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An all-atom protein generative model

Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discr...

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Autores principales: Chu, Alexander E., Cheng, Lucy, Nesr, Gina El, Xu, Minkai, Huang, Po-Ssu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245864/
https://www.ncbi.nlm.nih.gov/pubmed/37292974
http://dx.doi.org/10.1101/2023.05.24.542194
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author Chu, Alexander E.
Cheng, Lucy
Nesr, Gina El
Xu, Minkai
Huang, Po-Ssu
author_facet Chu, Alexander E.
Cheng, Lucy
Nesr, Gina El
Xu, Minkai
Huang, Po-Ssu
author_sort Chu, Alexander E.
collection PubMed
description Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which instantiates a “superposition” over the possible sidechain states, and collapses it to conduct reverse diffusion for sample generation. When combined with sequence design methods, our model is able to co-design all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.
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spelling pubmed-102458642023-06-08 An all-atom protein generative model Chu, Alexander E. Cheng, Lucy Nesr, Gina El Xu, Minkai Huang, Po-Ssu bioRxiv Article Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which instantiates a “superposition” over the possible sidechain states, and collapses it to conduct reverse diffusion for sample generation. When combined with sequence design methods, our model is able to co-design all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way. Cold Spring Harbor Laboratory 2023-05-25 /pmc/articles/PMC10245864/ /pubmed/37292974 http://dx.doi.org/10.1101/2023.05.24.542194 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
Chu, Alexander E.
Cheng, Lucy
Nesr, Gina El
Xu, Minkai
Huang, Po-Ssu
An all-atom protein generative model
title An all-atom protein generative model
title_full An all-atom protein generative model
title_fullStr An all-atom protein generative model
title_full_unstemmed An all-atom protein generative model
title_short An all-atom protein generative model
title_sort all-atom protein generative model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245864/
https://www.ncbi.nlm.nih.gov/pubmed/37292974
http://dx.doi.org/10.1101/2023.05.24.542194
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