Self-Avoiding Conformational Sampling Based on Histories of Past Conformational Searches

Abstract

Self-avoiding conformational sampling (SACS) is proposed as an enhanced conformational sampling method for proteins. In SACS, the following conformational resampling is repeated for a given protein: (1) identification of newly visited states in a subspace and (2) conformational resampling by restarting short-time molecular dynamics (MD) simulations from the newly visited states. To identify the newly visited states, a set of history-dependent histograms projected onto the subspace is used. One is constructed from the trajectories sampled at the current (ith) cycle, and the other is constructed from all of the trajectories accumulated up through the previous ((i - 1)th) cycle. By reference to the history-dependent histograms, the newly visited states appearing at the current (ith) cycle are defined as a difference set between them. By repeating the cycle of conformational resampling, SACS prevents the system from revisiting states that have already been visited for previous cycles, promoting structural transitions via resampling from the newly visited states. To verify the conformational sampling efficiency of SACS, the present method was applied to reveal underlying mechanisms of biologically important domain motions of maltodextrin binding protein in explicit water and successfully reproduced the open-closed transition with a reasonable (nanosecond-order) computational cost.

Frontier Expansion Sampling: A Method to Accelerate Conformational Search by Identifying Novel Seed Structures for Restart

“…Frontier Expansion Sampling. Similar to previous methods, [13][14][15]19 in each cycle, FEXS performs PCA on all sampled structures so as to reduce the dimensionality and then finds the seed structures to initialize multiple parallel simulations so as to expand the sampled space (Figure 1). However, the seed structures here are selected as the "frontier" points diversely distributed at the boundary of the whole point set in the conformational subspace, which could be identified by the combination of the convex hull algorithm and GMM.…”

“…Selecting seed structures following the dimensionality reduction of sampled conformations like the principal component analysis (PCA) is another option. For instance, structural dissimilarity sampling (SDS), self-avoiding conformational sampling (SACS), and CoCo-MD choose seed structures from those of high dissimilarity to sampled conformations based on the inner product of projection vectors, from the newly visited regions and from the unvisited regions, respectively. Albeit simple, such methods and their variants − are powerful for simulating the conformational changes of proteins .…”

“…In this work, we proposed a new enhanced sampling method, frontier expansion sampling (FEXS), to observe the large-scale conformational changes of proteins in computationally affordable time. Similar to previous methods like SDS, − , FEXS iteratively selects suitable seed structures and initiates multiple parallel simulations to improve sampling. However, different from all previous methods, the seed structures in FEXS are chosen as the ones that are diversely distributed at the boundary (or “frontier”) of the point set composed of all structures ever sampled previously, and thus the subsequent descendant simulations will proceed into the unvisited regions nearly uniformly from all directions in the conformational subspace.…”

Ligand Binding Path Sampling Based on Parallel Cascade Selection Molecular Dynamics: LB-PaCS-MD

“…Note here that this cutoff value was not the firm choice. We have often faced with the problem of deciding when to finish conformational search [59][60][61][62][63]. To be more precise, one may consider the convergence of the distribution evaluated from LB-PaCS-MD.…”

Temperature–pressure shuffling outlier flooding method enhances the conformational sampling of proteins

“…Owing to this feature, our method is suitable for completely distributed computing because multiple MD simulations are independently launched from the selected initial structures. Despite the simple procedure, we have numerically proven that our methods efficiently capture a wide variety of rare events such as protein folding, domain motions, and polymerizations of a nanocube by improving the insufficient conformational sampling of CMD. Although the detailed dynamics cannot be pursued, free energy landscapes (FEL) can be estimated by the umbrella sampling or a Markov state model (MSM) once the transition pathways among (meta‐) stable structures are obtained …”