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Fast and accurate Ab Initio Protein structure prediction using deep learning potentials

PLoS Computational Biology(2022)10.1371/journal.pcbi.1010539Source: DataRank Database

Fast and accurate Ab Initio Protein structure prediction using deep learning potentials is a research paper published in PLoS Computational Biology (2022). On theSindex it has a DataRank of 0. It has been cited 41 times.

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Open Access41 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (18 grants)Impact (FWCI 2.90)OA: goldTopics (52)IDs (PubMed, PMC, OpenAlex)SDGs (1)

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Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (4)

Yang Li ORCID,Gilbert S. Omenn ORCID,Yang Zhang ORCID,Robin Pearce ORCID

Live enrichment

PubMed:36112717 PMC:PMC9518900 OpenAlex:https://openalex.org/W4296164039

National Institute of General Medical Sciences

Grant: GM136422

National Institute of General Medical Sciences

Grant: S10OD026825

National Institute of Allergy and Infectious Diseases

Grant: AI134678

National Science Foundation

Grant: IIS1901191

National Science Foundation

Grant: DBI2030790

National Science Foundation

Grant: MTM2025426

National Institutes of Health

Grant: U24CA210967

National Institutes of Health

Grant: P30ES017885

NIGMS NIH HHS

Grant: R35 GM136422

NIAID NIH HHS

Grant: R01 AI134678

National Institutes of Health

Grant: 5R35GM136422-02

Advanced approaches to protein structure prediction

National Institutes of Health

Grant: 1S10OD026825-01A1

High-Performance Computing Cluster for Biomedical Research

National Science Foundation

Grant: 1901191

III: Medium: Collaborative Research: Multi-level computational approaches to protein function prediction

National Institutes of Health

Grant: 5U24CA210967-04

University of Michigan Proteogenomics Data Analysis Center

National Institutes of Health

Grant: 2P30ES017885-10A1

Michigan Center on Lifestage Environmental Exposures and Disease (M-LEEaD)

National Science Foundation

Grant: 2025426

MTM 2: Combining structural informatics and crosslinking mass spectrometry to predict the key protein-protein interactions shaping symbiotic microbial communities

National Science Foundation

Grant: 2030790

IIBR: Informatics: RAPID: Genome-wide Structure and Function Modeling of the SARS-CoV-2 Virus

National Institutes of Health

Grant: 5R01AI134678-03

Structure-based functional annotation of microbial genomes

FWCI

2.90

Citation Percentile

0.9%

Citation Trend

2023

2024

2025

2026

goldLicense: cc-by

journalhttps://doi.org/10.1371/journal.pcbi.1010539

publisherhttps://doi.org/10.1371/journal.pcbi.1010539

publisherhttps://dx.plos.org/10.1371/journal.pcbi.1010539

repositoryhttps://pubmed.ncbi.nlm.nih.gov/36112717

repositoryhttps://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010539

Fields of Study

Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Materials Science0301 basic medicine0303 health sciences03 medical and health sciencesAlgorithmsComputational BiologyDatabases, ProteinDeep LearningModels, MolecularProtein ConformationProtein FoldingProteinsSoftware

MeSH Terms

Deep LearningAlgorithmsModels, MolecularProtein ConformationProteinsSoftwareProtein FoldingComputational BiologyDatabases, Protein

Keywords

Protein structure predictionBenchmark (surveying)CASPComputer scienceArtificial intelligenceAb initioFolding (DSP implementation)Deep learningProtein foldingGradient descentResidualProtein structureMachine learningArtificial neural networkAlgorithmPattern recognition (psychology)PhysicsEngineeringModels, MolecularQH301-705.5Protein ConformationComputational BiologyProteinsBiology (General)Databases, ProteinAlgorithmsSoftwareResearch Article

Sustainable Development Goals

SDG 0: Affordable and clean energy