Accurate prediction of protein structures and interactions using a three-track neural network
Точное предсказание структур и взаимодействий белков с использованием трековой нейронной сети из трех каналов
2021-07-15
SCID: 54.1/mc55sf5t
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1D-2D-3D information integrationprotein structure predictionprotein-protein complex modelingthree-track neural networkx-ray crystallography and cryo-electron microscopy modeling
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Abstract (AI)
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.
Key Findings
1
A three-track neural network integrating 1D sequence, 2D distance map, and 3D coordinate information achieves top performance among explored architectures.
2
The authors are releasing the method to the scientific community to accelerate biological research.
3
The method enables rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems.
4
The model rapidly generates accurate protein-protein complex models from sequence alone, bypassing separate subunit modeling and docking.
5
The network provides functional insights for proteins of currently unknown structure.
6
The three-track network produces structure predictions with accuracies approaching DeepMind's CASP14 results.
Research Object
Protein structures and protein–protein complexes predicted from amino-acid sequences
Research Subject
Accurate prediction of 3D protein structures and protein–protein interactions using a three-track neural network that integrates 1D sequence, 2D distance maps, and 3D coordinates
Publication Details
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2021-07-15
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