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

Author:

Lin Zeming12ORCID,Akin Halil1ORCID,Rao Roshan1ORCID,Hie Brian13ORCID,Zhu Zhongkai1,Lu Wenting1,Smetanin Nikita1,Verkuil Robert1ORCID,Kabeli Ori1ORCID,Shmueli Yaniv1ORCID,dos Santos Costa Allan4ORCID,Fazel-Zarandi Maryam1,Sercu Tom1ORCID,Candido Salvatore1ORCID,Rives Alexander12ORCID

Affiliation:

1. FAIR, Meta AI, New York, NY, USA.

2. New York University, New York, NY, USA.

3. Stanford University, Palo Alto, CA, USA.

4. Massachusetts Institute of Technology, Cambridge, MA, USA.

Abstract

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.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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