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Instruments / Technical Area	Publication Details	Published	Actions
I04-Macromolecular Crystallography	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 Science 2 DOI: 10.1126/science.abj8754 Diamond Proposal Number(s): [20303] Show Abstract ▼ 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.	Jul 2021

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I04-Macromolecular Crystallography

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