8f53

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'''Unreleased structure'''
 
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The entry 8f53 is ON HOLD until Paper Publication
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==Top-down design of protein architectures with reinforcement learning==
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<StructureSection load='8f53' size='340' side='right'caption='[[8f53]], [[Resolution|resolution]] 2.93&Aring;' scene=''>
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== Structural highlights ==
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<table><tr><td colspan='2'>[[8f53]] is a 60 chain structure with sequence from [https://en.wikipedia.org/wiki/Synthetic_construct Synthetic construct]. Full crystallographic information is available from [http://oca.weizmann.ac.il/oca-bin/ocashort?id=8F53 OCA]. For a <b>guided tour on the structure components</b> use [https://proteopedia.org/fgij/fg.htm?mol=8F53 FirstGlance]. <br>
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</td></tr><tr id='resources'><td class="sblockLbl"><b>Resources:</b></td><td class="sblockDat"><span class='plainlinks'>[https://proteopedia.org/fgij/fg.htm?mol=8f53 FirstGlance], [http://oca.weizmann.ac.il/oca-bin/ocaids?id=8f53 OCA], [https://pdbe.org/8f53 PDBe], [https://www.rcsb.org/pdb/explore.do?structureId=8f53 RCSB], [https://www.ebi.ac.uk/pdbsum/8f53 PDBsum], [https://prosat.h-its.org/prosat/prosatexe?pdbcode=8f53 ProSAT]</span></td></tr>
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</table>
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<div style="background-color:#fffaf0;">
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== Publication Abstract from PubMed ==
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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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Authors: Borst, A.J., Baker, D.
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Top-down design of protein architectures with reinforcement learning.,Lutz ID, Wang S, Norn C, Courbet A, Borst AJ, Zhao YT, Dosey A, Cao L, Xu J, Leaf EM, Treichel C, Litvicov P, Li Z, Goodson AD, Rivera-Sanchez P, Bratovianu AM, Baek M, King NP, Ruohola-Baker H, Baker D Science. 2023 Apr 21;380(6642):266-273. doi: 10.1126/science.adf6591. Epub 2023 , Apr 20. PMID:37079676<ref>PMID:37079676</ref>
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Description: Top-down design of protein architectures with reinforcement learning
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From MEDLINE&reg;/PubMed&reg;, a database of the U.S. National Library of Medicine.<br>
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[[Category: Unreleased Structures]]
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</div>
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[[Category: Baker, D]]
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<div class="pdbe-citations 8f53" style="background-color:#fffaf0;"></div>
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[[Category: Borst, A.J]]
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== References ==
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<references/>
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__TOC__
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</StructureSection>
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[[Category: Large Structures]]
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[[Category: Synthetic construct]]
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[[Category: Baker D]]
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[[Category: Borst AJ]]

Revision as of 06:55, 10 May 2023

Top-down design of protein architectures with reinforcement learning

PDB ID 8f53

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