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Gavade A, Nagraj AK, Patel R, Pais R, Dhanure P, Scheele J, Seiz W, Patil J. Understanding the Specific Implications of Amino Acids in the Antibody Development. Protein J 2024; 43:405-424. [PMID: 38724751 DOI: 10.1007/s10930-024-10201-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2024] [Indexed: 06/01/2024]
Abstract
As the demand for immunotherapy to treat and manage cancers, infectious diseases and other disorders grows, a comprehensive understanding of amino acids and their intricate role in antibody engineering has become a prime requirement. Naturally produced antibodies may not have the most suitable amino acids at the complementarity determining regions (CDR) and framework regions, for therapeutic purposes. Therefore, to enhance the binding affinity and therapeutic properties of an antibody, the specific impact of certain amino acids on the antibody's architecture must be thoroughly studied. In antibody engineering, it is crucial to identify the key amino acid residues that significantly contribute to improving antibody properties. Therapeutic antibodies with higher binding affinity and improved functionality can be achieved through modifications or substitutions with highly suitable amino acid residues. Here, we have indicated the frequency of amino acids and their association with the binding free energy in CDRs. The review also analyzes the experimental outcome of two studies that reveal the frequency of amino acids in CDRs and provides their significant correlation between the outcomes. Additionally, it discusses the various bond interactions within the antibody structure and antigen binding. A detailed understanding of these amino acid properties should assist in the analysis of antibody sequences and structures needed for designing and enhancing the overall performance of therapeutic antibodies.
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Affiliation(s)
- Akshata Gavade
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India
| | - Anil Kumar Nagraj
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India
| | - Riya Patel
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India
| | - Roylan Pais
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India
| | - Pratiksha Dhanure
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India
| | | | | | - Jaspal Patil
- Innoplexus Consulting Services Pvt Ltd, 7Th Floor, Midas Tower, Hinjawadi, Pune, Maharashtra, 411057, India.
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Gupta P, Horspool AM, Trivedi G, Moretti G, Datar A, Huang ZF, Chiecko J, Kenny CH, Marlow MS. Matrixed CDR grafting: A neoclassical framework for antibody humanization and developability. J Biol Chem 2024; 300:105555. [PMID: 38072062 PMCID: PMC10805677 DOI: 10.1016/j.jbc.2023.105555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 12/02/2023] [Accepted: 12/05/2023] [Indexed: 01/02/2024] Open
Abstract
Discovery and optimization of a biotherapeutic monoclonal antibody requires a careful balance of target engagement and physicochemical developability properties. To take full advantage of the sequence diversity provided by different antibody discovery platforms, a rapid and reliable process for humanization of antibodies from nonhuman sources is required. Canonically, maximizing homology of the human variable region (V-region) to the original germline was believed to result in preservation of binding, often without much consideration for inherent molecular properties. We expand on this approach by grafting the complementary determining regions (CDRs) of a mouse anti-LAG3 antibody into an extensive matrix of human variable heavy chain (VH) and variable light chain (VL) framework regions with substantially broader sequence homology to assess the impact on complementary determining region-framework compatibility through progressive evaluation of expression, affinity, biophysical developability, and function. Specific VH and VL framework sequences were associated with major expression and purification phenotypes. Greater VL sequence conservation was correlated with retained or improved affinity. Analysis of grafts that bound the target demonstrated that initial developability criteria were significantly impacted by VH, but not VL. In contrast, cell binding and functional characteristics were significantly impacted by VL, but not VH. Principal component analysis of all factors identified multiple grafts that exhibited more favorable antibody properties, notably with nonoptimal sequence conservation. Overall, this study demonstrates that modern throughput systems enable a more thorough, customizable, and systematic analysis of graft-framework combinations, resulting in humanized antibodies with improved global properties that may progress through development more quickly and with a greater probability of success.
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Affiliation(s)
- Pankaj Gupta
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA.
| | - Alexander M Horspool
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Goral Trivedi
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Gina Moretti
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Akshita Datar
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Zhong-Fu Huang
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Jeffrey Chiecko
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Cynthia Hess Kenny
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Michael S Marlow
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA.
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Clark T, Subramanian V, Jayaraman A, Fitzpatrick E, Gopal R, Pentakota N, Rurak T, Anand S, Viglione A, Raman R, Tharakaraman K, Sasisekharan R. Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning. Commun Chem 2023; 6:244. [PMID: 37945793 PMCID: PMC10636138 DOI: 10.1038/s42004-023-01037-7] [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: 10/06/2023] [Accepted: 10/20/2023] [Indexed: 11/12/2023] Open
Abstract
The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-deleterious mutations on an in-house validation dataset that is free of biases seen in the publicly available training datasets. Furthermore, experimental screening of a limited number of predictions from the model (<10^2 designs) identified affinity-enhancing mutations in two unrelated SARS-CoV-2 antibodies, resulting in constructs with up to 1000-fold increased binding to the SARS-COV-2 RBD. Our findings indicate that accurate prediction and screening of non-deleterious mutations using machine learning offers a powerful approach to improving antibody affinity.
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Affiliation(s)
- Thomas Clark
- Altus Enterprises, 900 Middlesex Turnpike, Billerica, MA, USA
| | | | - Akila Jayaraman
- Altus Enterprises, 900 Middlesex Turnpike, Billerica, MA, USA
| | | | - Ranjani Gopal
- Altus Enterprises, 900 Middlesex Turnpike, Billerica, MA, USA
| | | | - Troy Rurak
- Altus Enterprises, 900 Middlesex Turnpike, Billerica, MA, USA
| | - Shweta Anand
- Altus Enterprises, 900 Middlesex Turnpike, Billerica, MA, USA
| | | | - Rahul Raman
- Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
| | | | - Ram Sasisekharan
- Department of Biological Engineering, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
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Gulland A. 'Gagged and blindsided': how an allegation of research misconduct affected our lab. Nature 2023:10.1038/d41586-023-02711-5. [PMID: 37626219 DOI: 10.1038/d41586-023-02711-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
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Yang YX, Wang P, Zhu BT. Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas. J Mol Graph Model 2023; 118:108364. [PMID: 36356467 DOI: 10.1016/j.jmgm.2022.108364] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022]
Abstract
Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.
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Affiliation(s)
- Yong Xiao Yang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China
| | - Pan Wang
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China
| | - Bao Ting Zhu
- Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China.
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Miller NL, Clark T, Raman R, Sasisekharan R. Learned features of antibody-antigen binding affinity. Front Mol Biosci 2023; 10:1112738. [PMID: 36895805 PMCID: PMC9989197 DOI: 10.3389/fmolb.2023.1112738] [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: 11/30/2022] [Accepted: 01/18/2023] [Indexed: 02/23/2023] Open
Abstract
Defining predictors of antigen-binding affinity of antibodies is valuable for engineering therapeutic antibodies with high binding affinity to their targets. However, this task is challenging owing to the huge diversity in the conformations of the complementarity determining regions of antibodies and the mode of engagement between antibody and antigen. In this study, we used the structural antibody database (SAbDab) to identify features that can discriminate high- and low-binding affinity across a 5-log scale. First, we abstracted features based on previously learned representations of protein-protein interactions to derive 'complex' feature sets, which include energetic, statistical, network-based, and machine-learned features. Second, we contrasted these complex feature sets with additional 'simple' feature sets based on counts of contacts between antibody and antigen. By investigating the predictive potential of 700 features contained in the eight complex and simple feature sets, we observed that simple feature sets perform comparably to complex feature sets in classification of binding affinity. Moreover, combining features from all eight feature-sets provided the best classification performance (median cross-validation AUROC and F1-score of 0.72). Of note, classification performance is substantially improved when several sources of data leakage (e.g., homologous antibodies) are not removed from the dataset, emphasizing a potential pitfall in this task. We additionally observe a classification performance plateau across diverse featurization approaches, highlighting the need for additional affinity-labeled antibody-antigen structural data. The findings from our present study set the stage for future studies aimed at multiple-log enhancement of antibody affinity through feature-guided engineering.
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Affiliation(s)
- Nathaniel L Miller
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.,Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Thomas Clark
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.,Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Rahul Raman
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.,Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Ram Sasisekharan
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States.,Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, United States
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Gopal R, Fitzpatrick E, Pentakota N, Jayaraman A, Tharakaraman K, Capila I. Optimizing Antibody Affinity and Developability Using a Framework-CDR Shuffling Approach-Application to an Anti-SARS-CoV-2 Antibody. Viruses 2022; 14:v14122694. [PMID: 36560698 PMCID: PMC9784564 DOI: 10.3390/v14122694] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/21/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022] Open
Abstract
The computational methods used for engineering antibodies for clinical development have undergone a transformation from three-dimensional structure-guided approaches to artificial-intelligence- and machine-learning-based approaches that leverage the large sequence data space of hundreds of millions of antibodies generated by next-generation sequencing (NGS) studies. Building on the wealth of available sequence data, we implemented a computational shuffling approach to antibody components, using the complementarity-determining region (CDR) and the framework region (FWR) to optimize an antibody for improved affinity and developability. This approach uses a set of rules to suitably combine the CDRs and FWRs derived from naturally occurring antibody sequences to engineer an antibody with high affinity and specificity. To illustrate this approach, we selected a representative SARS-CoV-2-neutralizing antibody, H4, which was identified and isolated previously based on the predominant germlines that were employed in a human host to target the SARS-CoV-2-human ACE2 receptor interaction. Compared to screening vast CDR libraries for affinity enhancements, our approach identified fewer than 100 antibody framework-CDR combinations, from which we screened and selected an antibody (CB79) that showed a reduced dissociation rate and improved affinity against the SARS-CoV-2 spike protein (7-fold) when compared to H4. The improved affinity also translated into improved neutralization (>75-fold improvement) of SARS-CoV-2. Our rapid and robust approach for optimizing antibodies from parts without the need for tedious structure-guided CDR optimization will have broad utility for biotechnological applications.
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Affiliation(s)
- Ranjani Gopal
- Discovery and Diagnostics Division, Peritia Inc., 12 Gill Street, Woburn, MA 01801, USA
| | - Emmett Fitzpatrick
- Discovery and Diagnostics Division, Peritia Inc., 12 Gill Street, Woburn, MA 01801, USA
| | - Niharika Pentakota
- Discovery and Diagnostics Division, Peritia Inc., 12 Gill Street, Woburn, MA 01801, USA
| | - Akila Jayaraman
- Discovery and Diagnostics Division, Peritia Inc., 12 Gill Street, Woburn, MA 01801, USA
| | - Kannan Tharakaraman
- Discovery and Diagnostics Division, Peritia Inc., 12 Gill Street, Woburn, MA 01801, USA
- Correspondence: (K.T.); (I.C.)
| | - Ishan Capila
- Celltas Biosciences, 900 Middlesex Turnpike, Billerica, MA 01821, USA
- Correspondence: (K.T.); (I.C.)
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Strohl WR, Ku Z, An Z, Carroll SF, Keyt BA, Strohl LM. Passive Immunotherapy Against SARS-CoV-2: From Plasma-Based Therapy to Single Potent Antibodies in the Race to Stay Ahead of the Variants. BioDrugs 2022; 36:231-323. [PMID: 35476216 PMCID: PMC9043892 DOI: 10.1007/s40259-022-00529-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/21/2022] [Indexed: 12/15/2022]
Abstract
The COVID-19 pandemic is now approaching 2 years old, with more than 440 million people infected and nearly six million dead worldwide, making it the most significant pandemic since the 1918 influenza pandemic. The severity and significance of SARS-CoV-2 was recognized immediately upon discovery, leading to innumerable companies and institutes designing and generating vaccines and therapeutic antibodies literally as soon as recombinant SARS-CoV-2 spike protein sequence was available. Within months of the pandemic start, several antibodies had been generated, tested, and moved into clinical trials, including Eli Lilly's bamlanivimab and etesevimab, Regeneron's mixture of imdevimab and casirivimab, Vir's sotrovimab, Celltrion's regdanvimab, and Lilly's bebtelovimab. These antibodies all have now received at least Emergency Use Authorizations (EUAs) and some have received full approval in select countries. To date, more than three dozen antibodies or antibody combinations have been forwarded into clinical trials. These antibodies to SARS-CoV-2 all target the receptor-binding domain (RBD), with some blocking the ability of the RBD to bind human ACE2, while others bind core regions of the RBD to modulate spike stability or ability to fuse to host cell membranes. While these antibodies were being discovered and developed, new variants of SARS-CoV-2 have cropped up in real time, altering the antibody landscape on a moving basis. Over the past year, the search has widened to find antibodies capable of neutralizing the wide array of variants that have arisen, including Alpha, Beta, Gamma, Delta, and Omicron. The recent rise and dominance of the Omicron family of variants, including the rather disparate BA.1 and BA.2 variants, demonstrate the need to continue to find new approaches to neutralize the rapidly evolving SARS-CoV-2 virus. This review highlights both convalescent plasma- and polyclonal antibody-based approaches as well as the top approximately 50 antibodies to SARS-CoV-2, their epitopes, their ability to bind to SARS-CoV-2 variants, and how they are delivered. New approaches to antibody constructs, including single domain antibodies, bispecific antibodies, IgA- and IgM-based antibodies, and modified ACE2-Fc fusion proteins, are also described. Finally, antibodies being developed for palliative care of COVID-19 disease, including the ramifications of cytokine release syndrome (CRS) and acute respiratory distress syndrome (ARDS), are described.
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Affiliation(s)
| | - Zhiqiang Ku
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, The University of Texas Health Sciences Center, Houston, TX USA
| | - Zhiqiang An
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, The University of Texas Health Sciences Center, Houston, TX USA
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