Robust deep learning–based protein sequence design using ProteinMPNN

Dauparas, J.; Anishchenko, I.; Bennett, N.; Bai, H.; Ragotte, R. J.; Milles, L. F.; Wicky, B. M.; Courbet, A.; de Haas, R. J.; Bethel, N.; Leung, P. Y.; Huddy, T. F.; Pellock, S.; Tischer, D.; Chan, F.; Koepnick, B.; Nguyen, H.; Kang, A.; Sankaran, B.; Bera, A. K.; King, N. P.; Baker, D.

doi:10.1126/science.add2187

Robust deep learning–based protein sequence design using ProteinMPNN

Journal Article · Thu Sep 15 00:00:00 EDT 2022 · Science

DOI:https://doi.org/10.1126/science.add2187· OSTI ID:2470608

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Univ. of Washington, Seattle, WA (United States)
Wageningen Univ. (Netherlands)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning–based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo–electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2470608

Journal Information:: Science, Journal Name: Science Journal Issue: 6615 Vol. 378; ISSN 0036-8075

Publisher:: AAASCopyright Statement

Country of Publication:: United States

Language:: English

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