Constructing metastable conformational states and kinetic transition networks from long to short time scales can greatly improve the understanding of biomolecules and furthermore develop coarse-grained models and advanced simulation techniques for these complex biological molecular systems. The high-dimensional thermodynamic and kinetic even dynamical properties of these systems and their intrinsic complexity can be honestly reproduced with the network representation without requiring a priori assumptions about reaction coordinates. We develop a method for naturally reconstruct the hierarchical transition networks among metastable states. Multiple (short) simulation trajectories are generated in parallel, and each trajectory is mapped into a high-dimensional vector with the averages of lots of conformational functions along the trajectory as its components. The linear space spanned by the trajectory-mapped vectors has the same structure as that spanned by the conformational probability density functions of these trajectories, thus simple linear algebraic analyses on the mapped vectors can identify metastable states, transition kinetics as well as transition pathways of the simulation trajectories. We apply the method in polypeptides to illustrate its application and to get folding/unfolding dynamics and mechanisms of small proteins.
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