Unified Approach to Enhanced Sampling

Invernizzi, Michele; Piaggi, Pablo M.; Parrinello, Michele

doi:10.1103/physrevx.10.041034

Cited by 92 publications

(165 citation statements)

References 81 publications

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“…OPES, like MetaD and many other methods 14 – 16 , relies on the identification of suitable order parameters or collective variables (CVs). In these methods 14 , 17 , the CV distribution is made to follow a preassigned law. This allows CV fluctuations to be amplified in a controlled way.…”

Section: Introductionmentioning

confidence: 99%

The role of water in host-guest interaction

et al. 2021

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One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system’s degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.

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Section: Introductionmentioning

confidence: 99%

The role of water in host-guest interaction

et al. 2021

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“…

. As pointed out in ( Invernizzi et al, 2020 ) when the weights of the blocks are unbalanced, using

instead of

can significantly underestimate the uncertainty.…”

Section: Methodsmentioning

confidence: 99%

“…By comparing the averages of a given quantity from each block we can calculate the error bar on our estimate of that quantity: for large enough blocks the averages should not be time correlated so that the estimate of the error converges. As our blocks could be characterized by different weights, this must be taken into account in the estimation of the error as described in ( Invernizzi et al, 2020 ). Given W b the weight of the block b , obtained as the sum of the weights of the frames composing the block, the statistical error on the observable O is:

, where

is the effective block size, the sums run on the number of blocks

is the average computed over the frames of block

and

is the average computed over all the frames, which corresponds to the average computed over the block averages, i.e.…”

Section: Methodsmentioning

confidence: 99%

How to Determine Accurate Conformational Ensembles by Metadynamics Metainference: A Chignolin Study Case

Paissoni

Camilloni

2021

Front. Mol. Biosci.

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The reliability and usefulness of molecular dynamics simulations of equilibrium processes rests on their statistical precision and their capability to generate conformational ensembles in agreement with available experimental knowledge. Metadynamics Metainference (M&M), coupling molecular dynamics with the enhanced sampling ability of Metadynamics and with the ability to integrate experimental information of Metainference, can in principle achieve both goals. Here we show that three different Metadynamics setups provide converged estimate of the populations of the three-states populated by a model peptide. Errors are estimated correctly by block averaging, but higher precision is obtained by performing independent replicates. One effect of Metadynamics is that of dramatically decreasing the number of effective frames resulting from the simulations and this is relevant for M&M where the number of replicas should be large enough to capture the conformational heterogeneity behind the experimental data. Our simulations allow also us to propose that monitoring the relative error associated with conformational averaging can help to determine the minimum number of replicas to be simulated in the context of M&M simulations. Altogether our data provides useful indication on how to generate sound conformational ensemble in agreement with experimental data.

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“…Rare trajectories reaching a target basin in configuration space are often of interest as transition paths for reactive events, and significant development has been undertaken to efficiently generate them. [85][86][87][88][89] Computing optimal drift forces for generating these rare trajectories enables the study of reactive dynamics in a direct manner. We expect these algorithms to find use in the study of diffusive dynamics where Monte Carlo approaches have difficulty sampling.…”

Section: Rare Fluctuations In Finite Timementioning

confidence: 99%

Reinforcement learning of rare diffusive dynamics

Das

Rose

Garrahan

et al. 2021

The Journal of Chemical Physics

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We present a method to probe rare molecular dynamics trajectories directly using reinforcement learning. We consider trajectories that are conditioned to transition between regions of configuration space in finite time, like those relevant in the study of reactive events, as well as trajectories exhibiting rare fluctuations of time-integrated quantities in the long time limit, like those relevant in the calculation of large deviation functions. In both cases, reinforcement learning techniques are used to optimize an added force that minimizes the Kullback-Leibler divergence between the conditioned trajectory ensemble and a driven one. Under the optimized added force, the system evolves the rare fluctuation as a typical one, affording a variational estimate of its likelihood in the original trajectory ensemble. Low variance gradients employing value functions are proposed to increase the convergence of the optimal force. The method we develop employing these gradients leads to efficient and accurate estimates of both the optimal force and the likelihood of the rare event for a variety of model systems.

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Unified Approach to Enhanced Sampling

Cited by 92 publications

References 81 publications

The role of water in host-guest interaction

The role of water in host-guest interaction

How to Determine Accurate Conformational Ensembles by Metadynamics Metainference: A Chignolin Study Case

Reinforcement learning of rare diffusive dynamics

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