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An all-atom protein generative model.
Chu, Alexander E; Kim, Jinho; Cheng, Lucy; El Nesr, Gina; Xu, Minkai; Shuai, Richard W; Huang, Po-Ssu.
Afiliação
  • Chu AE; Biophysics Program, Stanford University, Stanford, CA 94305.
  • Kim J; Department of Bioengineering, Stanford University, Stanford, CA 94305.
  • Cheng L; Department of Bioengineering, Stanford University, Stanford, CA 94305.
  • El Nesr G; Department of Physics, Stanford University, Stanford, CA 94305.
  • Xu M; Aquarium Learning, San Francisco, CA 94117.
  • Shuai RW; Biophysics Program, Stanford University, Stanford, CA 94305.
  • Huang PS; Department of Bioengineering, Stanford University, Stanford, CA 94305.
Proc Natl Acad Sci U S A ; 121(27): e2311500121, 2024 Jul 02.
Article em En | MEDLINE | ID: mdl-38916999
ABSTRACT
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 represents all sidechain states at once as a "superposition" state; superpositions defining a protein are collapsed into individual residue types and conformations during sample generation. When combined with sequence design methods, our model is able to codesign 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 to conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Conformação Proteica / Proteínas / Modelos Moleculares Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Conformação Proteica / Proteínas / Modelos Moleculares Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2024 Tipo de documento: Article