It is a central issue to find the slow dynamic modes of biological macromolecules via analyzing the large-scale data of molecular dynamics simulation (MD). While the MD data are high-dimensional time-successive series...It is a central issue to find the slow dynamic modes of biological macromolecules via analyzing the large-scale data of molecular dynamics simulation (MD). While the MD data are high-dimensional time-successive series involving all-atomic details and sub-picosecond time resolution, a few collective variables which characterizing the motions in longer than nanoseconds are needed to be chosen for an intuitive understanding of the dynamics of the system. The trajectory map (TM) was presented in our previous works to provide an efficient method to find the low-dimensional slow dynamic collective-motion modes from high-dimensional time series. In this paper, we present a more straight understanding about the principle of TM via the slow-mode linear space of the conformational probability distribution functions of MD trajectories and more clearly discuss the relation between the TM and the current other similar methods in finding slow modes.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No. 11904086).
文摘It is a central issue to find the slow dynamic modes of biological macromolecules via analyzing the large-scale data of molecular dynamics simulation (MD). While the MD data are high-dimensional time-successive series involving all-atomic details and sub-picosecond time resolution, a few collective variables which characterizing the motions in longer than nanoseconds are needed to be chosen for an intuitive understanding of the dynamics of the system. The trajectory map (TM) was presented in our previous works to provide an efficient method to find the low-dimensional slow dynamic collective-motion modes from high-dimensional time series. In this paper, we present a more straight understanding about the principle of TM via the slow-mode linear space of the conformational probability distribution functions of MD trajectories and more clearly discuss the relation between the TM and the current other similar methods in finding slow modes.
