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Cited by 1Bioinformatics and Genomic NetworksMachine Learning in BioinformaticsProtein Structure and Dynamics

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Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partner

Open access
Choi, Juyoung·2023·iScience
DOI 10.1016/j.isci.2023.106911PMC PMC10250834

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Abstract

Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.

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Metadata

Authors
Choi, J.
Affiliations
Sogang University (KR)
Corresponding author
Choi, Juyoung
ORCID
Choi, Juyoung
Venue
iScience
Publisher
Elsevier BV
Published
2023-06
Volume
26
Issue
6
Pages
106911
ISSN
2589-0042 (ISSN-L)
DOI
10.1016/j.isci.2023.106911
PMC
PMC10250834
OpenAlex
W4377086792
Cited by
1
References
7
Citations by year
2026: 1
FWCI
0.09
Type
journal article
Language
en
Open access
Licence
Locations
  • iScience (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Sogang University
  • Hallym University
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
protein-protein interaction prediction, kinase substrate prediction, TM-score, protein-protein interaction network

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