ArticleJournal of chemical theory and computation2023
Uncertainties in Markov State Models of Small Proteins.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Molecular Modeling of the Pathogenetic Mechanisms of Neuropsychiatric Disorders.International journal of molecular sciences · 2026Review
- Calculation of minimum energy pathways in transport proteins.Communications chemistry · 2025Article
- Nearl: extracting dynamic features from molecular dynamics trajectories for machine learning tasks.Bioinformatics (Oxford, England) · 2025Article
- A FAIR-Compliant Management Solution for Molecular Simulation Trajectories.Journal of chemical information and modeling · 2025Article
- Toward Grid-Based Models for Molecular Association.Journal of chemical theory and computation · 2025Article
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2 authors.
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Abstract
Markov state models are widely used to describe and analyze protein dynamics based on molecular dynamics simulations, specifically to extract functionally relevant characteristic time scales and motions. Particularly for larger biomolecules such as proteins, however, insufficient sampling is a notorious concern and often the source of large uncertainties that are difficult to quantify. Furthermore, there are several other sources of uncertainty, such as choice of the number of Markov states and lag time, choice and parameters of dimension reduction preprocessing step, and uncertainty due to the limited number of observed transitions; the latter is often estimated via a Bayesian approach. Here, we quantified and ranked all of these uncertainties for four small globular test proteins. We found that the largest uncertainty is due to insufficient sampling and initially increases with the total trajectory length
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