Aisima Chatzi Souleiman

The study presents AlloPool, a graph neural network framework that combines temporal attention with iterative edge pooling to learn minimal, time-evolving residue interaction networks from equilibrium and non-equilibrium molecular dynamics trajectories, reconstructing trajectories at sub-angstrom RMSD across Pin1, the GAIN mechanosensor domain, dopamine D2 and beta-1 adrenergic receptors, the SdrG adhesin, PDZ3, and the Engrailed homeodomain, and using those networks to map allosteric pathways, predict ligand pharmacology, mechanical loading states, and mutation effects.

The study presents AlloPool, a graph neural network framework that combines temporal attention with iterative edge pooling to learn minimal, time-evolving residue interaction networks from equilibrium and non-equilibrium molecular dynamics trajectories, reconstructing trajectories at sub-angstrom RMSD across Pin1, the GAIN mechanosensor domain, dopamine D2 and beta-1 adrenergic receptors, the SdrG adhesin, PDZ3, and the Engrailed homeodomain, and using those networks to map allosteric pathways, predict ligand pharmacology, mechanical loading states, and mutation effects.