drug development. Computational approaches can complement low-throughput, time-consuming, and costly experimental determination of epitopes. Currently available prediction methods, however, have moderate success predicting epitopes, which limits their applicability. Epitope prediction is further complicated by the fact that multiple epitopes may be located on the same antigen and complete experimental data is often unavailable. Results Here, we introduce the antigen epitope prediction program ISPIPab that combines information from two feature-based methods and a docking-based method. We demonstrate that ISPIPab outperforms each of its individual classifiers as well as other state-of-the-art methods, including those designed specifically for epitope prediction. By combining the prediction algorithm with hierarchical clustering, we show that we can effectively capture epitopes that align with available experimental data while also revealing additional novel targets for future experimental investigations. Contact raji@yu.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Pegoraro M, Dominé C, Rodolà E, Veličković P, Deac A. Geometric epitope and paratope prediction. Bioinformatics 2024; 40:btae405. [PMID: 38984742 PMCID: PMC11245313 DOI: 10.1093/bioinformatics/btae405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 05/14/2024] [Accepted: 07/09/2024] [Indexed: 07/11/2024] Open
Abstract
MOTIVATION Identifying the binding sites of antibodies is essential for developing vaccines and synthetic antibodies. In this article, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the importance of geometric information. RESULTS Specifically, we compare different geometric deep learning methods applied to proteins' inner (I-GEP) and outer (O-GEP) structures. We incorporate 3D coordinates and spectral geometric descriptors as input features to fully leverage the geometric information. Our research suggests that different geometrical representation information is useful for different tasks. Surface-based models are more efficient in predicting the binding of the epitope, while graph models are better in paratope prediction, both achieving significant performance improvements. Moreover, we analyze the impact of structural changes in antibodies and antigens resulting from conformational rearrangements or reconstruction errors. Through this investigation, we showcase the robustness of geometric deep learning methods and spectral geometric descriptors to such perturbations. AVAILABILITY AND IMPLEMENTATION The python code for the models, together with the data and the processing pipeline, is open-source and available at https://github.com/Marco-Peg/GEP.
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Affiliation(s)
- Marco Pegoraro
- Department of Computer Science, Sapienza University of Rome, 00185, Italy
| | - Clémentine Dominé
- Gatsby Computational Neuroscience Unit, University College London, W1T 4JG, United-Kingdom
| | - Emanuele Rodolà
- Department of Computer Science, Sapienza University of Rome, 00185, Italy
| | | | - Andreea Deac
- Département d’informatique et de recherche opérationelle, Université de Montréal, QC H2S 3H1, Canada
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Arriaza RH, Kapingidza AB, Dolamore C, Khatri K, O’Malley A, Glesner J, Wuenschmann S, Hyduke NP, Easley W, Chhiv C, Pomés A, Chruszcz M. Structural, Biophysical, and Computational Studies of a Murine Light Chain Dimer. Molecules 2024; 29:2885. [PMID: 38930950 PMCID: PMC11206851 DOI: 10.3390/molecules29122885] [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: 05/03/2024] [Revised: 06/05/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Antibodies are widely used in medicinal and scientific research due to their ability to bind to a specific antigen. Most often, antibodies are composed of heavy and light chain domains. Under physiological conditions, light chains are produced in excess, as compared to the heavy chain. It is now known that light chains are not silent partners of the heavy chain and can modulate the immune response independently. In this work, the first crystal structure of a light chain dimer originating from mice is described. It represents the light chain dimer of 6A8, a monoclonal antibody specific to the allergen Der f 1. Building on the unexpected occurrence of this kind of dimer, we have demonstrated that this light chain is stable in solution alone. Moreover, enzyme-linked immunosorbent assays (ELISA) have revealed that, when the light chain is not partnered to its corresponding heavy chain, it interacts non-specifically with a wide range of proteins. Computational studies were used to provide insight on the role of the 6A8 heavy chain domain in the specific binding to Der f 1. Overall, this work demonstrates and supports the ongoing notion that light chains can function by themselves and are not silent partners of heavy chains.
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Affiliation(s)
- Ricardo H. Arriaza
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48864, USA; (R.H.A.); (K.K.); (A.O.)
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA; (A.B.K.); (C.D.); (N.P.H.); (W.E.); (C.C.)
| | - A. Brenda Kapingidza
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA; (A.B.K.); (C.D.); (N.P.H.); (W.E.); (C.C.)
| | - Coleman Dolamore
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA; (A.B.K.); (C.D.); (N.P.H.); (W.E.); (C.C.)
| | - Kriti Khatri
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48864, USA; (R.H.A.); (K.K.); (A.O.)
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA; (A.B.K.); (C.D.); (N.P.H.); (W.E.); (C.C.)
| | - Andrea O’Malley
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48864, USA; (R.H.A.); (K.K.); (A.O.)
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA; (A.B.K.); (C.D.); (N.P.H.); (W.E.); (C.C.)
| | - Jill Glesner
- InBio, Charlottesville, VA 22903, USA; (J.G.); (S.W.); (A.P.)
| | | | - Noah P. Hyduke
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA; (A.B.K.); (C.D.); (N.P.H.); (W.E.); (C.C.)
| | - William Easley
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA; (A.B.K.); (C.D.); (N.P.H.); (W.E.); (C.C.)
| | - Charline Chhiv
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA; (A.B.K.); (C.D.); (N.P.H.); (W.E.); (C.C.)
| | - Anna Pomés
- InBio, Charlottesville, VA 22903, USA; (J.G.); (S.W.); (A.P.)
| | - Maksymilian Chruszcz
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48864, USA; (R.H.A.); (K.K.); (A.O.)
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA; (A.B.K.); (C.D.); (N.P.H.); (W.E.); (C.C.)
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8
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Mondal A, Singh B, Felkner RH, De Falco A, Swapna G, Montelione GT, Roth MJ, Perez A. A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries. Angew Chem Int Ed Engl 2024; 63:e202405767. [PMID: 38588243 DOI: 10.1002/anie.202405767] [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: 03/26/2024] [Revised: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 04/10/2024]
Abstract
Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF-CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.
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Affiliation(s)
- Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL, USA
| | - Bhumika Singh
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL, USA
| | - Roland H Felkner
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, 675 Hoes Lane Rm 636, Piscataway, NJ 08854, USA
| | - Anna De Falco
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
| | - Gvt Swapna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
| | - Monica J Roth
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, 675 Hoes Lane Rm 636, Piscataway, NJ 08854, USA
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL, USA
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Saini S, Pareekh S, Kumar Y. Investigating the structural impact of Omicron RBD mutation on antibody escape and receptor management. J Biomol Struct Dyn 2024; 42:4668-4678. [PMID: 37334729 DOI: 10.1080/07391102.2023.2222174] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 06/01/2023] [Indexed: 06/20/2023]
Abstract
The SARS-CoV-2 Variant B.1.1.5291 evolved rapidly in late November 2021 from the existing mutants sparking fear worldwide owing to its infamous immune escape from a varied class of neutralising antibodies. To assess the structural behaviour of Omicron-Receptor Binding Domain (RBD) upon interacting with cross-reactive CR3022 antibody, we investigated the computational approach of structural engagement in B.1.1529 RBD and wild-type RBD with CR3022 antibody. The current study investigates the interacting interface between the RBDs and CR3022 to decipher the key residues accompanying the potential mutational landscape of SARS-CoV-2 variants. We conducted in-silico docking followed by molecular dynamics simulation analysis to examine the dynamic behaviour of protein-protein interactions. Furthermore, the study unleashed possible interactions post energy decomposition analysis via MM-GBSA. Conclusively, the mutational landscape of RBD eases in designing and discovering the effective neutralisation accompanied by development of a universal vaccine.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Samvedna Saini
- Department of Biological Sciences and Engineering (BSE), Netaji Subhas University of Technology (NSUT), New Delhi, India
| | - Savita Pareekh
- High Performance Computing (HPC) & AI Innovation Lab, Dell EMC, Bengaluru, India
| | - Yatender Kumar
- Department of Biological Sciences and Engineering (BSE), Netaji Subhas University of Technology (NSUT), New Delhi, India
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10
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Joubbi S, Micheli A, Milazzo P, Maccari G, Ciano G, Cardamone D, Medini D. Antibody design using deep learning: from sequence and structure design to affinity maturation. Brief Bioinform 2024; 25:bbae307. [PMID: 38960409 PMCID: PMC11221890 DOI: 10.1093/bib/bbae307] [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: 03/03/2024] [Revised: 05/20/2024] [Accepted: 06/12/2024] [Indexed: 07/05/2024] Open
Abstract
Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody-antigen interactions docking and affinity maturation.
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Affiliation(s)
- Sara Joubbi
- Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Alessio Micheli
- Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy
| | - Paolo Milazzo
- Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy
| | - Giuseppe Maccari
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Giorgio Ciano
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Dario Cardamone
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Duccio Medini
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
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11
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Høie MH, Gade FS, Johansen J, Würtzen C, Winther O, Nielsen M, Marcatili P. DiscoTope-3.0: improved B-cell epitope prediction using inverse folding latent representations. Front Immunol 2024; 15:1322712. [PMID: 38390326 PMCID: PMC10882062 DOI: 10.3389/fimmu.2024.1322712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/08/2024] [Indexed: 02/24/2024] Open
Abstract
Accurate computational identification of B-cell epitopes is crucial for the development of vaccines, therapies, and diagnostic tools. However, current structure-based prediction methods face limitations due to the dependency on experimentally solved structures. Here, we introduce DiscoTope-3.0, a markedly improved B-cell epitope prediction tool that innovatively employs inverse folding structure representations and a positive-unlabelled learning strategy, and is adapted for both solved and predicted structures. Our tool demonstrates a considerable improvement in performance over existing methods, accurately predicting linear and conformational epitopes across multiple independent datasets. Most notably, DiscoTope-3.0 maintains high predictive performance across solved, relaxed and predicted structures, alleviating the need for experimental structures and extending the general applicability of accurate B-cell epitope prediction by 3 orders of magnitude. DiscoTope-3.0 is made widely accessible on two web servers, processing over 100 structures per submission, and as a downloadable package. In addition, the servers interface with RCSB and AlphaFoldDB, facilitating large-scale prediction across over 200 million cataloged proteins. DiscoTope-3.0 is available at: https://services.healthtech.dtu.dk/service.php?DiscoTope-3.0.
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Affiliation(s)
- Magnus Haraldson Høie
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Frederik Steensgaard Gade
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Julie Maria Johansen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Charlotte Würtzen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Ole Winther
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
- Center for Genomic Medicine, Rigshospitalet (Copenhagen University Hospital), Copenhagen, Denmark
- Department of Biology, Bioinformatics Centre, University of Copenhagen, Copenhagen, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Paolo Marcatili
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
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12
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Mondal A, Singh B, Felkner RH, De Falco A, Swapna GVT, Montelione GT, Roth MJ, Perez A. Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576374. [PMID: 38328039 PMCID: PMC10849530 DOI: 10.1101/2024.01.20.576374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners, but often include false positives. Furthermore, they provide no information about what the binding region is (e.g. the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competition Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment, along with the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies to AF and AF-CBA, to help users identify scenarios where the approach will be most useful. Given the speed and accuracy of the methodology, we expect it to be generally applicable to facilitate target selection for experimental verification starting from high-throughput protein libraries.
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Affiliation(s)
- Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL
| | - Bhumika Singh
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL
| | - Roland H. Felkner
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, 675 Hoes Lane Rm 636, Piscataway, NJ 08854
| | - Anna De Falco
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - GVT Swapna
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Monica J. Roth
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, 675 Hoes Lane Rm 636, Piscataway, NJ 08854
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Leigh Hall 240, Gainesville, FL
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13
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Saini S, Kumar Y. Structural and functional analysis of engineered antibodies for cancer immunotherapy: insights into protein compactness and solvent accessibility. J Biomol Struct Dyn 2024:1-14. [PMID: 38173178 DOI: 10.1080/07391102.2023.2300129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 12/22/2023] [Indexed: 01/05/2024]
Abstract
Antibodies are crucial tools in various biomedical applications, including immunotherapy. In this study, we focused on designing and engineering antibodies to enhance their structural dynamics and functional properties. By employing advanced computational techniques and experimental validation, we gained crucial insights into the impact of specific mutations on the engineered antibodies. This study investigates the design and engineering of antibodies to improve their structural dynamics and functional properties. Structural attributes, such as protein compactness and solvent accessibility, were assessed, revealing interesting trends in anti-CD3 and anti-HER2 antibodies. Mutations in CD3 antibodies resulted in a more stable conformation, while mutant HER2 antibodies exhibited altered interaction with the target. Analysis of secondary structure assignments demonstrated significant changes in the folding and stability of the mutant antibodies compared to the wild-type counterparts. The conformational landscape of the engineered antibodies was explored, providing insights into folding pathways and binding mechanisms. Overall, the current study highlights the significance of antibody design and engineering in modulating structural dynamics and functional properties. The findings contribute to developing improved immunotherapeutic strategies by optimising antibody-based therapeutics for targeted diseases with enhanced efficacy and precision.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Samvedna Saini
- Department of Biological Sciences and Engineering (BSE), Netaji Subhas University of Technology (NSUT), New Delhi, India
| | - Yatender Kumar
- Department of Biological Sciences and Engineering (BSE), Netaji Subhas University of Technology (NSUT), New Delhi, India
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14
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Kumar N, Bajiya N, Patiyal S, Raghava GPS. Multi-perspectives and challenges in identifying B-cell epitopes. Protein Sci 2023; 32:e4785. [PMID: 37733481 PMCID: PMC10578127 DOI: 10.1002/pro.4785] [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: 06/26/2023] [Revised: 09/11/2023] [Accepted: 09/16/2023] [Indexed: 09/23/2023]
Abstract
The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).
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Affiliation(s)
- Nishant Kumar
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
| | - Nisha Bajiya
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
| | - Sumeet Patiyal
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
| | - Gajendra P. S. Raghava
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
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15
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Bai G, Sun C, Guo Z, Wang Y, Zeng X, Su Y, Zhao Q, Ma B. Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects. Semin Cancer Biol 2023; 95:13-24. [PMID: 37355214 DOI: 10.1016/j.semcancer.2023.06.005] [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] [Received: 03/23/2023] [Revised: 06/09/2023] [Accepted: 06/18/2023] [Indexed: 06/26/2023]
Abstract
Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.
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Affiliation(s)
- Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chuance Sun
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziang Guo
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China
| | - Yangjing Wang
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xincheng Zeng
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuhong Su
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macao Special Administrative Region of China.
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Digiwiser BioTechnolgy, Limited, Shanghai 201203, China.
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16
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Jiang J, Boughter CT, Ahmad J, Natarajan K, Boyd LF, Meier-Schellersheim M, Margulies DH. SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain. Commun Biol 2023; 6:953. [PMID: 37726484 PMCID: PMC10509263 DOI: 10.1038/s42003-023-05332-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 09/07/2023] [Indexed: 09/21/2023] Open
Abstract
The COVID-19 pandemic and SARS-CoV-2 variants have dramatically illustrated the need for a better understanding of antigen (epitope)-antibody (paratope) interactions. To gain insight into the immunogenic characteristics of epitopic sites (ES), we systematically investigated the structures of 340 Abs and 83 nanobodies (Nbs) complexed with the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. We identified 23 distinct ES on the RBD surface and determined the frequencies of amino acid usage in the corresponding CDR paratopes. We describe a clustering method for analysis of ES similarities that reveals binding motifs of the paratopes and that provides insights for vaccine design and therapies for SARS-CoV-2, as well as a broader understanding of the structural basis of Ab-protein antigen (Ag) interactions.
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Affiliation(s)
- Jiansheng Jiang
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA.
| | - Christopher T Boughter
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA
| | - Javeed Ahmad
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA
| | - Kannan Natarajan
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA
| | - Lisa F Boyd
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA
| | - Martin Meier-Schellersheim
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA
| | - David H Margulies
- Molecular Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, 20892, USA.
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17
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Hoffstedt M, Stein MO, Baumann K, Wätzig H. Experimentally Observed Conformational Changes in Antibodies Due to Binding and Paratope-epitope Asymmetries. J Pharm Sci 2023; 112:2404-2411. [PMID: 37295605 DOI: 10.1016/j.xphs.2023.06.003] [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: 03/25/2023] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023]
Abstract
Understanding binding related changes in antibody conformations is important for epitope prediction and antibody refinement. The increase of available data in the PDB allowed a more detailed investigation of the conformational landscape for free and bound antibodies. A dataset containing a total of 835 unique PDB entries of antibodies that were crystallized in complex with their antigen and in a free state was constructed. It was examined for binding related conformation changes. We present further evidence supporting the theory of a pre-existing-equilibrium in experimental data. Multiple sequence alignments did not show binding induced tendencies in the solvent accessibility of residues in any specific position. Evaluating the changes in solvent accessibility per residue revealed a certain binding induced increase for several amino acids. Antibody-antigen interaction statistics were established and quantify a significant directional asymmetry between many interacting antibody and antigen residue pairs, especially a richness in tyrosine in the antibody epitope compared to its paratope. This asymmetry could potentially facilitate an increase in the success rate of computationally guided antibody refinement.
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Affiliation(s)
- Marc Hoffstedt
- Institute of Medicinal and Pharmaceutical Chemistry, TU Braunschweig, Braunschweig, Deutschland
| | - Matthias Oliver Stein
- Institute of Medicinal and Pharmaceutical Chemistry, TU Braunschweig, Braunschweig, Deutschland
| | - Knut Baumann
- Institute of Medicinal and Pharmaceutical Chemistry, TU Braunschweig, Braunschweig, Deutschland
| | - Hermann Wätzig
- Institute of Medicinal and Pharmaceutical Chemistry, TU Braunschweig, Braunschweig, Deutschland
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18
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Garcia-Calvo E, García-García A, Rodríguez Gómez S, Farrais S, Martín R, García T. Development of a new recombinant antibody, selected by phage-display technology from a celiac patient library, for detection of gluten in foods. Curr Res Food Sci 2023; 7:100578. [PMID: 37680694 PMCID: PMC10480589 DOI: 10.1016/j.crfs.2023.100578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 07/28/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023] Open
Abstract
Gluten, a group of ethanol-soluble proteins present in the endosperm of cereals, is extensively used in the food industry due to its ability to improve dough properties. However, gluten is also associated with a range of gluten-related diseases (GRDs), such as wheat allergies, celiac disease, and gluten intolerance. The recommended treatment for GRDs patients is a gluten-free diet. To monitor adherence to this diet, it is necessary to develop gluten-detection systems in food products. Among the available methods, immunodetection systems are the most popular due to their simplicity, reproducibility, and accuracy. The aim of this study was to generate novel high-affinity antibodies against gluten to be used as the primary reactant in an enzyme-linked immunosorbent assay (ELISA) test. These antibodies were developed by constructing an immune library from mRNA obtained from two celiac patients with a high humoral response to gluten-related proteins. The resulting library (composed by 1.1x107) was subjected to selection against gliadin using phage display technology. Following several rounds of selection, the Fab-C was selected, and demonstrated good functionality in ELISA tests, presenting a limit of detection of 15 mg/kg for detection of gluten in spiked mixtures and food products. The methodology can discriminate gluten-free products according to the current legislation.
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Affiliation(s)
- Eduardo Garcia-Calvo
- Departamento de Nutrición y Ciencia de los Alimentos, Facultad de Veterinaria, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Aina García-García
- Departamento de Nutrición y Ciencia de los Alimentos, Facultad de Veterinaria, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Santiago Rodríguez Gómez
- Departamento de Nutrición y Ciencia de los Alimentos, Facultad de Veterinaria, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Sergio Farrais
- Servicio de Medicina Digestiva, Hospital Universitario Fundación Jiménez Díaz, 28040, Madrid, Spain
| | - Rosario Martín
- Departamento de Nutrición y Ciencia de los Alimentos, Facultad de Veterinaria, Universidad Complutense de Madrid, 28040, Madrid, Spain
| | - Teresa García
- Departamento de Nutrición y Ciencia de los Alimentos, Facultad de Veterinaria, Universidad Complutense de Madrid, 28040, Madrid, Spain
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19
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Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
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Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
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20
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Makowski EK, Chen HT, Tessier PM. Simplifying complex antibody engineering using machine learning. Cell Syst 2023; 14:667-675. [PMID: 37591204 PMCID: PMC10733906 DOI: 10.1016/j.cels.2023.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/06/2023] [Accepted: 04/26/2023] [Indexed: 08/19/2023]
Abstract
Machine learning is transforming antibody engineering by enabling the generation of drug-like monoclonal antibodies with unprecedented efficiency. Unsupervised algorithms trained on massive and diverse protein sequence datasets facilitate the prediction of panels of antibody variants with native-like intrinsic properties (e.g., high stability), greatly reducing the amount of subsequent experimentation needed to identify specific candidates that also possess desired extrinsic properties (e.g., high affinity). Additionally, supervised algorithms, which are trained on deep sequencing datasets obtained after enrichment of in vitro antibody libraries for one or more specific extrinsic properties, enable the prediction of antibody variants with desired combinations of extrinsic properties without the need for additional screening. Here we review recent advances using both machine learning approaches and how they are impacting the field of antibody engineering as well as key outstanding challenges and opportunities for these paradigm-changing methods.
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Affiliation(s)
- Emily K Makowski
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hsin-Ting Chen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter M Tessier
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA; Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA.
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21
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Zeng X, Bai G, Sun C, Ma B. Recent Progress in Antibody Epitope Prediction. Antibodies (Basel) 2023; 12:52. [PMID: 37606436 PMCID: PMC10443277 DOI: 10.3390/antib12030052] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/23/2023] Open
Abstract
Recent progress in epitope prediction has shown promising results in the development of vaccines and therapeutics against various diseases. However, the overall accuracy and success rate need to be improved greatly to gain practical application significance, especially conformational epitope prediction. In this review, we examined the general features of antibody-antigen recognition, highlighting the conformation selection mechanism in flexible antibody-antigen binding. We recently highlighted the success and warning signs of antibody epitope predictions, including linear and conformation epitope predictions. While deep learning-based models gradually outperform traditional feature-based machine learning, sequence and structure features still provide insight into antibody-antigen recognition problems.
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Affiliation(s)
- Xincheng Zeng
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
| | - Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
| | - Chuance Sun
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; (X.Z.); (C.S.)
- Shanghai Digiwiser Biological, Inc., Shanghai 200131, China
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22
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Bauer J, Rajagopal N, Gupta P, Gupta P, Nixon AE, Kumar S. How can we discover developable antibody-based biotherapeutics? Front Mol Biosci 2023; 10:1221626. [PMID: 37609373 PMCID: PMC10441133 DOI: 10.3389/fmolb.2023.1221626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.
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Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
| | - Nandhini Rajagopal
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Priyanka Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Pankaj Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Andrew E. Nixon
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Sandeep Kumar
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
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23
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Sunny S, Prakash PB, Gopakumar G, Jayaraj PB. DeepBindPPI: Protein-Protein Binding Site Prediction Using Attention Based Graph Convolutional Network. Protein J 2023; 42:276-287. [PMID: 37198346 PMCID: PMC10191823 DOI: 10.1007/s10930-023-10121-9] [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] [Accepted: 04/25/2023] [Indexed: 05/19/2023]
Abstract
Due to the importance of protein-protein interactions in defence mechanism of living body, attempts were made to investigate its attributes, including, but not limited to, binding affinity, and binding region. Contemporary strategies for binding site prediction largely resort to deep learning techniques but turned out to be low precision models. As laboratory experiments for drug discovery tasks utilize this information, increased false positives devalue the computational methods. This emphasize the need to develop enhanced strategies. DeepBindPPI employs deep learning technique to predict the binding regions of proteins, particularly antigen-antibody interaction sites. The results obtained are applied in a docking environment to confirm their correctness. An integration of graph convolutional network with attention mechanism predicts interacting amino acids with improved precision. The model learns the determining factors in interaction from a general pool of proteins and is then fine-tuned using antigen-antibody data. Comparison of the proposed method with existing techniques shows that the developed model has comparable performance. The use of a separate spatial network clearly improved the precision of the proposed method from 0.4 to 0.5. An attempt to utilize the interface information for docking using the HDOCK server gives promising results, with high-quality structures appearing in the top10 ranks.
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Affiliation(s)
- Sharon Sunny
- Department of CSE, National Institute of Technology, Calicut, Kerala 673601 India
| | | | - G. Gopakumar
- Department of CSE, National Institute of Technology, Calicut, Kerala 673601 India
| | - P. B. Jayaraj
- Department of CSE, National Institute of Technology, Calicut, Kerala 673601 India
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24
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Durairaj R, Pageat P, Bienboire-Frosini C. Impact of Semiochemicals Binding to Fel d 1 on Its 3D Conformation and Predicted B-Cell Epitopes Using Computational Approaches. Int J Mol Sci 2023; 24:11685. [PMID: 37511444 PMCID: PMC10380945 DOI: 10.3390/ijms241411685] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/12/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
The major cat allergen Fel d 1 is a tetrameric glycoprotein from the secretoglobin superfamily. Fel d 1's biological role is unknown, but it has been previously shown that it participates in semiochemical binding/transportation. Fel d 1 has linear epitopes, but its conformational epitope sites remain unclear. In this study, we predicted the B-cell epitopes of Fel d 1 and explored semiochemical dynamics with epitopes using bioinformatics tools. The epitope residues were tabulated for chains 1 and 2 and the heterodimers of Fel d 1. The residual interactions of Fel d 1 with IgE were evaluated, and the prominent epitope sites were predicted. The molecular dynamics simulation (MDS) of Fel d 1 was performed with seven reported semiochemicals to evaluate the Fel d 1-ligand complex stability and decipher the semiochemical effect on Fel d 1 conformational epitopes. Fel d 1-lauric acid, Fel d 1-oleic acid, and Fel d 1-progesterone showed more stability and less fluctuation than other compounds. Fel d 1-linoleic acid and Fel d 1-pregnenolone displayed the most unstable complex with fluctuations. The effects of conformational changes on epitopes are discussed. All the ligand complexes drive substantial fluctuation towards the functionally exposed IgE-binding epitopes. Fel d 1 could be examined for its ligand-binding and conformational changes caused by mutations of B-cell epitopes.
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Affiliation(s)
- Rajesh Durairaj
- Department of Bioinformatics and Chemical Communication (D-BICC), Research Institute in Semiochemistry and Applied Ethology (IRSEA), Quartier Salignan, 84400 Apt, France
| | - Patrick Pageat
- Research and Education Board, Research Institute in Semiochemistry and Applied Ethology (IRSEA), Quartier Salignan, 84400 Apt, France
| | - Cécile Bienboire-Frosini
- Department of Molecular Biology and Chemical Communication (D-BMCC), Research Institute in Semiochemistry and Applied Ethology (IRSEA), Quartier Salignan, 84400 Apt, France
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25
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Jaszczyszyn I, Bielska W, Gawlowski T, Dudzic P, Satława T, Kończak J, Wilman W, Janusz B, Wróbel S, Chomicz D, Galson JD, Leem J, Kelm S, Krawczyk K. Structural modeling of antibody variable regions using deep learning-progress and perspectives on drug discovery. Front Mol Biosci 2023; 10:1214424. [PMID: 37484529 PMCID: PMC10361724 DOI: 10.3389/fmolb.2023.1214424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 06/12/2023] [Indexed: 07/25/2023] Open
Abstract
AlphaFold2 has hallmarked a generational improvement in protein structure prediction. In particular, advances in antibody structure prediction have provided a highly translatable impact on drug discovery. Though AlphaFold2 laid the groundwork for all proteins, antibody-specific applications require adjustments tailored to these molecules, which has resulted in a handful of deep learning antibody structure predictors. Herein, we review the recent advances in antibody structure prediction and relate them to their role in advancing biologics discovery.
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Affiliation(s)
- Igor Jaszczyszyn
- NaturalAntibody, Kraków, Poland
- Medical University of Warsaw, Warsaw, Poland
| | - Weronika Bielska
- NaturalAntibody, Kraków, Poland
- Medical University of Lodz, Lodz, Poland
| | | | | | | | | | | | | | | | | | | | - Jinwoo Leem
- Alchemab Therapeutics Ltd., London, United Kingdom
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26
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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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27
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Qiu T, Zhang L, Chen Z, Wang Y, Mao T, Wang C, Cun Y, Zheng G, Yan D, Zhou M, Tang K, Cao Z. SEPPA-mAb: spatial epitope prediction of protein antigens for mAbs. Nucleic Acids Res 2023:7175357. [PMID: 37216611 DOI: 10.1093/nar/gkad427] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/07/2023] [Accepted: 05/10/2023] [Indexed: 05/24/2023] Open
Abstract
Identifying the exact epitope positions for a monoclonal antibody (mAb) is of critical importance yet highly challenging to the Ab design of biomedical research. Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at http://www.badd-cao.net/seppa-mab/.
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Affiliation(s)
- Tianyi Qiu
- School of Life Sciences, Fudan University, Shanghai 200092, China
- Institute of Clinical Science, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Lu Zhang
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Zikun Chen
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yuan Wang
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Tiantian Mao
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Caicui Wang
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yewei Cun
- School of Life Sciences, Fudan University, Shanghai 200092, China
| | - Genhui Zheng
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Deyu Yan
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Mengdi Zhou
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Kailin Tang
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Zhiwei Cao
- School of Life Sciences, Fudan University, Shanghai 200092, China
- Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
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28
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Desta IT, Kotelnikov S, Jones G, Ghani U, Abyzov M, Kholodov Y, Standley DM, Sabitova M, Beglov D, Vajda S, Kozakov D. Mapping of antibody epitopes based on docking and homology modeling. Proteins 2023; 91:171-182. [PMID: 36088633 PMCID: PMC9822860 DOI: 10.1002/prot.26420] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/25/2022] [Accepted: 09/06/2022] [Indexed: 01/11/2023]
Abstract
Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template-based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template-based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x-ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER-Map, has been tested on a widely used antibody-antigen docking benchmark. The results show that PIPER-Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.
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Affiliation(s)
- Israel T. Desta
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | | | | | - Daron M. Standley
- Department of Genome Informatics, Osaka University, Osaka, 565-0871, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka, 565-0871, Japan
| | - Maria Sabitova
- Department of Mathematics, CUNY Queens College, Flushing, NY 11367, USA
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
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29
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Khan A, Cowen-Rivers AI, Grosnit A, Deik DGX, Robert PA, Greiff V, Smorodina E, Rawat P, Akbar R, Dreczkowski K, Tutunov R, Bou-Ammar D, Wang J, Storkey A, Bou-Ammar H. Toward real-world automated antibody design with combinatorial Bayesian optimization. CELL REPORTS METHODS 2023; 3:100374. [PMID: 36814835 PMCID: PMC9939385 DOI: 10.1016/j.crmeth.2022.100374] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/08/2022] [Accepted: 12/07/2022] [Indexed: 06/14/2023]
Abstract
Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.
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Affiliation(s)
- Asif Khan
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | | | | | | | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | | | | | - Dany Bou-Ammar
- American University of Beirut Medical Centre, Beirut 11-0236, Lebanon
| | - Jun Wang
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
| | - Amos Storkey
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Haitham Bou-Ammar
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
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30
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Cia G, Pucci F, Rooman M. Critical review of conformational B-cell epitope prediction methods. Brief Bioinform 2023; 24:6972295. [PMID: 36611255 DOI: 10.1093/bib/bbac567] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/17/2022] [Accepted: 11/19/2022] [Indexed: 01/09/2023] Open
Abstract
Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.
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Affiliation(s)
- Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
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31
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Kuri P, Goswami P. Current Update on Rotavirus in-Silico Multiepitope Vaccine Design. ACS OMEGA 2023; 8:190-207. [PMID: 36643547 PMCID: PMC9835168 DOI: 10.1021/acsomega.2c07213] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/14/2022] [Indexed: 06/06/2023]
Abstract
Rotavirus gastroenteritis is one of the leading causes of pediatric morbidity and mortality worldwide in infants and under-five populations. The World Health Organization (WHO) recommended global incorporation of the rotavirus vaccine in national immunization programs to alleviate the burden of the disease. Implementation of the rotavirus vaccination in certain regions of the world brought about a significant and consistent reduction of rotavirus-associated hospitalizations. However, the efficacy of licensed vaccines remains suboptimal in low-income countries where the incidences of rotavirus gastroenteritis continue to happen unabated. The problem of low efficacy of currently licensed oral rotavirus vaccines in low-income countries necessitates continuous exploration, design, and development of new rotavirus vaccines. Traditional vaccine development is a complex, expensive, labor-intensive, and time-consuming process. Reverse vaccinology essentially utilizes the genome and proteome information on pathogens and has opened new avenues for in-silico multiepitope vaccine design for a plethora of pathogens, promising time reduction in the complete vaccine development pipeline by complementing the traditional vaccinology approach. A substantial number of reviews on licensed rotavirus vaccines and those under evaluation are already available in the literature. However, a collective account of rotavirus in-silico vaccines is lacking in the literature, and such an account may further fuel the interest of researchers to use reverse vaccinology to expedite the vaccine development process. Therefore, the main focus of this review is to summarize the research endeavors undertaken for the design and development of rotavirus vaccines by the reverse vaccinology approach utilizing the tools of immunoinformatics.
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32
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Lusiany T, Xu Z, Saputri DS, Ismanto HS, Nazlica SA, Standley DM. Structural Modeling of Adaptive Immune Responses to Infection. Methods Mol Biol 2023; 2552:283-294. [PMID: 36346598 DOI: 10.1007/978-1-0716-2609-2_15] [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] [Indexed: 06/16/2023]
Abstract
Antibody and TCR modeling are becoming important as more and more sequence data becomes available to the public. One of the pressing questions now is how to use such data to understand adaptive immune responses to disease. Infectious disease is of particular interest because the antigens driving such responses are often known to some extent. Here, we describe tips for gathering data and cleaning it for use in downstream analysis. We present a method for high-throughput structural modeling of antibodies or TCRs using Repertoire Builder and its extensions. AbAdapt is an extension of Repertoire Builder for antibody-antigen docking from antibody and antigen sequences. ImmuneScape is a corresponding extension for TCR-pMHC 3D modeling. Together, these pipelines can help researchers to understand immune responses to infection from a structural point of view.
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Affiliation(s)
- Tina Lusiany
- Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Dianita S Saputri
- Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Hendra S Ismanto
- Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | | | - Daron M Standley
- Research Institute for Microbial Diseases, Osaka University, Suita, Japan.
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33
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Ambrosetti F, Jandova Z, Bonvin AMJJ. Information-Driven Antibody-Antigen Modelling with HADDOCK. Methods Mol Biol 2023; 2552:267-282. [PMID: 36346597 DOI: 10.1007/978-1-0716-2609-2_14] [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] [Indexed: 06/16/2023]
Abstract
In the recent years, therapeutic use of antibodies has seen a huge growth, "due to their inherent proprieties and technological advances in the methods used to study and characterize them. Effective design and engineering of antibodies for therapeutic purposes are heavily dependent on knowledge of the structural principles that regulate antibody-antigen interactions. Several experimental techniques such as X-ray crystallography, cryo-electron microscopy, NMR, or mutagenesis analysis can be applied, but these are usually expensive and time-consuming. Therefore computational approaches like molecular docking may offer a valuable alternative for the characterization of antibody-antigen complexes.Here we describe a protocol for the prediction of the 3D structure of antibody-antigen complexes using the integrative modelling platform HADDOCK. The protocol consists of (1) the identification of the antibody residues belonging to the hypervariable loops which are known to be crucial for the binding and can be used to guide the docking and (2) the detailed steps to perform docking with the HADDOCK 2.4 webserver following different strategies depending on the availability of information about epitope residues.
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Affiliation(s)
- Francesco Ambrosetti
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Zuzana Jandova
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands.
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34
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Zheng D, Liang S, Zhang C. B-Cell Epitope Predictions Using Computational Methods. Methods Mol Biol 2023; 2552:239-254. [PMID: 36346595 DOI: 10.1007/978-1-0716-2609-2_12] [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] [Indexed: 06/16/2023]
Abstract
Identifying protein antigenic epitopes that are recognizable by antibodies is a key step in immunologic research. This type of research has broad medical applications, such as new immunodiagnostic reagent discovery, vaccine design, and antibody design. However, due to the countless possibilities of potential epitopes, the experimental search through trial and error would be too costly and time-consuming to be practical. To facilitate this process and improve its efficiency, computational methods were developed to predict both linear epitopes and discontinuous antigenic epitopes. For linear B-cell epitope prediction, many methods were developed, including PREDITOP, PEOPLE, BEPITOPE, BepiPred, COBEpro, ABCpred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, LBEEP, DRREP, iBCE-EL, SVMTriP, etc. For the more challenging yet important task of discontinuous epitope prediction, methods were also developed, including CEP, DiscoTope, PEPITO, ElliPro, SEPPA, EPITOPIA, PEASE, EpiPred, SEPIa, EPCES, EPSVR, etc. In this chapter, we will discuss computational methods for B-cell epitope predictions of both linear and discontinuous epitopes. SVMTriP and EPCES/EPCSVR, the most successful among the methods for each type of the predictions, will be used as model methods to detail the standard protocols. For linear epitope prediction, SVMTriP was reported to achieve a sensitivity of 80.1% and a precision of 55.2% with a fivefold cross-validation based on a large dataset, yielding an AUC of 0.702. For discontinuous or conformational B-cell epitope prediction, EPCES and EPCSVR were both benchmarked by a curated independent test dataset in which all antigens had no complex structures with the antibody. The identified epitopes by these methods were later independently validated by various biochemical experiments. For these three model methods, webservers and all datasets are publicly available at http://sysbio.unl.edu/SVMTriP , http://sysbio.unl.edu/EPCES/ , and http://sysbio.unl.edu/EPSVR/ .
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Affiliation(s)
- Dandan Zheng
- Department of Radiation Oncology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Shide Liang
- Department of Research and Development, Bio-Thera Solutions, Guangzhou, China.
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA.
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35
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Huang Y, Zhang Z, Zhou Y. AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information. Front Immunol 2022; 13:1053617. [PMID: 36618397 PMCID: PMC9813736 DOI: 10.3389/fimmu.2022.1053617] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in antibody therapeutics is the experimental identification of antibody-antigen interactions, which is generally time-consuming, costly, and laborious. Although some computational methods have been proposed to screen potential antibodies, the dependence on 3D structures still limits the application of these methods. Methods Here, we developed a deep learning-assisted prediction method (i.e., AbAgIntPre) for fast identification of antibody-antigen interactions that only relies on amino acid sequences. A Siamese-like convolutional neural network architecture was established with the amino acid composition encoding scheme for both antigens and antibodies. Results and Discussion The generic model of AbAgIntPre achieved satisfactory performance with the Area Under Curve (AUC) of 0.82 on a high-quality generic independent test dataset. Besides, this approach also showed competitive performance on the more specific SARS-CoV dataset. We expect that AbAgIntPre can serve as an important complement to traditional experimental methods for antibody screening and effectively reduce the workload of antibody design. The web server of AbAgIntPre is freely available at http://www.zzdlab.com/AbAgIntPre.
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Affiliation(s)
- Yan Huang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China,Department of Biomedical Informatics, Key Laboratory of Molecular Cardiovascular Sciences of the Ministry of Education, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China,*Correspondence: Ziding Zhang, ; Yuan Zhou,
| | - Yuan Zhou
- Department of Biomedical Informatics, Key Laboratory of Molecular Cardiovascular Sciences of the Ministry of Education, School of Basic Medical Sciences, Peking University, Beijing, China,*Correspondence: Ziding Zhang, ; Yuan Zhou,
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36
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Cohen T, Halfon M, Carter L, Sharkey B, Jain T, Sivasubramanian A, Schneidman-Duhovny D. Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models. Methods Enzymol 2022; 678:237-262. [PMID: 36641210 DOI: 10.1016/bs.mie.2022.11.003] [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: 12/13/2022]
Abstract
Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD<4Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.
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Affiliation(s)
- Tomer Cohen
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Matan Halfon
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lester Carter
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, United States
| | - Beth Sharkey
- High-Throughput Expression, Adimab LLC, Lebanon, NH, United States
| | - Tushar Jain
- Computational Biology, Adimab LLC, Palo Alto, CA, United States
| | | | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
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37
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Tsuchiya Y, Yamamori Y, Tomii K. Protein-protein interaction prediction methods: from docking-based to AI-based approaches. Biophys Rev 2022; 14:1341-1348. [PMID: 36570321 PMCID: PMC9759050 DOI: 10.1007/s12551-022-01032-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Protein-protein interactions (PPIs), such as protein-protein inhibitor, antibody-antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody-antigen interactions, offering both historical and forward-looking perspectives.
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Affiliation(s)
- Yuko Tsuchiya
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Yu Yamamori
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Kentaro Tomii
- Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
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Waury K, Willemse EAJ, Vanmechelen E, Zetterberg H, Teunissen CE, Abeln S. Bioinformatics tools and data resources for assay development of fluid protein biomarkers. Biomark Res 2022; 10:83. [DOI: 10.1186/s40364-022-00425-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 10/25/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractFluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.
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Xu Z, Ismanto HS, Zhou H, Saputri DS, Sugihara F, Standley DM. Advances in antibody discovery from human BCR repertoires. FRONTIERS IN BIOINFORMATICS 2022; 2:1044975. [PMID: 36338807 PMCID: PMC9631452 DOI: 10.3389/fbinf.2022.1044975] [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: 09/15/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022] Open
Abstract
Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.
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Affiliation(s)
- Zichang Xu
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Hendra S. Ismanto
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Hao Zhou
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Dianita S. Saputri
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Fuminori Sugihara
- Core Instrumentation Facility, Immunology Frontier Research Center, Osaka University, Suita, Japan
| | - Daron M. Standley
- Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Suita, Japan
- Department Systems Immunology, Immunology Frontier Research Center, Osaka University, Suita, Japan
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Zhou J, Chen J, Peng Y, Xie Y, Xiao Y. A Promising Tool in Serological Diagnosis: Current Research Progress of Antigenic Epitopes in Infectious Diseases. Pathogens 2022; 11:1095. [PMID: 36297152 PMCID: PMC9609281 DOI: 10.3390/pathogens11101095] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 07/30/2023] Open
Abstract
Infectious diseases, caused by various pathogens in the clinic, threaten the safety of human life, are harmful to physical and mental health, and also increase economic burdens on society. Infections are a complex mechanism of interaction between pathogenic microorganisms and their host. Identification of the causative agent of the infection is vital for the diagnosis and treatment of diseases. Etiological laboratory diagnostic tests are therefore essential to identify pathogens. However, due to its rapidity and automation, the serological diagnostic test is among the methods of great significance for the diagnosis of infections with the basis of detecting antigens or antibodies in body fluids clinically. Epitopes, as a special chemical group that determines the specificity of antigens and the basic unit of inducing immune responses, play an important role in the study of immune responses. Identifying the epitopes of a pathogen may contribute to the development of a vaccine to prevent disease, the diagnosis of the corresponding disease, and the determination of different stages of the disease. Moreover, both the preparation of neutralizing antibodies based on useful epitopes and the assembly of several associated epitopes can be used in the treatment of disease. Epitopes can be divided into B cell epitopes and T cell epitopes; B cell epitopes stimulate the body to produce antibodies and are therefore commonly used as targets for the design of serological diagnostic experiments. Meanwhile, epitopes can fall into two possible categories: linear and conformational. This article reviews the role of B cell epitopes in the clinical diagnosis of infectious diseases.
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Wang Y, Tang H, Gao C, Ge M, Li Z, Dong Z, Zhao L. Flexibility-aware graph model for accurate epitope identification. Comput Biol Med 2022; 149:106064. [DOI: 10.1016/j.compbiomed.2022.106064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/05/2022] [Accepted: 08/27/2022] [Indexed: 11/25/2022]
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Wilman W, Wróbel S, Bielska W, Deszynski P, Dudzic P, Jaszczyszyn I, Kaniewski J, Młokosiewicz J, Rouyan A, Satława T, Kumar S, Greiff V, Krawczyk K. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Brief Bioinform 2022; 23:bbac267. [PMID: 35830864 PMCID: PMC9294429 DOI: 10.1093/bib/bbac267] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
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Lu S, Li Y, Ma Q, Nan X, Zhang S. A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features. Front Immunol 2022; 13:890943. [PMID: 35844532 PMCID: PMC9283778 DOI: 10.3389/fimmu.2022.890943] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/23/2022] [Indexed: 11/24/2022] Open
Abstract
B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks (GCNs) to capture information of spatial neighbors of a target residue. For global features, Attention-Based Bidirectional Long Short-Term Memory (Att-BLSTM) networks are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction. Our detailed case study on the BCEs prediction for SARS-Cov-2 receptor binding domain confirms that our method is effective for predicting and clustering true BCEs.
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Affiliation(s)
- Shuai Lu
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
| | - Yuguang Li
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
| | - Qiang Ma
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Xiaofei Nan
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
- *Correspondence: Xiaofei Nan, ; Shoutao Zhang,
| | - Shoutao Zhang
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
- Longhu Laboratory of Advanced Immunology, Zhengzhou, China
- *Correspondence: Xiaofei Nan, ; Shoutao Zhang,
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Gacias-Amengual N, Wohlschlager L, Csarman F, Ludwig R. Fluorescent Imaging of Extracellular Fungal Enzymes Bound onto Plant Cell Walls. Int J Mol Sci 2022; 23:ijms23095216. [PMID: 35563607 PMCID: PMC9105846 DOI: 10.3390/ijms23095216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 02/04/2023] Open
Abstract
Lignocelluloytic enzymes are industrially applied as biocatalysts for the deconstruction of recalcitrant plant biomass. To study their biocatalytic and physiological function, the assessment of their binding behavior and spatial distribution on lignocellulosic material is a crucial prerequisite. In this study, selected hydrolases and oxidoreductases from the white rot fungus Phanerochaete chrysosporium were localized on model substrates as well as poplar wood by confocal laser scanning microscopy. Two different detection approaches were investigated: direct tagging of the enzymes and tagging specific antibodies generated against the enzymes. Site-directed mutagenesis was employed to introduce a single surface-exposed cysteine residue for the maleimide site-specific conjugation. Specific polyclonal antibodies were produced against the enzymes and were labeled using N-hydroxysuccinimide (NHS) ester as a cross-linker. Both methods allowed the visualization of cell wall-bound enzymes but showed slightly different fluorescent yields. Using native poplar thin sections, we identified the innermost secondary cell wall layer as the preferential attack point for cellulose-degrading enzymes. Alkali pretreatment resulted in a partial delignification and promoted substrate accessibility and enzyme binding. The methods presented in this study are suitable for the visualization of enzymes during catalytic biomass degradation and can be further exploited for interaction studies of lignocellulolytic enzymes in biorefineries.
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Lim H, Cankara F, Tsai CJ, Keskin O, Nussinov R, Gursoy A. Artificial intelligence approaches to human-microbiome protein–protein interactions. Curr Opin Struct Biol 2022; 73:102328. [DOI: 10.1016/j.sbi.2022.102328] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/01/2021] [Accepted: 12/31/2021] [Indexed: 02/08/2023]
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Mahita J, Kim DG, Son S, Choi Y, Kim HS, Bailey-Kellogg C. Computational epitope binning reveals functional equivalence of sequence-divergent paratopes. Comput Struct Biotechnol J 2022; 20:2169-2180. [PMID: 35615020 PMCID: PMC9118127 DOI: 10.1016/j.csbj.2022.04.036] [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: 01/09/2022] [Revised: 04/27/2022] [Accepted: 04/27/2022] [Indexed: 11/26/2022] Open
Abstract
Epitope binning groups target-specific protein binders recognizing the same binding region. The “Epibin” method utilizes docking models to computationally predict competition and identify bins. Epibin recapitulated binding competition of repebody variants as determined by immunoassays. In addition, Epibin enabled identification of ‘paratope-equivalent’ residues in sequence-dissimilar variants. Computational epitope binning can scale to allow characterization of entire antigen-specific antibody repertoires.
The therapeutic efficacy of a protein binder largely depends on two factors: its binding site and its binding affinity. Advances in in vitro library display screening and next-generation sequencing have enabled accelerated development of strong binders, yet identifying their binding sites still remains a major challenge. The differentiation, or “binning”, of binders into different groups that recognize distinct binding sites on their target is a promising approach that facilitates high-throughput screening of binders that may show different biological activity. Here we study the extent to which the information contained in the amino acid sequences comprising a set of target-specific binders can be leveraged to bin them, inferring functional equivalence of their binding regions, or paratopes, based directly on comparison of the sequences, their modeled structures, or their modeled interactions. Using a leucine-rich repeat binding scaffold known as a “repebody” as the source of diversity in recognition against interleukin-6 (IL-6), we show that the “Epibin” approach introduced here effectively utilized structural modelling and docking to extract specificity information encoded in the repebody amino acid sequences and thereby successfully recapitulate IL-6 binding competition observed in immunoassays. Furthermore, our computational binning provided a basis for designing in vitro mutagenesis experiments to pinpoint specificity-determining residues. Finally, we demonstrate that the Epibin approach can extend to antibodies, retrospectively comparing its predictions to results from antigen-specific antibody competition studies. The study thus demonstrates the utility of modeling structure and binding from the amino acid sequences of different binders against the same target, and paves the way for larger-scale binning and analysis of entire repertoires.
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47
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Davila A, Xu Z, Li S, Rozewicki J, Wilamowski J, Kotelnikov S, Kozakov D, Teraguchi S, Standley DM. AbAdapt: an adaptive approach to predicting antibody-antigen complex structures from sequence. BIOINFORMATICS ADVANCES 2022; 2:vbac015. [PMID: 36699363 PMCID: PMC9710585 DOI: 10.1093/bioadv/vbac015] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 02/15/2022] [Accepted: 03/03/2022] [Indexed: 01/28/2023]
Abstract
Motivation The scoring of antibody-antigen docked poses starting from unbound homology models has not been systematically optimized for a large and diverse set of input sequences. Results To address this need, we have developed AbAdapt, a webserver that accepts antibody and antigen sequences, models their 3D structures, predicts epitope and paratope, and then docks the modeled structures using two established docking engines (Piper and Hex). Each of the key steps has been optimized by developing and training new machine-learning models. The sequences from a diverse set of 622 antibody-antigen pairs with known structure were used as inputs for leave-one-out cross-validation. The final set of cluster representatives included at least one 'Adequate' pose for 550/622 (88.4%) of the queries. The median (interquartile range) ranks of these 'Adequate' poses were 22 (5-77). Similar results were obtained on a holdout set of 100 unrelated antibody-antigen pairs. When epitopes were repredicted using docking-derived features for specific antibodies, the median ROC AUC increased from 0.679 to 0.720 in cross-validation and from 0.694 to 0.730 in the holdout set. Availability and implementation AbAdapt and related data are available at https://sysimm.org/abadapt/. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Ana Davila
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Songling Li
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - John Rozewicki
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Jan Wilamowski
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5252, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5252, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Shunsuke Teraguchi
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan,Faculty of Data Science, Shiga University, Hikone 522-8522, Japan
| | - Daron M Standley
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan,Immunology Frontier Research Center, Department of Systems Immunology, Osaka University, Suita 565-0871, Japan,To whom correspondence should be addressed.
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48
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Cha JH, Chan LC, Wang YN, Chu YY, Wang CH, Lee HH, Xia W, Shyu WC, Liu SP, Yao J, Chang CW, Cheng FR, Liu J, Lim SO, Hsu JL, Yang WH, Hortobagyi GN, Lin C, Yang L, Yu D, Jeng LB, Hung MC. Ephrin receptor A10 monoclonal antibodies and the derived chimeric antigen receptor T cells exert an antitumor response in mouse models of triple-negative breast cancer. J Biol Chem 2022; 298:101817. [PMID: 35278434 PMCID: PMC8988001 DOI: 10.1016/j.jbc.2022.101817] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 12/17/2022] Open
Abstract
Expression of the receptor tyrosine kinase ephrin receptor A10 (EphA10), which is undetectable in most normal tissues except for the male testis, has been shown to correlate with tumor progression and poor prognosis in several malignancies, including triple-negative breast cancer (TNBC). Therefore, EphA10 could be a potential therapeutic target, likely with minimal adverse effects. However, no effective clinical drugs against EphA10 are currently available. Here, we report high expression levels of EphA10 in tumor regions of breast, lung, and ovarian cancers as well as in immunosuppressive myeloid cells in the tumor microenvironment. Furthermore, we developed anti-EphA10 monoclonal antibodies (mAbs) that specifically recognize cell surface EphA10, but not other EphA family isoforms, and target tumor regions precisely in vivo with no apparent accumulation in other organs. In syngeneic TNBC mouse models, we found that anti-EphA10 mAb clone #4 enhanced tumor regression, therapeutic response rate, and T cell–mediated antitumor immunity. Notably, the chimeric antigen receptor T cells derived from clone #4 significantly inhibited TNBC cell viability in vitro and tumor growth in vivo. Together, our findings suggest that targeting EphA10 via EphA10 mAbs and EphA10-specific chimeric antigen receptor–T cell therapy may represent a promising strategy for patients with EphA10-positive tumors.
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Saini S, Agarwal M, Pradhan A, Pareek S, Singh AK, Dhawan G, Dhawan U, Kumar Y. Exploring the role of framework mutations in enabling breadth of a cross-reactive antibody (CR3022) against the SARS-CoV-2 RBD and its variants of concern. J Biomol Struct Dyn 2022; 41:2341-2354. [PMID: 35098888 DOI: 10.1080/07391102.2022.2030800] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Cross-reactive and broadly neutralizing antibodies against surface proteins of diverse strains of rapidly evolving viral pathogens like SARS-CoV-2 can prevent infection and therefore are crucial for the development of effective universal vaccines. While antibodies typically incorporate mutations in their complementarity determining regions during affinity maturation, mutations in the framework regions have been reported as players in determining properties of broadly neutralizing antibodies against HIV and the Influenza virus. We propose an increase in the cross-reactive potential of CR3022 against the emerging SARS- CoV-2 variants of concern through enhanced conformational flexibility. In this study, we use molecular dynamics simulations, in silico mutagenesis, structural modeling, and docking to explore the role of light chain FWR mutations in CR3022, a SARS-CoV anti-spike (S)-protein antibody cross-reactive to the S-protein receptor binding domain of SARS-CoV-2. Our study shows that single substitutions in the light chain framework region of CR3022 with conserved epitopes across SARS-CoV strains allow targeting of diverse antibody epitope footprints that align with the epitopes of recently-categorized neutralizing antibody classes while enabling binding to more than one strain of SARS-CoV-2. Our study has implications for rapid and evolution-based engineering of broadly neutralizing antibodies and reaffirms the role of framework mutations in effective change of antibody orientation and conformation via improved flexibility.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Samvedna Saini
- Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, New Delhi, India
| | - Manusmriti Agarwal
- Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, New Delhi, India.,Faculty of Technology, University of Delhi, New Delhi, India
| | - Amartya Pradhan
- Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, New Delhi, India.,Faculty of Technology, University of Delhi, New Delhi, India.,Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, India
| | - Savitha Pareek
- High Performance Computing (HPC) & AI Innovation Lab, Dell EMC, Bengaluru, India
| | - Ashish K Singh
- High Performance Computing (HPC) & AI Innovation Lab, Dell EMC, Bengaluru, India
| | - Gagan Dhawan
- Department of Biomedical Science, Acharya Narendra Dev College, University of Delhi, New Delhi, India
| | - Uma Dhawan
- Department of Biomedical Science, Bhaskaracharya College of Applied Sciences, University of Delhi, New Delhi, India
| | - Yatender Kumar
- Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, New Delhi, India
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50
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Bukhari SNH, Jain A, Haq E, Mehbodniya A, Webber J. Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review. Pathogens 2022; 11:146. [PMID: 35215090 PMCID: PMC8879824 DOI: 10.3390/pathogens11020146] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 02/01/2023] Open
Abstract
The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.
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Affiliation(s)
- Syed Nisar Hussain Bukhari
- University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India;
| | - Amit Jain
- University Institute of Computing, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Mohali 140413, India;
| | - Ehtishamul Haq
- Department of Biotechnology, University of Kashmir, Srinagar 190006, India;
| | - Abolfazl Mehbodniya
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Kuwait City 20185145, Kuwait;
| | - Julian Webber
- Graduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan;
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