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
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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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