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Parametrically guided design of beta barrels and transmembrane nanopores using deep learning

Proceedings of the National Academy of Sciences(2025)10.1073/pnas.2425459122Source: DataRank Database

Parametrically guided design of beta barrels and transmembrane nanopores using deep learning is a research paper published in Proceedings of the National Academy of Sciences (2025). On theSindex it has a DataRank of 0. It has been cited 11 times.

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Open Access11 citations

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Enrichment:Funding (2 grants)OA: hybridTopics (16)IDs (PubMed, PMC)Annotations (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 (10)

Joseph L. Watson ORCID,David Juergens ORCID,Sagardip Majumder ORCID,Ria Sonigra,Stacey Gerben ORCID

Live enrichment

PubMed:40953261 PMC:PMC12478100

NIGMS NIH HHS

Grant: P30 GM133893

National Institutes of Health

Grant: 5P30GM133893-04

The Center for Biomolecular Structure at NSLS-II

hybridLicense: cc-by-nc-nd

publisherhttps://www.pnas.org/doi/pdf/10.1073/pnas.2425459122

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/12478100

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12478100/

Europe_PMChttps://europepmc.org/articles/PMC12478100

Europe_PMChttps://europepmc.org/articles/PMC12478100?pdf=render

Fields of Study

0206 medical engineering02 engineering and technology

MeSH Terms

Protein FoldingModels, MolecularNanoporesProtein Conformation, beta-StrandDeep Learning

Keywords

Protein designNanoporesDeep LearningBeta BarrelsModels, MolecularProtein FoldingProtein Conformation, beta-StrandBiological SciencesArticle

Parametrically guided design of beta barrels and transmembrane nanopores using deep learning

FAIR Checklist

Authors (10)

Funding (2 grants)

Open Access

Topics

Annotations (Europe PMC)