Serial Generalized Ensemble Simulations of Biomolecules with Self-Consistent Determination of Weights

Abstract

Serial generalized ensemble simulations, such as simulated tempering, enhance phase space sampling through non-Boltzmann weighting protocols. The most critical aspect of these methods with respect to the popular replica exchange schemes is the difficulty in determining the weight factors which enter the criterion for accepting replica transitions between different ensembles. Recently, a method, called BAR-SGE, was proposed for estimating optimal weight factors by resorting to a self-consistent procedure applied during the simulation (J. Chem. Theory Comput.2010, 6, 1935-1950). Calculations on model systems have shown that BAR-SGE outperforms other approaches proposed for determining optimal weights in serial generalized ensemble simulations. However, extensive tests on real systems and on convergence features with respect to the replica exchange method are lacking. Here, we report on a thorough analysis of BAR-SGE by performing molecular dynamics simulations of a solvated alanine dipeptide, a system often used as a benchmark to test new computational methodologies, and comparing results to the replica exchange method. To this aim, we have supplemented the ORAC program, a FORTRAN suite for molecular dynamics simulations (J. Comput. Chem.2010, 31, 1106-1116), with several variants of the BAR-SGE technique. An illustration of the specific BAR-SGE algorithms implemented in the ORAC program is also provided.

Adaptive Ensemble Biomolecular Applications at Scale

“…In generalized ensemble simulation methods, different ensemble simulations employ distinct exchange algorithms [22] or specify diverse sampling parameters [23] to explore free-energy surfaces that are less accessible to non-adaptive methods. In metadynamics [24] and expanded ensemble [25], simulations traverse different states based on weights "learned" adaptively. Markov State Model [18] (MSM) approaches adaptively select initial configurations for simulations to reduce uncertainty of the resulting model.…”

Computing Free Energy Differences of Configurational Basins

“…Although several implementations of SGE simulation techniques have been provided during the years, [37][38][39][40][41] in our comparative analysis we have adopted the scheme proposed in Refs., 20,27 which is based on a "on the fly" update of ensemble free energies according to the Bennett acceptance ratio method. 42,43 The simulation run considered here results from extending in time the SGE simulation reported in Ref., 20 to which reference is made for a detailed description of the simulation setup.…”

“…In this respect, it is worth noting that the adopted SGE methodology has already been proved to be comparable in accuracy to the popular replica exchange method, [45][46][47][48][49][50] as the estimate of Φ(z) is concerned. 20 Moreover, we point out that it is not our aim here to present the PLD scheme as the best approach to study conformational distributions in peptides, or biopolymers in general, also because no systematic comparison is provided with other important methods for free energy calculations. 16 Rather, we limit our conclusions to observe that, in the treatment of small peptides, the PLD scheme outperforms the quite popular family of generalized ensemble simulations, offering interesting perspectives, alternative to methodologies already in use, for free energy calculations.…”

Combining path-breaking with bidirectional nonequilibrium simulations to improve efficiency in free energy calculations

“…Possibility of application of path-breaking is envisaged in both replica exchange simulations [50][51][52] and serial generalized-ensemble simulations with self-consistent determination of weights. [60][61][62] Future studies will be devoted to this subject.…”