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Robust deep learning based protein sequence design using ProteinMPNN
While 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, with outstan...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997061/ https://www.ncbi.nlm.nih.gov/pubmed/36108050 http://dx.doi.org/10.1126/science.add2187 |
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author | 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. |
author_facet | 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. |
author_sort | Dauparas, J. |
collection | PubMed |
description | While 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, with outstanding performance in both in silico and experimental tests. 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. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. |
format | Online Article Text |
id | pubmed-9997061 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-99970612023-03-09 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 Article While 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, with outstanding performance in both in silico and experimental tests. 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. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. 2022-10-07 2022-09-15 /pmc/articles/PMC9997061/ /pubmed/36108050 http://dx.doi.org/10.1126/science.add2187 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article 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. Robust deep learning based protein sequence design using ProteinMPNN |
title | Robust deep learning based protein sequence design using ProteinMPNN |
title_full | Robust deep learning based protein sequence design using ProteinMPNN |
title_fullStr | Robust deep learning based protein sequence design using ProteinMPNN |
title_full_unstemmed | Robust deep learning based protein sequence design using ProteinMPNN |
title_short | Robust deep learning based protein sequence design using ProteinMPNN |
title_sort | robust deep learning based protein sequence design using proteinmpnn |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997061/ https://www.ncbi.nlm.nih.gov/pubmed/36108050 http://dx.doi.org/10.1126/science.add2187 |
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