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Top-down design of protein architectures with reinforcement learning.
- Source :
-
Science (New York, N.Y.) [Science] 2023 Apr 21; Vol. 380 (6642), pp. 266-273. Date of Electronic Publication: 2023 Apr 20. - Publication Year :
- 2023
-
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.
Details
- Language :
- English
- ISSN :
- 1095-9203
- Volume :
- 380
- Issue :
- 6642
- Database :
- MEDLINE
- Journal :
- Science (New York, N.Y.)
- Publication Type :
- Academic Journal
- Accession number :
- 37079676
- Full Text :
- https://doi.org/10.1126/science.adf6591