ArticleProceedings of the National Academy of Sciences of the United States of America2020
Exploring the landscape of model representations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Bridging Scales: Coarse-Grained Protein Models in Computational Biology.Advances in experimental medicine and biology · 2026Review
- Optimizing a coarse-grained model for large-scale membrane protein simulation.Biophysical reports · 2025Article
- Bottom-up Coarse-Grained Models of Asymmetric Membranes.The journal of physical chemistry. B · 2025Article
- Enhancing the Assembly Properties of Bottom-Up Coarse-Grained Phospholipids.Journal of chemical theory and computation · 2024Article
- AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.Journal of chemical theory and computation · 2024Article
- Top-Down Machine Learning of Coarse-Grained Protein Force Fields.Journal of chemical theory and computation · 2023Article
- OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining.The journal of physical chemistry. B · 2023Article
- Machine learning coarse-grained potentials of protein thermodynamics.Nature communications · 2023Article
- Machine learned coarse-grained protein force-fields: Are we there yet?Current opinion in structural biology · 2023Review
- Slicing and Dicing: Optimal Coarse-Grained Representation to Preserve Molecular Kinetics.ACS central science · 2023Article
- Bottom-up Coarse-Graining: Principles and Perspectives.Journal of chemical theory and computation · 2022Review
- From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules.Frontiers in molecular biosciences · 2021Review
- A journey through mapping space: characterising the statistical and metric properties of reduced representations of macromolecules.The European physical journal. B · 2021Article
- A Deep Graph Network-Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins.Frontiers in molecular biosciences · 2021Article
- Model behaviour.Nature materials · 2020Article
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Authors and funding
4 authors.
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Abstract
The success of any physical model critically depends upon adopting an appropriate representation for the phenomenon of interest. Unfortunately, it remains generally challenging to identify the essential degrees of freedom or, equivalently, the proper order parameters for describing complex phenomena. Here we develop a statistical physics framework for exploring and quantitatively characterizing the space of order parameters for representing physical systems. Specifically, we examine the space of low-resolution representations that correspond to particle-based coarse-grained (CG) models for a simple microscopic model of protein fluctuations. We employ Monte Carlo (MC) methods to sample this space and determine the density of states for CG representations as a function of their ability to preserve the configurational information, I, and large-scale fluctuations, Q, of the microscopic model. These two metrics are uncorrelated in high-resolution representations but become anticorrelated at lower resolutions. Moreover, our MC simulations suggest an emergent length scale for coarse-graining proteins, as well as a qualitative distinction between good and bad representations of proteins. Finally, we relate our work to recent approaches for clustering graphs and detecting communities in networks.
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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.