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Solvent interaction energy calculations on molecular dynamics trajectories: increasing the efficiency using

Markus A Lill1, Jared J Thompson

  • 1Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Drive, West Lafayette, Indiana 47907, United States. mlill@purdue.edu

Journal of Chemical Information and Modeling
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Summary

This study explores efficient frame selection from molecular dynamics (MD) simulations to reduce computational costs for protein-ligand binding free energy calculations using solvent interaction energy (SIE) analysis.

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Area of Science:

  • Computational chemistry
  • Biophysics
  • Molecular modeling

Background:

  • End-point methods like LIE, MM/GBSA, and SIE are popular for protein-ligand binding free energy calculations.
  • These methods typically require molecular dynamics (MD) simulations to generate ensembles of protein structures.
  • The standard workflow is computationally expensive due to energy calculations for each frame in each trajectory.

Purpose of the Study:

  • To investigate methods for intelligent frame selection from MD simulations.
  • To reduce the computational cost associated with end-point free energy calculations.
  • To assess the impact of the number of selected frames on the accuracy of SIE calculations.

Main Methods:

  • Studied frame selection techniques, including clustering, from MD simulations.
  • Applied solvent interaction energy (SIE) analysis to selected frames.
  • Evaluated the influence of the number of selected frames on binding free energy estimates.

Main Results:

  • Identified frame selection strategies to mitigate computational expense.
  • Demonstrated a correlation between the number of selected frames and the accuracy of SIE results.
  • Provided insights into optimizing the trade-off between computational cost and accuracy.

Conclusions:

  • Intelligent frame selection can significantly reduce the computational burden of MD-based free energy calculations.
  • The number of selected frames is a critical parameter influencing the reliability of SIE-based binding free energy predictions.
  • Optimized frame selection protocols are essential for efficient and accurate computational drug discovery.