Enhanced modeling via network theory: Adaptive sampling of Markov state models

By Gregory R. Bowman, D. Ensign, Vijay S. Pande. 2010

244 citations recorded by the source corpus.

Computer simulations can complement experiments by providing insight into molecular kinetics with atomic resolution. Unfortunately, even the most powerful supercomputers can only simulate small systems for short timescales, leaving modeling of most biologically relevant systems and timescales intractable. In this work, however, we show that molecular simulations driven by adaptive sampling of networks called Markov State Models (MSMs) can yield tremendous time and resource savings, allowing previously intractable calculations to be performed on a routine basis on existing hardware. We also introduce a distance metric (based on the relative entropy) for comparing MSMs. We primarily employ this metric to judge the convergence of various sampling schemes but it could also be employed to assess the effects of perturbations to a system (e.g. determining how changing the temperature or making a mutation changes a system's dynamics).

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Deep Generative Markov State Models (2018, methodology): “This makes the method promising for performing active learning in MD [2, 21], and to predict the future evolution of the system in other contexts.”
Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview (2018, unknown): “…computation of MSM on the collected data can give a feedback about undersampled zones of the phase space, suggesting where to focus further simulation, adapting the sampling (adaptive sampling methods) and increasing the eﬃciency of simulations (Bowman et al., 2010; Doerr and De Fabritiis, 2014).”
Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations (2011, background): “Methodologies that increase the unbiased sampling on the states involved in the slowest modes could be used to improve the efficiency of sampling and possibly yield more accurate estimation of quantitative parameters (30, 31).”
Markov models of molecular kinetics: generation and validation. (2011, background): “When the relevant conformations have been found and a good discretization has been obtained, it is clear that the uncertainty estimates of the Markov model can be exploited in order to pick starting points of subsequent simulations so as to adaptively reduce the uncertainty of the quantities of inte”
Alchemical free energy methods for drug discovery: Progress and challenges (2011, background): “While the metastable states can be identified by many short parallel simulations [88], recent adaptive schemes allow these to be constructed very efficiently on a computer cluster [89,90].”