Evolutionary-scale prediction of atomic-level protein structure with a language model

• 成果类型: Article
• 署名作者: Lin, Zeming; Akin, Halil; Rao, Roshan; Hie, Brian; Zhu, Zhongkai; Lu, Wenting; Smetanin, Nikita; Verkuil, Robert; Kabeli, Ori; Shmueli, Yaniv; Costa, Allan dos Santos; Fazel-Zarandi, Maryam; Sercu, Tom; Candido, Salvatore; Rives, Alexander
• 署名单位: New York University; Stanford University; Massachusetts Institute of Technology (MIT)
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-11917
• DOI: 10.1126/science.ade2574
• 发表日期: 2023-03-17
• 页码: 1123-1130
• 关键词: contacts

摘要

Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model. As language models of protein sequences are scaled up to 15 billion parameters, an atomic-resolution picture of protein structure emerges in the learned representations. This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins. We apply this capability to construct the ESM Metagenomic Atlas by predicting structures for >617 million metagenomic protein sequences, including >225 million that are predicted with high confidence, which gives a view into the vast breadth and diversity of natural proteins.