Seq-InSite: sequence supersedes structure for protein interaction site prediction
Open accessPublished version of a preprint
Abstract
Motivation: Proteins accomplish cellular functions by interacting with each other, which makes the prediction of interaction sites a fundamental problem. As experimental methods are expensive and time consuming, computational prediction of the interaction sites has been studied extensively. Structure-based programs are the most accurate, while the sequence-based ones are much more widely applicable, as the sequences available outnumber the structures by two orders of magnitude. Ideally, we would like a tool that has the quality of the former and the applicability of the latter. Results: We provide here the first solution that achieves these two 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. The predictive power of Seq-InSite is illustrated using an analysis of evolutionary conservation for four protein sequences. Availability and implementation: Seq-InSite is freely available as a web server at http://seq-insite.csd.uwo.ca/ and as free source code, including trained models and all datasets used for training and testing, at https://github.com/lucian-ilie/Seq-InSite.
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Flagged first, then by citations
- Authors
- Hosseini, S., Golding, G. B., & Ilie, L.
- Affiliations
- Western University (CA)
- McMaster University (CA)
- Corresponding author
- Ilie, Lucian
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-01-01
- Volume
- 40
- Issue
- 1
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC10796176
- OpenAlex
- W4390793818
- Cited by
- 26
- References
- 11
- Citations by year
- 2026: 6 · 2025: 18 · 2024: 2
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- NSERC Discovery (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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