Abstract
The transition network provides a key to reveal the thermodynamic and kinetic properties of biomolecular systems. In this paper, we introduce a new method, named effective energy rescaling space trajectory mapping (EspcTM), to detect metastable states and construct transition networks based on the simulation trajectories of the complex biomolecular system. It mapped simulation trajectories into an orthogonal function space, whose bases were rescaled by effective energy, and clustered the interrelation between these trajectories to locate metastable states. By using the EspcTM method, we identified the metastable states and elucidated interstate transition kinetics of a Brownian particle and a dodecapeptide. It was found that the scaling parameters of effective energy also provided a clue to the dominating factors in dynamics. We believe that the EspcTM method is a useful tool for the studies of dynamics of the complex system and may provide new insight into the understanding of thermodynamics and kinetics of biomolecular systems.
Highlights
The biomolecules are fundamentally dynamic in nature (Chodera et al, 2007)
The effective energy, which was filtered from the total potential energy of simulation trajectories by fast Fourier transform (FFT) and multiple linear regression, is an efficacious order parameter to describe the slow conformational change of complex system
The energy rescaling space trajectory mapping (EspcTM) method was first illustrated with a toy model, i.e., the dynamics of a Brownian particle on a two-dimensional
Summary
The biomolecules are fundamentally dynamic in nature (Chodera et al, 2007). Protein folding, for example, involves the conformation change from polypeptide chain to a particular tertiary topology over microseconds to seconds, a process that can go awry and lead to misfolding and cause disease (Chiti and Dobson, 2006; Gregersen et al, 2006; Chodera et al, 2007; Guo et al, 2012; Wei et al, 2016; Zhou et al, 2019). We will present a new method, named effective energy rescaling space trajectory mapping (EspcTM), for detecting metastable states and constructing transition networks. It is a parameter-free analysis framework based on the previous TM method. The EspcTM method is an analysis framework to identify metastable states from simulation data in the effective energy rescaling space and construct the transition network between the states based on the theory of Markov chain. Dynamics of alanine dodecapeptide (Ala12), and a transition network between metastable states of Ala was constructed
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