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PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features

Methods(2023)10.1016/j.ymeth.2023.03.002Source: DataRank Database

PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features is a research paper published in Methods (2023). On theSindex it has a DataRank of 0. It has been cited 30 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access30 citations

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Enrichment:Funding (10 grants)Topics (8)IDs (PubMed, PMC)Annotations (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Daisuke Kihara ORCID,M. Michael Gromiha ORCID,K. Harini ORCID

Live enrichment

PubMed:36924867 PMC:PMC10563387

National Science Foundation

Grant: DMS2151678

National Science Foundation

Grant: DBI2146026

National Science Foundation

Grant: MCB1925643

Ministry of Science and Technology, Government of Nepal

Grant: OVDF 2022

NIGMS NIH HHS

Grant: R01 GM123055

NIGMS NIH HHS

Grant: R01 GM133840

National Science Foundation

Grant: CMMI1825941

Science and Engineering Research Board

Grant: CRG/2020/000314

National Science Foundation

Grant: DBI2003635

National Science Foundation

Grant: IIS2211598

MeSH Terms

ProteinsDNABinding SitesProtein Binding

Keywords

Binding free energyProtein–dna ComplexContact PotentialsStructure-based Features

PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features

FAIR Checklist

Authors (3)

Funding (10 grants)

Topics

Annotations (Europe PMC)