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Wang X, Gao X, Fan X, Huai Z, Zhang G, Yao M, Wang T, Huang X, Lai L. WUREN: Whole-modal union representation for epitope prediction. Comput Struct Biotechnol J 2024; 23:2122-2131. [PMID: 38817963 PMCID: PMC11137340 DOI: 10.1016/j.csbj.2024.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
B-cell epitope identification plays a vital role in the development of vaccines, therapies, and diagnostic tools. Currently, molecular docking tools in B-cell epitope prediction are heavily influenced by empirical parameters and require significant computational resources, rendering a great challenge to meet large-scale prediction demands. When predicting epitopes from antigen-antibody complex, current artificial intelligence algorithms cannot accurately implement the prediction due to insufficient protein feature representations, indicating novel algorithm is desperately needed for efficient protein information extraction. In this paper, we introduce a multimodal model called WUREN (Whole-modal Union Representation for Epitope predictioN), which effectively combines sequence, graph, and structural features. It achieved AUC-PR scores of 0.213 and 0.193 on the solved structures and AlphaFold-generated structures, respectively, for the independent test proteins selected from DiscoTope3 benchmark. Our findings indicate that WUREN is an efficient feature extraction model for protein complexes, with the generalizable application potential in the development of protein-based drugs. Moreover, the streamlined framework of WUREN could be readily extended to model similar biomolecules, such as nucleic acids, carbohydrates, and lipids.
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Affiliation(s)
| | | | - Xuezhe Fan
- XtalPi Innovation Center, Beijing, China
| | - Zhe Huai
- XtalPi Innovation Center, Beijing, China
| | | | | | | | | | - Lipeng Lai
- XtalPi Innovation Center, Beijing, China
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2
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McCoy KM, Ackerman ME, Grigoryan G. A comparison of antibody-antigen complex sequence-to-structure prediction methods and their systematic biases. Protein Sci 2024; 33:e5127. [PMID: 39167052 PMCID: PMC11337930 DOI: 10.1002/pro.5127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/24/2024] [Accepted: 07/14/2024] [Indexed: 08/23/2024]
Abstract
The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer and RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower quality display significant structural biases at the level of tertiary motifs (TERMs) toward having fewer structural matches in non-antibody-containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that the scarcity of interfacial geometry data in the structural database may currently limit the application of ML to the prediction of antibody-antigen interactions.
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Affiliation(s)
- Katherine Maia McCoy
- Molecular and Cell Biology Graduate ProgramDartmouth CollegeHanoverNew HampshireUSA
| | - Margaret E. Ackerman
- Molecular and Cell Biology Graduate ProgramDartmouth CollegeHanoverNew HampshireUSA
- Thayer School of EngineeringDartmouth CollegeHanoverNew HampshireUSA
| | - Gevorg Grigoryan
- Molecular and Cell Biology Graduate ProgramDartmouth CollegeHanoverNew HampshireUSA
- Department of Computer ScienceDartmouth CollegeHanoverNew HampshireUSA
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3
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He H, He B, Guan L, Zhao Y, Jiang F, Chen G, Zhu Q, Chen CYC, Li T, Yao J. De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model. Nat Commun 2024; 15:6867. [PMID: 39127753 PMCID: PMC11316817 DOI: 10.1038/s41467-024-50903-y] [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: 11/14/2023] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Artificial Intelligence (AI) techniques have made great advances in assisting antibody design. However, antibody design still heavily relies on isolating antigen-specific antibodies from serum, which is a resource-intensive and time-consuming process. To address this issue, we propose a Pre-trained Antibody generative large Language Model (PALM-H3) for the de novo generation of artificial antibodies heavy chain complementarity-determining region 3 (CDRH3) with desired antigen-binding specificity, reducing the reliance on natural antibodies. We also build a high-precision model antigen-antibody binder (A2binder) that pairs antigen epitope sequences with antibody sequences to predict binding specificity and affinity. PALM-H3-generated antibodies exhibit binding ability to SARS-CoV-2 antigens, including the emerging XBB variant, as confirmed through in-silico analysis and in-vitro assays. The in-vitro assays validate that PALM-H3-generated antibodies achieve high binding affinity and potent neutralization capability against spike proteins of SARS-CoV-2 wild-type, Alpha, Delta, and the emerging XBB variant. Meanwhile, A2binder demonstrates exceptional predictive performance on binding specificity for various epitopes and variants. Furthermore, by incorporating the attention mechanism inherent in the Roformer architecture into the PALM-H3 model, we improve its interpretability, providing crucial insights into the fundamental principles of antibody design.
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Affiliation(s)
- Haohuai He
- AI Lab, Tencent, Shenzhen, 518052, China
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Bing He
- AI Lab, Tencent, Shenzhen, 518052, China.
| | - Lei Guan
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Xi'an, China
| | - Yu Zhao
- AI Lab, Tencent, Shenzhen, 518052, China
| | - Feng Jiang
- AI Lab, Tencent, Shenzhen, 518052, China
| | - Guanxing Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Qingge Zhu
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Xi'an, China
| | - Calvin Yu-Chian Chen
- AI for Science (AI4S)-Preferred Program, School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
- State Key Laboratory of Chemical Oncogenomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
- Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan.
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354, Taiwan.
- Guangdong L-Med Biotechnology Co. Ltd, Meizhou, 514699, Guangdong, China.
| | - Ting Li
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Xi'an, China.
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4
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Nasaev SS, Mukanov AR, Mishkorez IV, Kuznetsov II, Leibin IV, Dolgusheva VA, Pavlyuk GA, Manasyan AL, Veselovsky AV. Molecular Modeling Methods in the Development of Affine and Specific Protein-Binding Agents. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:1451-1473. [PMID: 39245455 DOI: 10.1134/s0006297924080066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/12/2024] [Accepted: 07/11/2024] [Indexed: 09/10/2024]
Abstract
High-affinity and specific agents are widely applied in various areas, including diagnostics, scientific research, and disease therapy (as drugs and drug delivery systems). It takes significant time to develop them. For this reason, development of high-affinity agents extensively utilizes computer methods at various stages for the analysis and modeling of these molecules. The review describes the main affinity and specific agents, such as monoclonal antibodies and their fragments, antibody mimetics, aptamers, and molecularly imprinted polymers. The methods of their obtaining as well as their main advantages and disadvantages are briefly described, with special attention focused on the molecular modeling methods used for their analysis and development.
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Affiliation(s)
| | - Artem R Mukanov
- Research & Development Department, Xelari Ltd., Moscow, 121601, Russia
| | - Ivan V Mishkorez
- Research & Development Department, Xelari Ltd., Moscow, 121601, Russia
- Institute of Biomedical Chemistry, Moscow, 119121, Russia
| | - Ivan I Kuznetsov
- Research & Development Department, Xelari Ltd., Moscow, 121601, Russia
| | - Iosif V Leibin
- Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Moscow, 121205, Russia
| | | | - Gleb A Pavlyuk
- Research & Development Department, Xelari Ltd., Moscow, 121601, Russia
| | - Artem L Manasyan
- Research & Development Department, Xelari Ltd., Moscow, 121601, Russia
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5
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Liang T, Sun ZY, Hines MG, Penrose KJ, Hao Y, Chu X, Mellors JW, Dimitrov DS, Xie XQ, Li W, Feng Z. AI-based IsAb2.0 for antibody design. Brief Bioinform 2024; 25:bbae445. [PMID: 39285513 PMCID: PMC11405125 DOI: 10.1093/bib/bbae445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/06/2024] [Accepted: 08/27/2024] [Indexed: 09/22/2024] Open
Abstract
Therapeutic antibody design has garnered widespread attention, highlighting its interdisciplinary importance. Advancements in technology emphasize the critical role of designing nanobodies and humanized antibodies in antibody engineering. However, current experimental methods are costly and time-consuming. Computational approaches, while progressing, faced limitations due to insufficient structural data and the absence of a standardized protocol. To tackle these challenges, our lab previously developed IsAb1.0, an in silico antibody design protocol. Yet, IsAb1.0 lacked accuracy, had a complex procedure, and required extensive antibody bioinformation. Moreover, it overlooked nanobody and humanized antibody design, hindering therapeutic antibody development. Building upon IsAb1.0, we enhanced our design protocol with artificial intelligence methods to create IsAb2.0. IsAb2.0 utilized AlphaFold-Multimer (2.3/3.0) for accurate modeling and complex construction without templates and employed the precise FlexddG method for in silico antibody optimization. Validated through optimization of a humanized nanobody J3 (HuJ3) targeting HIV-1 gp120, IsAb2.0 predicted five mutations that can improve HuJ3-gp120 binding affinity. These predictions were confirmed by commercial software and validated through binding and neutralization assays. IsAb2.0 streamlined antibody design, offering insights into future techniques to accelerate immunotherapy development.
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Affiliation(s)
- Tianjian Liang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Ze-Yu Sun
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Margaret G Hines
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Kerri Jo Penrose
- Division of Infectious Diseases, Department of Medicine, Center for AIDS Research, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Yixuan Hao
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Xiaojie Chu
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - John W Mellors
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
- Division of Infectious Diseases, Department of Medicine, Center for AIDS Research, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Dimiter S Dimitrov
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
| | - Wei Li
- Division of Infectious Diseases, Department of Medicine, Center for Antibody Therapeutics, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, United States
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States
- National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Drug Discovery Institute, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA 15261, United States
- Department of Computational Biology, School of Medicine, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15261, United States
- Department of Structural Biology, School of Medicine, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, PA 15261, United States
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6
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Ananya, Panchariya DC, Karthic A, Singh SP, Mani A, Chawade A, Kushwaha S. Vaccine design and development: Exploring the interface with computational biology and AI. Int Rev Immunol 2024:1-20. [PMID: 38982912 DOI: 10.1080/08830185.2024.2374546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 06/26/2024] [Indexed: 07/11/2024]
Abstract
Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.
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Affiliation(s)
- Ananya
- National Institute of Animal Biotechnology, Hyderabad, India
| | | | | | | | - Ashutosh Mani
- Motilal Nehru National Institute of Technology, Prayagraj, India
| | - Aakash Chawade
- Swedish University of Agricultural Sciences, Alnarp, Sweden
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7
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McCoy KM, Ackerman ME, Grigoryan G. A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585121. [PMID: 38979267 PMCID: PMC11230293 DOI: 10.1101/2024.03.15.585121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer, RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower-quality display significant structural biases at the level of tertiary motifs (TERMs) towards having fewer structural matches in non-antibody containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that scarcity of interfacial geometry data in the structural database may currently limit application of machine learning to the prediction of antibody-antigen interactions.
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Affiliation(s)
- Katherine Maia McCoy
- Molecular and Cell Biology Graduate Program, Dartmouth College, Hanover, New Hampshire, USA
| | - Margaret E Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
- Molecular and Cell Biology Graduate Program, Dartmouth College, Hanover, New Hampshire, USA
| | - Gevorg Grigoryan
- Department of Computer Science, Dartmouth College, Hanover, New Hampshire, USA
- Molecular and Cell Biology Graduate Program, Dartmouth College, Hanover, New Hampshire, USA
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8
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Cheng J, Liang T, Xie XQ, Feng Z, Meng L. A new era of antibody discovery: an in-depth review of AI-driven approaches. Drug Discov Today 2024; 29:103984. [PMID: 38642702 DOI: 10.1016/j.drudis.2024.103984] [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: 10/12/2023] [Revised: 04/02/2024] [Accepted: 04/15/2024] [Indexed: 04/22/2024]
Abstract
Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.
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Affiliation(s)
- Jin Cheng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, 224005, China
| | - Tianjian Liang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Li Meng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, 224005, China.
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9
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Lima CP, Barreiros GM, Oliveira ASA, de Souza MM, Manieri TM, Moro AM. A Dual Strategy-In Vitro and In Silico-To Evaluate Human Antitetanus mAbs Addressing Their Potential Protective Action on TeNT Endocytosis in Primary Rat Neuronal Cells. Int J Mol Sci 2024; 25:5788. [PMID: 38891974 PMCID: PMC11171557 DOI: 10.3390/ijms25115788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/01/2024] [Accepted: 05/10/2024] [Indexed: 06/21/2024] Open
Abstract
Tetanus disease, caused by C. tetani, starts with wounds or mucous layer contact. Prevented by vaccination, the lack of booster shots throughout life requires prophylactic treatment in case of accidents. The incidence of tetanus is high in underdeveloped countries, requiring the administration of antitetanus antibodies, usually derived from immunized horses or humans. Heterologous sera represent risks such as serum sickness. Human sera can carry unknown viruses. In the search for human monoclonal antibodies (mAbs) against TeNT (Tetanus Neurotoxin), we previously identified a panel of mAbs derived from B-cell sorting, selecting two nonrelated ones that binded to the C-terminal domain of TeNT (HCR/T), inhibiting its interaction with the cellular receptor ganglioside GT1b. Here, we present the results of cellular assays and molecular docking tools. TeNT internalization in neurons is prevented by more than 50% in neonatal rat spinal cord cells, determined by quantitative analysis of immunofluorescence punctate staining of Alexa Fluor 647 conjugated to TeNT. We also confirmed the mediator role of the Synaptic Vesicle Glycoprotein II (SV2) in TeNT endocytosis. The molecular docking assays to predict potential TeNT epitopes showed the binding of both antibodies to the HCR/T domain. A higher incidence was found between N1153 and W1297 when evaluating candidate residues for conformational epitope.
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Affiliation(s)
- Cauã Pacheco Lima
- Laboratory of Biopharmaceuticals, Butantan Institute, Sao Paulo 05503-900, Brazil; (C.P.L.); (G.M.B.); (A.S.A.O.)
- Interunits Graduate Program in Biotechnology, University of Sao Paulo, Sao Paulo 05508-270, Brazil
| | - Gabriela Massaro Barreiros
- Laboratory of Biopharmaceuticals, Butantan Institute, Sao Paulo 05503-900, Brazil; (C.P.L.); (G.M.B.); (A.S.A.O.)
- Interunits Graduate Program in Biotechnology, University of Sao Paulo, Sao Paulo 05508-270, Brazil
| | - Adriele Silva Alves Oliveira
- Laboratory of Biopharmaceuticals, Butantan Institute, Sao Paulo 05503-900, Brazil; (C.P.L.); (G.M.B.); (A.S.A.O.)
| | - Marcelo Medina de Souza
- CENTD—Centre of Excellence in New Target Discovery, Butantan Institute, São Paulo 05503-900, Brazil;
| | - Tania Maria Manieri
- Laboratory of Biopharmaceuticals, Butantan Institute, Sao Paulo 05503-900, Brazil; (C.P.L.); (G.M.B.); (A.S.A.O.)
- CeRDI—Center for Research and Development in Immunobiologicals, Butantan Institute, São Paulo 05503-900, Brazil
| | - Ana Maria Moro
- Laboratory of Biopharmaceuticals, Butantan Institute, Sao Paulo 05503-900, Brazil; (C.P.L.); (G.M.B.); (A.S.A.O.)
- CeRDI—Center for Research and Development in Immunobiologicals, Butantan Institute, São Paulo 05503-900, Brazil
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10
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Li D, Pucci F, Rooman M. Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks. Int J Mol Sci 2024; 25:5434. [PMID: 38791470 PMCID: PMC11121317 DOI: 10.3390/ijms25105434] [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: 04/02/2024] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024] Open
Abstract
Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.
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Affiliation(s)
- Dong Li
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (D.L.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (D.L.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (D.L.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
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11
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Paul R, Kasahara K, Sasaki J, Pérez JF, Matsunaga R, Hashiguchi T, Kuroda D, Tsumoto K. Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction. Front Mol Biosci 2024; 10:1302737. [PMID: 38495738 PMCID: PMC10941800 DOI: 10.3389/fmolb.2023.1302737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/11/2023] [Indexed: 03/19/2024] Open
Abstract
Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.
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Affiliation(s)
- Rimpa Paul
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
- Research Center of Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan
| | - Keisuke Kasahara
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Jiei Sasaki
- Institute for Life and Medical Sciences, Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Jorge Fernández Pérez
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Ryo Matsunaga
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Takao Hashiguchi
- Institute for Life and Medical Sciences, Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
- Research Center of Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan
- The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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12
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Zhao N, Han B, Zhao C, Xu J, Gong X. ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody-antigen computational docking. Brief Bioinform 2024; 25:bbae048. [PMID: 38385879 PMCID: PMC10883643 DOI: 10.1093/bib/bbae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/05/2024] [Accepted: 01/15/2024] [Indexed: 02/23/2024] Open
Abstract
Accurate prediction of antibody-antigen complex structures is pivotal in drug discovery, vaccine design and disease treatment and can facilitate the development of more effective therapies and diagnostics. In this work, we first review the antibody-antigen docking (ABAG-docking) datasets. Then, we present the creation and characterization of a comprehensive benchmark dataset of antibody-antigen complexes. We categorize the dataset based on docking difficulty, interface properties and structural characteristics, to provide a diverse set of cases for rigorous evaluation. Compared with Docking Benchmark 5.5, we have added 112 cases, including 14 single-domain antibody (sdAb) cases and 98 monoclonal antibody (mAb) cases, and also increased the proportion of Difficult cases. Our dataset contains diverse cases, including human/humanized antibodies, sdAbs, rodent antibodies and other types, opening the door to better algorithm development. Furthermore, we provide details on the process of building the benchmark dataset and introduce a pipeline for periodic updates to keep it up to date. We also utilize multiple complex prediction methods including ZDOCK, ClusPro, HDOCK and AlphaFold-Multimer for testing and analyzing this dataset. This benchmark serves as a valuable resource for evaluating and advancing docking computational methods in the analysis of antibody-antigen interaction, enabling researchers to develop more accurate and effective tools for predicting and designing antibody-antigen complexes. The non-redundant ABAG-docking structure benchmark dataset is available at https://github.com/Zhaonan99/Antibody-antigen-complex-structure-benchmark-dataset.
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Affiliation(s)
- Nan Zhao
- Institute for Mathematical Sciences, School of Mathematics, Renmin University of China, Beijing, China
| | - Bingqing Han
- Institute for Mathematical Sciences, School of Mathematics, Renmin University of China, Beijing, China
| | - Cuicui Zhao
- Institute for Mathematical Sciences, School of Mathematics, Renmin University of China, Beijing, China
| | - Jinbo Xu
- MoleculeMind Ltd., Beijing, China
| | - Xinqi Gong
- Institute for Mathematical Sciences, School of Mathematics, Renmin University of China, Beijing, China
- Beijing Academy of Artificial Intelligence, Beijing, China
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13
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Yin R, Pierce BG. Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy. Protein Sci 2024; 33:e4865. [PMID: 38073135 PMCID: PMC10751731 DOI: 10.1002/pro.4865] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 12/26/2023]
Abstract
High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.
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Affiliation(s)
- Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Department of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology ResearchRockvilleMarylandUSA
- Department of Cell Biology and Molecular GeneticsUniversity of MarylandCollege ParkMarylandUSA
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14
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Keri D, Walker M, Singh I, Nishikawa K, Garces F. Next generation of multispecific antibody engineering. Antib Ther 2024; 7:37-52. [PMID: 38235376 PMCID: PMC10791046 DOI: 10.1093/abt/tbad027] [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: 07/31/2023] [Revised: 10/16/2023] [Accepted: 11/15/2023] [Indexed: 01/19/2024] Open
Abstract
Multispecific antibodies recognize two or more epitopes located on the same or distinct targets. This added capability through protein design allows these man-made molecules to address unmet medical needs that are no longer possible with single targeting such as with monoclonal antibodies or cytokines alone. However, the approach to the development of these multispecific molecules has been met with numerous road bumps, which suggests that a new workflow for multispecific molecules is required. The investigation of the molecular basis that mediates the successful assembly of the building blocks into non-native quaternary structures will lead to the writing of a playbook for multispecifics. This is a must do if we are to design workflows that we can control and in turn predict success. Here, we reflect on the current state-of-the-art of therapeutic biologics and look at the building blocks, in terms of proteins, and tools that can be used to build the foundations of such a next-generation workflow.
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Affiliation(s)
- Daniel Keri
- Department of Protein Therapeutics, Research, Gilead Research, 324 Lakeside Dr, Foster City, CA 94404, USA
| | - Matt Walker
- Department of Protein Therapeutics, Research, Gilead Research, 324 Lakeside Dr, Foster City, CA 94404, USA
| | - Isha Singh
- Department of Protein Therapeutics, Research, Gilead Research, 324 Lakeside Dr, Foster City, CA 94404, USA
| | - Kyle Nishikawa
- Department of Protein Therapeutics, Research, Gilead Research, 324 Lakeside Dr, Foster City, CA 94404, USA
| | - Fernando Garces
- Department of Protein Therapeutics, Research, Gilead Research, 324 Lakeside Dr, Foster City, CA 94404, USA
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15
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Sekar TV, Elghonaimy EA, Swancutt KL, Diegeler S, Gonzalez I, Hamilton C, Leung PQ, Meiler J, Martina CE, Whitney M, Aguilera TA. Simultaneous selection of nanobodies for accessible epitopes on immune cells in the tumor microenvironment. Nat Commun 2023; 14:7473. [PMID: 37978291 PMCID: PMC10656474 DOI: 10.1038/s41467-023-43038-z] [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: 02/16/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023] Open
Abstract
In the rapidly advancing field of synthetic biology, there exists a critical need for technology to discover targeting moieties for therapeutic biologics. Here we present INSPIRE-seq, an approach that utilizes a nanobody library and next-generation sequencing to identify nanobodies selected for complex environments. INSPIRE-seq enables the parallel enrichment of immune cell-binding nanobodies that penetrate the tumor microenvironment. Clone enrichment and specificity vary across immune cell subtypes in the tumor, lymph node, and spleen. INSPIRE-seq identifies a dendritic cell binding clone that binds PHB2. Single-cell RNA sequencing reveals a connection with cDC1s, and immunofluorescence confirms nanobody-PHB2 colocalization along cell membranes. Structural modeling and docking studies assist binding predictions and will guide nanobody selection. In this work, we demonstrate that INSPIRE-seq offers an unbiased approach to examine complex microenvironments and assist in the development of nanobodies, which could serve as active drugs, modified to become drugs, or used as targeting moieties.
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Affiliation(s)
- Thillai V Sekar
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Microbiology, Pondicherry University, Kalapet, Puducherry, India
| | - Eslam A Elghonaimy
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Katy L Swancutt
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Sebastian Diegeler
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Isaac Gonzalez
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Cassandra Hamilton
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Peter Q Leung
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jens Meiler
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Germany
| | - Cristina E Martina
- Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Michael Whitney
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA
| | - Todd A Aguilera
- Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX, USA.
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16
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Gaudreault F, Corbeil CR, Sulea T. Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2. Sci Rep 2023; 13:15107. [PMID: 37704686 PMCID: PMC10499836 DOI: 10.1038/s41598-023-42090-5] [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: 02/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023] Open
Abstract
Predicting the structure of antibody-antigen complexes has tremendous value in biomedical research but unfortunately suffers from a poor performance in real-life applications. AlphaFold2 (AF2) has provided renewed hope for improvements in the field of protein-protein docking but has shown limited success against antibody-antigen complexes due to the lack of co-evolutionary constraints. In this study, we used physics-based protein docking methods for building decoy sets consisting of low-energy docking solutions that were either geometrically close to the native structure (positives) or not (negatives). The docking models were then fed into AF2 to assess their confidence with a novel composite score based on normalized pLDDT and pTMscore metrics after AF2 structural refinement. We show benefits of the AF2 composite score for rescoring docking poses both in terms of (1) classification of positives/negatives and of (2) success rates with particular emphasis on early enrichment. Docking models of at least medium quality present in the decoy set, but not necessarily highly ranked by docking methods, benefitted most from AF2 rescoring by experiencing large advances towards the top of the reranked list of models. These improvements, obtained without any calibration or novel methodologies, led to a notable level of performance in antibody-antigen unbound docking that was never achieved previously.
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Affiliation(s)
- Francis Gaudreault
- Human Health Therapeutics Research Centre, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC, H4P 2R2, Canada
| | - Christopher R Corbeil
- Human Health Therapeutics Research Centre, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC, H4P 2R2, Canada
| | - Traian Sulea
- Human Health Therapeutics Research Centre, National Research Council Canada, 6100 Royalmount Avenue, Montreal, QC, H4P 2R2, Canada.
- Institute of Parasitology, McGill University, 21111 Lakeshore Road, Sainte-Anne-de-Bellevue, QC, H9X 3V9, Canada.
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17
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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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18
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Bauer J, Rajagopal N, Gupta P, Gupta P, Nixon AE, Kumar S. How can we discover developable antibody-based biotherapeutics? Front Mol Biosci 2023; 10:1221626. [PMID: 37609373 PMCID: PMC10441133 DOI: 10.3389/fmolb.2023.1221626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.
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Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
| | - Nandhini Rajagopal
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Priyanka Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Pankaj Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Andrew E. Nixon
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Sandeep Kumar
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
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19
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Yin R, Pierce BG. Evaluation of AlphaFold Antibody-Antigen Modeling with Implications for Improving Predictive Accuracy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547832. [PMID: 37461571 PMCID: PMC10349958 DOI: 10.1101/2023.07.05.547832] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 429 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. We show the importance of bound-like component modeling in complex assembly accuracy, and that the current version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training may further improve its performance.
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Affiliation(s)
- Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
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20
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Chen S, Liang Q, Zhuo Y, Hong Q. Human-murine chimeric autoantibodies with high affinity and specificity for systemic sclerosis. Front Immunol 2023; 14:1127849. [PMID: 37398644 PMCID: PMC10311643 DOI: 10.3389/fimmu.2023.1127849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/07/2023] [Indexed: 07/04/2023] Open
Abstract
Scleroderma 70 (Scl-70) is commonly used in the clinic for aiding systemic sclerosis (SSc) diagnosis due to its recognition as autoantibodies in the serum of SSc patients. However, obtaining sera positive for anti-Scl-70 antibody can be challenging; therefore, there is an urgent need to develop a specific, sensitive, and easily available reference for SSc diagnosis. In this study, murine-sourced scFv library were screened by phage display technology against human Scl-70, and the scFvs with high affinity were constructed into humanized antibodies for clinical application. Finally, ten high-affinity scFv fragments were obtained. Three fragments (2A, 2AB, and 2HD) were select for humanization. The physicochemical properties of the amino acid sequence, three-dimensional structural basis, and electrostatic potential distribution of the protein surface of different scFv fragments revealed differences in the electrostatic potential of their CDR regions determined their affinity for Scl-70 and expression. Notably, the specificity test showed the half-maximal effective concentration values of the three humanized antibodies were lower than that of positive patient serum. Moreover, these humanized antibodies showed high specificity for Scl-70 in diagnostic immunoassays for ANA. Among these three antibodies, 2A exhibited most positive electrostatic potential on the surface of the CDRs and highest affinity and specificity for Scl-70 but with least expression level; thus, it may provide new foundations for developing enhanced diagnostic strategies for SSc.
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Affiliation(s)
- Sunhui Chen
- Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
- Department of Pharmacy, Fujian Provincial Hospital, Fuzhou, China
| | - Qiong Liang
- Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
- Department of Pharmacy, Fujian Provincial Hospital, Fuzhou, China
| | - Yanhang Zhuo
- Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
- Center for Experimental Research in Clinical Medicine, Fujian Provincial Hospital, Fuzhou, China
| | - Qin Hong
- Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China
- Center for Experimental Research in Clinical Medicine, Fujian Provincial Hospital, Fuzhou, China
- Fujian Provincial Key Laboratory of Critical Care Medicine, Fujian Provincial Hospital, Fuzhou, China
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21
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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: 61] [Impact Index Per Article: 61.0] [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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22
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Shabani S, Rashidi M, Radgoudarzi S, Jebali A. The validation of artificial anti-monkeypox antibodies by in silico and experimental approaches. Immun Inflamm Dis 2023; 11:e834. [PMID: 37102640 PMCID: PMC10091375 DOI: 10.1002/iid3.834] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/06/2023] [Accepted: 03/25/2023] [Indexed: 04/28/2023] Open
Abstract
As a result of smallpox immunization programs that ended more than 40 years ago, a significant portion of the world's population is not immune. Moreover, due to the lack of anti-monkeypox drugs and vaccines against monkeypox, the spread of this virus may be the beginning of another challenge. In this study, novel antibodies against monkeypox virus were modeled based on a heavy chain of human antibody and a small peptide fragment. Docking of modeled antibodies with C19L protein showed the range of docking energy, and root-mean-square deviation (RMSD) was from -124 to -154 kcal/mL and 4-6 angstrom, respectively. Also, docking of modeled antibodies-C19L complex with gamma Fc receptor type I illustrated the range of docking energy, and RMSD was from -132 to -155 kcal/ml and 5-7 angstrom, respectively. Moreover, molecular dynamics simulation showed that antibody 62 had the highest stability with the lowest energy level and RMSD. Interestingly, no modeled antibodies had immunogenicity, allergenicity, and toxicity. Although all of them had good stability, only antibodies 25, 28, 54, and 62 had a half-life of >10 h. Moreover, the interaction between C19L protein and anti-C19L antibodies (wild-type and synthetic) was evaluated by the SPR method. We found that KD in synthetic antibodies was lower than wild antibody. In terms of δH°, TδS°, and δG°, the results were consistent with binding parameters. Here, the lowest value of thermodynamic parameters was obtained for antibody 62. These data show that the synthetic antibodies, especially antibody 62, had a higher affinity than the wild-type antibody.
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Affiliation(s)
- Sadeq Shabani
- Department of Biological SciencesFlorida International UniversityMiamiFloridaUSA
- Biomolecular Science InstituteFlorida International UniversityMiamiFloridaUSA
| | - Mohsen Rashidi
- Department Pharmacology, Faculty of MedicineMazandaran University of Medical SciencesSariIran
- The Health of Plant and Livestock Products Research CenterMazandaran University of Medical SciencesSariIran
| | - Shakila Radgoudarzi
- I.M. Sechenov First Moscow State Medical University (Первый МГМУ им)MoscowRussia
| | - Ali Jebali
- Department of Medical Nanotechnology, Faculty of Advanced Sciences and Technology, Tehran Medical ScienceIslamic Azad UniversityTehranIran
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23
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Boorla VS, Chowdhury R, Ramasubramanian R, Ameglio B, Frick R, Gray JJ, Maranas CD. De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD). Proteins 2023; 91:196-208. [PMID: 36111441 PMCID: PMC9538105 DOI: 10.1002/prot.26422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/17/2022] [Accepted: 09/06/2022] [Indexed: 01/11/2023]
Abstract
The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.
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Affiliation(s)
- Veda Sheersh Boorla
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802
| | - Ratul Chowdhury
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802
| | | | - Brandon Ameglio
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Rahel Frick
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park. PA 16802
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24
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Khan A, Cowen-Rivers AI, Grosnit A, Deik DGX, Robert PA, Greiff V, Smorodina E, Rawat P, Akbar R, Dreczkowski K, Tutunov R, Bou-Ammar D, Wang J, Storkey A, Bou-Ammar H. Toward real-world automated antibody design with combinatorial Bayesian optimization. CELL REPORTS METHODS 2023; 3:100374. [PMID: 36814835 PMCID: PMC9939385 DOI: 10.1016/j.crmeth.2022.100374] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/08/2022] [Accepted: 12/07/2022] [Indexed: 06/14/2023]
Abstract
Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.
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Affiliation(s)
- Asif Khan
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | | | | | | | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Eva Smorodina
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | - Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo 0315, Norway
| | | | | | - Dany Bou-Ammar
- American University of Beirut Medical Centre, Beirut 11-0236, Lebanon
| | - Jun Wang
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
| | - Amos Storkey
- School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Haitham Bou-Ammar
- Huawei Noah’s Ark Lab, London N1C 4AG, UK
- University College London, London WC1E 6BT, UK
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25
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Cohen T, Halfon M, Carter L, Sharkey B, Jain T, Sivasubramanian A, Schneidman-Duhovny D. Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models. Methods Enzymol 2022; 678:237-262. [PMID: 36641210 DOI: 10.1016/bs.mie.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD<4Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.
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Affiliation(s)
- Tomer Cohen
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Matan Halfon
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lester Carter
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, United States
| | - Beth Sharkey
- High-Throughput Expression, Adimab LLC, Lebanon, NH, United States
| | - Tushar Jain
- Computational Biology, Adimab LLC, Palo Alto, CA, United States
| | | | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
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26
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Ye C, Hu W, Gaeta B. Prediction of Antibody-Antigen Binding via Machine Learning: Development of Data Sets and Evaluation of Methods. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2022; 3:e29404. [PMID: 38935962 PMCID: PMC11135222 DOI: 10.2196/29404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 09/23/2021] [Accepted: 10/18/2022] [Indexed: 06/29/2024]
Abstract
BACKGROUND The mammalian immune system is able to generate antibodies against a huge variety of antigens, including bacteria, viruses, and toxins. The ultradeep DNA sequencing of rearranged immunoglobulin genes has considerable potential in furthering our understanding of the immune response, but it is limited by the lack of a high-throughput, sequence-based method for predicting the antigen(s) that a given immunoglobulin recognizes. OBJECTIVE As a step toward the prediction of antibody-antigen binding from sequence data alone, we aimed to compare a range of machine learning approaches that were applied to a collated data set of antibody-antigen pairs in order to predict antibody-antigen binding from sequence data. METHODS Data for training and testing were extracted from the Protein Data Bank and the Coronavirus Antibody Database, and additional antibody-antigen pair data were generated by using a molecular docking protocol. Several machine learning methods, including the weighted nearest neighbor method, the nearest neighbor method with the BLOSUM62 matrix, and the random forest method, were applied to the problem. RESULTS The final data set contained 1157 antibodies and 57 antigens that were combined in 5041 antibody-antigen pairs. The best performance for the prediction of interactions was obtained by using the nearest neighbor method with the BLOSUM62 matrix, which resulted in around 82% accuracy on the full data set. These results provide a useful frame of reference, as well as protocols and considerations, for machine learning and data set creation in the prediction of antibody-antigen binding. CONCLUSIONS Several machine learning approaches were compared to predict antibody-antigen interaction from protein sequences. Both the data set (in CSV format) and the machine learning program (coded in Python) are freely available for download on GitHub.
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Affiliation(s)
- Chao Ye
- School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia
| | - Wenxing Hu
- Department of Computer Science, School of Information Science and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Bruno Gaeta
- School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia
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27
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Manieri TM, Takata DY, Targino RC, Quintilio W, Batalha-Carvalho JV, da Silva CML, Moro AM. Characterization of Neutralizing Human Anti-Tetanus Monoclonal Antibodies Produced by Stable Cell Lines. Pharmaceutics 2022; 14:1985. [PMID: 36297421 PMCID: PMC9611486 DOI: 10.3390/pharmaceutics14101985] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 10/12/2023] Open
Abstract
Tetanus toxin (TeNT) is produced by C. tetani, a spore-forming bacillus broadly spread in the environment. Although an inexpensive and safe vaccine is available, tetanus persists because of a lack of booster shots and variable responses to vaccines due to immunocompromised status or age-decreased immune surveillance. Tetanus is most prevalent in low- and medium-income countries, where it remains a health problem. Neutralizing monoclonal antibodies (mAbs) can prevent the severity of illness and death caused by C. tetani infection. We identified a panel of mAbs that bind to TeNT, some of which were investigated in a preclinical assay, showing that a trio of mAbs that bind to different sites of TeNT can neutralize the toxin and prevent symptoms and death in mice. We also identified two mAbs that can impair the binding of TeNT to the GT1b ganglioside receptor in neurons. In this work, to generate a series of cell lines, we constructed vectors containing sequences encoding heavy and light constant regions that can receive the paired variable regions resulting from PCRs of human B cells. In this way, we generated stable cell lines for five mAbs and compared and characterized the antibody produced in large quantities, enabling the characterization experiments. We present the results regarding the cell growth and viability in a fed-batch culture, titer measurement, and specific productivity estimation. The affinity of purified mAbs was analyzed by kinetics and under steady-state conditions, as three mAbs could not dissociate from TeNT within 36,000 s. The binding of mAbs to TeNT was confirmed by ELISA and inhibition of toxin binding to GT1b. The use of the mAbs mixture confirmed the individual mAb contribution to inhibition. We also analyzed the binding of mAbs to FcγR by surface plasmon resonance (SPR) and the glycan composition. Molecular docking analyses showed the binding site of an anti-tetanus mAb.
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Affiliation(s)
- Tania Maria Manieri
- Biopharmaceuticals Laboratory, Butantan Institute, Sao Paulo 05503-900, Brazil
| | - Daniela Yumi Takata
- Biopharmaceuticals Laboratory, Butantan Institute, Sao Paulo 05503-900, Brazil
- Interunits Graduate Program in Biotechnology, University of Sao Paulo, Sao Paulo 05508-270, Brazil
| | | | - Wagner Quintilio
- Biopharmaceuticals Laboratory, Butantan Institute, Sao Paulo 05503-900, Brazil
| | - João Victor Batalha-Carvalho
- Biopharmaceuticals Laboratory, Butantan Institute, Sao Paulo 05503-900, Brazil
- Graduate Program in Immunology, University of Sao Paulo, Sao Paulo 05508-270, Brazil
| | | | - Ana Maria Moro
- Biopharmaceuticals Laboratory, Butantan Institute, Sao Paulo 05503-900, Brazil
- Center for Research and Development in Immunobiologicals (CeRDI), Butantan Institute, Sao Paulo 05503-900, Brazil
- National Institute for Science and Technology (INCT/iii), University of Sao Paulo, Sao Paulo 05403-900, Brazil
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28
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Mokhtary P, Pourhashem Z, Mehrizi AA, Sala C, Rappuoli R. Recent Progress in the Discovery and Development of Monoclonal Antibodies against Viral Infections. Biomedicines 2022; 10:biomedicines10081861. [PMID: 36009408 PMCID: PMC9405509 DOI: 10.3390/biomedicines10081861] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/21/2022] [Accepted: 07/29/2022] [Indexed: 01/09/2023] Open
Abstract
Monoclonal antibodies (mAbs), the new revolutionary class of medications, are fast becoming tools against various diseases thanks to a unique structure and function that allow them to bind highly specific targets or receptors. These specialized proteins can be produced in large quantities via the hybridoma technique introduced in 1975 or by means of modern technologies. Additional methods have been developed to generate mAbs with new biological properties such as humanized, chimeric, or murine. The inclusion of mAbs in therapeutic regimens is a major medical advance and will hopefully lead to significant improvements in infectious disease management. Since the first therapeutic mAb, muromonab-CD3, was approved by the U.S. Food and Drug Administration (FDA) in 1986, the list of approved mAbs and their clinical indications and applications have been proliferating. New technologies have been developed to modify the structure of mAbs, thereby increasing efficacy and improving delivery routes. Gene delivery technologies, such as non-viral synthetic plasmid DNA and messenger RNA vectors (DMabs or mRNA-encoded mAbs), built to express tailored mAb genes, might help overcome some of the challenges of mAb therapy, including production restrictions, cold-chain storage, transportation requirements, and expensive manufacturing and distribution processes. This paper reviews some of the recent developments in mAb discovery against viral infections and illustrates how mAbs can help to combat viral diseases and outbreaks.
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Affiliation(s)
- Pardis Mokhtary
- Monoclonal Antibody Discovery Laboratory, Fondazione Toscana Life Sciences, 53100 Siena, Italy;
- Department of Biochemistry and Molecular Biology, University of Siena, 53100 Siena, Italy
| | - Zeinab Pourhashem
- Student Research Committee, Pasteur Institute of Iran, Tehran 1316943551, Iran;
- Malaria and Vector Research Group, Biotechnology Research Center, Pasteur Institute of Iran, Tehran 1316943551, Iran;
| | - Akram Abouei Mehrizi
- Malaria and Vector Research Group, Biotechnology Research Center, Pasteur Institute of Iran, Tehran 1316943551, Iran;
| | - Claudia Sala
- Monoclonal Antibody Discovery Laboratory, Fondazione Toscana Life Sciences, 53100 Siena, Italy;
- Correspondence: (C.S.); (R.R.)
| | - Rino Rappuoli
- Monoclonal Antibody Discovery Laboratory, Fondazione Toscana Life Sciences, 53100 Siena, Italy;
- Correspondence: (C.S.); (R.R.)
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29
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Wilman W, Wróbel S, Bielska W, Deszynski P, Dudzic P, Jaszczyszyn I, Kaniewski J, Młokosiewicz J, Rouyan A, Satława T, Kumar S, Greiff V, Krawczyk K. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Brief Bioinform 2022; 23:bbac267. [PMID: 35830864 PMCID: PMC9294429 DOI: 10.1093/bib/bbac267] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody-antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
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30
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Lee JH, Yin R, Ofek G, Pierce BG. Structural Features of Antibody-Peptide Recognition. Front Immunol 2022; 13:910367. [PMID: 35874680 PMCID: PMC9302003 DOI: 10.3389/fimmu.2022.910367] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/08/2022] [Indexed: 11/22/2022] Open
Abstract
Antibody recognition of antigens is a critical element of adaptive immunity. One key class of antibody-antigen complexes is comprised of antibodies targeting linear epitopes of proteins, which in some cases are conserved elements of viruses and pathogens of relevance for vaccine design and immunotherapy. Here we report a detailed analysis of the structural and interface features of this class of complexes, based on a set of nearly 200 nonredundant high resolution antibody-peptide complex structures that were assembled from the Protein Data Bank. We found that antibody-bound peptides adopt a broad range of conformations, often displaying limited secondary structure, and that the same peptide sequence bound by different antibodies can in many cases exhibit varying conformations. Propensities of contacts with antibody loops and extent of antibody binding conformational changes were found to be broadly similar to those for antibodies in complex with larger protein antigens. However, antibody-peptide interfaces showed lower buried surface areas and fewer hydrogen bonds than antibody-protein antigen complexes, while calculated binding energy per buried interface area was found to be higher on average for antibody-peptide interfaces, likely due in part to a greater proportion of buried hydrophobic residues and higher shape complementarity. This dataset and these observations can be of use for future studies focused on this class of interactions, including predictive computational modeling efforts and the design of antibodies or epitope-based vaccine immunogens.
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Affiliation(s)
- Jessica H. Lee
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
| | - Rui Yin
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
| | - Gilad Ofek
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
| | - Brian G. Pierce
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, United States
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, United States
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, United States
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31
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Magi Meconi G, Sasselli IR, Bianco V, Onuchic JN, Coluzza I. Key aspects of the past 30 years of protein design. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 85:086601. [PMID: 35704983 DOI: 10.1088/1361-6633/ac78ef] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.
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Affiliation(s)
- Giulia Magi Meconi
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | - Ivan R Sasselli
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | | | - Jose N Onuchic
- Center for Theoretical Biological Physics, Department of Physics & Astronomy, Department of Chemistry, Department of Biosciences, Rice University, Houston, TX 77251, United States of America
| | - Ivan Coluzza
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Bld. Martina Casiano, UPV/EHU Science Park, Barrio Sarriena s/n, 48940 Leioa, Spain
- Basque Foundation for Science, Ikerbasque, 48009, Bilbao, Spain
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32
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Liu H, Wu W, Sun G, Chia T, Cao L, Liu X, Guan J, Fu F, Yao Y, Wu Z, Zhou S, Wang J, Lu J, Kuang Z, Wu M, He L, Shao Z, Wu D, Chen B, Xu W, Wang Z, He K. Optimal target saturation of ligand-blocking anti-GITR antibody IBI37G5 dictates FcγR-independent GITR agonism and antitumor activity. Cell Rep Med 2022; 3:100660. [PMID: 35732156 PMCID: PMC9245059 DOI: 10.1016/j.xcrm.2022.100660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 03/26/2022] [Accepted: 05/20/2022] [Indexed: 11/29/2022]
Abstract
Glucocorticoid-induced tumor necrosis factor receptor (GITR) is a co-stimulatory receptor and an important target for cancer immunotherapy. We herein present a potent FcγR-independent GITR agonist IBI37G5 that can effectively activate effector T cells and synergize with anti-programmed death 1 (PD1) antibody to eradicate established tumors. IBI37G5 depends on both antibody bivalency and GITR homo-dimerization for efficient receptor cross-linking. Functional analyses reveal bell-shaped dose responses due to the unique 2:2 antibody-receptor stoichiometry required for GITR activation. Antibody self-competition is observed after concentration exceeded that of 100% receptor occupancy (RO), which leads to antibody monovalent binding and loss of activity. Retrospective pharmacokinetics/pharmacodynamics analysis demonstrates that the maximal efficacy is achieved at medium doses with drug exposure near saturating GITR occupancy during the dosing cycle. Finally, we propose an alternative dose-finding strategy that does not rely on the traditional maximal tolerated dose (MTD)-based paradigm but instead on utilizing the RO-function relations as biomarker to guide the clinical translation of GITR and similar co-stimulatory agonists.
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Affiliation(s)
- Huisi Liu
- Department of Immunology, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Weiwei Wu
- Department of Pharmacology and Preclinical Studies, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Gangyu Sun
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Tiongsun Chia
- Department of Immunology, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Lei Cao
- Department of Pharmacology and Preclinical Studies, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Xiaodan Liu
- Department of Immunology, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Jian Guan
- Department of Immunology, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Fenggen Fu
- Department of Antibody Discovery and Protein Engineering, Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Ying Yao
- Department of Pharmacology and Preclinical Studies, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Zhihai Wu
- Department of Antibody Discovery and Protein Engineering, Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Shuaixiang Zhou
- Department of Antibody Discovery and Protein Engineering, Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Jie Wang
- Department of Pharmacology and Preclinical Studies, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Jia Lu
- Department of Pharmacology and Preclinical Studies, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Zhihui Kuang
- Department of Pharmacology and Preclinical Studies, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Min Wu
- Department of Pharmacology and Preclinical Studies, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Luan He
- Department of Immunology, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Zhiyuan Shao
- Department of Antibody Discovery and Protein Engineering, Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Dongdong Wu
- Department of Pharmacology and Preclinical Studies, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Bingliang Chen
- Department of Pharmacology and Preclinical Studies, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China
| | - Wenqing Xu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Zhizhi Wang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Kaijie He
- Department of Immunology, Innovent Guoqing Academy, Innovent Biologics (Suzhou) Co., Ltd., Suzhou, China.
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33
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Tran MH, Schoeder CT, Schey KL, Meiler J. Computational Structure Prediction for Antibody-Antigen Complexes From Hydrogen-Deuterium Exchange Mass Spectrometry: Challenges and Outlook. Front Immunol 2022; 13:859964. [PMID: 35720345 PMCID: PMC9204306 DOI: 10.3389/fimmu.2022.859964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 04/22/2022] [Indexed: 11/21/2022] Open
Abstract
Although computational structure prediction has had great successes in recent years, it regularly fails to predict the interactions of large protein complexes with residue-level accuracy, or even the correct orientation of the protein partners. The performance of computational docking can be notably enhanced by incorporating experimental data from structural biology techniques. A rapid method to probe protein-protein interactions is hydrogen-deuterium exchange mass spectrometry (HDX-MS). HDX-MS has been increasingly used for epitope-mapping of antibodies (Abs) to their respective antigens (Ags) in the past few years. In this paper, we review the current state of HDX-MS in studying protein interactions, specifically Ab-Ag interactions, and how it has been used to inform computational structure prediction calculations. Particularly, we address the limitations of HDX-MS in epitope mapping and techniques and protocols applied to overcome these barriers. Furthermore, we explore computational methods that leverage HDX-MS to aid structure prediction, including the computational simulation of HDX-MS data and the combination of HDX-MS and protein docking. We point out challenges in interpreting and incorporating HDX-MS data into Ab-Ag complex docking and highlight the opportunities they provide to build towards a more optimized hybrid method, allowing for more reliable, high throughput epitope identification.
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Affiliation(s)
- Minh H. Tran
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, United States
- Center of Structural Biology, Vanderbilt University, Nashville, TN, United States
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
| | - Clara T. Schoeder
- Center of Structural Biology, Vanderbilt University, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Institute for Drug Discovery, University Leipzig Medical School, Leipzig, Germany
| | - Kevin L. Schey
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
| | - Jens Meiler
- Center of Structural Biology, Vanderbilt University, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Institute for Drug Discovery, University Leipzig Medical School, Leipzig, Germany
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34
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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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35
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Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet TS, Flem-Karlsen K, Frank R, Mehta BB, Vu MH, Zengin T, Gutierrez-Marcos J, Lund-Johansen F, Andersen JT, Greiff V. Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. MAbs 2022; 14:2008790. [PMID: 35293269 PMCID: PMC8928824 DOI: 10.1080/19420862.2021.2008790] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/04/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022] Open
Abstract
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.
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Affiliation(s)
- Rahmad Akbar
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Habib Bashour
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Puneet Rawat
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Philippe A. Robert
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eva Smorodina
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia
| | | | - Karine Flem-Karlsen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Robert Frank
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Brij Bhushan Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Mai Ha Vu
- Department of Linguistics and Scandinavian Studies, University of Oslo, Norway
| | - Talip Zengin
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Bioinformatics, Mugla Sitki Kocman University, Turkey
| | | | | | - Jan Terje Andersen
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Department of Pharmacology, University of Oslo and Oslo University Hospital, Norway
| | - Victor Greiff
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
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36
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Koehler Leman J, Lyskov S, Lewis SM, Adolf-Bryfogle J, Alford RF, Barlow K, Ben-Aharon Z, Farrell D, Fell J, Hansen WA, Harmalkar A, Jeliazkov J, Kuenze G, Krys JD, Ljubetič A, Loshbaugh AL, Maguire J, Moretti R, Mulligan VK, Nance ML, Nguyen PT, Ó Conchúir S, Roy Burman SS, Samanta R, Smith ST, Teets F, Tiemann JKS, Watkins A, Woods H, Yachnin BJ, Bahl CD, Bailey-Kellogg C, Baker D, Das R, DiMaio F, Khare SD, Kortemme T, Labonte JW, Lindorff-Larsen K, Meiler J, Schief W, Schueler-Furman O, Siegel JB, Stein A, Yarov-Yarovoy V, Kuhlman B, Leaver-Fay A, Gront D, Gray JJ, Bonneau R. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nat Commun 2021; 12:6947. [PMID: 34845212 PMCID: PMC8630030 DOI: 10.1038/s41467-021-27222-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 11/02/2021] [Indexed: 01/14/2023] Open
Abstract
Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Steven M Lewis
- Cyrus Biotechnology, 1201 Second Ave, Suite 900, Seattle, WA, 98101, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kyle Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ziv Ben-Aharon
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Daniel Farrell
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Jason Fell
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - William A Hansen
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Jeliazko Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - Justyna D Krys
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Ajasja Ljubetič
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Amanda L Loshbaugh
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
| | - Morgan L Nance
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Phuong T Nguyen
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Shane Ó Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Shannon T Smith
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Frank Teets
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Johanna K S Tiemann
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Hope Woods
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, 37235, USA
| | - Brahm J Yachnin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Christopher D Bahl
- Institute for Protein Innovation, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | | | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08904, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
- Biophysics Graduate Program, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA
- Institute for Drug Discovery, Medical School, Leipzig University, 04103, Leipzig, Germany
| | - William Schief
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, 92037, USA
- IAVI Neutralizing Antibody Center, Scripps Research, La Jolla, CA, 92037, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Hebrew University, Hadassah Medical School, POB 12272, Jerusalem, 91120, Israel
| | - Justin B Siegel
- Genome Center, University of California, Davis, CA, 95616, USA
- Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, 95616, USA
- Department of Chemistry, University of California, Davis, CA, 95616, USA
| | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200, Copenhagen N., Denmark
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, School of Medicine, University of California, Davis, CA, 95616, USA
| | - Brian Kuhlman
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Andrew Leaver-Fay
- Department of Bioochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
- Department of Biology, New York University, New York, NY, 10003, USA.
- Department of Computer Science, New York University, New York, NY, 10003, USA.
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37
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Paratope states in solution improve structure prediction and docking. Structure 2021; 30:430-440.e3. [PMID: 34838187 DOI: 10.1016/j.str.2021.11.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 09/10/2021] [Accepted: 11/03/2021] [Indexed: 12/13/2022]
Abstract
Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations, we show that a single static X-ray structure is not sufficient to identify determinants of antibody-antigen recognition. Here, we investigate antibodies that undergo substantial conformational changes upon antigen binding and have been classified as difficult cases in an extensive benchmark for antibody-antigen docking. We present thermodynamics and transition kinetics of these conformational rearrangements and show that paratope states can be used to improve antibody-antigen docking. By using the unbound antibody X-ray structure as starting structure for molecular dynamics simulations, we retain a binding competent conformation substantially different to the unbound antibody X-ray structure. We also observe that the kinetically dominant antibody paratope conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that paratope states in solution can improve antibody-antigen docking and structure prediction.
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38
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Negi SS, Goldblum RM, Braun W, Midoro-Horiuti T. Design of peptides with high affinity binding to a monoclonal antibody as a basis for immunotherapy. Peptides 2021; 145:170628. [PMID: 34411692 PMCID: PMC8484066 DOI: 10.1016/j.peptides.2021.170628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/23/2022]
Abstract
About half of the US population is sensitized to one or more allergens, as found by a National Health and Nutrition Examination Survey (NHANES). The most common treatment for seasonal allergic responses is the daily use of oral antihistamines, which can control some of the symptoms, but are not effective for nasal congestion, and can be debilitating in many patients. Peptide immunotherapy is a promising new approach to treat allergic airway diseases. The small size of the immunogens cannot lead to an unwanted allergic reaction in sensitized patients, and the production of peptides with sufficient amounts for immunotherapy is time- and cost-effective. However, it is not known what peptides are the most effective for an immunotherapy of allergens. We previously produced a unique monoclonal antibody (mAb) E58, which can inhibit the binding of multiple groups of mAbs and human IgEs from patients affected by the major group 1 allergens of ragweed (Amb a 1) and conifer pollens (Jun a 1, Cup s 1, and Cry j 1). Here, we demonstrated that a combined approach, starting from two linear E58 epitopes of the tree pollen allergen Jun a 1 and the ragweed pollen allergen Amb a 1, and residue modifications suggested by molecular docking calculations and peptide design could identify a large number of high affinity binding peptides. We propose that this combined experimental and computational approach by structural analysis of linear IgE epitopes and peptide design, can lead to potential new candidates for peptide immunotherapy.
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Affiliation(s)
- Surendra S Negi
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, 301 University Blvd., Galveston, TX, 77555-0304, United States
| | - Randall M Goldblum
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, 301 University Blvd., Galveston, TX, 77555-0304, United States; Department of Pediatrics, University of Texas Medical Branch, 301 University Blvd., Galveston, TX, 77555-0372, United States
| | - Werner Braun
- Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, 301 University Blvd., Galveston, TX, 77555-0304, United States.
| | - Terumi Midoro-Horiuti
- Department of Pediatrics, University of Texas Medical Branch, 301 University Blvd., Galveston, TX, 77555-0372, United States.
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39
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Tam C, Kumar A, Zhang KYJ. NbX: Machine Learning-Guided Re-Ranking of Nanobody-Antigen Binding Poses. Pharmaceuticals (Basel) 2021; 14:ph14100968. [PMID: 34681192 PMCID: PMC8537642 DOI: 10.3390/ph14100968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 12/02/2022] Open
Abstract
Modeling the binding pose of an antibody is a prerequisite to structure-based affinity maturation and design. Without knowing a reliable binding pose, the subsequent structural simulation is largely futile. In this study, we have developed a method of machine learning-guided re-ranking of antigen binding poses of nanobodies, the single-domain antibody which has drawn much interest recently in antibody drug development. We performed a large-scale self-docking experiment of nanobody–antigen complexes. By training a decision tree classifier through mapping a feature set consisting of energy, contact and interface property descriptors to a measure of their docking quality of the refined poses, significant improvement in the median ranking of native-like nanobody poses by was achieved eightfold compared with ClusPro and an established deep 3D CNN classifier of native protein–protein interaction. We further interpreted our model by identifying features that showed relatively important contributions to the prediction performance. This study demonstrated a useful method in improving our current ability in pose prediction of nanobodies.
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Affiliation(s)
- Chunlai Tam
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan; (C.T.); (A.K.)
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan; (C.T.); (A.K.)
| | - Kam Y. J. Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan; (C.T.); (A.K.)
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
- Correspondence:
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40
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Zhang Y, Chen T, Zeng H, Yang X, Xu Q, Zhang Y, Chen Y, Wang M, Zhu Y, Lan C, Wang Q, Tang H, Zhang Y, Wang C, Xie W, Ma C, Guan J, Guo S, Chen S, Yang W, Wei L, Ren J, Yu X, Zhang Z. RAPID: A Rep-Seq Dataset Analysis Platform With an Integrated Antibody Database. Front Immunol 2021; 12:717496. [PMID: 34484220 PMCID: PMC8414647 DOI: 10.3389/fimmu.2021.717496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/27/2021] [Indexed: 12/12/2022] Open
Abstract
The antibody repertoire is a critical component of the adaptive immune system and is believed to reflect an individual’s immune history and current immune status. Delineating the antibody repertoire has advanced our understanding of humoral immunity, facilitated antibody discovery, and showed great potential for improving the diagnosis and treatment of disease. However, no tool to date has effectively integrated big Rep-seq data and prior knowledge of functional antibodies to elucidate the remarkably diverse antibody repertoire. We developed a Rep-seq dataset Analysis Platform with an Integrated antibody Database (RAPID; https://rapid.zzhlab.org/), a free and web-based tool that allows researchers to process and analyse Rep-seq datasets. RAPID consolidates 521 WHO-recognized therapeutic antibodies, 88,059 antigen- or disease-specific antibodies, and 306 million clones extracted from 2,449 human IGH Rep-seq datasets generated from individuals with 29 different health conditions. RAPID also integrates a standardized Rep-seq dataset analysis pipeline to enable users to upload and analyse their datasets. In the process, users can also select set of existing repertoires for comparison. RAPID automatically annotates clones based on integrated therapeutic and known antibodies, and users can easily query antibodies or repertoires based on sequence or optional keywords. With its powerful analysis functions and rich set of antibody and antibody repertoire information, RAPID will benefit researchers in adaptive immune studies.
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Affiliation(s)
- Yanfang Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou, China.,Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Tianjian Chen
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Huikun Zeng
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou, China.,Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiujia Yang
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou, China.,Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qingxian Xu
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yanxia Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yuan Chen
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Minhui Wang
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Nephrology, Hainan General Hospital, Haikou, China.,Hainan Affiliated Hospital of Hainan Medical College, Haikou, China
| | - Yan Zhu
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Chunhong Lan
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qilong Wang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Haipei Tang
- Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yan Zhang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Chengrui Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenxi Xie
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Cuiyu Ma
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Junjie Guan
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shixin Guo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Sen Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wei Yang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lai Wei
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Jian Ren
- State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xueqing Yu
- Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Division of Nephrology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenhai Zhang
- State Key Laboratory of Organ Failure Research, National Clinical Research, Center for Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.,Center for Precision Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou, China.,Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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41
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Frick R, Høydahl LS, Petersen J, du Pré MF, Kumari S, Berntsen G, Dewan AE, Jeliazkov JR, Gunnarsen KS, Frigstad T, Vik ES, Llerena C, Lundin KEA, Yaqub S, Jahnsen J, Gray JJ, Rossjohn J, Sollid LM, Sandlie I, Løset GÅ. A high-affinity human TCR-like antibody detects celiac disease gluten peptide-MHC complexes and inhibits T cell activation. Sci Immunol 2021; 6:6/62/eabg4925. [PMID: 34417258 DOI: 10.1126/sciimmunol.abg4925] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 07/22/2021] [Indexed: 12/12/2022]
Abstract
Antibodies specific for peptides bound to human leukocyte antigen (HLA) molecules are valuable tools for studies of antigen presentation and may have therapeutic potential. Here, we generated human T cell receptor (TCR)-like antibodies toward the immunodominant signature gluten epitope DQ2.5-glia-α2 in celiac disease (CeD). Phage display selection combined with secondary targeted engineering was used to obtain highly specific antibodies with picomolar affinity. The crystal structure of a Fab fragment of the lead antibody 3.C11 in complex with HLA-DQ2.5:DQ2.5-glia-α2 revealed a binding geometry and interaction mode highly similar to prototypic TCRs specific for the same complex. Assessment of CeD biopsy material confirmed disease specificity and reinforced the notion that abundant plasma cells present antigen in the inflamed CeD gut. Furthermore, 3.C11 specifically inhibited activation and proliferation of gluten-specific CD4+ T cells in vitro and in HLA-DQ2.5 humanized mice, suggesting a potential for targeted intervention without compromising systemic immunity.
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Affiliation(s)
- Rahel Frick
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | - Lene S Høydahl
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | - Jan Petersen
- Infection and Immunity Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia.,Australian Research Council Centre of Excellence for Advanced Molecular Imaging, Monash University, Clayton, Victoria, Australia
| | - M Fleur du Pré
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | | | | | - Alisa E Dewan
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | | | - Kristin S Gunnarsen
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | | | | | - Carmen Llerena
- Infection and Immunity Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Knut E A Lundin
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway.,Department of Gastroenterology, Oslo University Hospital-Rikshospitalet, Oslo, Norway
| | - Sheraz Yaqub
- Department of Gastrointestinal Surgery, Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jørgen Jahnsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Gastroenterology, Akershus University Hospital, Lørenskog, Norway
| | - Jeffrey J Gray
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA.,Department of Chemical and Biomolecular Engineering and Institute of NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jamie Rossjohn
- Infection and Immunity Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia.,Australian Research Council Centre of Excellence for Advanced Molecular Imaging, Monash University, Clayton, Victoria, Australia.,Institute of Infection and Immunity, Cardiff University School of Medicine, Heath Park, Cardiff, UK
| | - Ludvig M Sollid
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | - Inger Sandlie
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | - Geir Åge Løset
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway. .,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway.,Nextera AS, Oslo, Norway
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42
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Liang T, Chen H, Yuan J, Jiang C, Hao Y, Wang Y, Feng Z, Xie XQ. IsAb: a computational protocol for antibody design. Brief Bioinform 2021; 22:6238584. [PMID: 33876197 DOI: 10.1093/bib/bbab143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/24/2021] [Accepted: 03/23/2021] [Indexed: 12/15/2022] Open
Abstract
The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.
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Affiliation(s)
- Tianjian Liang
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Hui Chen
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Jiayi Yuan
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Chen Jiang
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yixuan Hao
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Yuanqiang Wang
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Pittsburgh, PA 15261, USA
| | - Zhiwei Feng
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang-Qun Xie
- Computational Drug Abuse Research and Computational Chemogenomics Screening Center at the University of Pittsburgh, Pittsburgh, PA 15261, USA
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43
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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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44
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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: 25] [Impact Index Per Article: 8.3] [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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45
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Guest JD, Vreven T, Zhou J, Moal I, Jeliazkov JR, Gray JJ, Weng Z, Pierce BG. An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants. Structure 2021; 29:606-621.e5. [PMID: 33539768 DOI: 10.1016/j.str.2021.01.005] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 11/15/2020] [Accepted: 01/11/2021] [Indexed: 01/04/2023]
Abstract
Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for biotherapeutics, immunity, and vaccines. Through a systematic search for high-resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.
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Affiliation(s)
- Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Jing Zhou
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Iain Moal
- Computational Sciences, GlaxoSmithKline Research and Development, Stevenage SG1 2NY, UK
| | - Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
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46
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Zinsli LV, Stierlin N, Loessner MJ, Schmelcher M. Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion. Comput Struct Biotechnol J 2020; 19:315-329. [PMID: 33425259 PMCID: PMC7779837 DOI: 10.1016/j.csbj.2020.12.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 12/11/2022] Open
Abstract
Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.
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Key Words
- ABR, Antigen-binding region
- ADA, Anti-drug antibody
- ANN, Artificial neural network
- APC, Antigen-presenting cell
- Anti-drug-antibody
- B cell epitope
- BCR, B cell receptor
- Bab, Binding antibody
- CDR, Complementarity determining region
- CRISPR, Clustered regularly interspaced short palindromic repeats
- DC, Dendritic cell
- ELP, Elastin-like polypeptide
- EPO, Erythropoietin
- ER, Endoplasmatic reticulum
- GLK, Gelatin-like protein
- HAP, Homo-amino-acid polymer
- HLA, Human leukocyte antigen
- HMM, Hidden Markov model
- IL, Interleukin
- Ig, Immunoglobulin
- Immunogenicity
- LPS, Lipopolysaccharide
- MHC, Major histocompatibility complex
- NMR, Nuclear magnetic resonance
- Nab, Neutralizing antibody
- PAMP, Pathogen-associated molecular pattern
- PAS, Polypeptide composed of proline, alanine, and/or serine
- PBMC, Peripheral blood mononuclear cell
- PD, Pharmacodynamics
- PEG, Polyethylene glycol
- PK, Pharmacokinetics
- PRR, Pattern recognition receptor
- PSA, Sialic acid polymers
- Protein therapeutic
- RNN, Recurrent artificial neural network
- SVM, Support vector machine
- T cell epitope
- TAP, Transporter associated with antigen processing
- TCR, T cell receptor
- TLR, Toll-like receptor
- XTEN, “Xtended” recombinant polypeptide
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Affiliation(s)
- Léa V. Zinsli
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Noël Stierlin
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Martin J. Loessner
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Mathias Schmelcher
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
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47
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Discovery of Marburg virus neutralizing antibodies from virus-naïve human antibody repertoires using large-scale structural predictions. Proc Natl Acad Sci U S A 2020; 117:31142-31148. [PMID: 33229516 DOI: 10.1073/pnas.1922654117] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Marburg virus (MARV) disease is lethal, with fatality rates up to 90%. Neutralizing antibodies (Abs) are promising drug candidates to prevent or treat the disease. Current efforts are focused in part on vaccine development to induce such MARV-neutralizing Abs. We analyzed the antibody repertoire from healthy unexposed and previously MARV-infected individuals to assess if naïve repertoires contain suitable precursor antibodies that could become neutralizing with a limited set of somatic mutations. We computationally searched the human Ab variable gene repertoire for predicted structural homologs of the neutralizing Ab MR78 that is specific to the receptor binding site (RBS) of MARV glycoprotein (GP). Eight Ab heavy-chain complementarity determining region 3 (HCDR3) loops from MARV-naïve individuals and one from a previously MARV-infected individual were selected for testing as HCDR3 loop chimeras on the MR78 Ab framework. Three of these chimerized antibodies bound to MARV GP. We then tested a full-length native Ab heavy chain encoding the same 17-residue-long HCDR3 loop that bound to the MARV GP the best among the chimeric Abs tested. Despite only 57% amino acid sequence identity, the Ab from a MARV-naïve donor recognized MARV GP and possessed neutralizing activity against the virus. Crystallization of both chimeric and full-length native heavy chain-containing Abs provided structural insights into the mechanism of binding for these types of Abs. Our work suggests that the MARV GP RBS is a promising candidate for epitope-focused vaccine design to induce neutralizing Abs against MARV.
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Mares-Mejía I, García-Ramírez B, Torres-Larios A, Rodríguez-Hernández A, Osornio-Hernández AI, Terán-Olvera G, Ortega E, Rodríguez-Romero A. Novel murine mAbs define specific and cross-reactive epitopes on the latex profilin panallergen Hev b 8. Mol Immunol 2020; 128:10-21. [PMID: 33045539 DOI: 10.1016/j.molimm.2020.09.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 09/10/2020] [Accepted: 09/25/2020] [Indexed: 01/06/2023]
Abstract
The production of specific antibodies able to recognize allergens from different sources or block interactions between allergens and antibodies mediating allergic reactions is crucial for developing successful tools for diagnostics and therapeutics. Panallergens are highly conserved proteins present in widely different species, implicated in relevant cross-reactions. The panallergen latex profilin (Hev b 8) has been associated with the latex-food-pollen syndrome. We generated five monoclonal IgGs and one IgE from murine hybridomas against recombinant Hev b 8 and evaluated their interaction with this allergen using ELISA and biolayer interferometry (BLI). Affinity purified mAbs exhibited high binding affinities towards rHev b 8, with KD1 values ranging from 10-10 M to 10-11 M. Some of these antibodies also recognized the recombinant profilins from maize and tomato (Zea m 12 and Sola l 1), and the ash tree pollen (Fra e 2). Competition ELISA demonstrated that some mAb pairs could bind simultaneously to rHev b 8. Using BLI, we detected competitive, non-competitive, and partial-competition interactions between pairs of mAbs with rHev b 8, suggesting the existence of at least two non-overlapping epitopes on the surface of this allergen. Three-dimensional models of the Fv of 1B4 and 2D10 IgGs and docking simulations of these Fvs with rHev b 8 revealed these epitopes. Furthermore, these two mAbs inhibited the interaction of polyclonal IgE and IgG4 antibodies from profilin-allergic patients with rHev b 8, indicating that the mAbs and the antibodies present in sera from allergic patients bind to overlapping epitopes on the allergen. These mAbs can be useful tools for immune-localization studies, immunoassay development, or standardization of allergenic products.
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Affiliation(s)
- Israel Mares-Mejía
- Instituto de Química, Universidad Nacional Autónoma de México, Circuito Exterior, Cd. Universitaria, Coyoacán, Ciudad de México, 04510, Mexico
| | - Benjamín García-Ramírez
- Instituto de Química, Universidad Nacional Autónoma de México, Circuito Exterior, Cd. Universitaria, Coyoacán, Ciudad de México, 04510, Mexico
| | - Alfredo Torres-Larios
- Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Circuito Exterior, Cd. Universitaria, Coyoacán, Ciudad de México, 04510, Mexico
| | - Annia Rodríguez-Hernández
- Instituto de Química, Universidad Nacional Autónoma de México, Circuito Exterior, Cd. Universitaria, Coyoacán, Ciudad de México, 04510, Mexico
| | - Ana Isabel Osornio-Hernández
- Instituto de Química, Universidad Nacional Autónoma de México, Circuito Exterior, Cd. Universitaria, Coyoacán, Ciudad de México, 04510, Mexico
| | - Gabriela Terán-Olvera
- Instituto de Química, Universidad Nacional Autónoma de México, Circuito Exterior, Cd. Universitaria, Coyoacán, Ciudad de México, 04510, Mexico
| | - Enrique Ortega
- Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Circuito Exterior, Cd. Universitaria, Coyoacán, Ciudad de México, 04510, Mexico.
| | - Adela Rodríguez-Romero
- Instituto de Química, Universidad Nacional Autónoma de México, Circuito Exterior, Cd. Universitaria, Coyoacán, Ciudad de México, 04510, Mexico.
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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: 113] [Impact Index Per Article: 28.3] [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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Brooks BD, Closmore A, Yang J, Holland M, Cairns T, Cohen GH, Bailey-Kellogg C. Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition. Molecules 2020; 25:molecules25163659. [PMID: 32796656 PMCID: PMC7464469 DOI: 10.3390/molecules25163659] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/27/2020] [Accepted: 07/28/2020] [Indexed: 11/16/2022] Open
Abstract
Vaccines and immunotherapies depend on the ability of antibodies to sensitively and specifically recognize particular antigens and specific epitopes on those antigens. As such, detailed characterization of antibody-antigen binding provides important information to guide development. Due to the time and expense required, high-resolution structural characterization techniques are typically used sparingly and late in a development process. Here, we show that antibody-antigen binding can be characterized early in a process for whole panels of antibodies by combining experimental and computational analyses of competition between monoclonal antibodies for binding to an antigen. Experimental "epitope binning" of monoclonal antibodies uses high-throughput surface plasmon resonance to reveal which antibodies compete, while a new complementary computational analysis that we call "dock binning" evaluates antibody-antigen docking models to identify why and where they might compete, in terms of possible binding sites on the antigen. Experimental and computational characterization of the identified antigenic hotspots then enables the refinement of the competitors and their associated epitope binding regions on the antigen. While not performed at atomic resolution, this approach allows for the group-level identification of functionally related monoclonal antibodies (i.e., communities) and identification of their general binding regions on the antigen. By leveraging extensive epitope characterization data that can be readily generated both experimentally and computationally, researchers can gain broad insights into the basis for antibody-antigen recognition in wide-ranging vaccine and immunotherapy discovery and development programs.
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Affiliation(s)
- Benjamin D. Brooks
- Department of Biomedical Sciences, Rocky Vista University, Ivins, UT 84738, USA
- Inovan Inc., Fargo, ND 58102, USA
- Department of Microbiology, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (T.C.); (G.H.C.)
- Correspondence: ; Tel.: +1-435-222-1403
| | - Adam Closmore
- Department of Pharmacy, North Dakota State University, Fargo, ND 58102, USA;
| | - Juechen Yang
- Department of Biomedical Engineering, North Dakota State University, Fargo, ND 58102, USA; (J.Y.); (M.H.)
| | - Michael Holland
- Department of Biomedical Engineering, North Dakota State University, Fargo, ND 58102, USA; (J.Y.); (M.H.)
| | - Tina Cairns
- Department of Microbiology, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (T.C.); (G.H.C.)
| | - Gary H. Cohen
- Department of Microbiology, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (T.C.); (G.H.C.)
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