研究人员使用深度网络幻觉来生成广泛的对称蛋白质同源异构体，只给定了原体数量和原体长度的规格。7个设计的晶体结构与计算模型非常接近（RMSD中值：0.6埃），还有3个10纳米巨环的冷冻电镜结构，其残基多达1550个，C33对称性；所有这些都与以前解决的结构有很大的不同。这些结果突出了使用深度学习可以生成的新蛋白质结构的丰富多样性，并为设计日益复杂的纳米机器和生物材料的组件铺平了道路。
据介绍，深度学习生成方法提供了一个机会，可以在天然蛋白质的序列和结构之外广泛探索蛋白质结构空间。
附：英文原文
Title: Hallucinating symmetric protein assemblies
Author: B. I. M. Wicky, L. F. Milles, A. Courbet, R. J. Ragotte, J. Dauparas, E. Kinfu, S. Tipps, R. D. Kibler, M. Baek, F. DiMaio, X. Li, L. Carter, A. Kang, H. Nguyen, A. K. Bera, D. Baker
Issue&Volume: 2022-09-15
Abstract: 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 7 designs are very close to the computational models (median RMSD: 0.6 ), as are 3 cryoEM structures of giant 10 nanometer rings with up to 1550 residues and C33 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.
DOI: add1964
Source: https://www.science.org/doi/10.1126/science.add1964