ArticleProceedings of the National Academy of Sciences of the United States of America2019
Dynamic graphical models of molecular kinetics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Mapping Allosteric Communication in the Nucleosome with Conditional Activity.Journal of chemical information and modeling · 2026Article
- Mapping Allosteric Communication in the Nucleosome with Conditional Activity.bioRxiv : the preprint server for biology · 2025Article
- Predicting rare DNA conformations via dynamical graphical models: a case study of the B→A transition.Nucleic acids research · 2025Article
- Accurate prediction of the kinetic sequence of physicochemical states using generative artificial intelligence.Chemical science · 2025Article
- Thermodynamic Interpolation: A Generative Approach to Molecular Thermodynamics and Kinetics.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
- Symmetry-adapted Markov state models of closing, opening, and desensitizing in α 7 nicotinic acetylcholine receptors.Nature communications · 2024Article
- Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution.Scientific reports · 2023Article
- Deep learning to decompose macromolecules into independent Markovian domains.Nature communications · 2022Article
- The Energy Landscape Perspective: Encoding Structure and Function for Biomolecules.Frontiers in molecular biosciences · 2022Article
- quicksom: Self-Organizing Maps on GPUs for clustering of molecular dynamics trajectories.Bioinformatics (Oxford, England) · 2021Article
- Independent Markov decomposition: Toward modeling kinetics of biomolecular complexes.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- The Two-Step Clustering Approach for Metastable States Learning.International journal of molecular sciences · 2021Review
- Computational methods for exploring protein conformations.Biochemical Society transactions · 2020Review
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2 authors.
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Abstract
Most current molecular dynamics simulation and analysis methods rely on the idea that the molecular system can be represented by a single global state (e.g., a Markov state in a Markov state model [MSM]). In this approach, molecules can be extensively sampled and analyzed when they only possess a few metastable states, such as small- to medium-sized proteins. However, this approach breaks down in frustrated systems and in large protein assemblies, where the number of global metastable states may grow exponentially with the system size. To address this problem, we here introduce dynamic graphical models (DGMs) that describe molecules as assemblies of coupled subsystems, akin to how spins interact in the Ising model. The change of each subsystem state is only governed by the states of itself and its neighbors. DGMs require fewer parameters than MSMs or other global state models; in particular, we do not need to observe all global system configurations to characterize them. Therefore, DGMs can predict previously unobserved molecular configurations. As a proof of concept, we demonstrate that DGMs can faithfully describe molecular thermodynamics and kinetics and predict previously unobserved metastable states for Ising models and protein simulations.
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