1
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Polonsky K, Pupko T, Freund NT. Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 211:1578-1588. [PMID: 37782047 DOI: 10.4049/jimmunol.2300150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 09/06/2023] [Indexed: 10/03/2023]
Abstract
Being able to accurately predict the three-dimensional structure of an Ab can facilitate Ab characterization and epitope prediction, with important diagnostic and clinical implications. In this study, we evaluated the ability of AlphaFold to predict the structures of 222 recently published, high-resolution Fab H and L chain structures of Abs from different species directed against different Ags. We show that although the overall Ab prediction quality is in line with the results of CASP14, regions such as the complementarity-determining regions (CDRs) of the H chain, which are prone to higher variation, are predicted less accurately. Moreover, we discovered that AlphaFold mispredicts the bending angles between the variable and constant domains. To evaluate the ability of AlphaFold to model Ab-Ag interactions based only on sequence, we used AlphaFold-Multimer in combination with ZDOCK to predict the structures of 26 known Ab-Ag complexes. ZDOCK, which was applied on bound components of both the Ab and the Ag, succeeded in assembling 11 complexes, whereas AlphaFold succeeded in predicting only 2 of 26 models, with significant deviations in the docking contacts predicted in the rest of the molecules. Within the 11 complexes that were successfully predicted by ZDOCK, 9 involved short-peptide Ags (18-mer or less), whereas only 2 were complexes of Ab with a full-length protein. Docking of modeled unbound Ab and Ag was unsuccessful. In summary, our study provides important information about the abilities and limitations of using AlphaFold to predict Ab-Ag interactions and suggests areas for possible improvement.
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Affiliation(s)
- Ksenia Polonsky
- Department of Clinical Microbiology and Immunology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Tal Pupko
- Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Natalia T Freund
- Department of Clinical Microbiology and Immunology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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2
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Zhou Y, Huang Z, Li W, Wei J, Jiang Q, Yang W, Huang J. Deep learning in preclinical antibody drug discovery and development. Methods 2023; 218:57-71. [PMID: 37454742 DOI: 10.1016/j.ymeth.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 03/20/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023] Open
Abstract
Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.
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Affiliation(s)
- Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wenzhen Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jinyi Wei
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qianhu Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Wei Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
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3
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Li J, Kang G, Wang J, Yuan H, Wu Y, Meng S, Wang P, Zhang M, Wang Y, Feng Y, Huang H, de Marco A. Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization. Int J Biol Macromol 2023; 247:125733. [PMID: 37423452 DOI: 10.1016/j.ijbiomac.2023.125733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/04/2023] [Accepted: 07/06/2023] [Indexed: 07/11/2023]
Abstract
Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.
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Affiliation(s)
- Jiaqi Li
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Guangbo Kang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Jiewen Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Haibin Yuan
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Yili Wu
- Zhejiang Provincial Clinical Research Center for Mental Disorders, School of Mental Health and the Affiliated Kangning Hospital, Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Oujiang Laboratory, Wenzhou, Zhejiang 325035, China
| | - Shuxian Meng
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Ping Wang
- New Technology R&D Department, Tianjin Modern Innovative TCM Technology Company Limited, Tianjin 300392, China
| | - Miao Zhang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; China Resources Biopharmaceutical Company Limited, Beijing 100029, China
| | - Yuli Wang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Tianjin Pharmaceutical Da Ren Tang Group Corporation Limited, Traditional Chinese Pharmacy Research Institute, Tianjin Key Laboratory of Quality Control in Chinese Medicine, Tianjin 300457, China; State Key Laboratory of Drug Delivery Technology and Pharmacokinetics, Tianjin Institute of Pharmaceutical Research, Tianjin 300193, China
| | - Yuanhang Feng
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - He Huang
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China.
| | - Ario de Marco
- Laboratory for Environmental and Life Sciences, University of Nova Gorica, Nova Gorica, Slovenia.
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4
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Ascher DB, Kaminskas LM, Myung Y, Pires DEV. Using Graph-Based Signatures to Guide Rational Antibody Engineering. Methods Mol Biol 2023; 2552:375-397. [PMID: 36346604 DOI: 10.1007/978-1-0716-2609-2_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.
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Affiliation(s)
- David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Department of Biochemistry, Cambridge University, Cambridge, UK
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, Queensland, Australia
| | - Lisa M Kaminskas
- School of Biological Sciences, University of Queensland, St Lucia, QLD, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, Queensland, Australia
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, Australia.
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- School of Computing and Information Systems, University of Melbourne, Parkville, VIC, Australia.
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5
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Ahmad B, Choi S. Unraveling the Tomaralimab Epitope on the Toll-like Receptor 2 via Molecular Dynamics and Deep Learning. ACS OMEGA 2022; 7:28226-28237. [PMID: 35990491 PMCID: PMC9386714 DOI: 10.1021/acsomega.2c02559] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/22/2022] [Indexed: 06/15/2023]
Abstract
Tomaralimab (OPN-305) is the first humanized immunoglobulin G4 monoclonal antibody against TLR2 and is designed to prevent inflammation that is driven by inappropriate or excessive activation of innate immune pathways. Here, we constructed a homology model of Tomaralimab and its complex with TLR2 at different mapped epitopes and unraveled their behavior at the atomistic level. Furthermore, we predicted a novel epitope (leucine-rich region 9-12) near the lipopeptide-binding site that can be targeted and studied for the utility of therapeutic antibodies. A geometric deep learning algorithm was used to envisage Tomaralimab binding affinity changes upon mutation. There was a significant difference in binding affinity for Tomaralimab following epitope-mutated alanine substitutions of Val266, Pro294, Arg295, Asn319, Pro326, and His372. Using deep learning-based ΔΔG prediction, we computationally contrasted human TLR2-TLR2, TLR2-TLR1, and TLR2-TLR6 dimerization. These results reveal the mechanism that underlies Tomaralimab binding to TLR2 and should help to design structure-based mimics or bispecific antibodies that can be used to inhibit both lipopeptide-binding and TLR2 dimerization.
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Affiliation(s)
- Bilal Ahmad
- Department
of Molecular Science and Technology, Ajou
University, Suwon 16499, Korea
- S&K
Therapeutics, Ajou University
Campus Plaza 418, 199 Worldcup-ro, Yeongtong-gu, Suwon 16502, Korea
| | - Sangdun Choi
- Department
of Molecular Science and Technology, Ajou
University, Suwon 16499, Korea
- S&K
Therapeutics, Ajou University
Campus Plaza 418, 199 Worldcup-ro, Yeongtong-gu, Suwon 16502, Korea
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6
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Zhou Y, Xie S, Yang Y, Jiang L, Liu S, Li W, Abagna HB, Ning L, Huang J. SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody. Front Genet 2022; 13:842127. [PMID: 35368659 PMCID: PMC8965096 DOI: 10.3389/fgene.2022.842127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 01/31/2022] [Indexed: 01/11/2023] Open
Abstract
Therapeutic antibodies play a crucial role in the treatment of various diseases. However, the success rate of antibody drug development is low partially because of unfavourable biophysical properties of antibody drug candidates such as the high aggregation tendency, which is mainly driven by hydrophobic interactions of antibody molecules. Therefore, early screening of the risk of hydrophobic interaction of antibody drug candidates is crucial. Experimental screening is laborious, time-consuming, and costly, warranting the development of efficient and high-throughput computational tools for prediction of hydrophobic interactions of therapeutic antibodies. In the present study, 131 antibodies with hydrophobic interaction experiment data were used to train a new support vector machine-based ensemble model, termed SSH2.0, to predict the hydrophobic interactions of antibodies. Feature selection was performed against CKSAAGP by using the graph-based algorithm MRMD2.0. Based on the antibody sequence, SSH2.0 achieved the sensitivity and accuracy of 100.00 and 83.97%, respectively. This approach eliminates the need of three-dimensional structure of antibodies and enables rapid screening of therapeutic antibody candidates in the early developmental stage, thereby saving time and cost. In addition, a web server was constructed that is freely available at http://i.uestc.edu.cn/SSH2/.
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Affiliation(s)
- Yuwei Zhou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiyang Xie
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yue Yang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lixu Jiang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Siqi Liu
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Li
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hamza Bukari Abagna
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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7
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Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2313:57-113. [PMID: 34478132 DOI: 10.1007/978-1-0716-1450-1_4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.
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8
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Depetris RS, Lu D, Polonskaya Z, Zhang Z, Luna X, Tankard A, Kolahi P, Drummond M, Williams C, Ebert MCCJC, Patel JP, Poyurovsky MV. Functional antibody characterization via direct structural analysis and information-driven protein-protein docking. Proteins 2021; 90:919-935. [PMID: 34773424 PMCID: PMC9544432 DOI: 10.1002/prot.26280] [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: 02/22/2021] [Revised: 08/28/2021] [Accepted: 11/07/2021] [Indexed: 12/02/2022]
Abstract
Detailed description of the mechanism of action of the therapeutic antibodies is essential for the functional characterization and future optimization of potential clinical agents. We recently developed KD035, a fully human antibody targeting vascular endothelial growth factor receptor 2 (VEGFR2). KD035 blocked VEGF‐A, and VEGF‐C‐mediated VEGFR2 activation, as demonstrated by the in vitro binding and competition assays and functional cellular assays. Here, we report a computational model of the complex between the variable fragment of KD035 (KD035(Fv)) and the domains 2 and 3 of the extracellular portion of VEGFR2 (VEGFR2(D2‐3)). Our modeling was guided by a priori experimental information including the X‐ray structures of KD035 and related antibodies, binding assays, target domain mapping and comparison of KD035 affinity for VEGFR2 from different species. The accuracy of the model was assessed by molecular dynamics simulations, and subsequently validated by mutagenesis and binding analysis. Importantly, the steps followed during the generation of this model can set a precedent for future in silico efforts aimed at the accurate description of the antibody–antigen and more broadly protein–protein complexes.
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Affiliation(s)
| | - Dan Lu
- Kadmon Corporation, LLC, New York, New York, USA
| | | | - Zhikai Zhang
- Kadmon Corporation, LLC, New York, New York, USA
| | - Xenia Luna
- Kadmon Corporation, LLC, New York, New York, USA
| | | | - Pegah Kolahi
- Kadmon Corporation, LLC, New York, New York, USA
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9
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Norman RA, Ambrosetti F, Bonvin AMJJ, Colwell LJ, Kelm S, Kumar S, Krawczyk K. Computational approaches to therapeutic antibody design: established methods and emerging trends. Brief Bioinform 2020; 21:1549-1567. [PMID: 31626279 PMCID: PMC7947987 DOI: 10.1093/bib/bbz095] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/07/2019] [Accepted: 07/05/2019] [Indexed: 12/31/2022] Open
Abstract
Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.
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10
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Krawczyk K, Raybould MIJ, Kovaltsuk A, Deane CM. Looking for therapeutic antibodies in next-generation sequencing repositories. MAbs 2019; 11:1197-1205. [PMID: 31216939 PMCID: PMC6748601 DOI: 10.1080/19420862.2019.1633884] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 06/14/2019] [Accepted: 06/14/2019] [Indexed: 12/20/2022] Open
Abstract
Recently it has become possible to query the great diversity of natural antibody repertoires using next-generation sequencing (NGS). These methods are capable of producing millions of sequences in a single experiment. Here we compare clinical-stage therapeutic antibodies to the ~1b sequences from 60 independent sequencing studies in the Observed Antibody Space database, which includes antibody sequences from NGS analysis of immunoglobulin gene repertoires. Of 242 post-Phase 1 antibodies, we found 16 with sequence identity matches of 95% or better for both heavy and light chains. There are also 54 perfect matches to therapeutic CDR-H3 regions in the NGS outputs, suggesting a nontrivial amount of convergence between naturally observed sequences and those developed artificially. This has potential implications for both the legal protection of commercial antibodies and the discovery of antibody therapeutics.
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11
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Arlotta KJ, Owen SC. Antibody and antibody derivatives as cancer therapeutics. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2019; 11:e1556. [DOI: 10.1002/wnan.1556] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 02/20/2019] [Accepted: 03/10/2019] [Indexed: 12/20/2022]
Affiliation(s)
- Keith J. Arlotta
- Department of Biomedical Engineering University of Utah Salt Lake City Utah
| | - Shawn C. Owen
- Department of Biomedical Engineering University of Utah Salt Lake City Utah
- Department of Pharmaceutics and Pharmaceutical Chemistry University of Utah Salt Lake City Utah
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12
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Garcia NK, Deperalta G, Wecksler AT. Current Trends in Biotherapeutic Higher Order Structure Characterization by Irreversible Covalent Footprinting Mass Spectrometry. Protein Pept Lett 2019; 26:35-43. [PMID: 30484396 DOI: 10.2174/0929866526666181128141953] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 10/01/2018] [Accepted: 10/29/2018] [Indexed: 12/26/2022]
Abstract
BACKGROUND Biotherapeutics, particularly monoclonal antibodies (mAbs), are a maturing class of drugs capable of treating a wide range of diseases. Therapeutic function and solutionstability are linked to the proper three-dimensional organization of the primary sequence into Higher Order Structure (HOS) as well as the timescales of protein motions (dynamics). Methods that directly monitor protein HOS and dynamics are important for mapping therapeutically relevant protein-protein interactions and assessing properly folded structures. Irreversible covalent protein footprinting Mass Spectrometry (MS) tools, such as site-specific amino acid labeling and hydroxyl radical footprinting are analytical techniques capable of monitoring the side chain solvent accessibility influenced by tertiary and quaternary structure. Here we discuss the methodology, examples of biotherapeutic applications, and the future directions of irreversible covalent protein footprinting MS in biotherapeutic research and development. CONCLUSION Bottom-up mass spectrometry using irreversible labeling techniques provide valuable information for characterizing solution-phase protein structure. Examples range from epitope mapping and protein-ligand interactions, to probing challenging structures of membrane proteins. By paring these techniques with hydrogen-deuterium exchange, spectroscopic analysis, or static-phase structural data such as crystallography or electron microscopy, a comprehensive understanding of protein structure can be obtained.
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Affiliation(s)
- Natalie K Garcia
- Department of Protein Analytical Chemistry, Genentech Inc., South San Francisco, CA 94080, United States
| | - Galahad Deperalta
- Department of Protein Analytical Chemistry, Genentech Inc., South San Francisco, CA 94080, United States
| | - Aaron T Wecksler
- Department of Protein Analytical Chemistry, Genentech Inc., South San Francisco, CA 94080, United States
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13
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Sormanni P, Aprile FA, Vendruscolo M. Third generation antibody discovery methods: in silico rational design. Chem Soc Rev 2018; 47:9137-9157. [PMID: 30298157 DOI: 10.1039/c8cs00523k] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.
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Affiliation(s)
- Pietro Sormanni
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK.
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14
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Murphy C, Stack E, Krivelo S, Breheny M, Ma H, O'Kennedy R. Enhancing recombinant antibody performance by optimally engineering its format. J Immunol Methods 2018; 463:127-133. [DOI: 10.1016/j.jim.2018.10.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 10/11/2018] [Accepted: 10/11/2018] [Indexed: 12/18/2022]
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15
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Abstract
The immune systems protect our bodies from foreign molecules or antigens, where antibodies play important roles. Antibodies evolve over time upon antigen encounter by somatically mutating their genome sequences. The end result is a series of antibodies that display higher affinities and specificities to specific antigens. This process is called affinity maturation. Recent improvements in computer hardware and modeling algorithms now enable the rational design of protein structures and functions, and several works on computer-aided antibody design have been published. In this chapter, we briefly describe computational methods for antibody affinity maturation, focusing on methods for sampling antibody conformations and for scoring designed antibody variants. We also discuss lessons learned from the successful computer-aided design of antibodies.
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Affiliation(s)
- Daisuke Kuroda
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
- Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
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16
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Leong SW, Lim TS, Ismail A, Choong YS. Integration of molecular dynamics simulation and hotspot residues grafting for de novo scFv design against Salmonella Typhi TolC protein. J Mol Recognit 2017; 31:e2695. [PMID: 29230887 DOI: 10.1002/jmr.2695] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 11/13/2017] [Accepted: 11/19/2017] [Indexed: 01/10/2023]
Abstract
With the development of de novo binders for protein targets from non-related scaffolds, many possibilities for therapeutics and diagnostics have been created. In this study, we described the use of de novo design approach to create single-chain fragment variable (scFv) for Salmonella enterica subspecies enterica serovar Typhi TolC protein. Typhoid fever is a global health concern in developing and underdeveloped countries. Rapid typhoid diagnostics will improve disease management and therapy. In this work, molecular dynamics simulation was first performed on a homology model of TolC protein in POPE membrane bilayer to obtain the central structure that was subsequently used as the target for scFv design. Potential hotspot residues capable of anchoring the binders to the target were identified by docking "disembodied" amino acid residues against TolC surface. Next, scFv scaffolds were selected from Protein Data Bank to harbor the computed hotspot residues. The hotspot residues were then incorporated into the scFv scaffold complementarity determining regions. The designs recapitulated binding energy, shape complementarity, and interface surface area of natural protein-antibody interfaces. This approach has yielded 5 designs with high binding affinity against TolC that may be beneficial for the future development of antigen-based detection agents for typhoid diagnostics.
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Affiliation(s)
- Siew Wen Leong
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Theam Soon Lim
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Asma Ismail
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Health Campus, Kubang Kerian, Kelantan, Malaysia
| | - Yee Siew Choong
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Minden, Penang, Malaysia
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17
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Kovaltsuk A, Krawczyk K, Galson JD, Kelly DF, Deane CM, Trück J. How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data. Front Immunol 2017; 8:1753. [PMID: 29276518 PMCID: PMC5727015 DOI: 10.3389/fimmu.2017.01753] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 11/27/2017] [Indexed: 12/24/2022] Open
Abstract
Next-generation sequencing of immunoglobulin gene repertoires (Ig-seq) allows the investigation of large-scale antibody dynamics at a sequence level. However, structural information, a crucial descriptor of antibody binding capability, is not collected in Ig-seq protocols. Developing systematic relationships between the antibody sequence information gathered from Ig-seq and low-throughput techniques such as X-ray crystallography could radically improve our understanding of antibodies. The mapping of Ig-seq datasets to known antibody structures can indicate structurally, and perhaps functionally, uncharted areas. Furthermore, contrasting naïve and antigenically challenged datasets using structural antibody descriptors should provide insights into antibody maturation. As the number of antibody structures steadily increases and more and more Ig-seq datasets become available, the opportunities that arise from combining the two types of information increase as well. Here, we review how these data types enrich one another and show potential for advancing our knowledge of the immune system and improving antibody engineering.
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Affiliation(s)
| | - Konrad Krawczyk
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Jacob D Galson
- Division of Immunology and the Children's Research Center, University Children's Hospital, University of Zürich, Zürich, Switzerland
| | - Dominic F Kelly
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford and the NIHR Oxford Biomedical Research Center, Oxford, United Kingdom
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Johannes Trück
- Division of Immunology and the Children's Research Center, University Children's Hospital, University of Zürich, Zürich, Switzerland
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18
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In silico methods for design of biological therapeutics. Methods 2017; 131:33-65. [PMID: 28958951 DOI: 10.1016/j.ymeth.2017.09.008] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/21/2017] [Accepted: 09/23/2017] [Indexed: 12/18/2022] Open
Abstract
It has been twenty years since the first rationally designed small molecule drug was introduced into the market. Since then, we have progressed from designing small molecules to designing biotherapeutics. This class of therapeutics includes designed proteins, peptides and nucleic acids that could more effectively combat drug resistance and even act in cases where the disease is caused because of a molecular deficiency. Computational methods are crucial in this design exercise and this review discusses the various elements of designing biotherapeutic proteins and peptides. Many of the techniques discussed here, such as the deterministic and stochastic design methods, are generally used in protein design. We have devoted special attention to the design of antibodies and vaccines. In addition to the methods for designing these molecules, we have included a comprehensive list of all biotherapeutics approved for clinical use. Also included is an overview of methods that predict the binding affinity, cell penetration ability, half-life, solubility, immunogenicity and toxicity of the designed therapeutics. Biotherapeutics are only going to grow in clinical importance and are set to herald a new generation of disease management and cure.
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