From MM-PBSA to H-MMGB: Multiscale Modeling for Biomolecular Structure and Drug Discovery.

Abstract

From early efforts to predict protein structure from simplified models, computational biophysics has progressed toward increasingly physics-based approaches for evaluating biomolecular structure, molecular interactions, and energetics. The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method provided one of the first broadly accessible ways to evaluate binding and folding energetics from molecular dynamics (MD) trajectories, with applications ranging from protein structure prediction benchmarks to protein-ligand affinity ranking. Building on this foundation, the hierarchical Molecular Mechanics Generalized Born (H-MMGB) approach was developed to provide MMGB-based binding free energy estimates more efficiently, employing the Generalized Born model in contrast to the Poisson-Boltzmann framework of MM-PBSA and thereby enabling prospective applications to ligand design. Case studies illustrate how these methods, ranging from protein folding assessment to intact-ligand modeling and to a deconstruction-reconstruction strategy using picofragments, enable hypothesis generation in the absence of experimental structures and in challenging protein-protein interaction targets. Together, these developments support a guiding principle: gradual incorporation of more physics into modeling workflows increases the probability of successfully meeting objectives across diverse computational simulation problems.