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Top-down design of protein architectures with reinforcement learning.
Lutz, Isaac D; Wang, Shunzhi; Norn, Christoffer; Courbet, Alexis; Borst, Andrew J; Zhao, Yan Ting; Dosey, Annie; Cao, Longxing; Xu, Jinwei; Leaf, Elizabeth M; Treichel, Catherine; Litvicov, Patrisia; Li, Zhe; Goodson, Alexander D; Rivera-Sánchez, Paula; Bratovianu, Ana-Maria; Baek, Minkyung; King, Neil P; Ruohola-Baker, Hannele; Baker, David.
Afiliación
  • Lutz ID; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Wang S; Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • Norn C; Department of Bioengineering, University of Washington, Seattle, WA, USA.
  • Courbet A; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Borst AJ; Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • Zhao YT; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Dosey A; Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • Cao L; BioInnovation Institute, DK2200 Copenhagen N, Denmark.
  • Xu J; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Leaf EM; Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • Treichel C; Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
  • Litvicov P; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Li Z; Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • Goodson AD; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Rivera-Sánchez P; Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.
  • Bratovianu AM; Oral Health Sciences, University of Washington, Seattle, WA, USA.
  • Baek M; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • King NP; Institute for Protein Design, University of Washington, Seattle, WA, USA.
  • Ruohola-Baker H; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Baker D; Institute for Protein Design, University of Washington, Seattle, WA, USA.
Science ; 380(6642): 266-273, 2023 04 21.
Article en En | MEDLINE | ID: mdl-37079676
As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a "top-down" reinforcement learning-based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo-electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ingeniería de Proteínas / Proteínas / Nanoestructuras / Aprendizaje Automático Idioma: En Revista: Science Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ingeniería de Proteínas / Proteínas / Nanoestructuras / Aprendizaje Automático Idioma: En Revista: Science Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos