SPPIDER-seq
SPPIDER-seq is a deep learning framework for partner-aware prediction of protein–protein interaction (PPI) sites directly from amino acid sequences. Given a pair of interacting proteins, the models predict residue-level interface propensities while explicitly accounting for the identity of the interaction partner.
Models
This repository contains two complementary pretrained models:
- Peptide-centric model – predicts interface residues on the peptide/ligand protein using the receptor sequence as context.
- Receptor-centric model – predicts interface residues on the receptor protein using the peptide/ligand sequence as context.
Model Architecture
Both models share the same neural network architecture described in the SPPIDER-seq publication:
- ESM-2 (650M) protein language model embeddings
- Multi-head cross-attention network
- Multi-layer perceptron (MLP) prediction head
Software
SPPIDER-seq software, command-line interface, and notebooks are available at:
Citation
If you use these models in your research, please cite:
Porollo A, Jadhav O, Alvarez A, Chen J.
SPPIDER-seq: sequence-based partner-aware predictor of protein–protein interaction sites. Bioinformatics. 2026;42(7):btag388.
https://doi.org/10.1093/bioinformatics/btag388
Model Version
Version: v2.2.0
Notes for Version 2.2.0
The neural network architecture is unchanged from the published SPPIDER-seq model.
Compared with the models accompanying the original publication, the v2.2.0 models were retrained using the complete curated datasets described in the manuscript. Training employed a 90%/10% train/validation split to maximize the amount of data available for model fitting while retaining an independent validation set for model selection.
Although overall predictive performance is expected to remain comparable to that reported in the publication, the updated models generally produce higher interface propensity scores. In addition, the peptide-centric model exhibits improved sensitivity toward interaction sites located within intrinsically disordered regions, consistent with observations made during internal benchmarking.
