Enhanced sampling methods for molecular dynamics simulations

Hénin, Jérôme; Lelièvre, Tony; Shirts, Michael R.; Valsson, Ómar; Delemotte, Lucie

doi:10.48550/arxiv.2202.04164

Cited by 18 publications

(21 citation statements)

References 337 publications

(500 reference statements)

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“…However, while molecular dynamics (MD) simulations can aid in elucidating molecular mechanisms, the active state half-life of free PAI-1 is 1 to 2 hours under physiological conditions (19) much longer than the millisecond time scales accessible with MD. To computationally study rare events such as serpin conformational changes one can turn to a variety of enhanced sampling methods (an incomplete list of possible methods is provided in (20, 21)).…”

Section: Introductionmentioning

confidence: 99%

Long range allostery mediates the regulation of plasminogen activator inhibitor-1 by vitronectin

Kihn

Marchiori

Spagnolli

et al. 2022

Preprint

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The serpin plasminogen activator inhibitor 1 (PAI-1) spontaneously undergoes a massive structural change from a metastable, active conformation, with a solvent accessible reactive center loop (RCL), to a stable, inactive or latent conformation in which the RCL has inserted into the central β sheet. Physiologically, conversion to the latent state is regulated by the binding of vitronectin which retards the rate of this latency transition approximately 2-fold. We investigated the effects of vitronectin on the PAI-1 latency transition using all-atom path sampling simulations in explicit solvent. In simulated latency transitions of free PAI-1, the RCL is quite mobile as is the gate, the region that impedes RCL access to the central β sheet. This mobility allows the formation of a transient salt bridge that facilitates the transition, and this finding rationalizes existing mutagenesis results. Vitronectin binding reduces RCL and gate mobility by allosterically rigidifying structural elements over 40 Å away from the binding site thus blocking the transition to the latent conformation. The effects of vitronectin are propagated by a network of dynamically correlated residues including a number of conserved sites that have previously been identified as important for PAI-1 stability. Simulations also revealed a transient pocket populated only in the vitronectin bound state which corresponds to a cryptic drug binding site identified by crystallography. Overall, these results shed new light on regulation of the PAI-1 latency transition by vitronectin and illustrate the potential of path sampling simulations for understanding functional conformational changes in proteins and for facilitating drug discovery.

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Section: Introductionmentioning

confidence: 99%

Long range allostery mediates the regulation of plasminogen activator inhibitor-1 by vitronectin

Kihn

Marchiori

Spagnolli

et al. 2022

Preprint

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“…CV-based enhanced sampling techniques overcome the sampling problem by introducing a bias potential V (z) acting in the CV space designed to enhance CV fluctuations. The functional form of the bias depends on the enhanced sampling method used [4,7,8,[41][42][43]. The bias potential can be static [8] or adaptively constructed on the fly during the simulation [4,7,[41][42][43].…”

Section: B Enhanced Samplingmentioning

confidence: 99%

“…A possible resolution to the sampling problem is given by enhanced sampling methods [3][4][5][6][7]. Over the years, various strategies for enhanced sampling have emerged, e.g., tempering, variational, or biasing approaches; see Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Over the years, various strategies for enhanced sampling have emerged, e.g., tempering, variational, or biasing approaches; see Ref. [7] for classification and references therein. In this article, we consider a class of such enhanced sampling methods based on the work by Torrie and Valleau [8], which devised a framework for enhanced sampling that modifies the Boltzmann probability distribution by introducing a bias potential acting in a low-dimensional space of collective variables (CVs) that correspond to slow degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling

Rydzewski

Valsson

2021

J. Phys. Chem. A

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Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial in enhanced sampling simulations. There we seek a few generalized degrees of freedom, referred to as collective variables (CVs), to represent and drive the sampling of the free energy landscape. In theory, these CVs should separate different metastable states and correspond to the slow degrees of freedom of the studied physical process. To this aim, we propose a new method that we call multiscale reweighted stochastic embedding (MRSE). Our work builds upon a parametric version of stochastic neighbor embedding. The technique automatically learns CVs that map a high-dimensional feature space to a low-dimensional latent space via a deep neural network. We introduce several new advancements to stochastic neighbor embedding methods that make MRSE especially suitable for enhanced sampling simulations: (1) weight-tempered random sampling as a landmark selection scheme to obtain training data sets that strike a balance between equilibrium representation and capturing important metastable states lying higher in free energy; (2) a multiscale representation of the high-dimensional feature space via a Gaussian mixture probability model; and (3) a reweighting procedure to account for training data from a biased probability distribution. We show that MRSE constructs low-dimensional CVs that can correctly characterize the different metastable states in three model systems: the Müller-Brown potential, alanine dipeptide, and alanine tetrapeptide.

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“…Molecular dynamics (MD) simulations [12] enable RNA dynamics to be predicted and characterized at the atomistic scale. Limitations on the accessible timescales can be alleviated by taking advantage of enhanced sampling methods [13]. However, despite recent improvements, the accuracy of the models used to describe interactions (force fields) for RNA is still limited, in particular for some structural motifs [14].…”

Section: Introductionmentioning

confidence: 99%

Combining simulations and experiments to investigate RNA dynamics

Bernetti,

Bussi

2022

Preprint

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Conformational dynamics is crucial for ribonucleic acid (RNA) function. Techniques such as nuclear magnetic resonance, cryo-electron microscopy, small-and wide-angle X-ray scattering, chemical probing, or even thermal denaturation experiments probe RNA dynamics at different time and space resolutions. Their combination with accurate atomistic molecular dynamics (MD) simulations paves the way for quantitative and detailed studies of RNA dynamics. First, experiments provide a quantitative validation tool for MD simulations. Second, available data can be used to refine simulated structural ensembles to match experiments. Finally, comparison with experiments allows for improving MD force fields that are transferable to new systems for which data is not available. Here we review the recent literature and provide our perspective on this field.

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Enhanced sampling methods for molecular dynamics simulations

Abstract: This LiveCoMS document is maintained online on GitHub at https:// github. com/ jhenin/ Methods-forenhanced-sampling-andfree-energy-calculations; to provide feedback, suggestions, or help improve it, please visit the GitHub repository and participate via the issue tracker.

Cited by 18 publications

References 337 publications

Long range allostery mediates the regulation of plasminogen activator inhibitor-1 by vitronectin

Long range allostery mediates the regulation of plasminogen activator inhibitor-1 by vitronectin

Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling

Combining simulations and experiments to investigate RNA dynamics

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