ArticlePloS one2017
Data-driven coarse graining of large biomolecular structures.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Top-Down Machine Learning of Coarse-Grained Protein Force Fields.Journal of chemical theory and computation · 2023Article
- 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
- Optimizing model representation for integrative structure determination of macromolecular assemblies.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Survey of the analysis of continuous conformational variability of biological macromolecules by electron microscopy.Acta crystallographica. Section F, Structural biology communications · 2019Review
- Advances in coarse-grained modeling of macromolecular complexes.Current opinion in structural biology · 2018Review
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Authors and funding
2 authors.
Funding
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Abstract
Advances in experimental and computational techniques allow us to study the structure and dynamics of large biomolecular assemblies at increasingly higher resolution. However, with increasing structural detail it can be challenging to unravel the mechanism underlying the function of molecular machines. One reason is that atomistic simulations become computationally prohibitive. Moreover it is difficult to rationalize the functional mechanism of systems composed of tens of thousands to millions of atoms by following each atom's movements. Coarse graining (CG) allows us to understand biological structures from a hierarchical perspective and to gradually zoom into the adequate level of structural detail. This article introduces a Bayesian approach for coarse graining biomolecular structures. We develop a probabilistic model that aims to represent the shape of an experimental structure as a cloud of bead particles. The particles interact via a pairwise potential whose parameters are estimated along with the bead positions and the CG mapping between atoms and beads. Our model can also be applied to density maps obtained by cryo-electron microscopy. We illustrate our approach on various test systems.
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