Structural dissimilarity sampling with dynamically self-guiding selection

Abstract

Structural dissimilarity sampling (SDS) has been proposed as an enhanced conformational sampling method for reproducing the structural transitions of a given protein. SDS consists of cycles of two steps: (1) Selections of initial structures with structural dissimilarities by referring to a measure. (2) Conformational resampling by restarting short-time molecular dynamics (MD) simulations from the initial structures. In the present study, an efficient measure is proposed as a dynamically self-guiding selection to accelerate the structural transitions from a reactant state to a product state as an extension to the original SDS. In the extended SDS, the inner product (IP) between the reactant and the snapshots generated by short-time MD simulations are evaluated and ranked according to the IPs at every cycle. Then, the snapshots with low IPs are selected as initial structures for the short-time MD simulations. This scheme enables one to choose dissimilar and distant initial structures from the reactant, and thus the initial structures dynamically head towards the product, promoting structural transitions from the reactant. To confirm the conformational sampling efficiency, the extended SDS was applied to maltodextrin binding protein (MBP), and we successfully reproduced the structural transition from the open to closed states with submicrosecond-order simulation times. However, a conventional long-time MD simulation failed to reproduce the same structural transition. We also compared the performance with that obtained by the ordinary SDS and other sampling techniques that have been developed by us to characterize the possible utility of the extended SDS for actual applications. © 2017 Wiley Periodicals, Inc.

Non-empirical weighted Langevin mechanics for the potential escape problem: Parallel algorithm and application to the Argon clusters

“…To check how the method behaves with increasing of dimensionality, we performed several simulations for LJ 13 and LJ 38 clusters. The parallel efficiency is increased with a number of atoms (Fig.…”

“…In fact the saturation of speedup rate for LJ 38 occurs when the number of walkers per process equals 36 and the computational cost to calculate the potential is increased by (37 · 38)/(6 · 7) ≈ 33.5. Still, practically this performance was enough in this work for completing the single path tracking utilizing only 16 processors with 32000 time steps in 5 minutes for LJ 7 and in no more than several ten minutes for LJ 13 . For LJ 38 we used 80 nodes (640 processes) with 80000 time steps in 7 hours.…”

“…Capped nido icosahedron Figure 11: (Color online) The snapshots of reaction path from icosahedron structure to the capped nido icosahedron structure for LJ 13 . For small clusters (like LJ 7 ) all reactions occur in accordance with diamondsquare-diamond (DSD) process [26], where is usually one bond is destroyed and then another bond forms (Figs.…”

“…Many existing methods can be applied when either the product (destination of the transition in the configuration space) or the reaction coordinate (collective coordinates which well characterize the transition) is given a priori, for example, the blue-moon [1,2], targeted dynamics [3], steered dynamics [4] hyperdynamics [5,6], metadynamics [7], adaptive biasing force [8] and many others. In the former case, various path optimization or sampling methods are available, whereas in the latter, one can add artificial force with respect to the reaction coordinates to the potential force [9] or execute a weighted sampling dynamics referring to the coordinates [10,11,12,13].…”

“…(Color online) The energy evolution of reaction paths for a argon cluster LJ13 during simulation under biasing potential and during the relaxation. The horizontal line shows the moment when the relaxation start off.…”