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Calculating protein-ligand binding affinities with MMPBSA: Method and error analysis.

Changhao Wang1,2,3, Peter H Nguyen2, Kevin Pham2

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|August 12, 2016
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Summary

Optimizing Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) calculations improves protein-ligand binding affinity predictions. A modern nonpolar solvation model significantly enhances accuracy, reducing errors in binding free energy estimations.

Keywords:
Poisson-Boltzmann implicit solvent modelsmolecular dynamicsnonpolar solvent models

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Area of Science:

  • Computational chemistry
  • Biophysics
  • Drug discovery

Background:

  • Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) methods are crucial for estimating protein-ligand binding affinities.
  • Existing MMPBSA protocols show variability in agreement with experimental data.
  • Computational efficiency and accuracy are key considerations in molecular modeling.

Purpose of the Study:

  • To investigate computational alternatives for MMPBSA calculations.
  • To assess the impact of various parameters on the agreement between MMPBSA and experimental binding affinities.
  • To identify optimal settings for improving the accuracy of MMPBSA predictions.

Main Methods:

  • Selection of seven receptor families with high-quality crystal structures and binding affinities.
  • Evaluation of different nonpolar solvation models, focusing on hydrophobic and dispersion interactions.
  • Analysis of Poisson-Boltzmann numerical setup, including grid spacing, atomic radii, and molecular surface definitions.
  • Investigation of solute dielectric constant and simulation convergence effects.

Main Results:

  • A modern nonpolar solvation model significantly reduces Root Mean Square Deviation (RMSD) in computed relative binding affinities.
  • Poisson-Boltzmann grid spacing has a negligible impact on MMPBSA calculation quality at 0.5 Å.
  • Atomic radius sets and molecular surface definitions show minor influences on experimental agreement.
  • Higher solute dielectric constants generally improve agreement, particularly for charged binding pockets.
  • Simulation convergence slightly reduced agreement with experimental data.

Conclusions:

  • The choice of nonpolar solvation model is critical for accurate MMPBSA binding affinity predictions.
  • Numerical parameters like grid spacing have minimal impact, while solute dielectric constant is influential.
  • Further refinement of MMPBSA methods is needed for precise absolute binding free energy estimation.