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Updated: Sep 7, 2025

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The performance of ensemble-based free energy protocols in computing binding affinities to ROS1 kinase.

Shunzhou Wan1, Agastya P Bhati1, David W Wright1

  • 1Department of Chemistry, Centre for Computational Science, University College London, London, UK.

Scientific Reports
|June 21, 2022
PubMed
Summary

Researchers optimized compound binding affinities for ROS1 kinase using enhanced sampling of molecular dynamics with approximation of continuum solvent (ESMACS) and thermodynamic integration with enhanced sampling (TIES) methods. TIES simulations showed excellent statistical rankings, correlating well with experimental activities.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Biophysics

Background:

  • Optimizing binding affinities is crucial for developing new drugs.
  • ROS1 kinase is a target in various diseases, making its inhibitors important.

Purpose of the Study:

  • To evaluate and compare ESMACS and TIES computational methods for ranking compound binding affinities to ROS1 kinase.
  • To assess the correlation between calculated binding free energies and experimental activities.

Main Methods:

  • Utilized enhanced sampling of molecular dynamics with approximation of continuum solvent (ESMACS) simulations.
  • Employed thermodynamic integration with enhanced sampling (TIES) protocols.
  • Compared computational predictions with experimental data for a set of compounds targeting ROS1 kinase.

Main Results:

  • ESMACS simulations demonstrated good correlation with experimental data for certain compound subsets.
  • Both TIES and ESMACS protocols generated consistent binding free energy differences.
  • The TIES protocol yielded excellent statistical rankings, with a Pearson correlation coefficient of 0.90 between calculated and experimental activities, despite an unexplained overestimation.

Conclusions:

  • The TIES protocol is a highly effective method for ranking compound binding affinities to ROS1 kinase.
  • Computational methods like TIES and ESMACS can accurately predict relative binding strengths, aiding drug discovery efforts.
  • Further investigation may be needed to address the observed overestimation in binding free energy calculations.