Predicting the Post-translational Modification Effects on Protein−Ligand Interactions via End-Point Binding Free Energy Calculation: Database Creation and Strategy Optimization
TL;DR
The experimentally validated PTM-mediated Ligand Activity Change (PLAC) dataset is curate and finds that the interaction-affecting PTMs usually occur spatially closer to ligands than the interaction-neutral ones.
Abstract
Post-translational modifications (PTMs) frequently alter protein structures and drug-binding affinities, yet a systematic dataset is lacking. To address this gap, we curate the experimentally validated PTM-mediated Ligand Activity Change (PLAC) dataset and find that the interaction-affecting PTMs usually occur spatially closer to ligands than the interaction-neutral ones. To accurately characterize the PTMs’ effects on protein−ligand interactions, we evaluate end-point binding free-energy protocols (MM/GBSA) with varying molecular dynamics (MD) simulation times and dielectric constants. Our results show that 100 ns MD with a high dielectric constant (εin = 4) yields the strongest correlation with experimental data (rp = −0.70), whereas a low dielectric constant (εin = 1) better classifies a PTM’s effect (enhancing, weakening, or neutral). Moreover, a mechanism analysis shows that long MD simulations can capture the conformational difference between the wild-type and PTM-involved systems, thereby improving the prediction result. The PLAC dataset is provided to advance PTM-informed drug design.