Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering

Gil-Ley, Alejandro; Bussi, Giovanni

doi:10.1021/ct5009087

Cited by 101 publications

(172 citation statements)

References 67 publications

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“…Three replica exchange molecular dynamics (REMD) protocols are compared in the figure, namely, temperature REMD (T-REMD), dihedral-scaling REMD (H dih -REMD), and the newly introduced replica exchange with collective-variable tempering (RECT) method. In general, for the three compared methods the agreement between the trajectories across the replica ladder is much lower than in Figure S6 of ref (1). This indicates that all of the simulations are still not fully converged.…”

contrasting

confidence: 62%

Correction to Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering

Gil-Ley¹,

Bussi²

2015

J. Chem. Theory Comput.

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contrasting

confidence: 62%

Correction to Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering

Gil-Ley¹,

Bussi²

2015

J. Chem. Theory Comput.

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“…The RE procedure used is similar in spirit to Hamiltonian RE, solute tempering, and RE with CV tempering (17,52,53). In each replica the system is biased by the converged bias potential from the optimization run, scaled by a constant factor.…”

Section: Methodsmentioning

confidence: 99%

“…This problem of interesting basins getting washed out is also a potential problem for RE with CV tempering (17) and biasing potential RE MD (18), which we discussed above. Ultimately, for an RE procedure to truly improve upon the efficiency of a standard simulation with comparable computational resources, it must lower the roundtrip time for exploring the basins of interest; this is a criterion which is often not demonstrated for such simulations (38).…”

Section: ]mentioning

confidence: 95%

“…A related method is the recently introduced parallel bias metadynamics, which also produces many low-dimensional free-energy surfaces but eliminates the exchange moves (16). Methods like replica exchange with CV tempering (17) and biasing potential replica exchange molecular dynamics (18) rely on a replica exchange scheme to collect statistics from an unbiased replica, and they use either a predefined or an iteratively constructed bias to help the system explore in biased replicas which can exchange with the unbiased replica. An alternative approach is to use some machine learning procedure to identify the important basins in a high-dimensional space, as is…”

Section: Introductionmentioning

confidence: 99%

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Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin

Shaffer

Valsson

Parrinello

2016

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

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The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute freeenergy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin.enhanced sampling | protein folding | free-energy calculation | biomolecular simulation M olecular simulation has become an increasingly useful tool for studying complex transformations in chemistry, biology, and materials science. Such simulations provide extremely high spatial and temporal resolution but suffer from inherent time-scale limitations. Molecular dynamics simulations of biomolecules performed on standard hardware struggle to exceed the microsecond time scale. Modern, purpose-built hardware can reach the millisecond time scale (1), but these tools are not generally available and they still prove insufficient for many important problems (2). Widely used enhanced sampling methods also allow one to simulate processes with a time scale of milliseconds (3) and there is significant interest in extending these methods. One important class of enhanced sampling methods involves applying an external bias to one or more collective variables (CVs) of the system. Umbrella sampling and metadynamics fall into this category (4, 5), but there are many other examples (6-13). These methods are most effective when the transformations of interest are in some sense collective and can be described by a very small number of CVs.When the choice of CV is not obvious or the free-energy landscape cannot be projected meaningfully on a low-dimensional manifold, new strategies are needed. Here we describe an enhanced sampling strategy that allows one to bias high-dimensional freeenergy landscapes by exploiting the flexibility inherent in the recently introduced variationally enhanced sampling (VES) method (14). This method casts free-energy computation as a functional minimization problem, and it solves two important problems associated with high-dimensional free-energy computation. The first problem is that when exploring a high-dimensional space a system might never visit the regions of interest, but the variational principle introduced in ref. 14 permits us to specifically target the region we are interested in through the use of a so-called "target distribution." The second problem is that one must adopt an approximation for the high-dimensional bias and the variational principle allows us to do this in an optimal way. Furthermore, as we show here, for the first time to our knowledge, choos...

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“…Furthermore, we would like to improve the extent and consistency of sampling by coupling the intermediate alchemical states via the Hamiltonian replica exchange method (HREM). 16–19 …”

Section: Introductionmentioning

confidence: 99%

A GPU-Accelerated Parameter Interpolation Thermodynamic Integration Free Energy Method

Giese

York

2018

J. Chem. Theory Comput.

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There has been a resurgence of interest in free energy methods motivated by the performance enhancements offered by molecular dynamics (MD) software written for specialized hardware, such as graphics processing units (GPUs). In this work, we exploit the properties of a parameter-interpolated thermodynamic integration (PI-TI) method to connect states by their molecular mechanical (MM) parameter values. This pathway is shown to be better behaved for Mg2+ → Ca2+ transformations than traditional linear alchemical pathways (with and without soft-core potentials). The PI-TI method has the practical advantage that no modification of the MD code is required to propagate the dynamics, and unlike with linear alchemical mixing, only one electrostatic evaluation is needed (e.g., single call to particle-mesh Ewald) leading to better performance. In the case of AMBER, this enables all the performance benefits of GPU-acceleration to be realized, in addition to unlocking the full spectrum of features available within the MD software, such as Hamiltonian replica exchange (HREM). The TI derivative evaluation can be accomplished efficiently in a post-processing step by reanalyzing the statistically independent trajectory frames in parallel for high throughput. We also show how one can evaluate the particle mesh Ewald contribution to the TI derivative evaluation without needing to perform two reciprocal space calculations. We apply the PI-TI method with HREM on GPUs in AMBER to predict pKa values in double stranded RNA molecules and make comparison with experiments. Convergence to under 0.25 units for these systems required 100 ns or more of sampling per window and coupling of windows with HREM. We find that MM charges derived from ab initio QM/MM fragment calculations improve the agreement between calculation and experimental results.

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Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering

Cited by 101 publications

References 67 publications

Correction to Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering

Correction to Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering

Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin

A GPU-Accelerated Parameter Interpolation Thermodynamic Integration Free Energy Method

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