Elastic network models capture the motions apparent within ensembles of RNA structures. RNA 2014 Jun;20(6):792-804
Date
04/25/2014Pubmed ID
24759093Pubmed Central ID
PMC4024634DOI
10.1261/rna.041269.113Scopus ID
2-s2.0-84901471787 (requires institutional sign-in at Scopus site) 27 CitationsAbstract
The role of structure and dynamics in mechanisms for RNA becomes increasingly important. Computational approaches using simple dynamics models have been successful at predicting the motions of proteins and are often applied to ribonucleo-protein complexes but have not been thoroughly tested for well-packed nucleic acid structures. In order to characterize a true set of motions, we investigate the apparent motions from 16 ensembles of experimentally determined RNA structures. These indicate a relatively limited set of motions that are captured by a small set of principal components (PCs). These limited motions closely resemble the motions computed from low frequency normal modes from elastic network models (ENMs), either at atomic or coarse-grained resolution. Various ENM model types, parameters, and structure representations are tested here against the experimental RNA structural ensembles, exposing differences between models for proteins and for folded RNAs. Differences in performance are seen, depending on the structure alignment algorithm used to generate PCs, modulating the apparent utility of ENMs but not significantly impacting their ability to generate functional motions. The loss of dynamical information upon coarse-graining is somewhat larger for RNAs than for globular proteins, indicating, perhaps, the lower cooperativity of the less densely packed RNA. However, the RNA structures show less sensitivity to the elastic network model parameters than do proteins. These findings further demonstrate the utility of ENMs and the appropriateness of their application to well-packed RNA-only structures, justifying their use for studying the dynamics of ribonucleo-proteins, such as the ribosome and regulatory RNAs.
Author List
Zimmermann MT, Jernigan RLAuthor
Michael T. Zimmermann PhD Director, Associate Professor in the Data Science Institute department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsComputer Simulation
Models, Molecular
Motion
Principal Component Analysis
Protein Conformation
Proteins
RNA
Ribosomes
