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Efficiency of Stratification for Ensemble Docking Using Reduced Ensembles.

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Selecting molecular dynamics snapshots for receptor flexibility in molecular docking is crucial. Medoids excel for few snapshots, while optimal and proportional allocations are better for larger sets, especially for estimating minima.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Molecular modeling

Background:

  • Receptor flexibility is key in molecular docking.
  • Molecular dynamics (MD) simulations provide snapshots of protein conformations.
  • Selecting representative snapshots is vital for accurate ensemble-based docking scores.

Purpose of the Study:

  • To evaluate common snapshot selection strategies for molecular docking.
  • To assess the efficiency of different sampling methods using stratified sampling metrics.
  • To extend evaluation to estimators of exponential averages and minima.

Main Methods:

  • Utilized stratified sampling efficiency to compare snapshot selection strategies.
  • Applied quality metrics to assess variance reduction compared to random sampling.
  • Extended metrics to exponential averages and minima estimation.
  • Tested strategies on molecular docking of over 500 ligands to four proteins of varying flexibility.

Main Results:

  • Clustering algorithms showed similar performance trends for ensemble and exponential averages.
  • Medoid selection was most efficient for fewer than 25 snapshots.
  • Optimal and proportional allocations improved efficiency with more snapshots.
  • Proportional allocation demonstrated consistent efficiency for estimating minima.

Conclusions:

  • Snapshot selection strategy significantly impacts the accuracy of ensemble-based docking.
  • Medoid, optimal, and proportional allocations offer distinct advantages depending on the number of snapshots and estimation type.
  • Proportional allocation is recommended for estimating minima in flexible receptor docking.