Unsupervised evolution of protein and antibody complexes with a structure-informed language model

• 成果类型: Article
• 署名作者: Shanker, Varun R.; Bruun, Theodora U. J.; Hie, Brian L.; Kim, Peter S.
• 署名单位: Stanford University; Stanford University; Stanford University; Stanford University; Chan Zuckerberg Initiative (CZI); Stanford University; Stanford University
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-10394
• DOI: 10.1126/science.adk8946
• 发表日期: 2024-07-05
• 页码: 46-53
• 关键词: fitness landscapes; sequence; DESIGN; selection; RECOGNITION; inhibition; generation; reveals; set

摘要

Large language models trained on sequence information alone can learn 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 can guide evolution for diverse proteins without the need to model individual functional tasks. We also demonstrate that ESM-IF1, which was only trained on single-chain structures, can be extended to engineer protein complexes. Using this approach, we screened about 30 variants of two therapeutic clinical antibodies used to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We achieved up to 25-fold improvement in neutralization and 37-fold improvement in affinity against antibody-escaped viral variants of concern BQ.1.1 and XBB.1.5, respectively. These findings highlight the advantage of integrating structural information to identify efficient protein evolution trajectories without requiring any task-specific training data.