SNOOPPI: A Sequence-Normalized Database of On- and Off-Target Protein-Protein Interactions

Clicks: 15
ID: 331752
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #854 of 983 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 983 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
MOTIVATION: Protein-protein interactions (PPIs) are central to biological processes and therapeutic interventions. While many databases document known PPIs, these resources often lack evidence-based negatives and clear identification of the specific proteoforms tested. RESULTS: Here, we introduce SNOOPPI, a Sequence-Normalized database of On- and Off-target Protein-Protein Interactions, which represents the first unified dataset of binary PPIs that is isoform, post-translational modification, mutation, and binding site aware. By defining a PPI as a direct, physical interaction between two amino acid sequences, SNOOPPI overcomes several persistent limitations of existing PPI databases. SNOOPPI was curated from the IntAct database, taking full advantage of its experimental metadata and feature annotations to reclassify and uncover new PPIs. The final dataset comprises over 33.6K positive interactions and 5.3K negative interactions. SNOOPPI also retains over 835K unresolved interactions, explicitly capturing gaps in the experimental literature. Beyond its usefulness as a reference dataset for the scientific community, SNOOPPI has the potential to serve as a high-confidence foundation for sequence-based modeling, benchmarking, and generative design of novel protein and peptide interactors. AVAILABILITY: The full database can be accessed and downloaded through an interactive Hugging Face page: https://huggingface.co/datasets/ChatterjeeLab/SNOOPPI. All code is open-source and maintained at https://github.com/sophievincoff/snooppi. The database and associated code will be updated periodically to incorporate new IntAct releases. The specific version of the code associated with this publication is archived at the following Zenodo repository: https://doi.org/10.5281/zenodo.22116350. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7204253591 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag736
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.