Markov state models (MSMs) have been used to investigate the folding pathways and free energy landscapes of large systems with many conformational degrees of freedom, such as proteins.  A crucial component in the building of MSMs (and many other models) is a distance metric which measures the distance between two geometries in the conformational space.  Here we report the development of a new distance metric which is effective for systems containing identical and exchangeable components (such as solvent molecules) by finding the optimal permutation of atomic indices that minimizes the root-mean-square distance in significantly less than factorial time.  We also present MSMs of proteins in explicit solvent and systems containing exchangeable protons using this distance metric.

Markov-state model analysis of systems containing identical and exchangable components using an optimally permuted RMSD metric

243rd National Meeting (2012)

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