With the rapid growth of computer power and new developments of algorithms, it is possible for molecular dynamics simulations at the atomic-level to reach millisecond timescale. How to analyze the huge amount of data to obtain structural information as well as thermodynamics and kinetics properties of the system? Many analyses depend on clustering methods, such as some graph approaches and Markov state model. Inherent structure (IS) and geometry-based clustering methods are commonly used. ISs are obtained by minimizing the sampled conformations into local minima on potential/effective energy surface. The conformations that are minimized into the same energy basin belong to one cluster. We investigate the influence of the applications of these two methods of trajectory decomposition on our understanding of the thermodynamics and kinetics of alanine tetrapeptide. We find that at the micro-cluster level, the IS approach and root-mean-square deviation (RMSD)-based clustering method give totally different results. Depending on the local features of energy landscape, the conformations with close RMSDs can be minimized into different minima, while the conformations with large RMSDs could be minimized into the same basin. However, the relaxation timescales calculated based on the transition matrices built from the micro-clusters are similar. The discrepancy at the micro-cluster level leads to different macro-clusters. Although the dynamic models established through both clustering methods are validated approximately Markovian, the IS approach seems to give a meaningful state space discretization at the macro-cluster level in terms of conformational features and kinetics.

Meaningful state space discretization at the macro-cluster level

253rd National Meeting (2017)

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