Weighted Ensemble Simulation: Review of Methodology, Applications, and Software

Zuckerman, Daniel M.; Chong, Lillian T.

doi:10.1146/annurev-biophys-070816-033834

Cited by 263 publications

(335 citation statements)

References 79 publications

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“…Below, we outline the basic ideas behind a number of algorithms for calculating binding rate constants [Figure 4; for a related review with emphasis on protein–drug binding, see Romanowska et al (66); also see the article by Zuckerman & Chong (111) in this volume]. As already emphasized, the concept of a rate constant itself implies the validity of the single protein–ligand pair description.…”

Section: Algorithms For Computing Binding Rate Constantsmentioning

confidence: 99%

Rate Constants and Mechanisms of Protein–Ligand Binding

Pang

Zhou

2017

Annu. Rev. Biophys.

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Whereas protein–ligand binding affinities have long-established prominence, binding rate constants and binding mechanisms have gained increasing attention in recent years. Both new computational methods and new experimental techniques have been developed to characterize the latter properties. It is now realized that binding mechanisms, like binding rate constants, can and should be quantitatively determined. In this review, we summarize studies and synthesize ideas on several topics in the hope of providing a coherent picture of and physical insight into binding kinetics. The topics include microscopic formulation of the kinetic problem and its reduction to simple rate equations; computation of binding rate constants; quantitative determination of binding mechanisms; and elucidation of physical factors that control binding rate constants and mechanisms.

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Section: Algorithms For Computing Binding Rate Constantsmentioning

confidence: 99%

Rate Constants and Mechanisms of Protein–Ligand Binding

Pang

Zhou

2017

Annu. Rev. Biophys.

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“…String methods can be combined with the weighted ensemble approach[76] to further enhance the sampling of drug binding. This weighted ensemble (WE)[77] approach utilizes an on-the-fly protocol of removing or dividing simulation replicas[78] to achieve a convergence probability distribution of states. It scales very well with a large number of CPUs to sample a ten thousand of trajectories per iteration[79].…”

Section: Thermodynamics and Statistics Of Drug Binding From MD Simmentioning

confidence: 99%

Computational membrane biophysics: From ion channel interactions with drugs to cellular function

Miranda

Ngo

Perissinotti

et al. 2017

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics

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The rapid development of experimental and computational techniques has changed fundamentally our understanding of cellular-membrane transport. The advent of powerful computers and refined force-fields for proteins, ions, and lipids has expanded the applicability of Molecular Dynamics (MD) simulations. A myriad of cellular responses is modulated through the binding of endogenous and exogenous ligands (e.g. neurotransmitters and drugs, respectively) to ion channels. Deciphering the thermodynamics and kinetics of the ligand binding processes to these membrane proteins is at the heart of modern drug development. The ever-increasing computational power has already provided insightful data on the thermodynamics and kinetics of drug-target interactions, free energies of solvation, and partitioning into lipid bilayers for drugs. This review aims to provide a brief summary about modeling approaches to map out crucial binding pathways with intermediate conformations and free-energy surfaces for drug-ion channel binding mechanisms that are responsible for multiple effects on cellular functions. We will discuss post-processing analysis of simulation-generated data, which are then transformed to kinetic models to better understand the molecular underpinning of the experimental observables under the influence of drugs or mutations in ion channels. This review highlights crucial mathematical frameworks and perspectives on bridging different well-established computational techniques to connect the dynamics and timescales from all-atom MD and free energy simulations of ion channels to the physiology of action potentials in cellular models.

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“…Here we report substantial progress in the application of the weighted ensemble (WE) path sampling method [32][33][34][35][36] to room-temperature folding at the microsecond (µs), millisecond (ms) and second (s) scales, exploiting the power of GPU and cluster computing. We study three atomistic implicitly solvated systems: NTL9 with low and high-friction solvent, as well as Protein G at low friction.…”

Section: Introductionmentioning

confidence: 99%

“…Although all these methods are theoretically well-grounded, WE does offer the pragmatic advantage of being fully independent of the dynamics engine employed, which has enabled its application with a wide range of both molecular and cell-scale simulation software. 34,[53][54][55][56][57][58][59] This versatility facilitated the integration of the WESTPA software package 60 with the GPUaccelerated version of the AMBER molecular dynamics package [61][62][63] as employed here. The WE method yields ensembles of fully continuous trajectories from which non-equilibrium observables can be calculated, including kinetic and mechanistic properties.…”

Section: Introductionmentioning

confidence: 99%

Computational estimation of ms-sec atomistic folding times

Adhikari

Mostofian

Copperman

et al. 2018

Preprint

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Despite the development of massively parallel computing hardware including inexpensive graphics processing units (GPUs), it has remained infeasible to simulate the folding of atomistic proteins at room temperature using conventional molecular dynamics (MD) beyond the µs scale.Here we report the folding of atomistic, implicitly solvated protein systems with folding times f ranging from ~100 µs to ~1s using the weighted ensemble (WE) strategy in combination with GPU computing. Starting from an initial structure or set of structures, WE organizes an ensemble of GPU-accelerated MD trajectory segments via intermittent pruning and replication events to generate statistically unbiased estimates of rate constants for rare events such as folding; no biasing forces are used. Although the variance among atomistic WE folding runs is significant, multiple independent runs are used to reduce and quantify statistical uncertainty. Folding times are estimated directly from WE probability flux and from history-augmented Markov analysis of the WE data. Three systems were examined: NTL9 at low solvent viscosity (yielding f = 0.8 − 9.0 s), NTL9 at water-like viscosity ( f = 0.2 − 1.9 ms), and Protein G at low viscosity ( f = 3.3 − 200 ms). In all cases the folding time, uncertainty, and ensemble properties could be estimated from WE simulation; for Protein G, this characterization required significantly less overall computing than would be required to observe a single folding event with conventional MD simulations. Our results suggest that the use and calibration of force fields and solvent models for precise estimation of kinetic quantities is becoming feasible.

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Weighted Ensemble Simulation: Review of Methodology, Applications, and Software

Cited by 263 publications

References 79 publications

Rate Constants and Mechanisms of Protein–Ligand Binding

Rate Constants and Mechanisms of Protein–Ligand Binding

Computational membrane biophysics: From ion channel interactions with drugs to cellular function

Computational estimation of ms-sec atomistic folding times

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