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

doi:10.1126/science.abj8754

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DOI	10.1126/science.abj8754
	Accurate prediction of protein structures and interactions using a three-track neural network
	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
	2021-08-20
发表期刊	Science
出版年	2021
英文摘要	In 1972, Anfinsen won a Nobel prize for demonstrating a connection between a protein's amino acid sequence and its three-dimensional structure. Since 1994, scientists have competed in the biannual Critical Assessment of Structure Prediction (CASP) protein-folding challenge. Deep learning methods took center stage at CASP14, with DeepMind's Alphafold2 achieving remarkable accuracy. Baek et al. explored network architectures based on the DeepMind framework. They used a three-track network to process sequence, distance, and coordinate information simultaneously and achieved accuracies approaching those of DeepMind. The method, RoseTTA fold, can solve challenging x-ray crystallography and cryo–electron microscopy modeling problems and generate accurate models of protein-protein complexes. Science , abj8754, this issue p. [871][1] DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (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–electron microscopy 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 that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. [1]: /lookup/doi/10.1126/science.abj8754
领域	气候变化 ; 资源环境
URL	查看原文
引用统计	被引频次：2122[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/336027
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	Minkyung Baek,Frank DiMaio,Ivan Anishchenko,et al. Accurate prediction of protein structures and interactions using a three-track neural network[J]. Science,2021.
APA	Minkyung Baek.,Frank DiMaio.,Ivan Anishchenko.,Justas Dauparas.,Sergey Ovchinnikov.,...&David Baker.(2021).Accurate prediction of protein structures and interactions using a three-track neural network.Science.
MLA	Minkyung Baek,et al."Accurate prediction of protein structures and interactions using a three-track neural network".Science (2021).