On the removal of initial state bias from simulation data
| Autor: | Andreas Vitalis, Amedeo Caflisch, Marco Bacci |
|---|---|
| Přispěvatelé: | University of Zurich, Vitalis, Andreas |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
010304 chemical physics
Markov chain Stochastic matrix General Physics and Astronomy Sampling (statistics) Inference 610 Medicine & health 010402 general chemistry 01 natural sciences Field (computer science) 3100 General Physics and Astronomy 0104 chemical sciences Set (abstract data type) Resampling 0103 physical sciences 10019 Department of Biochemistry 570 Life sciences biology Physical and Theoretical Chemistry 1606 Physical and Theoretical Chemistry Algorithm Curse of dimensionality |
| Popis: | Classical atomistic simulations of biomolecules play an increasingly important role in molecular life science. The structure of current computing architectures favors methods that run multiple trajectories at once without requiring extensive communication between them. Many advanced sampling strategies in the field fit this mold. These approaches often rely on an adaptive logic and create ensembles of comparatively short trajectories whose starting points are not distributed according to the correct Boltzmann weights. This type of bias is notoriously difficult to remove, and Markov state models (MSMs) are one of the few strategies available for recovering the correct kinetics and thermodynamics from these ensembles of trajectories. In this contribution, we analyze the performance of MSMs in the thermodynamic reweighting task for a hierarchical set of systems. We show that MSMs can be rigorous tools to recover the correct equilibrium distribution for systems of sufficiently low dimensionality. This is conditional upon not tampering with local flux imbalances found in the data. For a real-world application, we find that a pure likelihood-based inference of the transition matrix produces the best results. The removal of the bias is incomplete, however, and for this system, all tested MSMs are outperformed by an alternative albeit less general approach rooted in the ideas of statistical resampling. We conclude by formulating some recommendations for how to address the reweighting issue in practice. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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