Top-down design of protein architectures with reinforcement learning.
Science
; 380(6642): 266-273, 2023 04 21.
Article
em En
| MEDLINE
| ID: mdl-37079676
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Engenharia de Proteínas
/
Proteínas
/
Nanoestruturas
/
Aprendizado de Máquina
Idioma:
En
Revista:
Science
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos