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

Accepted version
Peer-reviewed

Type

Article

Change log

Abstract

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.

Description

Keywords

ADAM Proteins, Amino Acid Sequence, Computer Simulation, Cryoelectron Microscopy, Crystallography, X-Ray, Databases, Protein, Deep Learning, Membrane Proteins, Models, Molecular, Multiprotein Complexes, Neural Networks, Computer, Protein Conformation, Protein Folding, Protein Subunits, Proteins, Receptors, G-Protein-Coupled, Sphingosine N-Acyltransferase

Journal Title

Science

Conference Name

Journal ISSN

0036-8075
1095-9203

Volume Title

373

Publisher

American Association for the Advancement of Science (AAAS)
Sponsorship
Wellcome Trust (209407/Z/17/Z)
National Institute of General Medical Sciences (P01GM063210)