美国华盛顿大学David Baker团队利用三轨神经网络准确预测蛋白质的结构和相互作用。相关论文于2021年7月15日在线发表在《科学》杂志上。

研究人员探索了包含相关理念的网络架构，并通过一个三轨网络获得了最佳性能，其中一维序列层面、二维距离图层面和三维坐标层面的信息被连续转换和整合。三轨网络产生的结构预测精度接近CASP14中的DeepMind，能够快速解决具有挑战性的X射线晶体学和冷冻电镜结构建模问题，并对目前未知结构的蛋白质的功能提供见解。

该网络还能仅从序列信息中快速生成准确的蛋白质-蛋白质复合物模型，缩短了需要在对接后对单个亚单位进行建模的传统方法。研究人员将该方法提供给科学界，以便加速生物学研究。

据悉，DeepMind在最近的CASP14蛋白质结构预测评估会议上提出了非常准确的预测结果。

附：英文原文

Title: Accurate prediction of protein structures and interactions using a three-track neural network

Author: Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy DeGiovanni, Jose H. Pereira, Andria V. Rodrigues, Alberdina A. van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister, Christoph Buhlheller, Tea Pavkov-Keller, Manoj K. Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Christopher Garcia, Nick V. Grishin, Paul D. Adams, Randy J. Read, David Baker

Issue&Volume: 2021/07/15

Abstract: DeepMind presented remarkably accurate predictions at the recent CASP14 protein structure prediction assessment conference. We explored network architectures incorporating related ideas and obtained the best performance with a three-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.

DOI: 10.1126/science.abj8754

Source: https://science.sciencemag.org/content/early/2021/07/14/science.abj8754