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Science ; 373(6557): 871-876, 2021 08 20.
Artigo em Inglês | MEDLINE | ID: mdl-34282049


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.

Aprendizado Profundo , Conformação Proteica , Dobramento de Proteína , Proteínas/química , Proteínas ADAM/química , Sequência de Aminoácidos , Simulação por Computador , Microscopia Crioeletrônica , Cristalografia por Raios X , Bases de Dados de Proteínas , Proteínas de Membrana/química , Modelos Moleculares , Complexos Multiproteicos/química , Redes Neurais de Computação , Subunidades Proteicas/química , Proteínas/fisiologia , Receptores Acoplados a Proteínas G/química , Esfingosina N-Aciltransferase/química