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Chen H, Fan X, Zhu S, Pei Y, Zhang X, Zhang X, Liu L, Qian F, Tian B. Accurate prediction of CDR-H3 loop structures of antibodies with deep learning. eLife 2024; 12:RP91512. [PMID: 38921957 PMCID: PMC11208048 DOI: 10.7554/elife.91512] [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] [Indexed: 06/27/2024] Open
Abstract
Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody-antigen interactions. This structural prediction tool can be used to optimize antibody-antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.
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Affiliation(s)
- Hedi Chen
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
| | - Xiaoyu Fan
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
| | - Shuqian Zhu
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
| | - Yuchan Pei
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua UniversityBeijingChina
| | - Xiaochun Zhang
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
| | - Xiaonan Zhang
- Department of Natural Language Processing, Baidu International Technology (Shenzhen) Co LtdShenzhenChina
| | - Lihang Liu
- Department of Natural Language Processing, Baidu International Technology (Shenzhen) Co LtdShenzhenChina
| | - Feng Qian
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
| | - Boxue Tian
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua UniversityBeijingChina
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2
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Darbandi A, Abdi M, Dashtbin S, Yaghoubi S, Sholeh M, Kouhsari E, Darbandi T, Ghanavati R, Taheri B. Antibody-Antibiotic Conjugates: A Comprehensive Review on Their Therapeutic Potentials Against BacterialInfections. J Clin Lab Anal 2024; 38:e25071. [PMID: 38867639 PMCID: PMC11211676 DOI: 10.1002/jcla.25071] [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: 12/22/2022] [Revised: 04/09/2024] [Accepted: 05/09/2024] [Indexed: 06/14/2024] Open
Abstract
INTRODUCTION Antibodies are significant agents in the immune system and have proven to be effective in treating bacterial infections. With the advancement of antibody engineering in recent decades, antibody therapy has evolved widely. AIM This review aimed to investigate a new method as a therapeutic platform for the treatment of bacterial infections and explore the novel features of this method in conferring pathogen specificity to broad-spectrum antibiotics. MATERIAL AND METHODS A literature review was conducted addressing the following topics about antibody-antibiotic conjugates (AACs): (1) structure and mechanism of action; (2) clinical effectiveness; (3) advantages and disadvantages. RESULT Antibody conjugates are designed to build upon the progress made in the development of monoclonal antibodies for the treatment of diseases. Despite the growing emergence of antibiotic resistance among pathogenic bacteria worldwide, novel antimicrobials have not been sufficiently expanded to combat the global crisis of antibiotic resistance. A recently developed strategy for the treatment of infectious diseases is the use of AACs, which are specifically activated only in host cells. CONCLUSION A novel therapeutic AAC employs an antibody to deliver the antibiotic to the bacteria. The AACs can release potent antibacterial components that unconjugated forms may not exhibit with an appropriate therapeutic index. This review highlights how this science has guided the design principles of an impressive AAC and discusses how the AAC model promises to enhance the antibiotic effect against bacterial infections.
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Affiliation(s)
- Atieh Darbandi
- Molecular Microbiology Research CenterShahed UniversityTehranIran
| | - Milad Abdi
- Research Center of Tropical and Infectious DiseasesKerman University of Medical SciencesKermanIran
| | - Shirin Dashtbin
- Department of Microbiology, School of MedicineIran University of Medical SciencesTehranIran
| | - Sajad Yaghoubi
- Basic Sciences DepartmentNeyshabur University of Medical SciencesNeyshaburIran
| | - Mohammad Sholeh
- Department of BacteriologyPasteur Institute of IranTehranIran
| | - Ebrahim Kouhsari
- Laboratory Sciences Research CenterGolestan University of Medical SciencesGorganIran
| | - Talieh Darbandi
- Pharmaceutical Sciences BranchIslamic Azad University of Medical SciencesTehranIran
| | | | - Behrouz Taheri
- Department of Medical Biotechnology, School of MedicineAhvaz Jundishapur University of Medical SciencesAhvazIran
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3
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Tennenhouse A, Khmelnitsky L, Khalaila R, Yeshaya N, Noronha A, Lindzen M, Makowski EK, Zaretsky I, Sirkis YF, Galon-Wolfenson Y, Tessier PM, Abramson J, Yarden Y, Fass D, Fleishman SJ. Computational optimization of antibody humanness and stability by systematic energy-based ranking. Nat Biomed Eng 2024; 8:30-44. [PMID: 37550425 PMCID: PMC10842793 DOI: 10.1038/s41551-023-01079-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 07/13/2023] [Indexed: 08/09/2023]
Abstract
Conventional methods for humanizing animal-derived antibodies involve grafting their complementarity-determining regions onto homologous human framework regions. However, this process can substantially lower antibody stability and antigen-binding affinity, and requires iterative mutational fine-tuning to recover the original antibody properties. Here we report a computational method for the systematic grafting of animal complementarity-determining regions onto thousands of human frameworks. The method, which we named CUMAb (for computational human antibody design; available at http://CUMAb.weizmann.ac.il ), starts from an experimental or model antibody structure and uses Rosetta atomistic simulations to select designs by energy and structural integrity. CUMAb-designed humanized versions of five antibodies exhibited similar affinities to those of the parental animal antibodies, with some designs showing marked improvement in stability. We also show that (1) non-homologous frameworks are often preferred to highest-homology frameworks, and (2) several CUMAb designs that differ by dozens of mutations and that use different human frameworks are functionally equivalent.
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Affiliation(s)
- Ariel Tennenhouse
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Lev Khmelnitsky
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Razi Khalaila
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Noa Yeshaya
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ashish Noronha
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
- Department of Urology, University of California, San Francisco, San Francisco, CA, USA
| | - Moshit Lindzen
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Emily K Makowski
- Biointerfaces Institute and Departments of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Ira Zaretsky
- Antibody Engineering Unit, Weizmann Institute of Science, Rehovot, Israel
| | | | | | - Peter M Tessier
- Biointerfaces Institute and Departments of Chemical Engineering, Pharmaceutical Sciences and Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jakub Abramson
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Yosef Yarden
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Deborah Fass
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel.
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4
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Kumari S, Gupta R, Ambasta RK, Kumar P. Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme. Biochim Biophys Acta Rev Cancer 2023; 1878:188999. [PMID: 37858622 DOI: 10.1016/j.bbcan.2023.188999] [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: 06/30/2023] [Revised: 10/06/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023]
Abstract
Recent multi-omics studies, including proteomics, transcriptomics, genomics, and metabolomics have revealed the critical role of post-translational modifications (PTMs) in the progression and pathogenesis of Glioblastoma multiforme (GBM). Further, PTMs alter the oncogenic signaling events and offer a novel avenue in GBM therapeutics research through PTM enzymes as potential biomarkers for drug targeting. In addition, PTMs are critical regulators of chromatin architecture, gene expression, and tumor microenvironment (TME), that play a crucial function in tumorigenesis. Moreover, the implementation of artificial intelligence and machine learning algorithms enhances GBM therapeutics research through the identification of novel PTM enzymes and residues. Herein, we briefly explain the mechanism of protein modifications in GBM etiology, and in altering the biologics of GBM cells through chromatin remodeling, modulation of the TME, and signaling pathways. In addition, we highlighted the importance of PTM enzymes as therapeutic biomarkers and the role of artificial intelligence and machine learning in protein PTM prediction.
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Affiliation(s)
- Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India
| | - Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India; School of Medicine, University of South Carolina, Columbia, SC, United States of America
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India; Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India.
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, India.
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5
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Fernández-Quintero ML, Pomarici ND, Fischer ALM, Hoerschinger VJ, Kroell KB, Riccabona JR, Kamenik AS, Loeffler JR, Ferguson JA, Perrett HR, Liedl KR, Han J, Ward AB. Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines. Antibodies (Basel) 2023; 12:67. [PMID: 37873864 PMCID: PMC10594513 DOI: 10.3390/antib12040067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/25/2023] Open
Abstract
Antibodies and other new antibody-like formats have emerged as one of the most rapidly growing classes of biotherapeutic proteins. Understanding the structural features that drive antibody function and, consequently, their molecular recognition is critical for engineering antibodies. Here, we present the structural architecture of conventional IgG antibodies alongside other formats. We emphasize the importance of considering antibodies as conformational ensembles in solution instead of focusing on single-static structures because their functions and properties are strongly governed by their dynamic nature. Thus, in this review, we provide an overview of the unique structural and dynamic characteristics of antibodies with respect to their antigen recognition, biophysical properties, and effector functions. We highlight the numerous technical advances in antibody structure prediction and design, enabled by the vast number of experimentally determined high-quality structures recorded with cryo-EM, NMR, and X-ray crystallography. Lastly, we assess antibody and vaccine design strategies in the context of structure and dynamics.
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Affiliation(s)
- Monica L. Fernández-Quintero
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Nancy D. Pomarici
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Anna-Lena M. Fischer
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Valentin J. Hoerschinger
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Katharina B. Kroell
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Jakob R. Riccabona
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Anna S. Kamenik
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Johannes R. Loeffler
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - James A. Ferguson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Hailee R. Perrett
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Klaus R. Liedl
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Julianna Han
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Andrew B. Ward
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
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6
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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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7
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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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8
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Ruffolo JA, Chu LS, Mahajan SP, Gray JJ. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nat Commun 2023; 14:2389. [PMID: 37185622 PMCID: PMC10129313 DOI: 10.1038/s41467-023-38063-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge. Here, we present IgFold, a fast deep learning method for antibody structure prediction. IgFold consists of a pre-trained language model trained on 558 million natural antibody sequences followed by graph networks that directly predict backbone atom coordinates. IgFold predicts structures of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s). Accurate structure prediction on this timescale makes possible avenues of investigation that were previously infeasible. As a demonstration of IgFold's capabilities, we predicted structures for 1.4 million paired antibody sequences, providing structural insights to 500-fold more antibodies than have experimentally determined structures.
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Affiliation(s)
- Jeffrey A Ruffolo
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Lee-Shin Chu
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeffrey J Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, 21218, USA.
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, 21218, USA.
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9
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Nadaradjane AA, Diharce J, Rebehmed J, Cadet F, Gardebien F, Gelly JC, Etchebest C, de Brevern AG. Quality assessment of V HH models. J Biomol Struct Dyn 2023; 41:13287-13301. [PMID: 36752327 DOI: 10.1080/07391102.2023.2172613] [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: 10/13/2022] [Accepted: 01/19/2023] [Indexed: 02/09/2023]
Abstract
Heavy Chain Only Antibodies are specific to Camelid species. Despite the lack of the light chain variable domain, their heavy chain variable domain (VH) domain, named VHH or nanobody, has promising potential applications in research and therapeutic fields. The structural study of VHH is therefore of great interest. Unfortunately, considering the huge amount of sequences that might be produced, only about one thousand of VHH experimental structures are publicly available in the Protein Data Bank, implying that structural model prediction of VHH is a necessary alternative to obtaining 3D information besides its sequence. The present study aims to assess and compare the quality of predictions from different modelling methodologies. Established comparative & homology modelling approaches to recent Deep Learning-based modelling strategies were applied, i.e. Modeller using single or multiple structural templates, ModWeb, SwissModel (with two evaluation schema), RoseTTAfold, AlphaFold 2 and NanoNet. The prediction accuracy was evaluated using RMSD, TM-score, GDT-TS, GDT-HA and Protein Blocks distance metrics. Besides the global structure assessment, we performed specific analyses of Frameworks and CDRs structures. We observed that AlphaFold 2 and especially NanoNet performed better than the other evaluated softwares. Importantly, we performed molecular dynamics simulations of an experimental structure and a NanoNet predicted model of a VHH in order to compare the global structural flexibility and local conformations using Protein Blocks. Despite rather similar structures, substantial differences in dynamical properties were observed, which underlies the complexity of the task of model evaluation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Aravindan Arun Nadaradjane
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB, Paris, France
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB, Saint Denis Messag, France
| | - Julien Diharce
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB, Paris, France
| | - Joseph Rebehmed
- Department of Computer Science and Mathematics, Lebanese, American University, Beirut, Lebanon
| | - Frédéric Cadet
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB, Saint Denis Messag, France
- Artificial Intelligence Department, PEACCEL, Paris, France
| | - Fabrice Gardebien
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB, Saint Denis Messag, France
| | - Jean-Christophe Gelly
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB, Paris, France
| | - Catherine Etchebest
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB, Paris, France
| | - Alexandre G de Brevern
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB, Paris, France
- Université Paris Cité and Université de la Réunion and Université des Antilles, INSERM, BIGR, DSIMB, Saint Denis Messag, France
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10
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Lee FS, Anderson AG, Olafson BD. Benchmarking TriadAb using targets from the second antibody modeling assessment. Protein Eng Des Sel 2023; 36:gzad013. [PMID: 37864287 DOI: 10.1093/protein/gzad013] [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: 05/05/2023] [Revised: 08/10/2023] [Indexed: 10/22/2023] Open
Abstract
Computational modeling and design of antibodies has become an integral part of today's research and development in antibody therapeutics. Here we describe the Triad Antibody Homology Modeling (TriadAb) package, a functionality of the Triad protein design platform that predicts the structure of any heavy and light chain sequences of an antibody Fv domain using template-based modeling. To gauge the performance of TriadAb, we benchmarked against the results of the Second Antibody Modeling Assessment (AMA-II). On average, TriadAb produced main-chain carbonyl root-mean-square deviations between models and experimentally determined structures at 1.10 Å, 1.45 Å, 1.41 Å, 3.04 Å, 1.47 Å, 1.27 Å, 1.63 Å in the framework and the six complementarity-determining regions (H1, H2, H3, L1, L2, L3), respectively. The inaugural results are comparable to those reported in AMA-II, corroborating with our internal bench-based experiences that models generated using TriadAb are sufficiently accurate and useful for antibody engineering using the sequence design capabilities provided by Triad.
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11
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Fernández-Quintero ML, Kokot J, Waibl F, Fischer ALM, Quoika PK, Deane CM, Liedl KR. Challenges in antibody structure prediction. MAbs 2023; 15:2175319. [PMID: 36775843 PMCID: PMC9928471 DOI: 10.1080/19420862.2023.2175319] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023] Open
Abstract
Advances in structural biology and the exponential increase in the amount of high-quality experimental structural data available in the Protein Data Bank has motivated numerous studies to tackle the grand challenge of predicting protein structures. In 2020 AlphaFold2 revolutionized the field using a combination of artificial intelligence and the evolutionary information contained in multiple sequence alignments. Antibodies are one of the most important classes of biotherapeutic proteins. Accurate structure models are a prerequisite to advance biophysical property predictions and consequently antibody design. Specialized tools used to predict antibody structures based on different principles have profited from current advances in protein structure prediction based on artificial intelligence. Here, we emphasize the importance of reliable protein structure models and highlight the enormous advances in the field, but we also aim to increase awareness that protein structure models, and in particular antibody models, may suffer from structural inaccuracies, namely incorrect cis-amide bonds, wrong stereochemistry or clashes. We show that these inaccuracies affect biophysical property predictions such as surface hydrophobicity. Thus, we stress the importance of carefully reviewing protein structure models before investing further computing power and setting up experiments. To facilitate the assessment of model quality, we provide a tool "TopModel" to validate structure models.
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Affiliation(s)
| | - Janik Kokot
- Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Franz Waibl
- Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Anna-Lena M. Fischer
- Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
| | - Patrick K. Quoika
- Center for Protein Assemblies (CPA), Physics Department, Chair of Theoretical Biophysics, Technical University of Munich, Garching, Germany
| | | | - Klaus R. Liedl
- CONTACT Klaus R. Liedl Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria
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12
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Marin FI, Marcatili P. Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops). Methods Mol Biol 2023; 2552:83-100. [PMID: 36346586 DOI: 10.1007/978-1-0716-2609-2_3] [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
Antibodies and T-cell receptors have been a subject of much interest due to their central role in the immune system and their potential applications in several biotechnological and medical applications from cancer therapy to vaccine development. A unique feature of these two lymphocyte receptors is their ability to bind a huge variety of different (pathogen) targets. This ability stems from six short loops in the binding domain that have hypervariable sequence due to genetic recombination mechanism. Particularly one of these loops, the third complementarity determining region (CDR3), has the highest sequence variability and a dominant role in binding the target. However, it has also been proven the most difficult to be modeled structurally, which is vitally important for downstream tasks such as binding prediction. This difficulty stems from its variability in sequence that both reduces the possibility of finding homologues and introduces unique structural features in the loop. We present here a general protocol for modeling such loops in antibodies and T-cell receptors. We also discuss the difficulties in loop modeling and the advantages and limitations of different modeling methods.
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Affiliation(s)
- Frederikke I Marin
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.
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13
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Bansia H, Ramakumar S. Homology Modeling of Antibody Variable Regions: Methods and Applications. Methods Mol Biol 2023; 2627:301-319. [PMID: 36959454 DOI: 10.1007/978-1-0716-2974-1_16] [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] [Indexed: 03/25/2023]
Abstract
Adaptive immunity specifically protects us from antigenic challenges. Antibodies are key effector proteins of adaptive immunity, and they are remarkable in their ability to recognize a virtually limitless number of antigens. Fragment variable (FV), the antigen-binding region of antibodies, can be split into two main components, namely, framework and complementarity determining regions. The framework (FR) consists of light-chain framework (FRL) and heavy-chain framework (FRH). Similarly, the complementarity determining regions (CDRs) comprises of light-chain CDRs 1-3 (CDRs L1-3) and heavy-chain CDRs 1-3 (CDRs H1-3). While FRs are relatively constant in sequence and structure across diverse antibodies, sequence variation in CDRs leading to differential conformations of CDR loops accounts for the distinct antigenic specificities of diverse antibodies. The conserved structural features in FRs and conformity of CDRs to a limited set of standard conformations allow for the accurate prediction of FV models using homology modeling techniques. Antibody structure prediction from its amino acid sequence has numerous important applications including prediction of antibody-antigen interaction interfaces and redesign of therapeutically and biotechnologically useful antibodies with improved affinity. This chapter summarizes the current practices employed in the successful homology modeling of antibody variable regions and the potential applications of the generated homology models.
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Affiliation(s)
- Harsh Bansia
- Department of Physics, Indian Institute of Science, Bengaluru, India.
- Advanced Science Research Center at The Graduate Center of the City University of New York, New York, NY, USA.
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14
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Waight AB, Prihoda D, Shrestha R, Metcalf K, Bailly M, Ancona M, Widatalla T, Rollins Z, Cheng AC, Bitton DA, Fayadat-Dilman L. A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties. MAbs 2023; 15:2248671. [PMID: 37610144 PMCID: PMC10448975 DOI: 10.1080/19420862.2023.2248671] [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: 02/23/2023] [Revised: 07/28/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023] Open
Abstract
Identification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated in silico features has potential to reduce the time and cost of delivering clinical-grade material to patients, but nevertheless has remained an ongoing challenge to the field. Here, we demonstrate an automated and flexible machine learning workflow designed to compare and identify the most powerful features from computationally derived physiochemical feature sets, generated from popular commercial software packages. We implement this workflow with medium-sized datasets of human and humanized IgG molecules to generate predictive regression models for two key developability endpoints, hydrophobicity and poly-specificity. The most important features discovered through the automated workflow corroborate several previous literature reports, and newly discovered features suggest directions for further research and potential model improvement.
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Affiliation(s)
- Andrew B. Waight
- Discovery Biologics, Protein Sciences, Merck & Co., Inc, South San Francisco, CA, USA
| | - David Prihoda
- Discovery Informatics, MSD Czech Republic s.r.o, Prague, Czech Republic
| | - Rojan Shrestha
- Discovery Biologics, Protein Sciences, Merck & Co., Inc, South San Francisco, CA, USA
| | - Kevin Metcalf
- Discovery Biologics, Protein Sciences, Merck & Co., Inc, South San Francisco, CA, USA
| | - Marc Bailly
- Discovery Biologics, Protein Sciences, Merck & Co., Inc, South San Francisco, CA, USA
| | - Marco Ancona
- Discovery Informatics, MSD Czech Republic s.r.o, Prague, Czech Republic
| | - Talal Widatalla
- Computational and Structural Chemistry, Merck & Co., Inc, South San Francisco, CA, USA
| | - Zachary Rollins
- Computational and Structural Chemistry, Merck & Co., Inc, South San Francisco, CA, USA
| | - Alan C Cheng
- Computational and Structural Chemistry, Merck & Co., Inc, South San Francisco, CA, USA
| | - Danny A. Bitton
- Discovery Informatics, MSD Czech Republic s.r.o, Prague, Czech Republic
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15
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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] [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
- *Correspondence: Daron M. Standley,
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16
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Ahmad B, Choi S. Unraveling the Tomaralimab Epitope on the Toll-like Receptor 2 via Molecular Dynamics and Deep Learning. ACS OMEGA 2022; 7:28226-28237. [PMID: 35990491 PMCID: PMC9386714 DOI: 10.1021/acsomega.2c02559] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/22/2022] [Indexed: 06/15/2023]
Abstract
Tomaralimab (OPN-305) is the first humanized immunoglobulin G4 monoclonal antibody against TLR2 and is designed to prevent inflammation that is driven by inappropriate or excessive activation of innate immune pathways. Here, we constructed a homology model of Tomaralimab and its complex with TLR2 at different mapped epitopes and unraveled their behavior at the atomistic level. Furthermore, we predicted a novel epitope (leucine-rich region 9-12) near the lipopeptide-binding site that can be targeted and studied for the utility of therapeutic antibodies. A geometric deep learning algorithm was used to envisage Tomaralimab binding affinity changes upon mutation. There was a significant difference in binding affinity for Tomaralimab following epitope-mutated alanine substitutions of Val266, Pro294, Arg295, Asn319, Pro326, and His372. Using deep learning-based ΔΔG prediction, we computationally contrasted human TLR2-TLR2, TLR2-TLR1, and TLR2-TLR6 dimerization. These results reveal the mechanism that underlies Tomaralimab binding to TLR2 and should help to design structure-based mimics or bispecific antibodies that can be used to inhibit both lipopeptide-binding and TLR2 dimerization.
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Affiliation(s)
- Bilal Ahmad
- Department
of Molecular Science and Technology, Ajou
University, Suwon 16499, Korea
- S&K
Therapeutics, Ajou University
Campus Plaza 418, 199 Worldcup-ro, Yeongtong-gu, Suwon 16502, Korea
| | - Sangdun Choi
- Department
of Molecular Science and Technology, Ajou
University, Suwon 16499, Korea
- S&K
Therapeutics, Ajou University
Campus Plaza 418, 199 Worldcup-ro, Yeongtong-gu, Suwon 16502, Korea
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17
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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; 23:6643456. [PMID: 35830864 PMCID: PMC9294429 DOI: 10.1093/bib/bbac267] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [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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18
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Abduljaleel Z, Shahzad N, Aziz SA, Malik SM. Monoclonal antibody designed for SARS-nCoV-2 spike protein of receptor binding domain on antigenic targeted epitopes for inhibition to prevent viral entry. Mol Divers 2022; 27:695-708. [PMID: 35616802 PMCID: PMC9133318 DOI: 10.1007/s11030-022-10449-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/19/2022] [Indexed: 11/25/2022]
Abstract
SARS, or severe acute respiratory syndrome, is caused by a novel coronavirus (COVID-19). This situation has compelled many pharmaceutical R&D companies and public health research sectors to focus their efforts on developing effective therapeutics. SARS-nCoV-2 was chosen as a protein spike to targeted monoclonal antibodies and therapeutics for prevention and treatment. Deep mutational scanning created a monoclonal antibody to characterize the effects of mutations in a variable antibody fragment based on its expression levels, specificity, stability, and affinity for specific antigenic conserved epitopes to the Spike-S-Receptor Binding Domain (RBD). Improved contacts between Fv light and heavy chains and the targeted antigens of RBD could result in a highly potent neutralizing antibody (NAbs) response as well as cross-protection against other SARS-nCoV-2 strains. It undergoes multipoint core mutations that combine enhancing mutations, resulting in increased binding affinity and significantly increased stability between RBD and antibody. In addition, we improved. Structures of variable fragment (Fv) complexed with the RBD of Spike protein were subjected to our established in-silico antibody-engineering platform to obtain enhanced binding affinity to SARS-nCoV-2 and develop ability profiling. We found that the size and three-dimensional shape of epitopes significantly impacted the activity of antibodies produced against the RBD of Spike protein. Overall, because of the conformational changes between RBD and hACE2, it prevents viral entry. As a result of this in-silico study, the designed antibody can be used as a promising therapeutic strategy to treat COVID-19.
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Affiliation(s)
- Zainularifeen Abduljaleel
- Science and Technology Unit, Umm Al-Qura University, P.O. Box 715, Mecca, 21955, Kingdom of Saudi Arabia.
- Department of Medical Genetics, Faculty of Medicine, Umm Al-Qura University, P.O. Box 715, Mecca, 21955, Kingdom of Saudi Arabia.
- The Regional Laboratory, Molecular Diagnostics Unit, Department of Molecular Biology, Ministry of Health (MOH), P.O. Box 6251, Mecca, Saudi Arabia.
| | - Naiyer Shahzad
- Department of Pharmacology and Toxicology, Faculty of Medicine, Umm Al-Qura University, Mecca, Saudi Arabia
| | - Syed A Aziz
- Department of Pathology and Lab Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
| | - Shaheer M Malik
- Department of Chemistry, Faculty of Applied Sciences, Umm Al-Qura University, Mecca, Saudi Arabia
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19
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Blackler RJ, Müller-Loennies S, Pokorny-Lehrer B, Legg MSG, Brade L, Brade H, Kosma P, Evans SV. Antigen binding by conformational selection in near-germline antibodies. J Biol Chem 2022; 298:101901. [PMID: 35395245 PMCID: PMC9112003 DOI: 10.1016/j.jbc.2022.101901] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 01/20/2023] Open
Abstract
Conformational flexibility in antibody-combining sites has been hypothesized to facilitate polyspecificity toward multiple unique epitopes and enable the limited germline repertoire to match an overwhelming diversity of potential antigens; however, elucidating the mechanisms of antigen recognition by flexible antibodies has been understandably challenging. Here, multiple liganded and unliganded crystal structures of the near-germline anticarbohydrate antibodies S25–2 and S25–39 are reported, which reveal an unprecedented diversity of complementarity-determining region H3 conformations in apparent equilibrium. These structures demonstrate that at least some germline or near-germline antibodies are flexible entities sensitive to their chemical environments, with conformational selection available as an evolved mechanism that preserves the inherited ability to recognize common pathogens while remaining adaptable to new threats.
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Affiliation(s)
- Ryan J Blackler
- Department of Biochemistry and Microbiology, University of Victoria, Victoria BC, Canada
| | | | - Barbara Pokorny-Lehrer
- Department of Chemistry, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Max S G Legg
- Department of Biochemistry and Microbiology, University of Victoria, Victoria BC, Canada
| | - Lore Brade
- Research Center Borstel, Leibniz Lung Center, Borstel, Germany
| | - Helmut Brade
- Research Center Borstel, Leibniz Lung Center, Borstel, Germany
| | - Paul Kosma
- Department of Chemistry, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Stephen V Evans
- Department of Biochemistry and Microbiology, University of Victoria, Victoria BC, Canada.
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20
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V HH Structural Modelling Approaches: A Critical Review. Int J Mol Sci 2022; 23:ijms23073721. [PMID: 35409081 PMCID: PMC8998791 DOI: 10.3390/ijms23073721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 12/20/2022] Open
Abstract
VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.
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21
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Sankar K, Trainor K, Blazer L, Adams J, Sidhu S, Day T, Meiering E, Maier J. A Descriptor set for Quantitative Structure-Property Relationship Prediction in Biologics. Mol Inform 2022; 41:e2100240. [PMID: 35277930 DOI: 10.1002/minf.202100240] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 03/11/2022] [Indexed: 11/12/2022]
Abstract
In addition to attaining the desired binding to their targets, a crucial aspect in the development of biotherapeutics is 'developability', which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability and low immunogenicity. The lack of any of these properties can lead to significant obstacles in advancing them to clinic; thus in silico methods capable of raising warning flags in earlier stages of development are highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors ideal for making liability predictions using a machine-learning approach. This set offers a high degree of feature diversity classifiable by sequence, structure and surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results demonstrate that the descriptors are well suited to assist in the improvement of properties of systems that exhibit poor solubility or aggregation.
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22
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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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23
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Frick R, Høydahl LS, Hodnebrug I, Vik ES, Dalhus B, Sollid LM, Gray JJ, Sandlie I, Løset GÅ. Affinity maturation of TCR-like antibodies using phage display guided by structural modeling. Protein Eng Des Sel 2022; 35:6649134. [PMID: 35871543 PMCID: PMC9536190 DOI: 10.1093/protein/gzac005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 12/01/2022] Open
Abstract
TCR-like antibodies represent a unique type of engineered antibodies with specificity toward pHLA, a ligand normally restricted to the sensitive recognition by T cells. Here, we report a phage display-based sequential development path of such antibodies. The strategy goes from initial lead identification through in silico informed CDR engineering in combination with framework engineering for affinity and thermostability optimization, respectively. The strategy allowed the identification of HLA-DQ2.5 gluten peptide-specific TCR-like antibodies with low picomolar affinity. Our method outlines an efficient and general method for development of this promising class of antibodies, which should facilitate their utility including translation to human therapy.
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Affiliation(s)
- Rahel Frick
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- Centre for Immune Regulation and Department of Biosciences, University of Oslo , Blindernveien 31, 0371 Oslo, Norway
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University , 3400 N. Charles Street, Baltimore, MD 21218, USA
| | - Lene S Høydahl
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- Centre for Immune Regulation and Department of Biosciences, University of Oslo , Blindernveien 31, 0371 Oslo, Norway
- KG Jebsen Coeliac Disease Research Centre, University of Oslo , Sognsvannsveien 20, 0372 Oslo, Norway
| | - Ina Hodnebrug
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- Centre for Immune Regulation and Department of Biosciences, University of Oslo , Blindernveien 31, 0371 Oslo, Norway
| | - Erik S Vik
- Nextera AS , Gaustadalléen 21, 0349 Oslo, Norway
| | - Bjørn Dalhus
- Department for Medical Biochemistry , Institute for Clinical Medicine, , Sognsvannsveien 20, 0372 Oslo, Norway
- University of Oslo , Institute for Clinical Medicine, , Sognsvannsveien 20, 0372 Oslo, Norway
- Department for Microbiology , Clinic for Laboratory Medicine, , Sognsvannsveien 20, 0372 Oslo, Norway
- Oslo University Hospital , Clinic for Laboratory Medicine, , Sognsvannsveien 20, 0372 Oslo, Norway
| | - Ludvig M Sollid
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- KG Jebsen Coeliac Disease Research Centre, University of Oslo , Sognsvannsveien 20, 0372 Oslo, Norway
| | - Jeffrey J Gray
- Program in Molecular Biophysics, Johns Hopkins University , 3400 N. Charles Street, Baltimore, MD 21218, USA
- Department of Chemical and Biomolecular Engineering and Institute of NanoBioTechnology, Johns Hopkins University , 3400 N. Charles Street, Baltimore, MD 21218, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine , 733 N Broadway, Baltimore, MD 21205, USA
| | - Inger Sandlie
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- Centre for Immune Regulation and Department of Biosciences, University of Oslo , Blindernveien 31, 0371 Oslo, Norway
| | - Geir Åge Løset
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital , Sognsvannsveien 20, 0372 Oslo, Norway
- Centre for Immune Regulation and Department of Biosciences, University of Oslo , Blindernveien 31, 0371 Oslo, Norway
- Nextera AS , Gaustadalléen 21, 0349 Oslo, Norway
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24
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Antibody structure prediction using interpretable deep learning. PATTERNS (NEW YORK, N.Y.) 2022; 3:100406. [PMID: 35199061 PMCID: PMC8848015 DOI: 10.1016/j.patter.2021.100406] [Citation(s) in RCA: 77] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/03/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022]
Abstract
Therapeutic antibodies make up a rapidly growing segment of the biologics market. However, rational design of antibodies is hindered by reliance on experimental methods for determining antibody structures. Here, we present DeepAb, a deep learning method for predicting accurate antibody FV structures from sequence. We evaluate DeepAb on a set of structurally diverse, therapeutically relevant antibodies and find that our method consistently outperforms the leading alternatives. Previous deep learning methods have operated as “black boxes” and offered few insights into their predictions. By introducing a directly interpretable attention mechanism, we show our network attends to physically important residue pairs (e.g., proximal aromatics and key hydrogen bonding interactions). Finally, we present a novel mutant scoring metric derived from network confidence and show that for a particular antibody, all eight of the top-ranked mutations improve binding affinity. This model will be useful for a broad range of antibody prediction and design tasks. DeepAb, a deep learning method for antibody structure, is presented Structures from DeepAb are more accurate than alternatives Outputs of DeepAb provide interpretable insights into structure predictions DeepAb predictions should facilitate design of novel antibody therapeutics
Accurate structure models are critical for understanding the properties of potential therapeutic antibodies. Conventional methods for protein structure determination require significant investments of time and resources and may fail. Although greatly improved, methods for general protein structure prediction still cannot consistently provide the accuracy necessary to understand or design antibodies. We present a deep learning method for antibody structure prediction and demonstrate improvement over alternatives on diverse, therapeutically relevant benchmarks. In addition to its improved accuracy, our method reveals interpretable outputs about specific amino acids and residue interactions that should facilitate design of novel therapeutic antibodies.
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25
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Computational discovery of binding mode of anti-TRBC1 antibody and predicted key amino acids of TRBC1. Sci Rep 2022; 12:1760. [PMID: 35110642 PMCID: PMC8810837 DOI: 10.1038/s41598-022-05742-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/11/2022] [Indexed: 12/02/2022] Open
Abstract
Peripheral T-cell lymphoma (PTCL) is a type of non-Hodgkin lymphoma that progresses aggressively with poor survival rate. CAR T cell targeting T-cell receptor β-chain constant domains 1 (TRBC1) of malignant T cells has been developed recently by using JOVI.1 monoclonal antibody as a template. However, the mode of JOVI.1 binding is still unknown. This study aimed to investigate the molecular interaction between JOVI.1 antibody and TRBC1 by using computational methods and molecular docking. Therefore, the TRBC protein crystal structures (TRBC1 and TRBC2) as well as the sequences of JOVI.1 CDR were chosen as the starting materials. TRBC1 and TRBC2 epitopes were predicted, and molecular dynamic (MD) simulation was used to visualize the protein dynamic behavior. The structure of JOVI.1 antibody was also generated before the binding mode was predicted using molecular docking with an antibody mode. Epitope prediction suggested that the N3K4 region of TRBC1 may be a key to distinguish TRBC1 from TCBC2. MD simulation showed the major different surface conformation in this area between two TRBCs. The JOVI.1-TRBC1 structures with three binding modes demonstrated JOVI.1 interacted TRBC1 at N3K4 residues, with the predicted dissociation constant (Kd) ranging from 1.5 × 108 to 1.1 × 1010 M. The analysis demonstrated JOVI.1 needed D1 residues of TRBC1 for the interaction formation to N3K4 in all binding modes. In conclusion, we proposed the three binding modes of the JOVI.1 antibody to TRBC1 with the new key residue (D1) necessary for N3K4 interaction. This data was useful for JOVI.1 redesign to improve the PTCL-targeting CAR T cell.
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26
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Abstract
Monoclonal antibodies are susceptible to chemical and enzymatic modifications during manufacturing, storage, and shipping. Deamidation, isomerization, and oxidation can compromise the potency, efficacy, and safety of therapeutic antibodies. Recently, in silico tools have been used to identify liable residues and engineer antibodies with better chemical stability. Computational approaches for predicting deamidation, isomerization, oxidation, glycation, carbonylation, sulfation, and hydroxylation are reviewed here. Although liable motifs have been used to improve the chemical stability of antibodies, the accuracy of in silico predictions can be improved using machine learning and molecular dynamic simulations. In addition, there are opportunities to improve predictions for specific stress conditions, develop in silico prediction of novel modifications in antibodies, and predict the impact of modifications on physical stability and antigen-binding.
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Affiliation(s)
- Shabdita Vatsa
- Development Services, Lonza Biologics, Singapore, Singapore
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27
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Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences. Sci Rep 2021; 11:21362. [PMID: 34725391 PMCID: PMC8560851 DOI: 10.1038/s41598-021-00669-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/05/2021] [Indexed: 01/01/2023] Open
Abstract
The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, 10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.
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28
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Schneider C, Buchanan A, Taddese B, Deane CM. DLAB: deep learning methods for structure-based virtual screening of antibodies. Bioinformatics 2021; 38:377-383. [PMID: 34546288 PMCID: PMC8723137 DOI: 10.1093/bioinformatics/btab660] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 08/03/2021] [Accepted: 09/01/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in vivo and in vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders. RESULTS We demonstrate that DLAB can be used both to improve antibody-antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody-antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies. AVAILABILITY AND IMPLEMENTATION The DLAB source code and pre-trained models are available at https://github.com/oxpig/dlab-public. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Andrew Buchanan
- Antibody Discovery & Protein Engineering, R&D, AstraZeneca, Cambridge, CB2 0AA, UK
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29
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Tahir S, Bourquard T, Musnier A, Jullian Y, Corde Y, Omahdi Z, Mathias L, Reiter E, Crépieux P, Bruneau G, Poupon A. Accurate determination of epitope for antibodies with unknown 3D structures. MAbs 2021; 13:1961349. [PMID: 34432559 PMCID: PMC8405158 DOI: 10.1080/19420862.2021.1961349] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
MAbTope is a docking-based method for the determination of epitopes. It has been used to successfully determine the epitopes of antibodies with known 3D structures. However, during the antibody discovery process, this structural information is rarely available. Although we already have evidence that homology models of antibodies could be used instead of their 3D structure, the choice of the template, the methodology for homology modeling and the resulting performance still have to be clarified. Here, we show that MAbTope has the same performance when working with homology models of the antibodies as compared to crystallographic structures. Moreover, we show that even low-quality models can be used. We applied MAbTope to determine the epitope of dupilumab, an anti- interleukin 4 receptor alpha subunit therapeutic antibody of unknown 3D structure, that we validated experimentally. Finally, we show how the MAbTope-determined epitopes for a series of antibodies targeting the same protein can be used to predict competitions, and demonstrate the accuracy with an experimentally validated example. 3D: three-dimensionalRMSD: root mean square deviationCDR: complementary-determining regionCPU: central processing unitsVH: heavy chain variable regionVL: light chain variable regionscFv: single-chain variable fragmentsVHH: single-chain antibody variable regionIL4Rα: Interleukin 4 receptor alpha chainSPR: surface plasmon resonancePDB: protein data bankHEK293: Human embryonic kidney 293 cellsEDTA: Ethylenediaminetetraacetic acidFBS: Fetal bovine serumANOVA: Analysis of varianceEGFR: Epidermal growth factor receptorPE: PhycoerythrinAPC: AllophycocyaninFSC: forward scatterSSC: side scatterWT: wild type Keywords: MAbTope, Epitope Mapping, Molecular docking, Antibody modeling, Antibody-antigen docking
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Affiliation(s)
- Shifa Tahir
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France
| | - Thomas Bourquard
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France.,MAbSilico SAS, 1 Impasse Du Palais
| | - Astrid Musnier
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France.,MAbSilico SAS, 1 Impasse Du Palais
| | - Yann Jullian
- MAbSilico SAS, 1 Impasse Du Palais.,CaSciModOT, UFR De Sciences Et Techniques, Université De Tours
| | | | | | | | - Eric Reiter
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France.,France Inria, Inria Saclay-Île-de-France, Palaiseau, France.,Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
| | - Pascale Crépieux
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France.,France Inria, Inria Saclay-Île-de-France, Palaiseau, France.,Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
| | | | - Anne Poupon
- PRC, INRAE, CNRS, Université De Tours, Nouzilly, France.,MAbSilico SAS, 1 Impasse Du Palais.,France Inria, Inria Saclay-Île-de-France, Palaiseau, France.,Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
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30
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Kodali P, Schoeder CT, Schmitz S, Crowe JE, Meiler J. RosettaCM for antibodies with very long HCDR3s and low template availability. Proteins 2021; 89:1458-1472. [PMID: 34176159 PMCID: PMC8492515 DOI: 10.1002/prot.26166] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/26/2021] [Indexed: 11/07/2022]
Abstract
Antibody-antigen co-crystal structures are a valuable resource for the fundamental understanding of antibody-mediated immunity. Determination of structures with antibodies in complex with their antigens, however, is a laborious task without guarantee of success. Therefore, homology modeling of antibodies and docking to their respective antigens has become a very important technique to drive antibody and vaccine design. The quality of the antibody modeling process is critical for the success of these endeavors. Here, we compare different computational protocols for predicting antibody structure from sequence in the biomolecular modeling software Rosetta-all of which use multiple existing antibody structures to guide modeling. Specifically, we compare protocols developed solely to predict antibody structure (RosettaAntibody, AbPredict) with a universal homology modeling protocol (RosettaCM). Following recent advances in homology modeling with multiple templates simultaneously, we propose that the use of multiple templates over the same antibody regions may improve modeling performance. To evaluate whether multi-template comparative modeling with RosettaCM can improve the modeling accuracy of antibodies over existing methods, this study compares the performance of the three modeling algorithms when modeling human antibodies taken from antibody-antigen co-crystal structures. In these benchmarking experiments, RosettaCM outperformed other methods when modeling antibodies with long HCDR3s and few available templates.
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Affiliation(s)
- Pranav Kodali
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.,Center of Structural Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Clara T Schoeder
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.,Center of Structural Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Samuel Schmitz
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.,Center of Structural Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - James E Crowe
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Departments of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.,Center of Structural Biology, Vanderbilt University, Nashville, Tennessee, USA.,Institute for Drug Discovery, University Leipzig Medical School, Leipzig, Germany
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31
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Robustification of RosettaAntibody and Rosetta SnugDock. PLoS One 2021; 16:e0234282. [PMID: 33764990 PMCID: PMC7993800 DOI: 10.1371/journal.pone.0234282] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 01/11/2021] [Indexed: 11/19/2022] Open
Abstract
In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence–structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock—methods for antibody structure prediction and antibody–antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody–antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.
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32
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Schoeder C, Schmitz S, Adolf-Bryfogle J, Sevy AM, Finn JA, Sauer MF, Bozhanova NG, Mueller BK, Sangha AK, Bonet J, Sheehan JH, Kuenze G, Marlow B, Smith ST, Woods H, Bender BJ, Martina CE, del Alamo D, Kodali P, Gulsevin A, Schief WR, Correia BE, Crowe JE, Meiler J, Moretti R. Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design. Biochemistry 2021; 60:825-846. [PMID: 33705117 PMCID: PMC7992133 DOI: 10.1021/acs.biochem.0c00912] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 03/02/2021] [Indexed: 01/16/2023]
Abstract
Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.
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Affiliation(s)
- Clara
T. Schoeder
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Samuel Schmitz
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jared Adolf-Bryfogle
- Department
of Immunology and Microbiology, The Scripps
Research Institute, La Jolla, California 92037, United States
- IAVI
Neutralizing Antibody Center, The Scripps
Research Institute, La Jolla, California 92037, United States
| | - Alexander M. Sevy
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
| | - Jessica A. Finn
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
- Department
of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Marion F. Sauer
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
| | - Nina G. Bozhanova
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Benjamin K. Mueller
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Amandeep K. Sangha
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Jaume Bonet
- Institute
of Bioengineering, École Polytechnique
Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - Jonathan H. Sheehan
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Georg Kuenze
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Institute
for Drug Discovery, University Leipzig Medical
School, 04103 Leipzig, Germany
| | - Brennica Marlow
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Shannon T. Smith
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Hope Woods
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Brian J. Bender
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37212, United States
| | - Cristina E. Martina
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Diego del Alamo
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Chemical
and Physical Biology Program, Vanderbilt
University, Nashville, Tennessee 37232-0301, United States
| | - Pranav Kodali
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - Alican Gulsevin
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
| | - William R. Schief
- Department
of Immunology and Microbiology, The Scripps
Research Institute, La Jolla, California 92037, United States
- IAVI
Neutralizing Antibody Center, The Scripps
Research Institute, La Jolla, California 92037, United States
| | - Bruno E. Correia
- Institute
of Bioengineering, École Polytechnique
Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - James E. Crowe
- Vanderbilt
Vaccine Center, Vanderbilt University Medical
Center, Nashville, Tennessee 37232-0417, United States
- Department
of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
- Department
of Pediatrics, Vanderbilt University Medical
Center, Nashville, Tennessee 37232, United States
| | - Jens Meiler
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
- Institute
for Drug Discovery, University Leipzig Medical
School, 04103 Leipzig, Germany
| | - Rocco Moretti
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37212, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240-7917, United States
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33
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Ruffolo JA, Guerra C, Mahajan SP, Sulam J, Gray JJ. Geometric potentials from deep learning improve prediction of CDR H3 loop structures. Bioinformatics 2021; 36:i268-i275. [PMID: 32657412 PMCID: PMC7355305 DOI: 10.1093/bioinformatics/btaa457] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Motivation Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody and predict new CDR H3 loop structures de novo. Results When evaluated on the Rosetta antibody benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same and worse structures [measured by root-mean-squared distance (RMSD) from the experimental CDR H3 loop structure] than the standard Rosetta energy function for 33, 6 and 10 targets, respectively, and improved the average RMSD of predictions by 32.1% (1.4 Å). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loops. When applied to de novo prediction of CDR H3 loop structures, DeepH3 achieves an average RMSD of 2.2 ± 1.1 Å on the Rosetta antibody benchmark. Availability and Implementation DeepH3 source code and pre-trained model parameters are freely available at https://github.com/Graylab/deepH3-distances-orientations. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jeffrey A Ruffolo
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Carlos Guerra
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
| | - Sai Pooja Mahajan
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeremias Sulam
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.,Mathematical Institute for Data Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD 21218, USA.,Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
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34
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Waibl F, Fernández-Quintero ML, Kamenik AS, Kraml J, Hofer F, Kettenberger H, Georges G, Liedl KR. Conformational Ensembles of Antibodies Determine Their Hydrophobicity. Biophys J 2020; 120:143-157. [PMID: 33220303 PMCID: PMC7820740 DOI: 10.1016/j.bpj.2020.11.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 10/23/2020] [Accepted: 11/10/2020] [Indexed: 12/11/2022] Open
Abstract
A major challenge in the development of antibody biotherapeutics is their tendency to aggregate. One root cause for aggregation is exposure of hydrophobic surface regions to the solvent. Many current techniques predict the relative aggregation propensity of antibodies via precalculated scales for the hydrophobicity or aggregation propensity of single amino acids. However, those scales cannot describe the nonadditive effects of a residue’s surrounding on its hydrophobicity. Therefore, they are inherently limited in their ability to describe the impact of subtle differences in molecular structure on the overall hydrophobicity. Here, we introduce a physics-based approach to describe hydrophobicity in terms of the hydration free energy using grid inhomogeneous solvation theory (GIST). We apply this method to assess the effects of starting structures, conformational sampling, and protonation states on the hydrophobicity of antibodies. Our results reveal that high-quality starting structures, i.e., crystal structures, are crucial for the prediction of hydrophobicity and that conformational sampling can compensate errors introduced by the starting structure. On the other hand, sampling of protonation states only leads to good results when combined with high-quality structures, whereas it can even be detrimental otherwise. We conclude by pointing out that a single static homology model may not be adequate for predicting hydrophobicity.
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Affiliation(s)
- Franz Waibl
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Monica L Fernández-Quintero
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Anna S Kamenik
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Johannes Kraml
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Florian Hofer
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
| | - Hubert Kettenberger
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Guy Georges
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, Germany
| | - Klaus R Liedl
- Department of General, Inorganic and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria.
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35
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Norman RA, Ambrosetti F, Bonvin AMJJ, Colwell LJ, Kelm S, Kumar S, Krawczyk K. Computational approaches to therapeutic antibody design: established methods and emerging trends. Brief Bioinform 2020; 21:1549-1567. [PMID: 31626279 PMCID: PMC7947987 DOI: 10.1093/bib/bbz095] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/07/2019] [Accepted: 07/05/2019] [Indexed: 12/31/2022] Open
Abstract
Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.
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36
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Barozet A, Bianciotto M, Vaisset M, Siméon T, Minoux H, Cortés J. Protein loops with multiple meta-stable conformations: A challenge for sampling and scoring methods. Proteins 2020; 89:218-231. [PMID: 32920900 DOI: 10.1002/prot.26008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 08/10/2020] [Accepted: 08/25/2020] [Indexed: 12/25/2022]
Abstract
Flexible regions in proteins, such as loops, cannot be represented by a single conformation. Instead, conformational ensembles are needed to provide a more global picture. In this context, identifying statistically meaningful conformations within an ensemble generated by loop sampling techniques remains an open problem. The difficulty is primarily related to the lack of structural data about these flexible regions. With the majority of structural data coming from x-ray crystallography and ignoring plasticity, the conception and evaluation of loop scoring methods is challenging. In this work, we compare the performance of various scoring methods on a set of eight protein loops that are known to be flexible. The ability of each method to identify and select all of the known conformations is assessed, and the underlying energy landscapes are produced and projected to visualize the qualitative differences obtained when using the methods. Statistical potentials are found to provide considerable reliability despite their being designed to tradeoff accuracy for lower computational cost. On a large pool of loop models, they are capable of filtering out statistically improbable states while retaining those that resemble known (and thus likely) conformations. However, computationally expensive methods are still required for more precise assessment and structural refinement. The results also highlight the importance of employing several scaffolds for the protein, due to the high influence of small structural rearrangements in the rest of the protein over the modeled energy landscape for the loop.
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Affiliation(s)
- Amélie Barozet
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France.,Sanofi Recherche & Développement, Integrated Drug Discovery, Molecular Design Sciences, Vitry-sur-Seine, France
| | - Marc Bianciotto
- Sanofi Recherche & Développement, Integrated Drug Discovery, Molecular Design Sciences, Vitry-sur-Seine, France
| | - Marc Vaisset
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
| | - Thierry Siméon
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
| | - Hervé Minoux
- Sanofi Recherche & Développement, Integrated Drug Discovery, Molecular Design Sciences, Vitry-sur-Seine, France
| | - Juan Cortés
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
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37
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Teraguchi S, Saputri DS, Llamas-Covarrubias MA, Davila A, Diez D, Nazlica SA, Rozewicki J, Ismanto HS, Wilamowski J, Xie J, Xu Z, Loza-Lopez MDJ, van Eerden FJ, Li S, Standley DM. Methods for sequence and structural analysis of B and T cell receptor repertoires. Comput Struct Biotechnol J 2020; 18:2000-2011. [PMID: 32802272 PMCID: PMC7366105 DOI: 10.1016/j.csbj.2020.07.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/08/2020] [Accepted: 07/08/2020] [Indexed: 02/07/2023] Open
Abstract
B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.
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Affiliation(s)
- Shunsuke Teraguchi
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Dianita S. Saputri
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Mara Anais Llamas-Covarrubias
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Mexico
| | - Ana Davila
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Diego Diez
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Sedat Aybars Nazlica
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - John Rozewicki
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Hendra S. Ismanto
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jan Wilamowski
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jiaqi Xie
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | | | - Floris J. van Eerden
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Songling Li
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Daron M. Standley
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
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38
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Myung Y, Pires DEV, Ascher DB. mmCSM-AB: guiding rational antibody engineering through multiple point mutations. Nucleic Acids Res 2020; 48:W125-W131. [PMID: 32432715 PMCID: PMC7319589 DOI: 10.1093/nar/gkaa389] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/18/2020] [Accepted: 05/16/2020] [Indexed: 12/15/2022] Open
Abstract
While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.
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Affiliation(s)
- Yoochan Myung
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, VIC 3004, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
| | - Douglas E V Pires
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, VIC 3004, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
- School of Computing and Information Systems, University of Melbourne, Parkville, VIC 3052, Australia
| | - David B Ascher
- Computational Biology and Clinical Informatics, Baker Institute, Melbourne, VIC 3004, Australia
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC 3052, Australia
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
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39
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Lapidoth G, Parker J, Prilusky J, Fleishman SJ. AbPredict 2: a server for accurate and unstrained structure prediction of antibody variable domains. Bioinformatics 2020; 35:1591-1593. [PMID: 30951584 DOI: 10.1093/bioinformatics/bty822] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 09/05/2018] [Accepted: 09/19/2018] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Methods for antibody structure prediction rely on sequence homology to experimentally determined structures. Resulting models may be accurate but are often stereochemically strained, limiting their usefulness in modeling and design workflows. We present the AbPredict 2 web-server, which instead of using sequence homology, conducts a Monte Carlo-based search for low-energy combinations of backbone conformations to yield accurate and unstrained antibody structures. AVAILABILITY AND IMPLEMENTATION We introduce several important improvements over the previous AbPredict implementation: (i) backbones and sidechains are now modeled using ideal bond lengths and angles, substantially reducing stereochemical strain, (ii) sampling of the rigid-body orientation at the light-heavy chain interface is improved, increasing model accuracy and (iii) runtime is reduced 20-fold without compromising accuracy, enabling the implementation of AbPredict 2 as a fully automated web-server (http://abpredict.weizmann.ac.il). Accurate and unstrained antibody model structures may in some cases obviate the need for experimental structures in antibody optimization workflows.
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Affiliation(s)
- Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Jake Parker
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Australia.,CSIRO Synthetic Biology Future Science Platform, Weizmann Institute of Science, Rehovot, Israel
| | - Jaime Prilusky
- Bioinformatics & Biological Computing Unit, Weizmann Institute of Science, Rehovot, Israel
| | - Sarel J Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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40
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Kuroda D, Tsumoto K. Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design. J Pharm Sci 2020; 109:1631-1651. [DOI: 10.1016/j.xphs.2020.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/25/2019] [Accepted: 01/10/2020] [Indexed: 12/18/2022]
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41
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Esaki T, Kumazawa K, Takahashi K, Watanabe R, Masuda T, Watanabe H, Shimizu Y, Okada A, Takimoto S, Shimada T, Ikeda K. Open Innovation Platform using Cloud-based Applications and Collaborative Space: A Case Study of Solubility Prediction Model Development. CHEM-BIO INFORMATICS JOURNAL 2020. [DOI: 10.1273/cbij.20.5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Tsuyoshi Esaki
- The Center for Data Science Education and Research, Shiga University
| | - Keiko Kumazawa
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited
| | - Kazutoshi Takahashi
- Research Institute for Bioscience Products and Fine Chemicals, Ajinomoto Co. Inc
| | - Reiko Watanabe
- Laboratory of Bioinformatics, AI Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition
| | | | - Hirofumi Watanabe
- Education Center on Computational Science and Engineering, Kobe University
| | - Yugo Shimizu
- Division of Physics for Life Functions, Keio University Faculty of Pharmacy
| | - Akitoshi Okada
- Central Pharmaceutical Research Institute Takatsuki Research Center, Japan Tobacco Inc
| | - Seisuke Takimoto
- Central Pharmaceutical Research Institute Takatsuki Research Center, Japan Tobacco Inc
| | - Tomohiro Shimada
- Administration Department, Planning & amp; Control Division, Teijin Pharma Limited
| | - Kazuyoshi Ikeda
- Division of Physics for Life Functions, Keio University Faculty of Pharmacy
- Center for Life Science Technologies, RIKEN
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42
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Kovaltsuk A, Raybould MIJ, Wong WK, Marks C, Kelm S, Snowden J, Trück J, Deane CM. Structural diversity of B-cell receptor repertoires along the B-cell differentiation axis in humans and mice. PLoS Comput Biol 2020; 16:e1007636. [PMID: 32069281 PMCID: PMC7048297 DOI: 10.1371/journal.pcbi.1007636] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 02/28/2020] [Accepted: 01/07/2020] [Indexed: 01/18/2023] Open
Abstract
Most current analysis tools for antibody next-generation sequencing data work with primary sequence descriptors, leaving accompanying structural information unharnessed. We have used novel rapid methods to structurally characterize the complementary-determining regions (CDRs) of more than 180 million human and mouse B-cell receptor (BCR) repertoire sequences. These structurally annotated CDRs provide unprecedented insights into both the structural predetermination and dynamics of the adaptive immune response. We show that B-cell types can be distinguished based solely on these structural properties. Antigen-unexperienced BCR repertoires use the highest number and diversity of CDR structures and these patterns of naïve repertoire paratope usage are highly conserved across subjects. In contrast, more differentiated B-cells are more personalized in terms of CDR structure usage. Our results establish the CDR structure differences in BCR repertoires and have applications for many fields including immunodiagnostics, phage display library generation, and “humanness” assessment of BCR repertoires from transgenic animals. The software tool for structural annotation of BCR repertoires, SAAB+, is available at https://github.com/oxpig/saab_plus. B-cell receptors (BCR) are the major components of the adaptive immune system. These are immunoglobulin molecules that bind to foreign substances known as antigens. Each individual has a huge BCR repertoire, where each individual BCR has a specific binding site composed of the complementary-determining regions (CDRs) capable of recognising a specific antigen. Drug discovery and immunodiagnostics inspired by the adaptive immune system rely on our ability to accurately interrogate the structural diversity of the binding sites of the BCR repertoire. Here we report our novel rapid pipeline, SAAB+, which has enabled us to interrogate how the structure of the CDR changes in BCR repertoires along the B-cell differentiation axis. By analysing human and mouse BCR repertoires at an unprecedented scale, we observed species-specific structural predetermination and detected CDR dynamics across multiple stages of B-cell differentiation. We showed that naïve repertoires share the highest number and diversity of CDR structures, a pattern which was highly conserved in all B-cell donors. Our results suggest that increased B-cell differentiation is associated with a personalization of CDR structure usages. Finally, we established the differences in CDR usages between humans and mice, analysis with immediate relevance for BCR repertoire “humanness” assessment and rational immunotherapeutic engineering.
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Affiliation(s)
| | | | - Wing Ki Wong
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Claire Marks
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | | | | | - Johannes Trück
- Division of Immunology, University Children's Hospital, University of Zurich, Zurich, Switzerland
| | - Charlotte M. Deane
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- * E-mail:
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43
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Chiu ML, Goulet DR, Teplyakov A, Gilliland GL. Antibody Structure and Function: The Basis for Engineering Therapeutics. Antibodies (Basel) 2019; 8:antib8040055. [PMID: 31816964 PMCID: PMC6963682 DOI: 10.3390/antib8040055] [Citation(s) in RCA: 229] [Impact Index Per Article: 45.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 11/25/2019] [Accepted: 11/28/2019] [Indexed: 12/11/2022] Open
Abstract
Antibodies and antibody-derived macromolecules have established themselves as the mainstay in protein-based therapeutic molecules (biologics). Our knowledge of the structure–function relationships of antibodies provides a platform for protein engineering that has been exploited to generate a wide range of biologics for a host of therapeutic indications. In this review, our basic understanding of the antibody structure is described along with how that knowledge has leveraged the engineering of antibody and antibody-related therapeutics having the appropriate antigen affinity, effector function, and biophysical properties. The platforms examined include the development of antibodies, antibody fragments, bispecific antibody, and antibody fusion products, whose efficacy and manufacturability can be improved via humanization, affinity modulation, and stability enhancement. We also review the design and selection of binding arms, and avidity modulation. Different strategies of preparing bispecific and multispecific molecules for an array of therapeutic applications are included.
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Affiliation(s)
- Mark L. Chiu
- Drug Product Development Science, Janssen Research & Development, LLC, Malvern, PA 19355, USA
- Correspondence:
| | - Dennis R. Goulet
- Department of Medicinal Chemistry, University of Washington, P.O. Box 357610, Seattle, WA 98195-7610, USA;
| | - Alexey Teplyakov
- Biologics Research, Janssen Research & Development, LLC, Spring House, PA 19477, USA; (A.T.); (G.L.G.)
| | - Gary L. Gilliland
- Biologics Research, Janssen Research & Development, LLC, Spring House, PA 19477, USA; (A.T.); (G.L.G.)
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44
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Krokhotin A, Du H, Hirabayashi K, Popov K, Kurokawa T, Wan X, Ferrone S, Dotti G, Dokholyan NV. Computationally Guided Design of Single-Chain Variable Fragment Improves Specificity of Chimeric Antigen Receptors. MOLECULAR THERAPY-ONCOLYTICS 2019; 15:30-37. [PMID: 31650023 PMCID: PMC6804740 DOI: 10.1016/j.omto.2019.08.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 08/24/2019] [Indexed: 01/24/2023]
Abstract
Chimeric antigen receptor (CAR)-T cell-based immunotherapy of malignant disease relies on the specificity and association constant of single-chain variable fragments (scFvs). The latter are synthesized from parent antibodies by fusing their light (VL) and heavy (VH)-chain variable domains into a single chain using a flexible linker peptide. The fusion of VL and VH domains can distort their relative orientation, thereby compromising specificity and association constant of scFv, and reducing the lytic efficacy of CAR-T cells. Here, we circumvent the complications of domains' fusion by designing scFv mutants that stabilize interaction between scFv and its target, thereby rescuing scFv efficacy. We employ an iterative approach, based on structural modeling and mutagenesis driven by computational protein design. To demonstrate the power of this approach, we use the scFv derived from an antibody specific to a human leukocyte antigen A2 (HLA-A2)-HER2-derived peptide complex. Whereas the parental antibody is highly specific to its target, the scFv showed reduced specificity. Using our approach, we design mutations into scFvs that restore specificity of the original antibody.
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Affiliation(s)
- Andrey Krokhotin
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongwei Du
- Lineberger Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Koichi Hirabayashi
- Lineberger Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Konstantin Popov
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tomohiro Kurokawa
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Xinhui Wan
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Soldano Ferrone
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Gianpietro Dotti
- Lineberger Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Microbiology and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Nikolay V Dokholyan
- Departments of Pharmacology and Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, PA 17033, USA
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45
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Schrade A, Bujotzek A, Spick C, Wagner M, Goerl J, Wezler X, Georges G, Kontermann RE, Brinkmann U. Back-to-Germline (B2G) Procedure for Antibody Devolution. Antibodies (Basel) 2019; 8:antib8030045. [PMID: 31544851 PMCID: PMC6784197 DOI: 10.3390/antib8030045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/13/2019] [Accepted: 08/16/2019] [Indexed: 11/16/2022] Open
Abstract
Bispecific antibodies (bsAbs) with avidity-enhanced specificity can be used to address target cells with increased specificity, ideally binding efficiently to cells that express two cognate antigens, yet not to cells that express only one of those. Building blocks required to generate such bsAbs are binders that recognize the two antigens with high specificity yet with various (including very low monovalent) affinities. The herein described ‘back-to-germline’ (B2G) procedure defines such derivatives. It converts parent antibodies with high specificity to derivatives that retain specificity but modulate affinity. The approach defines mutations to be introduced into antibody complementarity-determining regions (CDRs) regions without requiring structures of antibody-antigen complexes. Instead, it reverses the B-cell maturation process that increases affinities, with preference on CDR residues with high antigen contact probability. Placing germline residues at those positions generates VH and VL domains and Fv-combinations thereof that retain specificities but are ‘de-matured’ to different degrees. De-maturation influences on-rates and off-rates, and can produce entities with extremely low affinity for which binding can only be detected in bivalent formats. A comparison with alanine replacement in CDRs (so far, the most frequently applied technology) indicates that B2G may be more reliable/predictable without introduction of stickiness or poly-reactivity. The applicability for generating sets of affinity-modulated monospecific variants is exemplarily shown for antibodies that bind CD138, Her2/neu, and EGFR.
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Affiliation(s)
- Anja Schrade
- Roche Pharma Research and Early Development (pRED), Large Molecule Research (LMR), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Alexander Bujotzek
- Roche Pharma Research and Early Development (pRED), Large Molecule Research (LMR), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Christian Spick
- Roche Pharma Research and Early Development (pRED), Large Molecule Research (LMR), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Martina Wagner
- Roche Pharma Research and Early Development (pRED), Large Molecule Research (LMR), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Johannes Goerl
- Roche Pharma Research and Early Development (pRED), Large Molecule Research (LMR), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Xenia Wezler
- Roche Pharma Research and Early Development (pRED), Large Molecule Research (LMR), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Guy Georges
- Roche Pharma Research and Early Development (pRED), Large Molecule Research (LMR), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Roland E Kontermann
- Institute of Cell Biology & Immunology, Stuttgart University, 70569 Stuttgart, Germany
| | - Ulrich Brinkmann
- Roche Pharma Research and Early Development (pRED), Large Molecule Research (LMR), Roche Innovation Center Munich, 82377 Penzberg, Germany.
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46
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Almagro JC, Pedraza-Escalona M, Arrieta HI, Pérez-Tapia SM. Phage Display Libraries for Antibody Therapeutic Discovery and Development. Antibodies (Basel) 2019; 8:antib8030044. [PMID: 31544850 PMCID: PMC6784186 DOI: 10.3390/antib8030044] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 08/14/2019] [Accepted: 08/15/2019] [Indexed: 01/24/2023] Open
Abstract
Phage display technology has played a key role in the remarkable progress of discovering and optimizing antibodies for diverse applications, particularly antibody-based drugs. This technology was initially developed by George Smith in the mid-1980s and applied by John McCafferty and Gregory Winter to antibody engineering at the beginning of 1990s. Here, we compare nine phage display antibody libraries published in the last decade, which represent the state of the art in the discovery and development of therapeutic antibodies using phage display. We first discuss the quality of the libraries and the diverse types of antibody repertoires used as substrates to build the libraries, i.e., naïve, synthetic, and semisynthetic. Second, we review the performance of the libraries in terms of the number of positive clones per panning, hit rate, affinity, and developability of the selected antibodies. Finally, we highlight current opportunities and challenges pertaining to phage display platforms and related display technologies.
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Affiliation(s)
- Juan C Almagro
- GlobalBio, Inc., 320, Cambridge, MA 02138, USA.
- UDIBI, ENCB, Instituto Politécnico Nacional, Prolongación de Carpio y Plan de Ayala S/N, Colonia Casco de Santo Tomas, Delegación Miguel Hidalgo, Ciudad de Mexico 11340, Mexico.
| | - Martha Pedraza-Escalona
- CONACyT-UDIBI, ENCB, Instituto Politécnico Nacional, Prolongación de Carpio y Plan de Ayala S/N, Colonia Casco de Santo Tomas, Delegación Miguel Hidalgo, Ciudad de Mexico 11340, Mexico
| | - Hugo Iván Arrieta
- CONACyT-UDIBI, ENCB, Instituto Politécnico Nacional, Prolongación de Carpio y Plan de Ayala S/N, Colonia Casco de Santo Tomas, Delegación Miguel Hidalgo, Ciudad de Mexico 11340, Mexico
| | - Sonia Mayra Pérez-Tapia
- CONACyT-UDIBI, ENCB, Instituto Politécnico Nacional, Prolongación de Carpio y Plan de Ayala S/N, Colonia Casco de Santo Tomas, Delegación Miguel Hidalgo, Ciudad de Mexico 11340, Mexico
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47
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Warszawski S, Borenstein Katz A, Lipsh R, Khmelnitsky L, Ben Nissan G, Javitt G, Dym O, Unger T, Knop O, Albeck S, Diskin R, Fass D, Sharon M, Fleishman SJ. Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces. PLoS Comput Biol 2019; 15:e1007207. [PMID: 31442220 PMCID: PMC6728052 DOI: 10.1371/journal.pcbi.1007207] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 09/05/2019] [Accepted: 06/21/2019] [Indexed: 11/18/2022] Open
Abstract
Antibodies developed for research and clinical applications may exhibit suboptimal stability, expressibility, or affinity. Existing optimization strategies focus on surface mutations, whereas natural affinity maturation also introduces mutations in the antibody core, simultaneously improving stability and affinity. To systematically map the mutational tolerance of an antibody variable fragment (Fv), we performed yeast display and applied deep mutational scanning to an anti-lysozyme antibody and found that many of the affinity-enhancing mutations clustered at the variable light-heavy chain interface, within the antibody core. Rosetta design combined enhancing mutations, yielding a variant with tenfold higher affinity and substantially improved stability. To make this approach broadly accessible, we developed AbLIFT, an automated web server that designs multipoint core mutations to improve contacts between specific Fv light and heavy chains (http://AbLIFT.weizmann.ac.il). We applied AbLIFT to two unrelated antibodies targeting the human antigens VEGF and QSOX1. Strikingly, the designs improved stability, affinity, and expression yields. The results provide proof-of-principle for bypassing laborious cycles of antibody engineering through automated computational affinity and stability design.
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Affiliation(s)
- Shira Warszawski
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | | | - Rosalie Lipsh
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Lev Khmelnitsky
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Gili Ben Nissan
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Gabriel Javitt
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Orly Dym
- Israel Structural Proteomics Center, Weizmann Institute of Science, Rehovot, Israel
| | - Tamar Unger
- Israel Structural Proteomics Center, Weizmann Institute of Science, Rehovot, Israel
| | - Orli Knop
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shira Albeck
- Israel Structural Proteomics Center, Weizmann Institute of Science, Rehovot, Israel
| | - Ron Diskin
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Deborah Fass
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Michal Sharon
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Sarel J. Fleishman
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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48
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Lepore R, Olimpieri PP, Messih MA, Tramontano A. PIGSPro: prediction of immunoGlobulin structures v2. Nucleic Acids Res 2019; 45:W17-W23. [PMID: 28472367 PMCID: PMC5570210 DOI: 10.1093/nar/gkx334] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2017] [Accepted: 04/19/2017] [Indexed: 01/14/2023] Open
Abstract
PIGSpro is a significant upgrade of the popular PIGS server for the prediction of the structure of immunoglobulins. The software has been completely rewritten in python following a similar pipeline as in the original method, but including, at various steps, relevant modifications found to improve its prediction accuracy, as demonstrated here. The steps of the pipeline include the selection of the appropriate framework for predicting the conserved regions of the molecule by homology; the target template alignment for this portion of the molecule; the selection of the main chain conformation of the hypervariable loops according to the canonical structure model, the prediction of the third loop of the heavy chain (H3) for which complete canonical structures are not available and the packing of the light and heavy chain if derived from different templates. Each of these steps has been improved including updated methods developed along the years. Last but not least, the user interface has been completely redesigned and an automatic monthly update of the underlying database has been implemented. The method is available as a web server at http://biocomputing.it/pigspro.
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Affiliation(s)
- Rosalba Lepore
- Department of Physics, Sapienza University, Piazzale Aldo Moro 500-184 Rome, Italy.,Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Viale Regina Elena 291, 00161 Rome, Italy
| | - Pier P Olimpieri
- Department of Physics, Sapienza University, Piazzale Aldo Moro 500-184 Rome, Italy
| | - Mario A Messih
- Department of Physics, Sapienza University, Piazzale Aldo Moro 500-184 Rome, Italy
| | - Anna Tramontano
- Department of Physics, Sapienza University, Piazzale Aldo Moro 500-184 Rome, Italy.,Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Viale Regina Elena 291, 00161 Rome, Italy
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49
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Finlay WJ, Lugovskoy AA. De novo discovery of antibody drugs - great promise demands scrutiny. MAbs 2019; 11:809-811. [PMID: 31122133 PMCID: PMC6601558 DOI: 10.1080/19420862.2019.1622926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 05/01/2019] [Accepted: 05/21/2019] [Indexed: 01/10/2023] Open
Abstract
We live in an era of rapidly advancing computing capacity and algorithmic sophistication. "Big data" and "artificial intelligence"find progressively wider use in all spheres of human activity, including healthcare. A diverse array of computational technologies is being applied with increasing frequency to antibody drug research and development (R&D). Their successful applications are met with great interest due to the potential for accelerating and streamlining the antibody R&D process. While this excitement is very likely justified in the long term, it is less likely that the transition from the first use to routine practice will escape challenges that other new technologies had experienced before they began to blossom. This transition typically requires many cycles of iterative learning that rely on the deconstruction of the technology to understand its pitfalls and define vectors for optimization. The study by Vasquez et al. identifies a key obstacle to such learning: the lack of transparency regarding methodology in computational antibody design reports, which has the potential to mislead the community efforts.
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50
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Leem J, Deane CM. High-Throughput Antibody Structure Modeling and Design Using ABodyBuilder. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2019; 1851:367-380. [PMID: 30298409 DOI: 10.1007/978-1-4939-8736-8_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Antibodies are proteins of the adaptive immune system; they can be designed to bind almost any molecule, and are increasingly being used as biotherapeutics. Experimental antibody design is an expensive and time-consuming process, and computational antibody design methods can now be used to help develop new therapeutics and diagnostics. Within the design pipeline, accurate antibody structure modeling is essential, as it provides the basis for antibody-antigen docking, binding affinity prediction, and estimating thermal stability. Ideally, models should be rapidly generated, allowing the exploration of the breadth of antibody space. This allows methods to replicate the natural processes of antibody diversification (e.g., V(D)J recombination and somatic hypermutation), and cope with large volumes of data that are typical of next-generation sequencing datasets. Here we describe ABodyBuilder and PEARS, algorithms that build and mutate antibody model structures. These methods take ~30 s to generate a model antibody structure.
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Affiliation(s)
- Jinwoo Leem
- Department of Statistics, University of Oxford, Oxford, UK
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