ArticleThe Journal of chemical physics2013
Quantitative comparison of alternative methods for coarse-graining biological networks.
Article in The Journal of chemical physics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A continuous-space analytical framework for committor functions from molecular dynamics.The Journal of chemical physics · 2026Article
- AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.Journal of chemical theory and computation · 2025Article
- Biomolecular dynamics in the 21st century.Biochimica et biophysica acta. General subjects · 2024Review
- Folding@home: Achievements from over 20 years of citizen science herald the exascale era.Biophysical journal · 2023Review
- Exploring the Regulatory Function of theAdvanced theory and simulations · 2021Article
- Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning.JACS Au · 2021Review
- Dimeric allostery mechanism of the plant circadian clock photoreceptor ZEITLUPE.PLoS computational biology · 2021Article
- The Two-Step Clustering Approach for Metastable States Learning.International journal of molecular sciences · 2021Review
- Hierarchical Markov State Model Building to Describe Molecular Processes.Journal of chemical theory and computation · 2020Article
- EspcTM: Kinetic Transition Network Based on Trajectory Mapping in Effective Energy Rescaling Space.Frontiers in molecular biosciences · 2020Article
- Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge.Journal of chemical theory and computation · 2016Article
- Elucidation of the Dynamics of Transcription Elongation by RNA Polymerase II using Kinetic Network Models.Accounts of chemical research · 2016Article
- Using Kinetic Network Models To Probe Non-Native Salt-Bridge Effects on α-Helix Folding.The journal of physical chemistry. B · 2016Article
- Systematically constructing kinetic transition network in polypeptide from top to down: trajectory mapping.PloS one · 2015Article
- Perspective: Markov models for long-timescale biomolecular dynamics.The Journal of chemical physics · 2014Article
- Quantitatively characterizing the ligand binding mechanisms of choline binding protein using Markov state model analysis.PLoS computational biology · 2014Article
- Markov state models of biomolecular conformational dynamics.Current opinion in structural biology · 2014Review
- Perspective: Reaches of chemical physics in biology.The Journal of chemical physics · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Markov models and master equations are a powerful means of modeling dynamic processes like protein conformational changes. However, these models are often difficult to understand because of the enormous number of components and connections between them. Therefore, a variety of methods have been developed to facilitate understanding by coarse-graining these complex models. Here, we employ Bayesian model comparison to determine which of these coarse-graining methods provides the models that are most faithful to the original set of states. We find that the Bayesian agglomerative clustering engine and the hierarchical Nyström expansion graph (HNEG) typically provide the best performance. Surprisingly, the original Perron cluster cluster analysis (PCCA) method often provides the next best results, outperforming the newer PCCA+ method and the most probable paths algorithm. We also show that the differences between the models are qualitatively significant, rather than being minor shifts in the boundaries between states. The performance of the methods correlates well with the entropy of the resulting coarse-grainings, suggesting that finding states with more similar populations (i.e., avoiding low population states that may just be noise) gives better results.
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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.