Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution.

ABSTRACT: Large language models trained on sequence information alone are capable of learning high level principles of protein design. However, beyond sequence, the three-dimensional structures of proteins determine their specific function, activity, and evolvability. Here we show that a general protein language model augmented with protein structure backbone coordinates and trained on the inverse folding problem can guide evolution for diverse proteins without needing to explicitly model individual functional tasks. We demonstrate inverse folding to be an effective unsupervised, structure-based sequence optimization strategy that also generalizes to multimeric complexes by implicitly learning features of binding and amino acid epistasis. Using this approach, we screened ~30 variants of two therapeu

SUBMITTER: Shanker VR 

PROVIDER: S-EPMC10769282 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature