Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations.

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Proteins can exist in multiple conformations, affecting their function.
• Understanding how protein dynamics influence ligand binding is crucial for drug discovery.

Purpose of the Study:

• To develop a computational approach for characterizing protein conformational gating.
• To quantify the impact of gating on ligand binding kinetics, specifically for HIV-1 protease.

Main Methods:

• Constructed a Markov state model from molecular dynamics simulations of apo-HIV-1 protease.
• Identified and characterized protein macrostates based on ligand accessibility and kinetics.
• Integrated rate constants into multistate gating theory to derive a gating factor (γ).
• Performed Brownian dynamics simulations to model inhibitor association kinetics.

Main Results:

• Identified a kinetic network of five HIV-1 protease macrostates, including a novel "parted" conformation.
• Found that conformational gating modestly slows ligand binding to HIV-1 protease (γ = 0.75).
• Simulated association rates for eight inhibitors, revealing deviations from simple gating models for many ligands.

Conclusions:

• The developed approach effectively quantifies conformational gating effects on ligand binding.
• Conformational gating plays a moderate role in HIV-1 protease inhibitor binding kinetics.
• The method is computationally efficient and scalable for screening large numbers of ligands in systems modulated by gating.