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Cheng J, Liang T, Xie XQ, Feng Z, Meng L. A new era of antibody discovery: an in-depth review of AI-driven approaches. Drug Discov Today 2024; 29:103984. [PMID: 38642702 DOI: 10.1016/j.drudis.2024.103984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 04/02/2024] [Accepted: 04/15/2024] [Indexed: 04/22/2024]
Abstract
Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.
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Affiliation(s)
- Jin Cheng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, 224005, China
| | - Tianjian Liang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA; Department of Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, and Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA.
| | - Li Meng
- School of Pharmacy, Jiangsu Vocational College of Medicine, Yancheng, 224005, China.
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2
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Parkinson J, Wang W. For antibody sequence generative modeling, mixture models may be all you need. Bioinformatics 2024; 40:btae278. [PMID: 38652603 PMCID: PMC11093529 DOI: 10.1093/bioinformatics/btae278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/02/2024] [Accepted: 04/19/2024] [Indexed: 04/25/2024] Open
Abstract
MOTIVATION Antibody therapeutic candidates must exhibit not only tight binding to their target but also good developability properties, especially low risk of immunogenicity. RESULTS In this work, we fit a simple generative model, SAM, to sixty million human heavy and seventy million human light chains. We show that the probability of a sequence calculated by the model distinguishes human sequences from other species with the same or better accuracy on a variety of benchmark datasets containing >400 million sequences than any other model in the literature, outperforming large language models (LLMs) by large margins. SAM can humanize sequences, generate new sequences, and score sequences for humanness. It is both fast and fully interpretable. Our results highlight the importance of using simple models as baselines for protein engineering tasks. We additionally introduce a new tool for numbering antibody sequences which is orders of magnitude faster than existing tools in the literature. AVAILABILITY AND IMPLEMENTATION All tools developed in this study are available at https://github.com/Wang-lab-UCSD/AntPack.
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Affiliation(s)
- Jonathan Parkinson
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359, United States
- MAP Bioscience, La Jolla, CA 92093, United States
| | - Wei Wang
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093-0359, United States
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093-0359, United States
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3
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Marinov TM, Abu-Shmais AA, Janke AK, Georgiev IS. Design of Antigen-Specific Antibody CDRH3 Sequences Using AI and Germline-Based Templates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.22.586241. [PMID: 38562698 PMCID: PMC10983980 DOI: 10.1101/2024.03.22.586241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Antibody-antigen specificity is engendered and refined through a number of complex B cell processes, including germline gene recombination and somatic hypermutation. Here, we present an AI-based technology for de novo generation of antigen-specific antibody CDRH3 sequences using germline-based templates, and validate this technology through the generation of antibodies against SARS-CoV-2. AI-based processes that mimic the outcome, but bypass the complexity of natural antibody generation, can be efficient and effective alternatives to traditional experimental approaches for antibody discovery.
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Affiliation(s)
- Toma M. Marinov
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Program in Computational Microbiology and Immunology, Vanderbilt University Medical Center; Nashville, TN, 37232, USA
| | - Alexandra A. Abu-Shmais
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Alexis K. Janke
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Ivelin S. Georgiev
- Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Program in Computational Microbiology and Immunology, Vanderbilt University Medical Center; Nashville, TN, 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Infection, Immunology and Inflammation, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN 37232, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
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4
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Hill A, True JM, Jones CH. Transforming drug development with synthetic biology and AI. Trends Biotechnol 2024:S0167-7799(24)00028-3. [PMID: 38383215 DOI: 10.1016/j.tibtech.2024.01.008] [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: 11/14/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/23/2024]
Abstract
The COVID-19 pandemic has thrust RNA as a platform for drug development into the spotlight. However, identifying promising drug candidates is challenging. With advances in synthetic biology and artificial intelligence (AI) models, we can overcome this hurdle, transforming drug development and ushering in a new era in the pharmaceutical industry.
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Affiliation(s)
- Andrew Hill
- Pfizer, 66 Hudson Boulevard, New York, NY 10001, USA
| | - Jane M True
- Pfizer, 66 Hudson Boulevard, New York, NY 10001, USA
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5
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Kim DN, McNaughton AD, Kumar N. Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions. Bioengineering (Basel) 2024; 11:185. [PMID: 38391671 PMCID: PMC10886287 DOI: 10.3390/bioengineering11020185] [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: 12/30/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.
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Affiliation(s)
- Doo Nam Kim
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
| | - Andrew D McNaughton
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
| | - Neeraj Kumar
- Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352, USA
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6
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Sulea T, Kumar S, Kuroda D. Editorial: Progress and challenges in computational structure-based design and development of biologic drugs. Front Mol Biosci 2024; 11:1360267. [PMID: 38389897 PMCID: PMC10883042 DOI: 10.3389/fmolb.2024.1360267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/01/2024] [Indexed: 02/24/2024] Open
Affiliation(s)
- Traian Sulea
- Human Health Therapeutics Research Centre, National Research Council Canada, Montreal, QC, Canada
| | - Sandeep Kumar
- Computational Protein Design and Modeling, Computational Science, Moderna Therapeutics, Cambridge, MA, United States
| | - Daisuke Kuroda
- Research Center of Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan
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7
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Notin P, Rollins N, Gal Y, Sander C, Marks D. Machine learning for functional protein design. Nat Biotechnol 2024; 42:216-228. [PMID: 38361074 DOI: 10.1038/s41587-024-02127-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 01/05/2024] [Indexed: 02/17/2024]
Abstract
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.
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Affiliation(s)
- Pascal Notin
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Computer Science, University of Oxford, Oxford, UK.
| | | | - Yarin Gal
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Chris Sander
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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8
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Hung TI, Hsieh YJ, Lu WL, Wu KP, Chang CEA. What Strengthens Protein-Protein Interactions: Analysis and Applications of Residue Correlation Networks. J Mol Biol 2023; 435:168337. [PMID: 37918563 DOI: 10.1016/j.jmb.2023.168337] [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: 09/05/2023] [Revised: 10/13/2023] [Accepted: 10/26/2023] [Indexed: 11/04/2023]
Abstract
Identifying residues critical to protein-protein binding and efficient design of stable and specific protein binders are challenging tasks. Extending beyond the direct contacts in a protein-protein binding interface, our study employs computational modeling to reveal the essential network of residue interactions and dihedral angle correlations critical in protein-protein recognition. We hypothesized that mutating residues exhibiting highly correlated dynamic motion within the interaction network could efficiently optimize protein-protein interactions to create tight and selective protein binders. We tested this hypothesis using the ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complex, since Ub is a central player in multiple cellular functions and PLpro is an antiviral drug target. Our designed ubiquitin variant (UbV) hosting three mutated residues displayed a ∼3,500-fold increase in functional inhibition relative to wild-type Ub. Further optimization of two C-terminal residues within the Ub network resulted in a KD of 1.5 nM and IC50 of 9.7 nM for the five-point Ub mutant, eliciting 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study highlights residue correlation and interaction networks in protein-protein interactions, and introduces an effective approach to design high-affinity protein binders for cell biology research and future therapeutics.
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Affiliation(s)
- Ta I Hung
- Department of Chemistry, University of California, Riverside, United States; Department of Bioengineering, University of California, Riverside, United States
| | - Yun-Jung Hsieh
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan; Institute of Biochemical Sciences, National Taiwan University, Taipei, Taiwan
| | - Wei-Lin Lu
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan
| | - Kuen-Phon Wu
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan; Institute of Biochemical Sciences, National Taiwan University, Taipei, Taiwan.
| | - Chia-En A Chang
- Department of Chemistry, University of California, Riverside, United States.
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9
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Matsunaga R, Ujiie K, Inagaki M, Fernández Pérez J, Yasuda Y, Mimasu S, Soga S, Tsumoto K. High-throughput analysis system of interaction kinetics for data-driven antibody design. Sci Rep 2023; 13:19417. [PMID: 37990030 PMCID: PMC10663500 DOI: 10.1038/s41598-023-46756-y] [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: 09/01/2023] [Accepted: 11/04/2023] [Indexed: 11/23/2023] Open
Abstract
Surface plasmon resonance (SPR) is widely used for antigen-antibody interaction kinetics analysis. However, it has not been used in the screening phase because of the low throughput of measurement and analysis. Herein, we proposed a high-throughput SPR analysis system named "BreviA" using the Brevibacillus expression system. Brevibacillus was transformed using a plasmid library containing various antibody sequences, and single colonies were cultured in 96-well plates. Sequence analysis was performed using bacterial cells, and recombinant antibodies secreted in the supernatant were immobilized on a sensor chip to analyze their interactions with antigens using high-throughput SPR. Using this system, the process from the transformation to 384 interaction analyses can be performed within a week. This system utility was tested using an interspecies specificity design of an anti-human programmed cell death protein 1 (PD-1) antibody. A plasmid library containing alanine and tyrosine mutants of all complementarity-determining region residues was generated. A high-throughput SPR analysis was performed against human and mouse PD-1, showing that the mutation in the specific region enhanced the affinity for mouse PD-1. Furthermore, deep mutational scanning of the region revealed two mutants with > 100-fold increased affinity for mouse PD-1, demonstrating the potential efficacy of antibody design using data-driven approach.
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Affiliation(s)
- Ryo Matsunaga
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan
| | - Kan Ujiie
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan
| | - Mayuko Inagaki
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan
| | - Jorge Fernández Pérez
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan
| | - Yoshiki Yasuda
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan
| | - Shinya Mimasu
- Biologics Engineering, Discovery Intelligence, Astellas Pharma Inc., 21, Miyukigaoka, Tsukuba-shi, Ibaraki, 305-8585, Japan
| | - Shinji Soga
- Biologics Engineering, Discovery Intelligence, Astellas Pharma Inc., 21, Miyukigaoka, Tsukuba-shi, Ibaraki, 305-8585, Japan
| | - Kouhei Tsumoto
- Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan.
- Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan.
- The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan.
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10
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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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11
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Arras P, Yoo HB, Pekar L, Clarke T, Friedrich L, Schröter C, Schanz J, Tonillo J, Siegmund V, Doerner A, Krah S, Guarnera E, Zielonka S, Evers A. AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study. Front Mol Biosci 2023; 10:1249247. [PMID: 37842638 PMCID: PMC10575757 DOI: 10.3389/fmolb.2023.1249247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/31/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles. Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production. Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.
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Affiliation(s)
- Paul Arras
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
| | - Han Byul Yoo
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Lukas Pekar
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Thomas Clarke
- Bioinformatics, EMD Serono, Billerica, MA, United States
| | - Lukas Friedrich
- Computational Chemistry and Biologics, Merck Healthcare KGaA, Darmstadt, Germany
| | | | - Jennifer Schanz
- ADCs & Targeted NBE Therapeutics, Merck KGaA, Darmstadt, Germany
| | - Jason Tonillo
- ADCs & Targeted NBE Therapeutics, Merck KGaA, Darmstadt, Germany
| | - Vanessa Siegmund
- Early Protein Supply and Characterization, Merck Healthcare KGaA, Darmstadt, Germany
| | - Achim Doerner
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Simon Krah
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Enrico Guarnera
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Stefan Zielonka
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
| | - Andreas Evers
- Antibody Discovery and Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
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12
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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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13
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Arras P, Yoo HB, Pekar L, Schröter C, Clarke T, Krah S, Klewinghaus D, Siegmund V, Evers A, Zielonka S. A library approach for the de novo high-throughput isolation of humanized VHH domains with favorable developability properties following camelid immunization. MAbs 2023; 15:2261149. [PMID: 37766540 PMCID: PMC10540653 DOI: 10.1080/19420862.2023.2261149] [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: 05/27/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
In this study, we generated a novel library approach for high throughput de novo identification of humanized single-domain antibodies following camelid immunization. To achieve this, VHH-derived complementarity-determining regions-3 (CDR3s) obtained from an immunized llama (Lama glama) were grafted onto humanized VHH backbones comprising moderately sequence-diversified CDR1 and CDR2 regions similar to natural immunized and naïve antibody repertoires. Importantly, these CDRs were tailored toward favorable in silico developability properties, by considering human-likeness as well as excluding potential sequence liabilities and predicted immunogenic motifs. Target-specific humanized single-domain antibodies (sdAbs) were readily obtained by yeast surface display. We demonstrate that, by exploiting this approach, high affinity sdAbs with an optimized in silico developability profile can be generated. These sdAbs display favorable biophysical, biochemical, and functional attributes and do not require any further sequence optimization. This approach is generally applicable to any antigen upon camelid immunization and has the potential to significantly accelerate candidate selection and reduce risks and attrition rates in sdAb development.
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Affiliation(s)
- Paul Arras
- Antibody Discovery & Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
| | - Han Byul Yoo
- Antibody Discovery & Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Early Protein Supply & Characterization, Merck Healthcare KGaA, Darmstadt, Germany
| | - Lukas Pekar
- Antibody Discovery & Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | | | | | - Simon Krah
- Antibody Discovery & Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Daniel Klewinghaus
- Early Protein Supply & Characterization, Merck Healthcare KGaA, Darmstadt, Germany
| | - Vanessa Siegmund
- Early Protein Supply & Characterization, Merck Healthcare KGaA, Darmstadt, Germany
| | - Andreas Evers
- Antibody Discovery & Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
| | - Stefan Zielonka
- Antibody Discovery & Protein Engineering, Merck Healthcare KGaA, Darmstadt, Germany
- Institute for Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
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