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Inverse folding for antibody sequence design using deep learning
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We consider the problem of antibody sequence design given 3D structural information. Building on previous work, we propose a fine-tuned inverse folding model that is specifically optimised for antibody structures and outperforms generic protein models on sequence recovery and structure robustness when applied on antibodies, with notable improvement on the hypervariable CDR-H3 loop. We study the canonical conformations of complementarity-determining regions and find improved encoding of these loops into known clusters. Finally, we consider the applications of our model to drug discovery and binder design and evaluate the quality of proposed sequences using physics-based methods.
Forward citations
Cited by 2 Pith papers
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Tokenizing Loops of Antibodies
Igloo is a multimodal antibody loop tokenizer that, when plugged into protein language models, modestly improves loop retrieval, affinity prediction, and structure-consistent loop generation.
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Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design
AAMFM combines ESM3, an antigen-geometry adapter, and Cal-DPO preference optimization rewarded by AlphaFold3-style scores to design antibody CDRs and structures, reporting higher predicted binding scores than prior methods.
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