SNOOPPI: a sequence-normalized database of on- and off-target protein-protein interactions.
- 1Department of Bioengineering, University of Pennsylvania , 210 South 33rd Street, Skirkanich Hall, Philadelphia, PA 19104, United States.
- 2Department of Computer and Information Science, University of Pennsylvania , 3330 Walnut Street, Levine Hall, Phildelphia, PA 19104, United States.
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 and implementation
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.
Related
- ProPepBA: a binding affinity database for canonical and chemically modified protein–peptide interactions
- Representation learning for low-data protein fitness prediction using sequence–structure embeddings and graph neural networks
- Sequence‐encoded dynamics diversify allostery within a conserved protein scaffold
- An advanced LLM-powered article extraction pipeline delivers a database of buffers for protein studies and protein expression conditions.
- Sequence Engineering at Non-motif Modulator Residues Yields a Peptide That Effectively Targets a Single PDZ Protein in a Disease-relevant Cellular Context
BibTeX
@article{vincoff2026snooppi,
title = {{SNOOPPI: a sequence-normalized database of on- and off-target protein-protein interactions.}},
author = {Sophia Vincoff and Pranam Chatterjee},
journal = {Bioinformatics (Oxford, England)},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag736},
url = {https://europepmc.org/article/MED/42834527},
note = {CC BY},
}
RIS
TY - JOUR
TI - SNOOPPI: a sequence-normalized database of on- and off-target protein-protein interactions.
AU - Sophia Vincoff
AU - Pranam Chatterjee
PY - 2026
DA - 2026/10/01
JO - Bioinformatics (Oxford, England)
DO - 10.1093/bioinformatics/btag736
UR - https://europepmc.org/article/MED/42834527
AB - 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 and implementation 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.
SN - 1367-4803
SN - 1367-4811
ER -