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Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features

International Journal of Molecular Sciences(2024)10.3390/ijms25105247Source: DataRank Database

Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features is a research paper published in International Journal of Molecular Sciences (2024). On theSindex it has a DataRank of 0. It has been cited 5 times.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)OA: goldTopics (26)IDs (PubMed, PMC)

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Authors (3)

Jiefu Li ORCID,Li Liao ORCID,Aman Sawhney ORCID

Live enrichment

PubMed:38791287 PMC:PMC11121315

National Science Foundation

Grant: NSF-MCB1820103

National Science Foundation

Grant: 1820103

Toward the molecular unpacking of the PaaC domain, a novel Plasmodesmata-association & activation Cassette

National Institutes of Health

Grant: 5P20GM103446-15

Bioinformatics Core

goldLicense: CC BY

publisherhttps://www.mdpi.com/1422-0067/25/10/5247/pdf

Europe_PMChttps://europepmc.org/articles/PMC11121315

Europe_PMChttps://europepmc.org/articles/PMC11121315?pdf=render

https://doi.org/10.3390/ijms25105247

https://pubmed.ncbi.nlm.nih.gov/38791287

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciences

MeSH Terms

Membrane ProteinsComputational BiologyBinding SitesProtein FoldingModels, MolecularDatabases, ProteinProtein Conformation, alpha-Helical

Keywords

Neural networksProtein structure modelingprotein structureMachine LearningTransmembrane ProteinsAlpha HelixContact Map PredictionAlphafoldModels, MolecularProtein Conformation, alpha-HelicalProtein FoldingBinding SitesMembrane ProteinsComputational BiologyArticleDatabases, Protein