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

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

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

Open access
Choi, Juyoung·2022·iScience

Preprint — published as 10.1016/j.isci.2023.106911 · Paper page

Abstract

Biological mechanisms comprise protein interaction networks. However, most protein interaction predictions are based on interspecies biological evidence. These knowledge-based predictions are biased and have limited predictive ranges. Moreover, protein interaction prediction using in silico docking exhibits low accuracy for weak interactions and requires high computational power. This study suggests a novel method to identify protein interaction using interaction energy distribution. Protein interactions with specific partners can be predicted by searching narrow funnel-like interaction energy distribution. Kinases and E3 ubiquitin ligase are important signaling components. However, because their interactions vary and are weak, the interaction predictions are highly limited. However, this study shows that their interaction can be identified by analyzing interaction energy distribution with high accuracy and low computational cost. Furthermore, other specific protein interactions have significantly narrower funnel-like interaction energy distribution than those of noninteracting protein pairs.

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Crossref · 10.21203/rs.3.rs-1412786/v1

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Metadata

Authors
Choi, J.
Affiliations
Sogang University (KR)
Corresponding author
Choi, Juyoung
Venue
iScience
Published
2022-03-03
Volume
26
Issue
6
Pages
106911-106911
ISSN
2589-0042 (ISSN-L)
OpenAlex
W4377086792
Cited by
1
References
1
Citations by year
2026: 1
FWCI
0.09
Type
journal article
Language
en
Open access
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
  • Seoul National University
  • Hallym University
  • Sogang 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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