Accurate prediction of protein structures and interactions using a three-track neural network
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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.
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The record
- Venue
- Science
- Topic
- Protein Structure and Dynamics
- Field
- Biochemistry, Genetics and Molecular Biology
- Canadian institutions
- University of British ColumbiaUniversity of Victoria
- Funders
- National Institute of Allergy and Infectious DiseasesNational Science Foundation of Sri LankaDeutsches Elektronen-SynchrotronScience and Technology Facilities CouncilNational Institutes of HealthAustrian Science FundEuropean Synchrotron Radiation FacilityWellcome TrustMicrosoftSchmidt Family FoundationCanadian Institutes of Health ResearchNational Science FoundationOpen Philanthropy ProjectNational Institute of General Medical SciencesDiamond Light SourceG. Harold and Leila Y. Mathers FoundationTU Graz, Internationale Beziehungen und MobilitätsprogrammeGlobal Challenges Research FundWashington Research FoundationWelch Foundation
- Keywords
- CASPProtein structure predictionArtificial neural networkComputer scienceFolding (DSP implementation)Artificial intelligenceSequence (biology)Protein foldingDeep learningProtein structureComputational biologyMachine learningChemistryBiologyEngineeringBiochemistry
- Has abstract in OpenAlex
- yes