An automatic classification of molecular dynamics simulation data into states, and its application to the construction of a markov state model

Markov state models (MSMs) have enabled us to analyze the long-time behaviors of protein motions using molecular dynamics (MD) simulation data. To construct the model, protein conformations must be classified into states. We have recently shown that an MSM constructed using the classification results obtained via manifold learning performs better when calculating the time series of an observable as compared with an MSM constructed using the K-center method for classification [R. Ito and T. Yoshidome, Chem. Phys. Lett. 691, 22 (2018)]. Our classification protocol involved a visual classification step. We investigated the performance of an MSM, constructed using an automatic classification method that was a hybrid of the manifold learning, K-means method, and the silhouette score. The time evolution of an observable obtained using MSM was in good agreement with that obtained directly through MD.