Ke Yan

KGIPA improves prediction of peptide-protein non-covalent interactions by integrating biological knowledge and multimodal features, outperforming existing methods in binding residue and affinity prediction.

This paper introduces iMFP-LG, a method combining protein language models and graph attention networks to identify multi-functional peptides, identifying 8 candidates and confirming one with antibacterial and anticancer effects.