Highly Efficient Computation of the Basal kon using Direct Simulation of Protein–Protein Association with Flexible Molecular Models

Saglam, Ali S.; Chong, Lillian T.

doi:10.1021/acs.jpcb.5b10747

Cited by 32 publications

(45 citation statements)

References 30 publications

(92 reference statements)

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“…24 Third, we applied the weighted ensemble (WE) path sampling strategy, 25–27 which has been demonstrated to be orders of magnitude more efficient than standard Brownian dynamics simulations in generating pathways and rate constants for protein binding processes. 28 Full details of the protein model, simulations, and analysis are below.…”

Section: Methodsmentioning

confidence: 99%

Flexibility vs Preorganization: Direct Comparison of Binding Kinetics for a Disordered Peptide and Its Exact Preorganized Analogues

et al. 2017

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Many intrinsically disordered proteins, which are prevalent in nature, fold only upon binding their structured partner proteins. Such proteins have been hypothesized to have a kinetic advantage over their folded, preorganized analogues in binding their partner proteins. Here we determined the effects of ligand preorganization on the kon for a biomedically important system: an intrinsically disordered p53 peptide ligand and the MDM2 protein receptor. Based on direct simulations of binding pathways, computed kon values for fully disordered and preorganized p53 peptide analogues were within error of each other, indicating little if any kinetic advantage to being disordered or preorganized for binding the MDM2 protein. We also examined the effects of increasing the concentration of MDM2 on the extent to which its mechanism of binding to the p53 peptide is induced fit vs conformational selection. Results predict that the mechanism is solely induced fit if the unfolded state of the peptide is more stable than its folded state; otherwise, the mechanism shifts from being dominated by conformational selection at low MDM2 concentration to induced fit at high MDM2 concentration. Taken together, our results are relevant to any protein binding process that involves a disordered peptide of a similar length that forms a single α-helix upon binding a partner protein. Such disorder-to-helix transitions are common among protein interactions of disordered proteins and are therefore of fundamental biological interest.

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Section: Methodsmentioning

confidence: 99%

Flexibility vs Preorganization: Direct Comparison of Binding Kinetics for a Disordered Peptide and Its Exact Preorganized Analogues

et al. 2017

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“…Several years later, the WE strategy was proven to yield continuous pathways and rate constants in a rigorous manner for any type of stochastic dynamics, including Monte Carlo, BD, and MD, thereby highlighting the generality of the strategy (70). Regardless of the type of dynamics or scale of the system, numerous studies have demonstrated that the WE strategy exhibits significant super-linear scaling in estimating observables; that is, quantities such as rate constants can be estimated with orders of magnitude fewer overall computing resources compared to standard parallelized simulations because WE-spawned trajectories can be focused on sampling bottlenecks (3, 17, 21, 22, 53, 55, 69, 75). Given this efficiency, there has been a resurgence of interest in the WE strategy over the past decade, resulting in the successful generation of pathways and calculation of rate constants for long-timescale processes that would otherwise be infeasible to simulate.…”

Section: Applicationsmentioning

confidence: 99%

“…Using flexible molecular models that reproduce the molecular shapes, electrostatic potentials, and diffusion properties of all-atom models, the WE strategy in combination with the Northrup-Allison-McCammon (NAM) approach (48) has reproduced the experimental k on for wild-type and mutant pairs of the barnase and barstar proteins (55). Notably, this study reported highly efficient simulation of the slow association between the exact hydrophobic, uncharged isosteres of the proteins, yielding the basal k on —a quantity of fundamental interest for determining the extent to which electrostatic interactions enhance the k on of the wild-type proteins.…”

Section: Applicationsmentioning

confidence: 99%

“…However, as the motivation for using the WE approach is to enable otherwise unfeasible applications, converged standard simulations are typically not available for determining the efficiency of WE. In these cases, the efficiency of WE has been calculated by estimating the aggregate simulations time that would be required by standard simulations to yield the same observable of interest, for example, the same number of distinct pathways (55, 76) or the same rate constant and statistical precision as predicted by single-exponential kinetics, which is a reasonable model for the kinetics of two-state processes (21). …”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Here, we review recent advances and applications of the WE path sampling approach, which has enabled a number of otherwise unfeasible applications (2, 17, 19, 22, 55, 59, 76). We present the theoretical basis of WE and also discuss ongoing challenges and future directions for this promising approach.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zuckerman

Chong

2017

Annu. Rev. Biophys.

Self Cite

287

330

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The weighted ensemble (WE) methodology orchestrates quasi-independent parallel simulations run with intermittent communication that can enhance sampling of rare events such as protein conformational changes, folding, and binding. The WE strategy can achieve superlinear scaling—the unbiased estimation of key observables such as rate constants and equilibrium state populations to greater precision than would be possible with ordinary parallel simulation. WE software can be used to control any dynamics engine, such as standard molecular dynamics and cell-modeling packages. This article reviews the theoretical basis of WE and goes on to describe successful applications to a number of complex biological processes—protein conformational transitions, (un)binding, and assembly processes, as well as cell-scale processes in systems biology. We furthermore discuss the challenges that need to be overcome in the next phase of WE methodological development. Overall, the combined advances in WE methodology and software have enabled the simulation of long-timescale processes that would otherwise not be practical on typical computing resources using standard simulation.

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Predicting ligand binding affinity using on- and off-rates for the SAMPL6 SAMPLing challenge

Dixon

Lotz

Dickson

2018

J Comput Aided Mol Des

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Interest in ligand binding kinetics has been growing rapidly, as it is being discovered in more and more systems that ligand residence time is the crucial factor governing drug efficacy. Many enhanced sampling methods have been developed with the goal of predicting ligand binding rates ([Formula: see text]) and/or ligand unbinding rates ([Formula: see text]) through explicit simulation of ligand binding pathways, and these methods work by very different mechanisms. Although there is not yet a blind challenge for ligand binding kinetics, here we take advantage of experimental measurements and rigorously computed benchmarks to compare estimates of [Formula: see text] calculated as the ratio of two rates: [Formula: see text]. These rates were determined using a new enhanced sampling method based on the weighted ensemble framework that we call "REVO": Reweighting of Ensembles by Variance Optimization. This is a further development of the WExplore enhanced sampling method, in which trajectory cloning and merging steps are guided not by the definition of sampling regions, but by maximizing trajectory variance. Here we obtain estimates of [Formula: see text] and [Formula: see text] that are consistent across multiple simulations, with an average log10-scale standard deviation of 0.28 for on-rates and 0.56 for off-rates, which is well within an order of magnitude and far better than previously observed for previous applications of the WExplore algorithm. Our rank ordering of the three host-guest pairs agrees with the reference calculations, however our predicted [Formula: see text] values were systematically lower than the reference by an average of 4.2 kcal/mol. Using tree network visualizations of the trajectories in the REVO algorithm, and conformation space networks for each system, we analyze the results of our sampling, and hypothesize sources of discrepancy between our [Formula: see text] values and the reference. We also motivate the direct inclusion of [Formula: see text] and [Formula: see text] challenges in future iterations of SAMPL, to further develop the field of ligand binding kinetics prediction and modeling.

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Highly Efficient Computation of the Basal k_on using Direct Simulation of Protein–Protein Association with Flexible Molecular Models

Cited by 32 publications

References 30 publications

Flexibility vs Preorganization: Direct Comparison of Binding Kinetics for a Disordered Peptide and Its Exact Preorganized Analogues

Flexibility vs Preorganization: Direct Comparison of Binding Kinetics for a Disordered Peptide and Its Exact Preorganized Analogues

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

Predicting ligand binding affinity using on- and off-rates for the SAMPL6 SAMPLing challenge

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