Protein sequence design with a learned potential

Anand, Namrata; Eguchi, Raphael; Mathews, Irimpan I.; Perez, Carla P.; Derry, Alexander; Altman, Russ B.; Huang, Po-Ssu

doi:10.1038/s41467-022-28313-9

Title: Protein sequence design with a learned potential

Journal Article · 07 February 2022 · Nature Communications

DOI: https://doi.org/10.1038/s41467-022-28313-9 · OSTI ID:1869814

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^[1]; Mathews, Irimpan I. ^[2];

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^[1]; Altman, Russ B. ^[1];

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Stanford Univ., CA (United States)
SLAC National Accelerator Lab., Menlo Park, CA (United States)

The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

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Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Institutes of Health (NIH); National Library of Medicine; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 1869814

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 13; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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