Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0]

Grossfield, Alan; Patrone, Paul N.; Roe, Daniel R.; Schultz, Andrew J.; Siderius, Daniel W.; Zuckerman, Daniel M.

doi:10.33011/livecoms.1.1.5067

Cited by 134 publications

(184 citation statements)

References 55 publications

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“…It is important that the difference between subsequent t 0 values (δt 0 ) be longer than the correlation time so that the different time intervals are independent (see Ref. [32] for details regarding correlation time). Both methods involve some judgment on the part of the user and results can vary depending on where the slope is taken (Einstein approach) and for how long the integral is carried out (Green-Kubo approach).…”

Section: Improved Precisionmentioning

confidence: 99%

“…For this reason, in addition to the discussion provided below, we recommend a close study of Refs. [33] and [32]. Although force field deficiencies can lead to large systematic deviations with respect to the "true" experimental value, we limit our discussion to random uncertainties associated with fluctuations in the simulation output.…”

Section: Uncertainty Quantificationmentioning

confidence: 99%

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Best Practices for Computing Transport Properties 1. Self-Diffusivity and Viscosity from Equilibrium Molecular Dynamics [Article v1.0]

et al. 2020

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The ability to predict transport properties (e.g., diffusivity, viscosity, and conductivity) is one of the primary benefits of molecular simulation. Although most studies focus on the accuracy of the simulation output compared to experimental data, such a comparison primarily tests the adequacy of the force field (i.e., the model). By contrast, the reliability of different simulation methodologies for predicting transport properties is the focus of this manuscript. Unfortunately, obtaining reproducible estimates of transport properties from molecular simulation is not as straightforward as static properties. Therefore, this manuscript discusses the best practices that should be followed to ensure that the simulation output is reliable, i.e., is a valid representation of the force field implemented. We also discuss procedures to use so that the results are reproducible (i.e., can be obtained by other researchers following the same methods and procedures). There are two classes by which transport properties are predicted: equilibrium molecular dynamics (EMD) and non-equilibrium molecular dynamics (NEMD). This manuscript presents the best practices for EMD, leaving NEMD for a future publication. As self-diffusivity and shear viscosity are the most prevalent transport properties found in the literature, the discussion will also be limited to these properties with the expectation that future publications will discuss best practices for thermal conductivity, ionic conductivity, and multicomponent diffusivity.

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Section: Improved Precisionmentioning

confidence: 99%

Section: Uncertainty Quantificationmentioning

confidence: 99%

Best Practices for Computing Transport Properties 1. Self-Diffusivity and Viscosity from Equilibrium Molecular Dynamics [Article v1.0]

et al. 2020

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“…Besides the presence of unexplored relevant areas of configurational space, the noise in the trajectory can hide very slow decays (see YANK calculation in CB8-G3). More recommendations about how to detect convergence issues can be found in [114,115].…”

Section: Bias Is Critical When Comparing the Efficiency Of Different mentioning

confidence: 99%

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

Rizzi

Jensen

Slochower

et al. 2020

J Comput Aided Mol Des

125

189

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Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

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“…The lack of a common comparative method for molecular dynamics data has been problematic. Many reviews of best practices in molecular dynamics often warn users of the danger of comparisons based upon inadequate sampling, but seldom offer proper statistical framework to mitigate this problem (41,42). Recently, our lab has developed a platform for statistical comparison and machine learning applications to molecular dynamics (15,16).…”

Section: Discussionmentioning

confidence: 99%

Information theoretics for the machine learning detection of functionally conserved and coordinated protein motions

Babbitt

2020

Preprint

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The application of machine learning classification to the molecular dynamics of the functional states of protein allows for application of an information theoretic framework similar to traditional bioinformatics. The functional states of proteins involving binding interactions with partners comprised of protein, DNA or small molecules can first be defined in a binary fashion (i.e. bound vs unbound), subsequently simulated in molecular dynamics software, and then employed as a comparative training set for a binary machine learning classifier capable of discerning the complex dynamical consequences of binding interaction. This learner can subsequently be deployed on new simulations of the functionally bound state to validate its ability to recognize the molecular motions that are supporting binding function. Regions of proteins with functionally conserved dynamics will induce significant local correlations in learning performance across independent validation runs. Through case studies of Rbp subunit 4/7 interaction in RNA Pol II and DNA-protein interactions of TATA binding protein, we demonstrate this method of detecting functionally conserved protein dynamics. We also demonstrate how Shannon information, relative entropy and mutual information can be applied to these binary classification states of dynamic simulations in order to compare dynamics and identify coordinated motions involved in dynamic interactions across sites.

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Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0]

Cited by 134 publications

References 55 publications

Best Practices for Computing Transport Properties 1. Self-Diffusivity and Viscosity from Equilibrium Molecular Dynamics [Article v1.0]

Best Practices for Computing Transport Properties 1. Self-Diffusivity and Viscosity from Equilibrium Molecular Dynamics [Article v1.0]

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

Information theoretics for the machine learning detection of functionally conserved and coordinated protein motions

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