Robust deep learning-based protein sequence design using ProteinMPNN

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...

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Published in:Science (American Association for the Advancement of Science) Vol. 378; no. 6615; p. 49
Main Authors: Dauparas, J, Anishchenko, I, Bennett, N, Bai, H, Ragotte, R J, Milles, L F, Wicky, B I M, Courbet, A, de Haas, R J, Bethel, N, Leung, P J 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
Format: Journal Article
Language:English
Published: United States 07.10.2022
Subjects:
Amino Acid Sequence
Cryoelectron Microscopy
Crystallography, X-Ray
Deep Learning
Protein Conformation
Protein Engineering - methods
Proteins - chemistry
ISSN:1095-9203, 1095-9203
Online Access:Get more information
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Summary: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.
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ISSN:1095-9203
1095-9203
DOI:10.1126/science.add2187