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The ExTASY Approach

The ExTASY Approach utilizes swarm/ensemble simulation strategies and smart collective coordinate strategies to enhance sampling in interesting regions. It utilizes machine learning methods for on-the-fly selection and refinement of collective coordinates. It is compatible with standard MD codes without the need for software patches. CoCo (Complementary Coordinates), a simulation-analysis workflow, is used to identify under-sampled regions and perform enhanced sampling through random walks in a shape space.

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The ExTASY Approach

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  1. The ExTASY Approach • Use swarm/ensemble simulation strategies that map efficiently onto HPC services. • Use smart collective coordinate strategies to focus sampling in interesting regions. • Rely on machine learning methods rather than user expertise to select and refine (on the fly) the collective coordinates. • Be compatible with standard MD codes out of the box (without software patches).

  2. CoCo: “Complementary Coordinates” Proteins 2009; 75:206–216.

  3. CoCo in a simulation-analysis workflow Generate new start structures for MD Run N MD simulations Trajectory data Use CoCo to identify Nundersampled regions

  4. CoCo workflow Initial structure Replicate N times Add to compilation of trajectory data Generate new start structures for MD Run N MD simulations Prepare input files Trajectory data Perform PCA analysis on all data so far Use CoCo to identify Nundersampled regions Conver-ged? NO YES END

  5. MD as a Random Walk through Shape Space

  6. Enhanced sampling using CoCo MD as random walks in a shape space 1 walker

  7. Enhanced sampling using CoCo MD as random walks in a shape space 1 walker 5 walkers

  8. Enhanced sampling using CoCo MD as random walks in a shape space 1 walker 5 walkers 5 walkers + CoCo

  9. CoCo Performance A B D C “Hard to reach” states found 10 x faster using CoCo

  10. Mean first passage times (ps) to penta-alanine local minima COCO Performance Standard MD CoCo

  11. CoCo structure refinement restrained EM/MD raw CoCo output refined CoCo structure low energy conformation

  12. Summary • Simulation/analysis workflows using the CoCo method can significantly enhance sampling. • Ideally suited to ensemble/swarm approaches (individual MD simulations are independent). • Does not require any “hooks” in the MD code. • Limitation: does not yield a thermodynamic ensemble (yet…)

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