TvTPPIMI: A Boundary-Aware Dual-View Framework for Predicting PPI-Modulator Interactions

Protein-protein interactions (PPIs) regulate essential cellular processes and represent an important class of therapeutic targets; however, discovering effective modulators of PPIs remains a formidable challenge. Although deep learning approaches have been widely explored for PPI modulator discovery, many rely on simplified representations that obscure interchain boundary information and fine-grained PPI-modulator interaction (PPIMI) patterns, limiting their robustness under distribution shifts. To address this challenge, we introduce TvTPPIMI, a framework that leverages learnable boundary tokens to encode partner-aware boundary information and models PPIMI at atom-residue resolution. In a case study targeting the AURKA-TPX2 interaction, TvTPPIMI prioritized putative modulatory candidates from a small-molecule screening library. Structure-based docking, attention analysis, multireplica molecular dynamics simulations, MM/GBSA binding free-energy estimation, and noncovalent interaction analyses provided post hoc physical support for the stable AURKA binding of selected candidates, highlighting CE02-6266 as the most favorable compound among the tested hits. Together, these results suggest that TvTPPIMI provides a generalizable computational framework with coarse-grained, attention-based interpretive cues for PPIMI prediction and can be integrated with structure- and dynamics-based analyses to support PPI modulator discovery.