Ward Clustering Improves Cross-Validated Markov State Models of Protein Folding.

Abstract

Markov state models (MSMs) are a powerful framework for analyzing protein dynamics. MSMs require the decomposition of conformation space into states via clustering, which can be cross-validated when a prediction method is available for the clustering method. We present an algorithm for predicting cluster assignments of new data points with Ward's minimum variance method. We then show that clustering with Ward's method produces better or equivalent cross-validated MSMs for protein folding than other clustering algorithms.