[Folding@home] Improved coarse-graining of Markov state models via explicit consideration of statistical uncertainty

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J Chem Phys. 2012 Oct 7;137(13):134111. doi: 10.1063/1.4755751. ABSTRACT Markov state models (MSMs)–or discrete-time master equation models–are a powerful way of modeling the structure and function of molecular systems like proteins. Unfortunately, MSMs with sufficiently many states to make a quantitative connection with experiments (often tens of thousands of states even for small systems) are generally too complicated to understand. Here, I present a bayesian agglomerative clustering engine (BACE) for coarse-graining such Markov models, thereby reducing their complexity and making them more comprehensible. An important feature of this algorithm is its ability to explicitly account for statistical uncertainty in model parameters that arises from finite sampling. This advance builds on a number of...

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