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

Authors

Sophia Vincoff, Pranam Chatterjee

Published in

Bioinformatics (Oxford, England). Oct 05, 2026. Epub Oct 05, 2026.

Abstract

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.
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.
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 data are available at Bioinformatics online.

PMID:
42834527
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.

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