Communication: Consistent interpretation of molecular simulation kinetics using Markov state models biased with external information

Rudzinski, Joseph F.; Kremer, Kurt; Bereau, Tristan

doi:10.1063/1.4941455

Cited by 37 publications

(64 citation statements)

References 59 publications

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“…We monitor the transition kinetics between the helical (H) and extended (E) metastable basins ( Fig. 5a and c), identified from a Markov state model analysis of the reference AA simulation [34,35]. We focus on ratios of mean-first-passage times to factor out any homogeneous speedup factor due to coarse-graining.…”

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confidence: 99%

Accurate Structure-Based Coarse Graining Leads to Consistent Barrier-Crossing Dynamics

Bereau

Rudzinski

2018

Phys. Rev. Lett.

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Structure-based coarse-graining of molecular systems offers a systematic route to reproduce the many-body potential of mean force. Unfortunately, common strategies are inherently limited by the molecular-mechanics force field employed. Here we extend the concept of multisurface dynamics, initially developed to describe electronic transitions in chemical reactions, to accurately sample the conformational ensemble of a classical system in equilibrium. In analogy to describing different electronic configurations, a surface-hopping scheme couples distinct conformational basins beyond the additivity of the Hamiltonian. The incorporation of more surfaces leads systematically toward improved cross-correlations. The resulting models naturally achieve consistent long-time dynamics for systems governed by barrier-crossing events.

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confidence: 99%

Accurate Structure-Based Coarse Graining Leads to Consistent Barrier-Crossing Dynamics

Bereau

Rudzinski

2018

Phys. Rev. Lett.

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“…2 in the work of Dalgicdir and Sayar 36 ], which is surprising considering that CG models usually have accelerated dynamics due to softer interaction potentials compared to the AA case. As shown in a recent study, 63 similar to the structural and thermodynamic properties, recapturing the kinetics of the underlying AA system is also highly dependent on the specific parameterization of the CG model.…”

Section: A a Single Lkα14 Peptide In Bulk Watermentioning

confidence: 88%

A multi-state coarse grained modeling approach for an intrinsically disordered peptide

Ramezanghorbani

Dalgıçdir

Sayar

2017

The Journal of Chemical Physics

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Many proteins display a marginally stable tertiary structure, which can be altered via external stimuli. Since a majority of coarse grained (CG) models are aimed at structure prediction, their success for an intrinsically disordered peptide's conformational space with marginal stability and sensitivity to external stimuli cannot be taken for granted. In this study, by using the LKα14 peptide as a test system, we demonstrate a bottom-up approach for constructing a multi-state CG model, which can capture the conformational behavior of this peptide in three distinct environments with a unique set of interaction parameters. LKα14 is disordered in dilute solutions; however, it strictly adopts the α-helix conformation upon aggregation or when in contact with a hydrophobic/hydrophilic interface. Our bottom-up approach combines a generic base model, that is unbiased for any particular secondary structure, with nonbonded interactions which represent hydrogen bonds, electrostatics, and hydrophobic forces. We demonstrate that by using carefully designed all atom potential of mean force calculations from all three states of interest, one can get a balanced representation of the nonbonded interactions. Our CG model behaves intrinsically disordered in bulk water, folds into an α-helix in the presence of an interface or a neighboring peptide, and is stable as a tetrameric unit, successfully reproducing the all atom molecular dynamics simulations and experimental results.

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“…These two results open up the possibility of establishing more elaborate models for integrative structural biology where full thermodynamic and kinetic descriptions may be obtained. Future developments may allow for the integration of dynamic observables, such as relaxation rates or correlation functions, into AMMs by adopting a maximum caliber functional (56) or Bayesian methods (33,34) to account for the systematic errors in these quantities.…”

Section: Resultsmentioning

confidence: 99%

“…Further, most existing approaches do not clearly distinguish between systematic (forcefield) error and statistical (sampling) error, which may result in unexpected behavior or involve user-specified weighting factors. Several Markov state model estimators have been developed that are conditioned on auxiliary data, especially microscopic quantities such as the stationary distribution or functions of the transition probability matrix (33)(34)(35)(36)(37). However, the direct augmentation of Markov state models with experimental data using a judicious treatment of force-field and sampling errors is still an open issue.…”

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Combining experimental and simulation data of molecular processes via augmented Markov models

Olsson

Paul

et al. 2017

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

103

105

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Accurate mechanistic description of structural changes in biomolecules is an increasingly important topic in structural and chemical biology. Markov models have emerged as a powerful way to approximate the molecular kinetics of large biomolecules while keeping full structural resolution in a divide-and-conquer fashion. However, the accuracy of these models is limited by that of the force fields used to generate the underlying molecular dynamics (MD) simulation data. Whereas the quality of classical MD force fields has improved significantly in recent years, remaining errors in the Boltzmann weights are still on the order of a few kT, which may lead to significant discrepancies when comparing to experimentally measured rates or state populations. Here we take the view that simulations using a sufficiently good force-field sample conformations that are valid but have inaccurate weights, yet these weights may be made accurate by incorporating experimental data a posteriori. To do so, we propose augmented Markov models (AMMs), an approach that combines concepts from probability theory and information theory to consistently treat systematic force-field error and statistical errors in simulation and experiment. Our results demonstrate that AMMs can reconcile conflicting results for protein mechanisms obtained by different force fields and correct for a wide range of stationary and dynamical observables even when only equilibrium measurements are incorporated into the estimation process. This approach constitutes a unique avenue to combine experiment and computation into integrative models of biomolecular structure and dynamics. molecular dynamics | integrative structural biology | maximum entropy | Markov state models | augmented Markov models A tomistic molecular dynamics (MD) simulation is a popular tool to investigate mechanisms underlying biomolecular function, including ligand binding (1, 2) and allostery (3), whereas coarse-grained molecular models are often used when studying assembly and interactions of supramolecular systems (4, 5). With recent advances in massively paralleled computation, simulating thousands of short-to medium-length realizations of many biomolecular systems has become feasible (6, 7). Systematic analysis of such large sets of MD data in terms of Markov state models (MSMs) (8, 9) now enables the study of slow dynamic processes, including protein folding (10, 11), conformational transitions (12, 13), and quantitative comparison with experiments (14-16) otherwise affordable only on specialpurpose supercomputers (17).Whereas these technologies are closing the gap as to which macromolecular systems and timescales can be directly simulated, it is becoming increasingly evident that systematic errors in empirical models-force fields-limit our ability to predict experimental data quantitatively (18). Although force fields are undergoing continuous improvement, high accuracy needs to be balanced with computational efficiency. A viable approach is to aim at force fields that are good enough such that...

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Communication: Consistent interpretation of molecular simulation kinetics using Markov state models biased with external information

Cited by 37 publications

References 59 publications

Accurate Structure-Based Coarse Graining Leads to Consistent Barrier-Crossing Dynamics

Accurate Structure-Based Coarse Graining Leads to Consistent Barrier-Crossing Dynamics

A multi-state coarse grained modeling approach for an intrinsically disordered peptide

Combining experimental and simulation data of molecular processes via augmented Markov models

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