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

Area of Science:

• Computational biophysics
• Molecular modeling
• Biomolecular simulations

Background:

• Early protein structure prediction relied on simplified models.
• Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) enabled accessible binding and folding energetics evaluation from molecular dynamics (MD) trajectories.
• MM-PBSA has applications in protein structure prediction and protein-ligand affinity ranking.

Purpose of the Study:

• To develop more efficient methods for binding free energy estimation.
• To enable prospective applications in ligand design.
• To demonstrate the utility of physics-based modeling in hypothesis generation.

Main Methods:

• Development of the hierarchical Molecular Mechanics Generalized Born (H-MMGB) approach.
• Utilizing the Generalized Born model for binding free energy estimation.
• Application of MM-PBSA and H-MMGB to case studies including protein folding, ligand modeling, and protein-protein interactions.

Main Results:

• H-MMGB provides efficient MMGB-based binding free energy estimates.
• Case studies demonstrate successful hypothesis generation using these methods, even without experimental structures.
• The methods are applicable to challenging protein-protein interaction targets.

Conclusions:

• Gradually incorporating more physics into modeling workflows enhances success probability across diverse computational simulation problems.
• Physics-based computational biophysics is crucial for advancing biomolecular structure and interaction evaluation.
• These advanced methods support hypothesis generation and drug design.