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Putting Humpty–Dumpty Together: Clustering the Functional Dynamics of Single Biomolecular Machines Such as the Spliceosome

Rohlman, C. E. and Blanco, M. R. and Walter, N. G. (2016) Putting Humpty–Dumpty Together: Clustering the Functional Dynamics of Single Biomolecular Machines Such as the Spliceosome. In: Single-Molecule Enzymology: Fluorescence-Based and High-Throughput Methods. Methods in Enzymology. No.581. Elsevier , Cambridge, pp. 257-283. ISBN 9780128092675. https://resolver.caltech.edu/CaltechAUTHORS:20170622-082559488

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Abstract

The spliceosome is a biomolecular machine that, in all eukaryotes, accomplishes site-specific splicing of introns from precursor messenger RNAs (pre-mRNAs) with high fidelity. Operating at the nanometer scale, where inertia and friction have lost the dominant role they play in the macroscopic realm, the spliceosome is highly dynamic and assembles its active site around each pre-mRNA anew. To understand the structural dynamics underlying the molecular motors, clocks, and ratchets that achieve functional accuracy in the yeast spliceosome (a long-standing model system), we have developed single-molecule fluorescence resonance energy transfer (smFRET) approaches that report changes in intra- and intermolecular interactions in real time. Building on our work using hidden Markov models (HMMs) to extract kinetic and conformational state information from smFRET time trajectories, we recognized that HMM analysis of individual state transitions as independent stochastic events is insufficient for a biomolecular machine as complex as the spliceosome. In this chapter, we elaborate on the recently developed smFRET-based Single-Molecule Cluster Analysis (SiMCAn) that dissects the intricate conformational dynamics of a pre-mRNA through the splicing cycle in a model-free fashion. By leveraging hierarchical clustering techniques developed for Bioinformatics, SiMCAn efficiently analyzes large datasets to first identify common molecular behaviors. Through a second level of clustering based on the abundance of dynamic behaviors exhibited by defined functional intermediates that have been stalled by biochemical or genetic tools, SiMCAn then efficiently assigns pre-mRNA FRET states and transitions to specific splicing complexes, with the potential to find heretofore undescribed conformations. SiMCAn thus arises as a general tool to analyze dynamic cellular machines more broadly.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/bs.mie.2016.08.022DOIArticle
http://www.sciencedirect.com/science/article/pii/S0076687916302695PublisherArticle
Additional Information:© 2016 Elsevier Inc. Available online 13 October 2016. This work was supported by NIH Grant GM098023 to N.G.W. We thank Dr. Paul Lund for helpful insights.
Funders:
Funding AgencyGrant Number
NIHGM098023
Subject Keywords:Cluster analysis; Conformational dynamics; RNA folding; Single-molecule FRET; Spliceosome
Series Name:Methods in Enzymology
Issue or Number:581
Record Number:CaltechAUTHORS:20170622-082559488
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170622-082559488
Official Citation:C.E. Rohlman, M.R. Blanco, N.G. Walter, Chapter Nine - Putting Humpty–Dumpty Together: Clustering the Functional Dynamics of Single Biomolecular Machines Such as the Spliceosome, In: Maria Spies and Yann R. Chemla, Editor(s), Methods in Enzymology, Academic Press, 2016, Volume 581, Pages 257-283, ISSN 0076-6879, ISBN 9780128092675, https://doi.org/10.1016/bs.mie.2016.08.022. (http://www.sciencedirect.com/science/article/pii/S0076687916302695)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:78458
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:22 Jun 2017 17:46
Last Modified:03 Oct 2019 18:08

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