Protein-Protein Interaction Stabilizers from MD Simulation-Derived Pharmacophores

Abstract

Protein-protein interactions (PPI) play a crucial role in nearly all cellular processes, and their dysregulation often leads to diseases. Stabilizing rather than inhibiting PPIs by small drug-like molecules offers a promising route to modulate PPIs. Here, we present an effective workflow (PPIS-MDPharma) to identify PPI stabilizers solely from molecular dynamic (MD) simulation trajectories of protein-protein (PP) complexes in the absence of a stabilizer and large database pharmacophore screening. Our approach involves extracting pharmacophore features, namely, hydrogen bonding, electrostatic, hydrophobic, and aromatic features from MD simulation by analyzing the interaction of the interface pocket residues with water and ions. The resulting pharmacophore model, along with tens of thousands of derived subsets, is ranked and screened against a local database of 50 million compounds using rapid pharmacophore screening. It yields tens of thousands of stabilizer candidates followed by rescoring using the molecular mechanics generalized Born surface area (MMGBSA) method. For seven PP complexes, the top-ranked ligands exhibited MMGBSA scores similar to experimentally known stabilizers. The approach is computationally more efficient than alternative docking based methods, making it a promising tool for discovering novel PPI stabilizers for various therapeutic applications.