Quantitative and interface-aware prediction of peptide–protein interactions by VITAL

TL;DR - VITAL is a dual-channel deep learning framework for quantitatively predicting peptide–protein interactions. It jointly models sequence and structural context to estimate binding affinity and identify interaction interfaces.

• Co-learns complementary sequence and structural representations.
• Predicts peptide–protein interactions quantitatively rather than only classifying binding.
• Maps likely binding interfaces between peptides and proteins.
• Could support computational studies of molecular recognition and peptide-based discovery.