Qualitative Prediction of Ligand Dissociation Kinetics from Focal Adhesion Kinase Using Steered Molecular Dynamics

Area of Science:

• Computational Chemistry and Molecular Modeling
• Drug Discovery and Development
• Biophysics

Background:

• Traditional drug discovery prioritizes equilibrium binding affinity.
• Increasing interest in nonequilibrium binding kinetics for drug optimization.
• Focal adhesion kinase (FAK) is a significant therapeutic target.

Purpose of the Study:

• To evaluate the utility of steered molecular dynamics (SMD) for predicting drug-binding kinetics.
• To identify drug candidates with desirable binding kinetics for FAK.
• To establish a predictive model for ligand dissociation rates from FAK.

Main Methods:

• Simulated dissociation of 14 ligands from FAK using SMD.
• Empirical power-law relationship analysis between simulated unbinding time and experimental dissociation rate constants.
• Development of regression models correlating simulation data with experimental dissociation rates.

Main Results:

• An empirical power-law relationship was identified between simulated ligand unbinding time and experimental dissociation rate constants.
• A strong correlation was observed, dependent on the SMD force applied.
• Developed regression models demonstrated predictive capability for FAK ligand dissociation rates.

Conclusions:

• Steered molecular dynamics (SMD) shows promise for early-stage prediction of drug-binding kinetics.
• The developed models can guide the selection of drug candidates with favorable dissociation kinetics for FAK.
• This approach can accelerate the identification of effective FAK inhibitors.