ArticleJournal of chemical theory and computation2021
What Markov State Models Can and Cannot Do: Correlation versus Path-Based Observables in Protein-Folding Models.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- Reducing weighted ensemble variance with optimal trajectory management.The Journal of chemical physics · 2026Article
- Building multiscale Markov state models by systematic mapping of temporal communities.Bioinformatics (Oxford, England) · 2026Article
- Enhanced Sampling and Free Energy Calculations in Protein Simulations.Advances in experimental medicine and biology · 2026Review
- Markov-Type State Models to Describe Non-Markovian Dynamics.Journal of chemical theory and computation · 2025Article
- Markov State Models with Weighted Ensemble Simulation: How to Eliminate the Trajectory Merging Bias.Journal of chemical theory and computation · 2025Article
- Advances in the Directed Evolution of Computer-aided Enzymes.Current topics in medicinal chemistry · 2025Review
- Accounting for Fast vs Slow Exchange in Single Molecule FRET Experiments Reveals Hidden Conformational States.Journal of chemical theory and computation · 2024Article
- Rare-Event Sampling using a Reinforcement Learning-Based Weighted Ensemble Method.bioRxiv : the preprint server for biology · 2024Article
- CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules.JACS Au · 2024Article
- Accounting for fast vs slow exchange in single molecule FRET experiments reveals hidden conformational states.bioRxiv : the preprint server for biology · 2024Article
- Functional protein dynamics in a crystal.Nature communications · 2024Article
- Functional Protein Dynamics in a Crystal.bioRxiv : the preprint server for biology · 2024Article
- Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants.Protein science : a publication of the Protein Society · 2024Review
- Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics.The journal of physical chemistry. B · 2024Article
- Markov State Models: To Optimize or Not to Optimize.Journal of chemical theory and computation · 2024Article
- Article
- Uncertainties in Markov State Models of Small Proteins.Journal of chemical theory and computation · 2023Article
- Optimization of non-equilibrium self-assembly protocols using Markov state models.The Journal of chemical physics · 2022Article
- Automated Path Searching Reveals the Mechanism of Hydrolysis Enhancement by T4 Lysozyme Mutants.International journal of molecular sciences · 2022Article
- Markov State Study of Electrostatic Channeling within the Tricarboxylic Acid Cycle Supercomplex.ACS nanoscience Au · 2022Article
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6 authors.
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Abstract
Markov state models (MSMs) have been widely applied to study the kinetics and pathways of protein conformational dynamics based on statistical analysis of molecular dynamics (MD) simulations. These MSMs coarse-grain both configuration space and time in ways that limit what kinds of observables they can reproduce with high fidelity over different spatial and temporal resolutions. Despite their popularity, there is still limited understanding of which biophysical observables can be computed from these MSMs in a robust and unbiased manner, and which suffer from the space-time coarse-graining intrinsic in the MSM model. Most theoretical arguments and practical validity tests for MSMs rely on long-time equilibrium kinetics, such as the slowest relaxation time scales and experimentally observable time-correlation functions. Here, we perform an extensive assessment of the ability of well-validated protein folding MSMs to accurately reproduce path-based observable such as mean first-passage times (MFPTs) and transition path mechanisms compared to a direct trajectory analysis. We also assess a recently proposed class of history-augmented MSMs (haMSMs) that exploit additional information not accounted for in standard MSMs. We conclude with some practical guidance on the use of MSMs to study various problems in conformational dynamics of biomolecules. In brief, MSMs can accurately reproduce correlation functions slower than the lag time, but path-based observables can only be reliably reproduced if the lifetimes of states exceed the lag time, which is a much stricter requirement. Even in the presence of short-lived states, we find that haMSMs reproduce path-based observables more reliably.
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