ArticleThe Journal of chemical physics2020
Adaptive Markov state model estimation using short reseeding trajectories.
Article in The Journal of chemical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Tyrosine kinases sample unique activation ensembles.bioRxiv : the preprint server for biology · 2026Article
- Metastable Protein-Protein Interactions as a Design Principle for PROTACs: Insights from the RIPK1-VHL System.JACS Au · 2026Article
- Building multiscale Markov state models by systematic mapping of temporal communities.Bioinformatics (Oxford, England) · 2026Article
- Predicting rare DNA conformations via dynamical graphical models: a case study of the B→A transition.Nucleic acids research · 2025Article
- Markov-Type State Models to Describe Non-Markovian Dynamics.Journal of chemical theory and computation · 2025Article
- Adaptive CVgen: Leveraging reinforcement learning for advanced sampling in protein folding and chemical reactions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Non-Markovian Dynamic Models Identify Non-Canonical KRAS-VHL Encounter Complex Conformations for Novel PROTAC Design.JACS Au · 2024Article
- Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes.The Journal of chemical physics · 2024Article
- Submillisecond Atomistic Molecular Dynamics Simulations Reveal Hydrogen Bond-Driven Diffusion of a Guest Peptide in Protein-RNA Condensate.The journal of physical chemistry. B · 2024Article
- Biomolecular dynamics in the 21st century.Biochimica et biophysica acta. General subjects · 2024Review
- Markov State Models: To Optimize or Not to Optimize.Journal of chemical theory and computation · 2024Article
- Markov State Model of Solvent Features Reveals Water Dynamics in Protein-Peptide Binding.The journal of physical chemistry. B · 2023Article
- Integrative generalized master equation: A method to study long-timescale biomolecular dynamics via the integrals of memory kernels.The Journal of chemical physics · 2023Article
- Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness.Journal of chemical theory and computation · 2023Review
- Flexible Topology: A Dynamic Model of a Continuous Chemical Space.Journal of chemical theory and computation · 2023Article
- An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes.Journal of chemical theory and computation · 2023Article
- Deep learning to decompose macromolecules into independent Markovian domains.Nature communications · 2022Article
- Estimation of binding rates and affinities from multiensemble Markov models and ligand decoupling.The Journal of chemical physics · 2022Article
- Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective.Frontiers in molecular biosciences · 2022Review
- Energy penalties enhance flexible receptor docking in a model cavity.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In the last decade, advances in molecular dynamics (MD) and Markov State Model (MSM) methodologies have made possible accurate and efficient estimation of kinetic rates and reactive pathways for complex biomolecular dynamics occurring on slow time scales. A promising approach to enhanced sampling of MSMs is to use "adaptive" methods, in which new MD trajectories are "seeded" preferentially from previously identified states. Here, we investigate the performance of various MSM estimators applied to reseeding trajectory data, for both a simple 1D free energy landscape and mini-protein folding MSMs of WW domain and NTL9(1-39). Our results reveal the practical challenges of reseeding simulations and suggest a simple way to reweight seeding trajectory data to better estimate both thermodynamic and kinetic quantities.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.