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

Harada, Ryuhei; Yoshino, Ryunosuke; Nishizawa, Hiroaki; Shigeta, Yasuteru

doi:10.1002/jcc.25806

Cited by 4 publications

(4 citation statements)

References 58 publications

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“…The idea of temperature shuffling has been proposed in our preceding studies. We have addressed the conformational sampling efficiency of the temperature shuffling algorithm in our several enhanced sampling methods. ,, Therefore, the simple extension (temperature shuffling) in the nt-PaCS-MD will further enhance the conformational sampling efficiency.…”

Section: Resultsmentioning

confidence: 99%

Nontargeted Parallel Cascade Selection Molecular Dynamics Using Time-Localized Prediction of Conformational Transitions in Protein Dynamics

Harada

Sládek

Shigeta

2019

J. Chem. Theory Comput.

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Nontargeted parallel cascade selection molecular dynamics (nt-PaCS-MD) is an enhanced conformational sampling method of proteins, which does not rely on knowledge of the target structure. It makes use of cyclic resampling from some relevant initial structures to expand the searched conformational subspace. The efficiency of nt-PaCS-MD depends on the selections of these initial structures. They are usually stochastically occurring perturbed structures at which larger conformation transitions are about to happen. Reliable identification of these is the key to using nt-PaCS-MD. Two new parameters, the moving root-mean-square deviation (mRMSD) and the inner products of the backbone dihedral angles Φ and Ψ, are introduced as indicators of conformational outliers in MD trajectories. Both are based on the analysis of a time-localized set of coordinates, overcoming the need for a target structure while still capturing the complexity of the conformational transition. The reference to which the mRMSD relates is the close surrounding of the i-th conformation, often the (i-1)st one. Hence the name “time-localized” analysis. In this work, we focus on its interplay with nt-PaCS-MD and show that it increases its effectiveness compared to older versions. The target system is the midsized protein T4 lysozyme (in explicit water) on which we demonstrate the open-closed transition without referring to any target configuration. Additionally, we show that the short MD trajectories can be used for the construction of a free energy landscape of the conformational transition based on the Markov state model.

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Section: Resultsmentioning

confidence: 99%

Nontargeted Parallel Cascade Selection Molecular Dynamics Using Time-Localized Prediction of Conformational Transitions in Protein Dynamics

Harada

Sládek

Shigeta

2019

J. Chem. Theory Comput.

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“…In the sampled dissociation processes, the hexamer was decomposed into monomers. As a post-processing treatment after sampling the dissociation processes based on other rare-event sampling methods [72,73], LB-PaCS-MD might inversely sample a set of association processes from the dissociated monomers toward the self-assembled hexamer. Therefore, LB-PaCS-MD might allow one to sample a set of self-assembly processes of organic molecules toward a higher-order structure.…”

Section: Discussionmentioning

confidence: 99%

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

2022

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Parallel cascade selection molecular dynamics (PaCS-MD) is a rare-event sampling method that generates transition pathways between a reactant and product. To sample the transition pathways, PaCS-MD repeats short-time MD simulations from important configurations as conformational resampling cycles. In this study, PaCS-MD was extended to sample ligand binding pathways toward a target protein, which is referred to as LB-PaCS-MD. In a ligand-concentrated environment, where multiple ligand copies are randomly arranged around the target protein, LB-PaCS-MD allows for the frequent sampling of ligand binding pathways. To select the important configurations, we specified the center of mass (COM) distance between each ligand and the relevant binding site of the target protein, where snapshots generated by the short-time MD simulations were ranked by their COM distance values. From each cycle, snapshots with smaller COM distance values were selected as the important configurations to be resampled using the short-time MD simulations. By repeating conformational resampling cycles, the COM distance values gradually decreased and converged to constants, meaning that a set of ligand binding pathways toward the target protein was sampled by LB-PaCS-MD. To demonstrate relative efficiency, LB-PaCS-MD was applied to several proteins, and their ligand binding pathways were sampled more frequently than conventional MD simulations.

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“…After the conformational sampling based on OFLOOD, structural stabilities of sampled protein configurations should be quantitatively evaluated, meaning that possible structural transitions pathways are reevaluated with free energy landscapes (FELs). To calculate FELs, we have proposed a straightforward strategy based on the Markov state model (MSM) combined with OFLOOD; i.e., an MSM is constructed from protein configurations sampled by OFLOOD. ,, To construct a reliable MSM in calculating an FEL, the following three steps are required: (1) defining appropriate microstates, (2) choosing a reasonable lag time, and (3) checking its validity of constructed MSM. First, all the sampled protein configurations are classified by the k -means algorithm to yield N clusters (microstates), where N corresponds to the number of the microstates, and then each protein configuration is assigned to the closest microstate.…”

Section: Methodsmentioning

confidence: 99%

Selection Rules for Outliers in Outlier Flooding Method Regulate Its Conformational Sampling Efficiency

Harada

Shigeta

2019

J. Chem. Inf. Model.

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The outlier flooding method (OFLOOD) has been proposed as an enhanced conformational sampling method of proteins. In OFLOOD, rarely occurring states of proteins are detected as sparse conformational distributions (outliers) with a clustering algorithm. The detected outliers are intensively resampled with short-time molecular dynamics (MD) simulations. As a set of cycles, OFLOOD repeats selections of outliers and their conformational resampling. Herein, as an essential issue to be tackled to perform OFLOOD efficiently, a selection rule for outliers should be carefully specified. Generally, many outliers are detected from distributions on conformational subspaces with the clustering. Judging from its computational costs, it is unreasonable to select all the detected outliers upon the conformational resampling. Therefore, it is important to consider which outliers should be selected from the sparse distributions when restarting their short-time MD simulations with limited computational costs. In this sense, we investigated the conformational sampling efficiency of OFLOOD by changing the selection rules for outliers. To address the conformational sampling efficiency of OFLOOD depending on its selection rules, outliers to be resampled were selected by focusing their probability occurrences (populations of outliers). As a comparison, a random selection rule for outliers was also considered. Through the present assessment, the random selection of outliers showed the most efficient conformational sampling efficiency compared to the other OFLOOD trials using the biased selection rules, indicating that a variety of outliers should be selected and resampled during the OFLOOD cycles. In conclusion, the random outlier selection rule is the best strategy to perform OFLOOD efficiently.

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

Cited by 4 publications

References 58 publications

Nontargeted Parallel Cascade Selection Molecular Dynamics Using Time-Localized Prediction of Conformational Transitions in Protein Dynamics

Nontargeted Parallel Cascade Selection Molecular Dynamics Using Time-Localized Prediction of Conformational Transitions in Protein Dynamics

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

Selection Rules for Outliers in Outlier Flooding Method Regulate Its Conformational Sampling Efficiency

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