An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen

TL;DR

This paper introduces an integrated deep-learning framework combining peptide-protein interaction prediction and target-conditioned peptide generation, enhancing early-stage peptide screening with improved accuracy and interpretability.

Contribution

It presents novel models ConGA-PepPI and TC-PepGen that jointly enable partner-aware interaction prediction and conditioned peptide generation, addressing limitations of prior separate approaches.

Findings

ConGA-PepPI achieved 0.839 accuracy and 0.921 AUROC in cross-validation.

TC-PepGen's peptides exceeded native templates in 40.39% of cases based on AlphaFold ipTM.

The framework remained competitive on external benchmarks and preserved target-conditioned signals.

Abstract

Motivation: Peptide-protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterization remains too slow for large-scale screening. Existing methods usually emphasize either interaction prediction or peptide generation, leaving candidate prioritization, residue-level interpretation, and target-conditioned expansion insufficiently integrated. Results: We present an integrated framework for early-stage peptide screening that combines a partner-aware prediction and localization model (ConGA-PepPI) with a target-conditioned generative model (TC-PepGen). ConGA-PepPI uses asymmetric encoding, bidirectional cross-attention, and progressive transfer from pair prediction to binding-site localization, while TC-PepGen preserves target information throughout autoregressive decoding via layerwise conditioning. In five-fold cross-validation, ConGA-PepPI achieved 0.839 accuracy and 0.921 AUROC, with binding-site AUPR values of 0.601 on the protein side and 0.950 on the peptide side, and remained competitive on external benchmarks. Under a controlled length-conditioned benchmark, 40.39% of TC-PepGen peptides exceeded native templates in AlphaFold 3 ipTM, and unconstrained generation retained evidence of target-conditioned signal.