Equilibrium sampling of self-associating polymer solutions: a parallel selective tempering approach

We present a novel simulation algorithm based on tempering a fraction of relaxation-limiting interactions to accelerate the process of obtaining uncorrelated equilibrium configurations of self-associating polymer solutions. This approach consists of tempering (turning off) the attractive interactions for a fraction of self-associating groups determined by a biasing field h. A number of independent configurations (replicas) with overlapping Hamiltonian distributions in the expanded (NVTh) ensemble with constant NVT but different biasing fields, forming a chain of Hamiltonians, were simulated in parallel with occasional attempts to exchange the replicas associated with adjacent fields. Each field had an associated distribution of tempered interactions, average fraction of tempered interactions, and structural decorrelation time. Tempering parameters (number of replicas, fields, and exchange frequencies) were chosen to obtain the highest efficiency in sampling equilibrium configurations of a self-association polymer solution based on short serial simulation runs and a statistical model. Depending on the strength of the relaxation-limiting interactions, system size, and thermodynamic conditions, the algorithm can be orders of magnitude more efficient than conventional canonical simulation and is superior to conventional temperature parallel tempering.     

Combining smart darting with parallel tempering using Eckart space: application to Lennard-Jones clusters

The smart-darting algorithm is a Monte Carlo based simulation method used to overcome quasiergodicity problems associated with disconnected regions of configurations space separated by high energy barriers. As originally implemented, the smart-darting method works well for clusters at low temperatures with the angular momentum restricted to zero and where there are no transitions to permutational isomers. If the rotational motion of the clusters is unrestricted or if permutational isomerization becomes important, the acceptance probability of darting moves in the original implementation of the method becomes vanishingly small. In this work the smart-darting algorithm is combined with the parallel tempering method in a manner where both rotational motion and permutational isomerization events are important. To enable the combination of parallel tempering with smart darting so that the smart-darting moves have a reasonable acceptance probability, the original algorithm is modified by using a restricted space for the smart-darting moves. The restricted space uses a body-fixed coordinate system first introduced by Eckart, and moves in this Eckart space are coupled with local moves in the full 3N-dimensional space. The modified smart-darting method is applied to the calculation of the heat capacity of a seven-atom Lennard-Jones cluster. The smart-darting moves yield significant improvement in the statistical fluctuations of the calculated heat capacity in the region of temperatures where the system isomerizes. When the modified smart-darting algorithm is combined with parallel tempering, the statistical fluctuations of the heat capacity of a seven-atom Lennard-Jones cluster using the combined method are smaller than parallel tempering when used alone.     

On the convergence of parallel tempering Monte Carlo simulations of LJ38

The convergence of parallel tempering Monte Carlo simulations of the 38-atom Lennard-Jones cluster starting from the Oh global minimum and from the C(5v) second-lowest-energy minimum is investigated. It is found that achieving convergence is appreciably more difficult, particularly at temperatures in the vicinity of the Oh --> C(5v) transformation when starting from the C(5v) structure. A strategy combining the Tsallis generalized ensemble and the parallel tempering algorithm is implemented and used to improve the convergence of the simulations in the vicinity of the Oh --> C(5v) transformation.     

Free-energy landscape for beta hairpin folding from combined parallel tempering and metadynamics

We develop a new free-energy method, based on the combination of parallel tempering and metadynamics, and apply this method to the calculation of the free-energy landscape of the folding beta hairpin in explicit water. We show that the combined method greatly improves the performance of both parallel tempering and metadynamics. In particular, we are able to sample the high free-energy regions, which are not accessible with conventional parallel tempering. We use our results to calculate the difference in entropy and enthalpy between the folded and the unfolded state and to characterize the most populated configurations in the relevant free-energy basins.     

A constant entropy increase model for the selection of parallel tempering ensembles

The present paper explores a simple approach to the question of parallel tempering temperature selection. We argue that to optimize the performance of parallel tempering it is reasonable to require that the increase in entropy between successive temperatures be uniform over the entire ensemble. An estimate of the system's heat capacity, obtained either from experiment, a preliminary simulation, or a suitable physical model, thus provides a means for generating the desired tempering ensemble. Applications to the two-dimensional Ising problem indicate that the resulting method is effective, simple to implement, and robust with respect to its sensitivity to the quality of the underlying heat capacity model.     

The incomplete beta function law for parallel tempering sampling of classical canonical systems

We show that the acceptance probability for swaps in the parallel tempering Monte Carlo method for classical canonical systems is given by a universal function that depends on the average statistical fluctuations of the potential and on the ratio of the temperatures. The law, called the incomplete beta function law, is valid in the limit that the two temperatures involved in swaps are close to one another. An empirical version of the law, which involves the heat capacity of the system, is developed and tested on a Lennard-Jones cluster. We argue that the best initial guess for the distribution of intermediate temperatures for parallel tempering is a geometric progression and we also propose a technique for the computation of optimal temperature schedules. Finally, we demonstrate that the swap efficiency of the parallel tempering method for condensed-phase systems decreases naturally to zero at least as fast as the inverse square root of the dimensionality of the physical system.     

Equilibrium density of states and thermodynamic properties of a model glass former

This paper presents an analysis of the thermodynamics of a model glass former. We have performed equilibrium sampling of a popular binary Lennard-Jones model, employing parallel tempering Monte Carlo to cover the crystalline, amorphous, and liquid regions of configuration space. Disconnectivity graphs are used to visualize the potential energy landscape in the vicinity of a crystalline geometry and in an amorphous region of configuration space. The crystalline global minimum is separated from the bulk of the minima by a large potential energy gap, leading to broken ergodicity in conventional simulations. Our sampling reveals crystalline global minima that are lower in potential energy than some of the previous candidates. We present equilibrium thermodynamic properties based on parallel tempering simulations, including heat capacities and free energy profiles, which depend explicitly on the crystal structure. We also report equilibrium melting temperatures.     

A temperature predictor for parallel tempering simulations

An algorithm is proposed that generates a set of temperatures for use in parallel tempering simulations (also known as temperature-replica exchange molecular dynamics simulations) of proteins to obtain a desired exchange probability Pdes. The input consists of the number of protein atoms and water molecules in the system, information about the use of constraints and virtual sites and the lower temperature limits. The temperatures generated yield probabilities which are very close to Pdes (correlation 97%), independent of force field and over a wide temperature range. To facilitate its use, the algorithm has been implemented as a web server at .     

Structural behavior and self-assembly of Lennard-Jones clusters on rigid surfaces

The phase behavior and surface pattern formation for intermediate size Lennard-Jones clusters on rigid surfaces are examined. We use a parallel tempering Monte Carlo algorithm, in the canonical ensemble. Tempering is done over the temperature domain in most of the calculations. A two-dimensional temperature and Hamiltonian tempering algorithm is also implemented, to examine its usefulness in investigating this type of problem. In general, we observe gas phase systems as they undergo a condensation transition on the surface, followed by a freezing transition. The final solid state pattern formed by the cluster on the surface is the result of a number of competing effects. First, there is a competition between attraction within the cluster and that between cluster and surface atoms. Second, a monolayer of Lennard-Jones atoms tends to pack in a hexadic geometry. This geometry is frustrated on a surface with a different symmetry. The molecular organization of the substrate has a serious impact on the cluster packing. The surface morphology and the size mismatch between cluster and surface atoms, along with the relative interaction strengths, determine which of the effects prevail. When the surface atoms are small enough, the interactions within the cluster determine the symmetry of the pattern. In such a case, the substrate behaves similarly to a continuous surface, and the low-temperature pattern is a hexadic monolayer. When the sizes of the surface and cluster atoms are comparable, the low-temperature adsorbed geometry mimics the substrate symmetry. On a face-centered cubic surface, face-centered cubic monolayers or droplets are obtained.     

Optimal allocation of replicas in parallel tempering simulations

We have studied the efficiency of parallel tempering simulations for a variety of systems including a coarse-grained protein, an atomistic model polypeptide, and the Lennard-Jones fluid. A scheme is proposed for the optimal allocation of temperatures in these simulations. The method is compared to the existing empirical approaches used for this purpose. Accuracy associated with the computed thermodynamic quantities such as specific heat is also computed and their dependence on the trial-exchange acceptance rate is reported.     

Multiple‐Replica Exchange with Information Retrieval

The parallel tempering simulation method was recently extended to allow for possible exchanges between non‐adjacent replicas. We introduce a multiple‐exchange variant which naturally incorporates the information from all replicas when calculating statistical averages, building on the related virtual‐move method of Coluzza and Frenkel (ChemPhysChem 2005 , 6, 1779). The method is extensively tested on three model systems, namely, a Lennard‐Jones cluster exhibiting a finite size phase transition, the Lennard‐Jones fluid, and the 2D ferromagnetic Ising model. In all cases, the present method performs significantly better and converges faster than conventional parallel tempering Monte Carlo simulations. The standard deviations are also systematically decreased with respect to virtual moves.     

A rare event sampling method for diffusion Monte Carlo using smart darting

We identify a set of multidimensional potential energy surfaces sufficiently complex to cause both the classical parallel tempering and the guided or unguided diffusion Monte Carlo methods to converge too inefficiently for practical applications. The mathematical model is constructed as a linear combination of decoupled Double Wells [(DDW)(n)]. We show that the set (DDW)(n) provides a serious test for new methods aimed at addressing rare event sampling in stochastic simulations. Unlike the typical numerical tests used in these cases, the thermodynamics and the quantum dynamics for (DDW)(n) can be solved deterministically. We use the potential energy set (DDW)(n) to explore and identify methods that can enhance the diffusion Monte Carlo algorithm. We demonstrate that the smart darting method succeeds at reducing quasiergodicity for n ? 100 using just 1 × 10(6) moves in classical simulations (DDW)(n). Finally, we prove that smart darting, when incorporated into the regular or the guided diffusion Monte Carlo algorithm, drastically improves its convergence. The new method promises to significantly extend the range of systems computationally tractable by the diffusion Monte Carlo algorithm.     

Chiral separation: mechanism modeling in two-dimensional systems

Fluid phase separations of racemates are difficult because the subtle, short-ranged differences in intermolecular interactions of like and unlike pairs of chiral molecules are typically smaller than the thermal energy. A surface restricts the configurational space available to the pair of interacting molecules, thus changing the effective interactions between them. Because of this restriction, a surface can promote chiral separation of mixtures that are racemic in bulk. In this paper, we investigate chiral symmetry breaking induced by an achiral surface in a racemate. A parallel tempering Monte Carlo algorithm with tempering over the temperature domain is used to examine the interplay between molecular geometry and energetics in promoting chiral separations. The system is restricted to evolve in two dimensions. By controlling the balance between electrostatic and steric interactions, one can direct the surface assembly of the chiral molecules toward formation of small clusters of identical molecules. When molecular shape asymmetry is complemented by dipolar alignment, chiral micellar clusters of like molecules are assembled on the surface. We examine the case of small model molecules for which the two-dimensional restriction of the pair potential is sufficient to induce chiral segregation. An increase in molecular complexity can change the balance of intermolecular interactions to the point that heterochiral pairs are energetically more favored. In this case, we find conditions in which formation of homochiral micelles is still achieved, due to a combination of multibody and entropic effects. In such systems, an examination of the pair potential alone is insufficient to predict whether the multimolecular racemate will or will not segregate.     

On the efficiency of exchange in parallel tempering monte carlo simulations

We introduce the concept of effective fraction, defined as the expected probability that a configuration from the lowest index replica successfully reaches the highest index replica during a replica exchange Monte Carlo simulation. We then argue that the effective fraction represents an adequate measure of the quality of the sampling technique, as far as swapping is concerned. Under the hypothesis that the correlation between successive exchanges is negligible, we propose a technique for the computation of the effective fraction, a technique that relies solely on the values of the acceptance probabilities obtained at the end of the simulation. The effective fraction is then utilized for the study of the efficiency of a popular swapping scheme in the context of parallel tempering in the canonical ensemble. For large dimensional oscillators, we show that the swapping probability that minimizes the computational effort is 38.74%. By studying the parallel tempering swapping efficiency for a 13-atom Lennard-Jones cluster, we argue that the value of 38.74% remains roughly the optimal probability for most systems with continuous distributions that are likely to be encountered in practice.     

Comparing different protocols of temperature selection in the parallel tempering method

Parallel tempering Monte Carlo simulations have been applied to a variety of systems presenting rugged free-energy landscapes. Despite this, its efficiency depends strongly on the temperature set. With this query in mind, we present a comparative study among different temperature selection schemes in three lattice-gas models. We focus our attention in the constant entropy method (CEM), proposed by Sabo et al. In the CEM, the temperature is chosen by the fixed difference of entropy between adjacent replicas. We consider a method to determine the entropy which avoids numerical integrations of the specific heat and other thermodynamic quantities. Different analyses for first- and second-order phase transitions have been undertaken, revealing that the CEM may be an useful criterion for selecting the temperatures in the parallel tempering.     

Entropic stabilization of isolated beta-sheets

Temperature-dependent electric deflection measurements have been performed for a series of unsolvated alanine-based peptides (Ac-WA(n)-NH(2), where Ac = acetyl, W = tryptophan, A = alanine, and n = 3, 5, 10, 13, and 15). The measurements are interpreted using Monte Carlo simulations performed with a parallel tempering algorithm. Despite alanine's high helix propensity in solution, the results suggest that unsolvated Ac-WA(n)-NH(2) peptides with n > 10 adopt beta-sheet conformations at room temperature. Previous studies have shown that protonated alanine-based peptides adopt helical or globular conformations in the gas phase, depending on the location of the charge. Thus, the charge more than anything else controls the structure.     

Thermodynamics of folding and association of lattice-model proteins

Closely related to the "protein folding problem" is the issue of protein misfolding and aggregation. Protein aggregation has been associated with the pathologies of nearly 20 human diseases and presents serious difficulties during the manufacture of pharmaceutical proteins. Computational studies of multiprotein systems have recently emerged as a powerful complement to experimental efforts aimed at understanding the mechanisms of protein aggregation. We describe the thermodynamics of systems containing two lattice-model 64-mers. A parallel tempering algorithm abates problems associated with glassy systems and the weighted histogram analysis method improves statistical quality. The presence of a second chain has a substantial effect on single-chain conformational preferences. The melting temperature is substantially reduced, and the increase in the population of unfolded states is correlated with an increase in interactions between chains. The transition from two native chains to a non-native aggregate is entropically favorable. Non-native aggregates receive approximately 25% of their stabilizing energy from intraprotein contacts not found in the lowest-energy structure. Contact maps show that for non-native dimers, nearly 50% of the most probable interprotein contacts involve pairs of residues that form native contacts, suggesting that a domain-swapping mechanism is involved in self-association.     

Monte Carlo methods for small molecule high-throughput experimentation

By analogy with Monte Carlo algorithms, we propose new strategies for design and redesign of small molecule libraries in high-throughput experimentation, or combinatorial chemistry. Several Monte Carlo methods are examined, including Metropolis, three types of biased schemes, and composite moves that include swapping or parallel tempering. Among them, the biased Monte Carlo schemes exhibit particularly high efficiency in locating optimal compounds. The Monte Carlo strategies are compared to a genetic algorithm approach. Although the best compounds identified by the genetic algorithm are comparable to those from the better Monte Carlo schemes, the diversity of favorable compounds identified is reduced by roughly 60%.     

Replica-exchange extensions of simulated tempering method

In this paper we consider combinations of two well-known generalized-ensemble algorithms, namely, simulated tempering and replica-exchange method. We discuss two examples of such combinations. One is the replica-exchange simulated tempering and the other is the simulated tempering replica-exchange method. In the former method, a short replica-exchange simulation is first performed and the simulated tempering weight factor is obtained by the multiple-histogram reweighting techniques. This process of simulated tempering weight factor determination is faster and simpler than that in the usual iterative process. A long simulated tempering production run is then performed with this weight factor. The latter method is a further extension of the former in which a simulated tempering replica-exchange simulation is performed with a small number of replicas. These algorithms are particularly useful for studying frustrated systems with rough energy landscape. We give the formulations of these two methods in detail and demonstrate their effectiveness taking the example of the system of a 17-residue helical peptide.     

Freezing and folding behavior in simple off-lattice heteropolymers

We have performed parallel tempering Monte Carlo simulations using a simple continuum heteropolymer model for proteins. All 10 heteropolymer sequences which we have studied have shown first-order transitions at low temperature to ordered states dominated by single chain conformations. These results are in contrast with the theoretical predictions of the random energy model for heteropolymers, from which we would expect continuous transitions to glassy behavior at low temperatures.