Binding affinity estimation from restrained umbrella sampling simulations.

Area of Science:

• Computational chemistry and molecular modeling
• Biophysics and structural biology

Background:

• Protein-ligand binding affinity is crucial for understanding biological processes and drug development.
• Estimating binding affinity computationally often involves complex data- or physics-driven simulations.
• Existing enhanced sampling methods can be intricate and system-specific.

Purpose of the Study:

• To present a generalized and simplified physics-based sampling approach for estimating protein-ligand binding affinity.
• To introduce a flexible computational scheme adaptable to various protein-ligand systems.
• To validate the proposed method by comparing simulation results with experimental binding affinity data.

Main Methods:

• Utilized biased molecular dynamics simulations as a purely physics-based sampling strategy.
• Developed a generalized stratification approach simplifying previous umbrella sampling and enhanced sampling techniques.
• Applied the method to estimate the binding affinity of human fibroblast growth factor 1 to heparin hexasaccharide.

Main Results:

• The proposed method offers a flexible and simplified alternative to existing enhanced sampling simulations.
• Four variations of the method were tested, demonstrating its adaptability.
• Estimated binding affinities were compared against experimental data from isothermal titration calorimetry.

Conclusions:

• The presented physics-based sampling approach effectively estimates protein-ligand binding affinity.
• This method provides a more accessible and versatile tool for computational biophysics research.
• The findings contribute to advancing computational strategies for drug discovery and molecular interaction studies.