SPPIDER-seq: Sequence-based partner-aware predictor of protein-protein interaction sites.
Alexey Porollo1, Om Jadhav2, Aaron Alvarez2
1Department of Biostatistics, Health Informatics and Data Sciences, the University of Cincinnati College of Medicine, Cincinnati, OH, USA 45267.
Biorxiv : the Preprint Server for Biology
|May 7, 2026
Summary
SPPIDER-seq is a new framework for predicting protein-protein interaction sites by considering partner context. It outperforms existing methods on disordered interfaces and reveals partner-specific binding patterns.
Area of Science:
- Computational Biology
- Structural Biology
- Bioinformatics
Background:
- Traditional protein-protein interaction (PPI) site predictors often ignore crucial partner-specific context.
- This limitation is particularly significant for transient and intrinsically disordered protein interactions.
Purpose of the Study:
- To introduce SPPIDER-seq, a novel partner-aware framework for predicting PPI sites.
- To develop models that account for interacting partner context in residue-level predictions.
Main Methods:
- Utilized pretrained ESM-2 embeddings and a cross-attention architecture for partner conditioning.
- Curated non-redundant protein-peptide interaction datasets from BioLiP for training.
- Developed and benchmarked two complementary models: receptor-centric and peptide-centric.
Main Results:
- SPPIDER-seq achieved high performance on blind benchmarks (AUROC up to 0.797, MCC up to 0.269).
- Outperformed AlphaFold3 on peptide-mediated and disordered interfaces, showing complementarity on globular complexes.
- Identified coherent, partner-specific interface patterns in TP53 interaction partners across different regions.
Conclusions:
- SPPIDER-seq effectively predicts PPI sites by incorporating partner-specific information.
- The framework advances the prediction of interactions involving disordered regions and protein-peptide binding.
- Demonstrated the utility of partner-aware context in understanding protein interaction specificity.
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