ArticleThe Journal of chemical physics2016
Optimized parameter selection reveals trends in Markov state models for protein folding.
Article in The Journal of chemical physics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- Unveiling hidden intermediate states in protein folding with AI-based conditional transition clustering.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics.The Journal of chemical physics · 2025Article
- Article
- Information Bottleneck Approach for Markov Model Construction.Journal of chemical theory and computation · 2024Article
- Article
- Markov State Models: To Optimize or Not to Optimize.Journal of chemical theory and computation · 2024Article
- Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry.Nature communications · 2022Article
- GraphVAMPNet, using graph neural networks and variational approach to Markov processes for dynamical modeling of biomolecules.The Journal of chemical physics · 2022Article
- Molecular dynamics analysis of the structural properties of the transglutaminases ofComputational and structural biotechnology journal · 2022Article
- Unsupervised Learning Methods for Molecular Simulation Data.Chemical reviews · 2021Article
- What Markov State Models Can and Cannot Do: Correlation versus Path-Based Observables in Protein-Folding Models.Journal of chemical theory and computation · 2021Article
- TorchMD: A Deep Learning Framework for Molecular Simulations.Journal of chemical theory and computation · 2021Article
- A critical perspective on Markov state model treatments of protein-protein association using coarse-grained simulations.The Journal of chemical physics · 2021Article
- Specific PIPCommunications biology · 2021Article
- Coarse graining molecular dynamics with graph neural networks.The Journal of chemical physics · 2020Article
- Accelerated Estimation of Long-Timescale Kinetics from Weighted Ensemble Simulation via Non-Markovian "Microbin" Analysis.Journal of chemical theory and computation · 2020Article
- Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.Journal of chemical theory and computation · 2020Review
- Article
- Article
- Detecting early stage structural changes in wild type, pathogenic and non-pathogenic prion variants using Markov state model.RSC advances · 2019Article
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4 authors.
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Abstract
As molecular dynamics simulations access increasingly longer time scales, complementary advances in the analysis of biomolecular time-series data are necessary. Markov state models offer a powerful framework for this analysis by describing a system's states and the transitions between them. A recently established variational theorem for Markov state models now enables modelers to systematically determine the best way to describe a system's dynamics. In the context of the variational theorem, we analyze ultra-long folding simulations for a canonical set of twelve proteins [K. Lindorff-Larsen et al., Science 334, 517 (2011)] by creating and evaluating many types of Markov state models. We present a set of guidelines for constructing Markov state models of protein folding; namely, we recommend the use of cross-validation and a kinetically motivated dimensionality reduction step for improved descriptions of folding dynamics. We also warn that precise kinetics predictions rely on the features chosen to describe the system and pose the description of kinetic uncertainty across ensembles of models as an open issue.
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