Learning the language of antibody hypervariability

• 成果类型: Article
• 署名作者: Singh, Rohit; Im, Chiho; Qiu, Yu; Mackness, Brian; Gupta, Abhinav; Joren, Taylor; Sledzieski, Samuel; Erlach, Lena; Wendt, Maria; Nanfack, Yves Fomekong; Bryson, Bryan; Berger, Bonnie
• 署名单位: Massachusetts Institute of Technology (MIT); Sanofi-Aventis; Sanofi USA; Sanofi-Aventis; Sanofi USA; Swiss Federal Institutes of Technology Domain; ETH Zurich; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11531
• DOI: 10.1073/pnas.2418918121
• 发表日期: 2025-01-07
• 关键词: sequences

摘要

Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose foundational PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody MutagenesisAugmented Processing (AbMAP), which fine-tunes foundational models for antibody- sequence inputs by supervising on antibody structure and binding specificity examples. Our learned feature representations accurately predict mutational effects on antigen binding, paratope identification, and other key antibody properties. We experimentally validate AbMAP for antibody optimization by applying it to refine a set of antibodies that bind to a SARS-CoV-2 peptide, and obtain an 82% hit-rate and up to 22-fold increase in binding affinity. AbMAP also unlocks large-scale analyses of immune repertoires, revealing that B-cell receptor repertoires of individuals, while remarkably different in sequence, converge toward similar structural and functional coverage. Importantly, AbMAP's transfer learning approach can be readily adapted to advances in foundational PLMs. We anticipate AbMAP will accelerate the efficient design and modeling of antibodies, expedite the discovery of antibody-based therapeutics, and deepen our understanding of humoral immunity.