1
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Keating SM, Higgins BW. New technologies in therapeutic antibody development: The next frontier for treating infectious diseases. Antiviral Res 2024; 227:105902. [PMID: 38734210 DOI: 10.1016/j.antiviral.2024.105902] [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: 01/29/2024] [Revised: 05/02/2024] [Accepted: 05/05/2024] [Indexed: 05/13/2024]
Abstract
Adaptive immunity to viral infections requires time to neutralize and clear viruses to resolve infection. Fast growing and pathogenic viruses are quickly established, are highly transmissible and cause significant disease burden making it difficult to mount effective responses, thereby prolonging infection. Antibody-based passive immunotherapies can provide initial protection during acute infection, assist in mounting an adaptive immune response, or provide protection for those who are immune suppressed or immune deficient. Historically, plasma-derived antibodies have demonstrated some success in treating diseases caused by viral pathogens; nonetheless, limitations in access to product and antibody titer reduce success of this treatment modality. Monoclonal antibodies (mAbs) have proven an effective alternative, as it is possible to manufacture highly potent and specific mAbs against viral targets on an industrial scale. As a result, innovative technologies to discover, engineer and manufacture specific and potent antibodies have become an essential part of the first line of treatment in pathogenic viral infections. However, a mAb targeting a specific epitope will allow escape variants to outgrow, causing new variant strains to become dominant and resistant to treatment with that mAb. Methods to mitigate escape have included combining mAbs into cocktails, creating bi-specific or antibody drug conjugates but these strategies have also been challenged by the potential development of escape mutations. New technologies in developing antibodies made as recombinant polyclonal drugs can integrate the strength of poly-specific antibody responses to prevent mutational escape, while also incorporating antibody engineering to prevent antibody dependent enhancement and direct adaptive immune responses.
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Affiliation(s)
- Sheila M Keating
- GigaGen, Inc. (A Grifols Company), 75 Shoreway Road, San Carlos, CA, 94070, USA.
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2
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Joubbi S, Micheli A, Milazzo P, Maccari G, Ciano G, Cardamone D, Medini D. Antibody design using deep learning: from sequence and structure design to affinity maturation. Brief Bioinform 2024; 25:bbae307. [PMID: 38960409 PMCID: PMC11221890 DOI: 10.1093/bib/bbae307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 05/20/2024] [Accepted: 06/12/2024] [Indexed: 07/05/2024] Open
Abstract
Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody-antigen interactions docking and affinity maturation.
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Affiliation(s)
- Sara Joubbi
- Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Alessio Micheli
- Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy
| | - Paolo Milazzo
- Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy
| | - Giuseppe Maccari
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Giorgio Ciano
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Dario Cardamone
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
| | - Duccio Medini
- Data Science for Health (DaScH) Lab, Fondazione Toscana Life Sciences, Via Fiorentina, 1, 53100, Siena, Italy
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3
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Terranova N, Renard D, Shahin MH, Menon S, Cao Y, Hop CECA, Hayes S, Madrasi K, Stodtmann S, Tensfeldt T, Vaddady P, Ellinwood N, Lu J. Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices. Clin Pharmacol Ther 2024; 115:658-672. [PMID: 37716910 DOI: 10.1002/cpt.3053] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Abstract
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.
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Affiliation(s)
- Nadia Terranova
- Quantitative Pharmacology, Merck KGaA, Lausanne, Switzerland
| | - Didier Renard
- Full Development Pharmacometrics, Novartis Pharma AG, Basel, Switzerland
| | | | - Sujatha Menon
- Clinical Pharmacology, Pfizer Inc., Groton, Connecticut, USA
| | - Youfang Cao
- Clinical Pharmacology and Translational Medicine, Eisai Inc., Nutley, New Jersey, USA
| | | | - Sean Hayes
- Quantitative Pharmacology & Pharmacometrics, Merck & Co. Inc., Rahway, New Jersey, USA
| | - Kumpal Madrasi
- Modeling & Simulation, Sanofi, Bridgewater, New Jersey, USA
| | - Sven Stodtmann
- Pharmacometrics, AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany
| | | | - Pavan Vaddady
- Quantitative Clinical Pharmacology, Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA
| | | | - James Lu
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
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4
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Gallo E. The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design. J Biomed Sci 2024; 31:29. [PMID: 38491519 PMCID: PMC10943851 DOI: 10.1186/s12929-024-01018-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/05/2024] [Indexed: 03/18/2024] Open
Abstract
Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.
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Affiliation(s)
- Eugenio Gallo
- Department of Medicinal Chemistry, Avance Biologicals, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- Department of Protein Engineering, RevivAb, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
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5
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Barton J, Gaspariunas A, Galson JD, Leem J. Building Representation Learning Models for Antibody Comprehension. Cold Spring Harb Perspect Biol 2024; 16:a041462. [PMID: 38012013 PMCID: PMC10910360 DOI: 10.1101/cshperspect.a041462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. It is challenging to predict the functional and biophysical properties of antibodies from their amino acid sequence alone, requiring numerous experiments for full characterization. Machine learning, specifically deep representation learning, has emerged as a family of methods that can complement wet lab approaches and accelerate the overall discovery and engineering process. Here, we review advances in antibody sequence representation learning, and how this has improved antibody structure prediction and facilitated antibody optimization. We discuss challenges in the development and implementation of such models, such as the lack of publicly available, well-curated antibody function data and highlight opportunities for improvement. These and future advances in machine learning for antibody sequences have the potential to increase the success rate in developing new therapeutics, resulting in broader access to transformative medicines and improved patient outcomes.
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Affiliation(s)
- Justin Barton
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | | | - Jacob D Galson
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
| | - Jinwoo Leem
- Alchemab Therapeutics Ltd, London N1C 4AX, United Kingdom
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6
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Gallo E. Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances. Mol Biotechnol 2024:10.1007/s12033-024-01064-2. [PMID: 38308755 DOI: 10.1007/s12033-024-01064-2] [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: 11/03/2023] [Accepted: 01/02/2024] [Indexed: 02/05/2024]
Abstract
Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.
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Affiliation(s)
- Eugenio Gallo
- Avance Biologicals, Department of Medicinal Chemistry, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- RevivAb, Department of Protein Engineering, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
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7
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Gallo E. Current advancements in B-cell receptor sequencing fast-track the development of synthetic antibodies. Mol Biol Rep 2024; 51:134. [PMID: 38236361 DOI: 10.1007/s11033-023-08941-0] [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: 10/07/2023] [Accepted: 11/13/2023] [Indexed: 01/19/2024]
Abstract
Synthetic antibodies (Abs) are a class of engineered proteins designed to mimic the functions of natural Abs. These are produced entirely in vitro, eliminating the need for an immune response. As such, synthetic Abs have transformed the traditional methods of raising Abs. Likewise, deep sequencing technologies have revolutionized genomics and molecular biology. These enable the rapid and cost-effective sequencing of DNA and RNA molecules. They have allowed for accurate and inexpensive analysis of entire genomes and transcriptomes. Notably, via deep sequencing it is now possible to sequence a person's entire B-cell receptor immune repertoire, termed BCR sequencing. This procedure allows for big data explorations of natural Abs associated with an immune response. Importantly, the identified sequences have the ability to improve the design and engineering of synthetic Abs by offering an initial sequence framework for downstream optimizations. Additionally, machine learning algorithms can be introduced to leverage the vast amount of BCR sequencing datasets to rapidly identify patterns hidden in big data to effectively make in silico predictions of antigen selective synthetic Abs. Thus, the convergence of BCR sequencing, machine learning, and synthetic Ab development has effectively promoted a new era in Ab therapeutics. The combination of these technologies is driving rapid advances in precision medicine, diagnostics, and personalized treatments.
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Affiliation(s)
- Eugenio Gallo
- Avance Biologicals, Department of Medicinal Chemistry, 950 Dupont Street, Toronto, ON, M6H 1Z2, Canada.
- RevivAb, Department of Protein Engineering, Av. Ipiranga, 6681, Partenon, Porto Alegre, RS, 90619-900, Brazil.
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8
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Dudzic P, Chomicz D, Kończak J, Satława T, Janusz B, Wrobel S, Gawłowski T, Jaszczyszyn I, Bielska W, Demharter S, Spreafico R, Schulte L, Martin K, Comeau SR, Krawczyk K. Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery. MAbs 2024; 16:2361928. [PMID: 38844871 PMCID: PMC11164219 DOI: 10.1080/19420862.2024.2361928] [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: 01/30/2024] [Accepted: 05/27/2024] [Indexed: 06/12/2024] Open
Abstract
The naïve human antibody repertoire has theoretical access to an estimated > 1015 antibodies. Identifying subsets of this prohibitively large space where therapeutically relevant antibodies may be found is useful for development of these agents. It was previously demonstrated that, despite the immense sequence space, different individuals can produce the same antibodies. It was also shown that therapeutic antibodies, which typically follow seemingly unnatural development processes, can arise independently naturally. To check for biases in how the sequence space is explored, we data mined public repositories to identify 220 bioprojects with a combined seven billion reads. Of these, we created a subset of human bioprojects that we make available as the AbNGS database (https://naturalantibody.com/ngs/). AbNGS contains 135 bioprojects with four billion productive human heavy variable region sequences and 385 million unique complementarity-determining region (CDR)-H3s. We find that 270,000 (0.07% of 385 million) unique CDR-H3s are highly public in that they occur in at least five of 135 bioprojects. Of 700 unique therapeutic CDR-H3, a total of 6% has direct matches in the small set of 270,000. This observation extends to a match between CDR-H3 and V-gene call as well. Thus, the subspace of shared ('public') CDR-H3s shows utility for serving as a starting point for therapeutic antibody design.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Lukas Schulte
- Global Computational Biology & Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
| | - Kyle Martin
- Biotherapeutics Discovery, Boehringer Ingelheim, Ridgefield, CT, USA
| | - Stephen R. Comeau
- Biotherapeutics Discovery, Boehringer Ingelheim, Ridgefield, CT, USA
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9
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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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10
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Erasmus MF, Ferrara F, D'Angelo S, Spector L, Leal-Lopes C, Teixeira AA, Sørensen J, Nagpal S, Perea-Schmittle K, Choudhary A, Honnen W, Calianese D, Antonio Rodriguez Carnero L, Cocklin S, Greiff V, Pinter A, Bradbury ARM. Insights into next generation sequencing guided antibody selection strategies. Sci Rep 2023; 13:18370. [PMID: 37884618 PMCID: PMC10603065 DOI: 10.1038/s41598-023-45538-w] [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: 08/09/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023] Open
Abstract
Therapeutic antibody discovery often relies on in-vitro display methods to identify lead candidates. Assessing selected output diversity traditionally involves random colony picking and Sanger sequencing, which has limitations. Next-generation sequencing (NGS) offers a cost-effective solution with increased read depth, allowing a comprehensive understanding of diversity. Our study establishes NGS guidelines for antibody drug discovery, demonstrating its advantages in expanding the number of unique HCDR3 clusters, broadening the number of high affinity antibodies, expanding the total number of antibodies recognizing different epitopes, and improving lead prioritization. Surprisingly, our investigation into the correlation between NGS-derived frequencies of CDRs and affinity revealed a lack of association, although this limitation could be moderately mitigated by leveraging NGS clustering, enrichment and/or relative abundance across different regions to enhance lead prioritization. This study highlights NGS benefits, offering insights, recommendations, and the most effective approach to leverage NGS in therapeutic antibody discovery.
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Affiliation(s)
| | | | - Sara D'Angelo
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | - Laura Spector
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | | | | | | | | | | | - Alok Choudhary
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | - William Honnen
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | - David Calianese
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
| | | | - Simon Cocklin
- Specifica LLC, a Q2 Solutions Company, Santa Fe, USA
| | | | - Abraham Pinter
- Public Health Research Institute, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, 07103, USA
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11
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Bai G, Sun C, Guo Z, Wang Y, Zeng X, Su Y, Zhao Q, Ma B. Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects. Semin Cancer Biol 2023; 95:13-24. [PMID: 37355214 DOI: 10.1016/j.semcancer.2023.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/09/2023] [Accepted: 06/18/2023] [Indexed: 06/26/2023]
Abstract
Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.
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Affiliation(s)
- Ganggang Bai
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chuance Sun
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziang Guo
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China
| | - Yangjing Wang
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xincheng Zeng
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuhong Su
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macao Special Administrative Region of China.
| | - Buyong Ma
- Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Digiwiser BioTechnolgy, Limited, Shanghai 201203, China.
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12
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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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Hederman AP, Ackerman ME. Leveraging deep learning to improve vaccine design. Trends Immunol 2023; 44:333-344. [PMID: 37003949 PMCID: PMC10485910 DOI: 10.1016/j.it.2023.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/05/2023] [Accepted: 03/05/2023] [Indexed: 04/03/2023]
Abstract
Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the biomedical sciences, however, the revolutionary results seen in other fields are only now beginning to be realized. Vaccine research and development efforts represent an application with high public health significance. Protein structure prediction, immune repertoire analysis, and phylogenetics are three principal areas in which deep learning is poised to provide key advances. Here, we opine on some of the current challenges with deep learning and how they are being addressed. Despite the nascent stage of deep learning applications in immunological studies, there is ample opportunity to utilize this new technology to address the most challenging and burdensome infectious diseases confronting global populations.
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Affiliation(s)
| | - Margaret E Ackerman
- Thayer School of Engineering, Dartmouth College, Hanover, NH, USA; Department of Microbiology and Immunology, Geisel School of Medicine, Hanover, NH, USA.
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Chen Z, Wang X, Chen X, Huang J, Wang C, Wang J, Wang Z. Accelerating therapeutic protein design with computational approaches toward the clinical stage. Comput Struct Biotechnol J 2023; 21:2909-2926. [PMID: 38213894 PMCID: PMC10781723 DOI: 10.1016/j.csbj.2023.04.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/11/2023] [Accepted: 04/27/2023] [Indexed: 01/13/2024] Open
Abstract
Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.
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Affiliation(s)
- Zhidong Chen
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xinpei Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xu Chen
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Juyang Huang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Chenglin Wang
- Shenzhen Qiyu Biotechnology Co., Ltd, Shenzhen 518107, China
| | - Junqing Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Zhe Wang
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
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15
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Li T, Li Y, Zhu X, He Y, Wu Y, Ying T, Xie Z. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. Semin Cancer Biol 2023; 91:50-69. [PMID: 36870459 DOI: 10.1016/j.semcancer.2023.02.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.
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Affiliation(s)
- Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yupeng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyi Zhu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Yao He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yanling Wu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Tianlei Ying
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China.
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.
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Computational and artificial intelligence-based methods for antibody development. Trends Pharmacol Sci 2023; 44:175-189. [PMID: 36669976 DOI: 10.1016/j.tips.2022.12.005] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 01/19/2023]
Abstract
Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.
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17
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Huang Y, Zhang Z, Zhou Y. AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information. Front Immunol 2022; 13:1053617. [PMID: 36618397 PMCID: PMC9813736 DOI: 10.3389/fimmu.2022.1053617] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in antibody therapeutics is the experimental identification of antibody-antigen interactions, which is generally time-consuming, costly, and laborious. Although some computational methods have been proposed to screen potential antibodies, the dependence on 3D structures still limits the application of these methods. Methods Here, we developed a deep learning-assisted prediction method (i.e., AbAgIntPre) for fast identification of antibody-antigen interactions that only relies on amino acid sequences. A Siamese-like convolutional neural network architecture was established with the amino acid composition encoding scheme for both antigens and antibodies. Results and Discussion The generic model of AbAgIntPre achieved satisfactory performance with the Area Under Curve (AUC) of 0.82 on a high-quality generic independent test dataset. Besides, this approach also showed competitive performance on the more specific SARS-CoV dataset. We expect that AbAgIntPre can serve as an important complement to traditional experimental methods for antibody screening and effectively reduce the workload of antibody design. The web server of AbAgIntPre is freely available at http://www.zzdlab.com/AbAgIntPre.
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Affiliation(s)
- Yan Huang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China,Department of Biomedical Informatics, Key Laboratory of Molecular Cardiovascular Sciences of the Ministry of Education, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China,*Correspondence: Ziding Zhang, ; Yuan Zhou,
| | - Yuan Zhou
- Department of Biomedical Informatics, Key Laboratory of Molecular Cardiovascular Sciences of the Ministry of Education, School of Basic Medical Sciences, Peking University, Beijing, China,*Correspondence: Ziding Zhang, ; Yuan Zhou,
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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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