幻觉
埃
蛋白质结构
结晶学
生成语法
计算机科学
纳米技术
人工智能
化学
材料科学
生物化学
作者
Basile I. M. Wicky,Lukas F. Milles,Alexis Courbet,Robert J. Ragotte,Justas Dauparas,Elias S Kinfu,S. Tipps,Ryan D. Kibler,Minkyung Baek,Frank DiMaio,Xinting Li,Lauren Carter,Alex Kang,Hannah Nguyen,Asim K. Bera,David Baker
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2022-09-15
卷期号:378 (6615): 56-61
被引量:126
标识
DOI:10.1126/science.add1964
摘要
Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here, we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of seven designs are very similar to the computational models (median root mean square deviation: 0.6 angstroms), as are three cryo–electron microscopy structures of giant 10-nanometer rings with up to 1550 residues and C 33 symmetry; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be generated using deep learning and pave the way for the design of increasingly complex components for nanomachines and biomaterials.
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