ArticleProceedings of the National Academy of Sciences of the United States of America2017
Combining experimental and simulation data of molecular processes via augmented Markov models.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
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- Interactions between Inhibitors and 5-Lipoxygenase: Insights from Gaussian Accelerated Molecular Dynamics and Markov State Models.International journal of molecular sciences · 2024Article
- Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Cryo-electron Microscopy and Molecular Modeling Methods to Characterize the Dynamics of Tau Bound to Microtubules.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Determination of the Structure and Dynamics of the Fuzzy Coat of an Amyloid Fibril of IAPP Using Cryo-Electron Microscopy.Biochemistry · 2023Article
- Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model.bioRxiv : the preprint server for biology · 2023Article
- BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations.Journal of chemical information and modeling · 2023Article
- Deep learning to decompose macromolecules into independent Markovian domains.Nature communications · 2022Article
- A litmus test for classifying recognition mechanisms of transiently binding proteins.Nature communications · 2022Article
- gmxapi: A GROMACS-native Python interface for molecular dynamics with ensemble and plugin support.PLoS computational biology · 2022Article
- Enhanced sampling without borders: on global biasing functions and how to reweight them.Physical chemistry chemical physics : PCCP · 2022Review
- Inference of Joint Conformational Distributions from Separately Acquired Experimental Measurements.The journal of physical chemistry letters · 2021Article
- Discovery of a hidden transient state in all bromodomain families.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A method of incorporating rate constants as kinetic constraints in molecular dynamics simulations.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Multi-Scale Flexible Fitting of Proteins to Cryo-EM Density Maps at Medium Resolution.Frontiers in molecular biosciences · 2021Article
- Molecular Dynamics to Predict Cryo-EM: Capturing Transitions and Short-Lived Conformational States of Biomolecules.Frontiers in molecular biosciences · 2021Article
- Protein Allostery at Atomic Resolution.Angewandte Chemie (International ed. in English) · 2020Article
- Target search and recognition mechanisms of glycosylase AlkD revealed by scanning FRET-FCS and Markov state models.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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5 authors.
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Abstract
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 [Formula: see text], 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.
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