SPPIDER-seq: Sequence-based Partner-aware Predictor of Protein-Protein Interaction Sites

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ID: 317385
2026
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Abstract
MOTIVATION: Sequence-based protein-protein interaction (PPI) site predictors typically analyze proteins in isolation, neglecting partner-specific context that is critical for interface specificity, particularly in transient and disordered interactions. RESULTS: We introduce SPPIDER-seq, a partner-aware PPI site prediction framework that combines pretrained ESM-2 embeddings with a cross-attention architecture to enable residue-level conditioning on interacting partners. We curated non-redundant protein-peptide interaction datasets from BioLiP and used them to train and benchmark two complementary models: a receptor-centric model optimized for structured interfaces and a peptide-centric model tailored to disordered, motif-driven binding. On blind benchmarks, SPPIDER-seq achieved AUROC values up to 0.797 and MCC values up to 0.269, outperforming AlphaFold3 on peptide-mediated and disordered interfaces while remaining complementary on globular complexes. Application to 341 TP53 interaction partners revealed coherent, partner-specific interface patterns across both structured and intrinsically disordered regions. AVAILABILITY: SPPIDER-seq models, datasets, and the Python code are freely available on the web at https://github.com/aporollo-lab/SPPIDER-seq and archived on Zenodo at DOI: 10.5281/zenodo.19835990, corresponding to GitHub release v2.0-manuscript. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7164822299 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aleksey Porollo, Om Jadhav, Aaron Alvarez, Jichao Chen
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag388
URL
https://doi.org/10.1093/bioinformatics/btag388
Keywords Keywords not found

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