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-Structure paper
タイトル | Top-down design of protein architectures with reinforcement learning. |
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ジャーナル・号・ページ | Science, Vol. 380, Issue 6642, Page 266-273, Year 2023 |
掲載日 | 2023年4月21日 |
著者 | 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 要旨 | 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. |
リンク | Science / PubMed:37079676 |
手法 | EM (単粒子) |
解像度 | 2.5 - 3.01 Å |
構造データ | EMDB-28858, PDB-8f4x: EMDB-28859, PDB-8f53: EMDB-28860, PDB-8f54: |
由来 |
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キーワード | DE NOVO PROTEIN / nanoparticle / capsid / oligomer / de novo design / rosetta / cryoEM / reinforcement learning |