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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 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

Science (American Association for the Advancement of Science), 2022-10, Vol.378 (6615), p.49-56 [Revista revisada por pares]

United States: The American Association for the Advancement of Science

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Título:
Robust deep learning-based protein sequence design using ProteinMPNN
Autor: 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
Materias: Amino Acid Sequence ; Cryoelectron Microscopy ; Crystallography, X-Ray ; Cyclic oligomers ; Deep Learning ; Design ; Inverse problems ; Oligomers ; Optimization ; Protein Conformation ; Protein Engineering - methods ; Protein structure ; Proteins ; Proteins - chemistry
Es parte de: Science (American Association for the Advancement of Science), 2022-10, Vol.378 (6615), p.49-56
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Formal analysis: JD, LFM, BIMW, RJR, NBen
Validation: JD, NBen, HB, AKB, BS, AK, HN, SP, PJYL, NBeth, RJdH, LFM, BIMW, AC, RJR
Data curation: IA, JD, HB
Conceptualization: JD, LFM, BIMW, AC, RJdH, HB, NBen
Writing – original draft: JD, DB
Funding acquisition: JD, DB
Project administration: JD
Software: JD, TFH, DT, BK, FC
Resources: JD, DB
Visualization: JD, RJR, RJdH, HB, LFM, BIMW, PJYL, HB
Methodology: JD, IA, PJYL
Writing – review & editing: JD, DB
Supervision: DB, NPK
Author contributions
Descripción: 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.
Editor: United States: The American Association for the Advancement of Science
Idioma: Inglés

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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 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

United States: The American Association for the Advancement of Science

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