Replica Exchange with Solute Scaling: A More Efficient Version of Replica Exchange with Solute Tempering (REST2)

Wang, Lingle; Friesner, Richard A.; Berne, B. J.

doi:10.1021/jp204407d

Cited by 691 publications

(954 citation statements)

References 16 publications

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“…In this way, a small number of replicas are sufficient to maintain the sampling efficiency, in contrast to the large number of replicas needed in the usual temperature replica exchange. Here, we are using a more recently developed version of REST (called REST2) where the effective temperature of the hot region is achieved at the Hamiltonian level through scaling the potential energy terms of the hot region (16).…”

Section: Discussionmentioning

confidence: 99%

“…In this article, we introduce a very efficient protocol called free energy perturbation/replica exchange with solute tempering (FEP/REST), which combines the recently developed enhanced sampling method REST (15)(16)(17) into normal FEP to deal with the structural reorganization problem and use it to calculate relative protein-ligand binding affinities in some troublesome cases. The method assumes that the slow degrees of freedom are located within a close neighborhood of the bound ligand without prior knowledge.…”

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confidence: 99%

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On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities

Wang

Berne²,

Friesner³

2012

Proc. Natl. Acad. Sci. U.S.A.

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230

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We apply a free energy perturbation simulation method, free energy perturbation/replica exchange with solute tempering, to two modifications of protein-ligand complexes that lead to significant conformational changes, the first in the protein and the second in the ligand. The approach is shown to facilitate sampling in these challenging cases where high free energy barriers separate the initial and final conformations and leads to superior convergence of the free energy as demonstrated both by consistency of the results (independence from the starting conformation) and agreement with experimental binding affinity data. The second case, consisting of two neutral thrombin ligands that are taken from a recent medicinal chemistry program for this interesting pharmaceutical target, is of particular significance in that it demonstrates that good results can be obtained for large, complex ligands, as opposed to relatively simple model systems. To achieve quantitative agreement with experiment in the thrombin case, a next generation force field, Optimized Potentials for Liquid Simulations 2.0, is required, which provides superior charges and torsional parameters as compared to earlier alternatives.enhanced sampling | protein-ligand binding affinity | structural reorganization | lead optimization | drug design B iological processes often depend on protein-ligand binding so that accurate prediction of protein-ligand binding affinities is of central importance in structural based drug design (1-4). Among the existing methods used to calculate these binding affinities in explicit solvent, free energy perturbation (FEP) simulations provide one of the most rigorous simulation techniques. Usually FEP is applied in the lead optimization stage of structure based drug design and is used to rank-order a series of congeneric ligands to choose the most potent ones for further investigation (1-4).Despite the potentially large impact that FEP could have on structure based drug design projects, practical applications in an industrial context have been limited over the past decade. High accuracy and reliability in the methodology are required to make productive decisions about compound modification during late stage lead optimization, but neither has yet been demonstrated by existing implementations. Two types of challenges stand in the way of developing FEP into a true engineering platform for drug candidate optimization. Firstly, converging explicit solvent simulations to the desired precision is far from trivial, even with the immense computing power that is currently available using low cost multiprocessor clusters or cloud computing platforms of various types. Secondly, errors in the potential energy models must be reduced to the point where they lead to errors in a converged calculation that are smaller than the desired errors in relative binding affinities compared to experiment. In our estimation these errors are on the order of 0.5-1.0 kcal∕mole mean unsigned error for a typical late stage lead optimization effort in a drug dis...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities

Wang

Berne²,

Friesner³

2012

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

230

351

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“…Several recipes for choosing the modified Hamiltonian have been proposed in the literature [207][208][209][210][211][212][213][214][215][216][217][218][219]. Among these, a notable idea is that of solute tempering [208,217] which is used for the simulation of solvated biomolecules.…”

Section: Generalized Replica Exchangementioning

confidence: 99%

“…In the original formulation, the authors used a factor (1 + λ)/2 for the solute-solvent interaction. This choice however was later shown to be suboptimal [217,220], and refined to be √ λ. This latter choice appears to be more physically sound, since it allows one to just simulate the biased replicas with a modified force-field.…”

Section: Generalized Replica Exchangementioning

confidence: 99%

Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration

Abrams

Bussi

2013

Entropy

399

405

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Abstract:We review a selection of methods for performing enhanced sampling in molecular dynamics simulations. We consider methods based on collective variable biasing and on tempering, and offer both historical and contemporary perspectives. In collective-variable biasing, we first discuss methods stemming from thermodynamic integration that use mean force biasing, including the adaptive biasing force algorithm and temperature acceleration.We then turn to methods that use bias potentials, including umbrella sampling and metadynamics. We next consider parallel tempering and replica-exchange methods.We conclude with a brief presentation of some combination methods.

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“…Replica-exchange method (REM) (or parallel tempering) is one of the most popular ways to improve sampling efficiency [1][2][3][4] including biomolecular system in explicit solvent [5,6] or biomembrane [7,8] (for reviews, see, e.g., Refs. [9,10]).…”

Section: Introductionmentioning

confidence: 99%

Deterministic replica-exchange method without pseudo random numbers for simulations of complex systems

Urano

Okamoto

2015

Computer Physics Communications

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We propose a replica-exchange method (REM) which does not use pseudo random numbers. For this purpose, we first give a conditional probability for Gibbs sampling replica-exchange method (GSREM) based on the heat bath method. In GSREM, replica exchange is performed by conditional probability based on the weight of states using pseudo random numbers. From the conditional probability, we propose a new method called deterministic replica-exchange method (DETREM) that produces thermal equilibrium distribution based on a differential equation instead of using pseudo random numbers. This method satisfies the detailed balance condition using a conditional probability of Gibbs heat bath method and thus results can reproduce the Boltzmann distribution within the condition of the probability. We confirmed that the equivalent results were obtained by REM and DETREM with two-dimensional Ising model. DETREM can avoid problems of choice of seeds in pseudo random numbers for parallel computing of REM and gives analytic method for REM using a differential equation.Keywords: generalized-ensemble algorithm, replica-exchange method (REM), simulated tempering (ST), Monte Carlo (MC) simulation, differential equation, gibbs sampling, heat-bath method, conditional probability, pseudo random numbers, Ising model INTRODUCTIONThe enhancement of sampling during Monte Carlo (MC) and molecular dynamics (MD) simulations is very important for complex systems. Replica-exchange method (REM) (or parallel tempering) is one of the most popular ways to improve sampling efficiency [1][2][3][4] including biomolecular system in explicit solvent [5,6] or biomembrane [7,8] (for reviews, see, e.g., Refs. [9,10]). To realize a thermal equilibrium distribution, REM uses Metropolis criterion with pseudo random numbers. However, random numbers sometimes give inaccurate results of simulations [11]. Moreover, generation of high quality random numbers is often difficult and does not assure good simulation results [12]. REM and its extension is suited for parallel computing [13][14][15][16]. Most of pseudo random number generators decrease the scalability in parallelization [17]. Hence, the complementary method producing the same results without pseudo random numbers is meaningful.In addition, the analytic approach for temperature selections in REM have been performed [18,19]. For performance and the condition of REM, several works were also performed. For example, Nymeyer [20] showed how efficient REM is than conventional simulations using the number of independent configurations. Abraham and Gready introduced some measurement and compared the results [21]. Rosta and Hummer [22] evaluated the practical efficiency of REM simulation for protein folding with a two-state model. However, the examination of the condition for convergence of REM is difficult partly because the mixing of temperature in REM is determined by pseudo random numbers with Metropolis criteria. As a result, most of analyses estimated the REM performance by simulation results.Recently, Suzuki et...

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Replica Exchange with Solute Scaling: A More Efficient Version of Replica Exchange with Solute Tempering (REST2)

Cited by 691 publications

References 16 publications

On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities

On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities

Enhanced Sampling in Molecular Dynamics Using Metadynamics, Replica-Exchange, and Temperature-Acceleration

Deterministic replica-exchange method without pseudo random numbers for simulations of complex systems

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