Database of articles cited by EMDB/PDB/SASBDB data

Keywords
Structure methods	All X-ray diffraction NMR electron microscopy small angle scattering neutron diffraction fiber diffraction powder diffraction infrared spectroscopy EPR fluorescence transfer theoretical model Hybrid
Author
Journal
IF	>30 >20 >10 >5 all

Title	Top-down design of protein architectures with reinforcement learning.
Journal, issue, pages	Science, Vol. 380, Issue 6642, Page 266-273, Year 2023
Publish date	Apr 21, 2023
Authors	Isaac D Lutz / Shunzhi Wang / Christoffer Norn / Alexis Courbet / Andrew J Borst / Yan Ting Zhao / Annie Dosey / Longxing Cao / Jinwei Xu / Elizabeth M Leaf / Catherine Treichel / Patrisia Litvicov / Zhe Li / Alexander D Goodson / Paula Rivera-Sánchez / Ana-Maria Bratovianu / Minkyung Baek / Neil P King / Hannele Ruohola-Baker / David Baker /
PubMed 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 ...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.
External links	Science / PubMed:37079676
Methods	EM (single particle)
Resolution	2.5 - 3.01 Å
Structure data	28858 8f4x EMDB-28858, PDB-8f4x: Top-down design of protein architectures with reinforcement learning Method: EM (single particle) / Resolution: 3.01 Å 28859 8f53 EMDB-28859, PDB-8f53: Top-down design of protein architectures with reinforcement learning Method: EM (single particle) / Resolution: 2.93 Å 28860 8f54 EMDB-28860, PDB-8f54: Top-down design of protein architectures with reinforcement learning Method: EM (single particle) / Resolution: 2.5 Å
Source	Escherichia coli (E. coli) synthetic construct (others)
Keywords	DE NOVO PROTEIN / nanoparticle / capsid / oligomer / de novo design / rosetta / cryoEM / reinforcement learning