Path-integral method for predicting relative binding affinities of protein-ligand complexes

Area of Science:

• Computational chemistry
• Biophysics
• Molecular modeling

Background:

• Calculating protein-ligand binding affinities is crucial for structure-based drug design.
• Traditional methods based on statistical thermodynamics are often computationally intensive and time-consuming.
• Accurate free energy calculations are essential for reliable drug design predictions.

Purpose of the Study:

• To develop a novel, rapid computational approach for determining biomolecular interaction binding affinities.
• To overcome the limitations of traditional methods in terms of computational time.
• To provide a faster alternative for free energy calculations in drug design.

Main Methods:

• A novel approach based on a path integral solution of the Fokker-Planck equation.
• Utilizing a stochastic kinetic formalism, extending Feynman's path integral formulation to classical systems.
• Modeling ligands as Brownian particles interacting with receptor potentials.

Main Results:

• The method calculates relative binding affinities based on ligand diffusivity and potential surface curvature.
• Achieved a correlation coefficient greater than 0.93 between computed and actual free energies on test datasets.
• Demonstrated an exceedingly rapid calculation process compared to traditional methods.

Conclusions:

• The presented approach offers a computationally efficient and accurate method for binding affinity calculations.
• This novel technique can significantly expedite the structure-based drug design process.
• The method shows high correlation with experimental data, validating its predictive power.