Robust deep learning-based protein sequence design using ProteinMPNN

J Dauparas; I Anishchenko; N Bennett; H Bai; R J Ragotte; L F Milles; B I M Wicky; A Courbet; R J de Haas; N Bethel; P J Y Leung; T F Huddy; S Pellock; D Tischer; F Chan; B Koepnick; H Nguyen; A Kang; B Sankaran; A K Bera; N P King; D Baker; Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

doi:10.1126/science.add2187

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Robust deep learning-based protein sequence design using ProteinMPNN

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Robust deep learning-based protein sequence design using ProteinMPNN

J Dauparas, I Anishchenko, N Bennett, H Bai, R J Ragotte, L F Milles, B I M Wicky, A Courbet, R J de Haas, N Bethel, …

Science (American Association for the Advancement of Science), Vol.378(6615), pp.49-56

07/10/2022

DOI: https://doi.org/10.1126/science.add2187

PMID: 36108050

Résumé

Amino Acid Sequence

Cryoelectron Microscopy

Crystallography, X-Ray

Deep Learning

Protein Conformation

Protein Engineering - methods

Proteins - chemistry

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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Détails

Titre: Robust deep learning-based protein sequence design using ProteinMPNN
Créateurs - sans rôle: J Dauparas - Institute for Protein Design, University of Washington, Seattle, WA, USA
I Anishchenko - Institute for Protein Design, University of Washington, Seattle, WA, USA
N Bennett - Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
H Bai - Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
R J Ragotte - Institute for Protein Design, University of Washington, Seattle, WA, USA
L F Milles - Institute for Protein Design, University of Washington, Seattle, WA, USA
B I M Wicky - Institute for Protein Design, University of Washington, Seattle, WA, USA
A Courbet - Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
R J de Haas - Department of Physical Chemistry and Soft Matter, Wageningen University and Research, Wageningen, Netherlands
N Bethel - Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
P J Y Leung - Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA
T F Huddy - Institute for Protein Design, University of Washington, Seattle, WA, USA
S Pellock - Institute for Protein Design, University of Washington, Seattle, WA, USA
D Tischer - Institute for Protein Design, University of Washington, Seattle, WA, USA
F Chan - Institute for Protein Design, University of Washington, Seattle, WA, USA
B Koepnick - Institute for Protein Design, University of Washington, Seattle, WA, USA
H Nguyen - Institute for Protein Design, University of Washington, Seattle, WA, USA
A Kang - Institute for Protein Design, University of Washington, Seattle, WA, USA
B Sankaran - Berkeley Center for Structural Biology, Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley Laboratory, Berkeley, CA, USA
A K Bera - Institute for Protein Design, University of Washington, Seattle, WA, USA
N P King - Institute for Protein Design, University of Washington, Seattle, WA, USA
D Baker - Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Détails de publication: Science (American Association for the Advancement of Science), Vol.378(6615), pp.49-56
Note de subvention: Howard Hughes Medical Institute P30 GM124169 / NIGMS NIH HHS
Identifiants: 99136156309311
Unité académique: Centre de Biologie Structurale - CBS
Langue: English
Type de ressource: Journal article