Accurate prediction of protein structures and interactions using a three-track neural network.
Journal:
Science (New York, N.Y.)
PMID:
34282049
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.
Authors
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