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PreprintCited by 26Machine Learning in BioinformaticsProtein Structure and DynamicsBioinformatics and Genomic Networks

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Seq-InSite: sequence supersedes structure for protein interaction site prediction

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Hosseini, SeyedMohsen, Golding, G. Brian, Ilie, Lucian·2023·Bioinformatics
DOI 10.1101/2023.06.19.545575PMC PMC10796176

Preprint — published as 10.1093/bioinformatics/btad738 · Paper page

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Abstract

Proteins accomplish cellular functions by interacting with each other, which makes the prediction of interaction sites a fundamental problem. Computational prediction of the interaction sites has been studied extensively, with the structure-based programs being the most accurate, while the sequence-based ones being much more widely applicable, as the sequences available outnumber the structures by two orders of magnitude. We provide here the first solution that achieves both goals. Our new sequence-based program, Seq-InSite, greatly surpasses the performance of sequence-based models, matching the quality of state-of-the-art structure-based predictors, thus effectively superseding the need for models requiring structure. Seq-InSite is illustrated using an analysis of four protein sequences. Seq-InSite is freely available as a web server at seq-insite.csd.uwo.ca and as free source code, including trained models and all datasets used for training and testing, at github.com/lucian-ilie/seq-insite .

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Metadata

Authors
Hosseini, S., Golding, G. B., & Ilie, L.
Affiliations
  • Western University (CA)
  • McMaster University (CA)
Corresponding author
Ilie, Lucian
ORCID
Venue
Bioinformatics
Publisher
openRxiv
Published
2023-06-21
Volume
40
Issue
1
ISSN
1367-4803 (ISSN-L)
DOI
10.1101/2023.06.19.545575
PMC
PMC10796176
OpenAlex
W4390793818
Cited by
26
References
1
Citations by year
2026: 6 · 2025: 18 · 2024: 2
FWCI
3.54
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Compute Canada
  • Natural Sciences and Engineering Research Council of Canada (RGPIN 2020, rgpin-2020-05733, rgpin-2021-03978)
Topics
  • 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
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
protein-protein interaction site prediction, sequence-based prediction, structure-based prediction, evolutionary conservation, web server, protein interaction sites
MeSH terms
Amino Acid Sequence, Proteins, Software

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