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 3-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 3-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.
Highlights
With the recent considerable progress in protein structure prediction, a key question is what accurate protein structure models can be used for
Intrigued by the DeepMind results, and with the goal of increasing protein structure prediction accuracy for structural biology research and advancing protein design [4], we explored network architectures incorporating different combinations of these five properties
We investigated the utility of the RoseTTAFold to facilitate experimental structure determination by X-ray crystallography and cryo-electron microscopy and to build models providing biological insights for key proteins of currently unknown structures
Summary
With the recent considerable progress in protein structure prediction, a key question is what accurate protein structure models can be used for. We investigated the utility of the RoseTTAFold to facilitate experimental structure determination by X-ray crystallography and cryo-electron microscopy and to build models providing biological insights for key proteins of currently unknown structures. Benchmark tests on GPCR sequences with determined structures showed that RoseTTAFold models for both active and inactive states can be quite accurate even in the absence of close homologs with known structures We provide RoseTTAFold models and accompanying accuracy predictions for closed and open states of all human GPCRs of currently unknown structure
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