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In vivo-like nearest neighbor parameters accurately predict fractional RNA base-pairing in cells

ABSTRACT: In this study, we conducted a thermodynamic analysis of RNA helix stability in the Eco80 artificial cytoplasm, which mimics in vivo conditions. Eco80 contains 80% of Escherichia coli metabolites, with biological concentrations of metal ions including 2 mM free Mg2+ and 29 mM metabolite-chelated Mg2+. We determined a set of Watson-Crick free energy nearest-neighbor parameters in Eco80 and found that helices are less stable by ∆∆Go37 ~ 1 kcal/mol in comparison to the traditional 1 M NaCl condition. Analysis indicates that Eco80 reduces the stability of three nearest-neighbor pairs. We applied this information to update the nearest-neighbor model using the RNAstructure package. We determined that our in vivo-like corrections have minimal effects on the prediction of RNA secondary structures determined in vitro and in silico. In contrast, in vivo-like corrections markedly improve prediction of fractional RNA base pairing in E. coli, as benchmarked with in vivo RNA chemical probing data using both DMS and EDC. In summary, our thermodynamic and chemical probing analyses of RNA helices in Eco80 indicate that RNA secondary structures are less stable in cells than in artificially stable in vitro buffer conditions such as 1 M NaCl.

ORGANISM(S): Escherichia coli str. K-12 substr. MG1655

PROVIDER: GSE233607 | GEO | 2023/09/14

REPOSITORIES: GEO

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Project description:This a model from the article: Different myofilament nearest-neighbor interactions have distinctive effects on contractile behavior. Razumova MV, Bukatina AE, Campbell KB. Biophys J. 2000;78(6):3120-37 10827989 , Abstract: Cooperativity in contractile behavior of myofilament systems almost assuredly arises because of interactions between neighboring sites. These interactions may be of different kinds. Tropomyosin thin-filament regulatory units may have neighbors in steric blocking positions (off) or steric permissive positions (on). The position of these neighbors influence the tendency for the regulatory unit to assume the on or off state. Likewise, the tendency of a myosin cross-bridge to achieve a force-bearing state may be influenced by whether neighboring cross-bridges are in force-bearing states. Also, a cross-bridge in the force-bearing state may influence the tendency of a regulatory unit to enter the on state. We used a mathematical model to examine the influence of each of these three kinds of neighbor interactions on the steady-state force-pCa relation and on the dynamic force redevelopment process. Each neighbor interaction was unique in its effects on maximal Ca(2+)-activated force, position, and symmetry of the force-pCa curve and on the Hill coefficient. Also, each neighbor interaction had a distinctive effect on the time course of force development as assessed by its rate coefficient, k(dev). These diverse effects suggest that variations in all three kinds of nearest-neighbor interactions may be responsible for a wide variety of currently unexplained observations of myofilament contractile behavior. This model was taken from the CellML repository and automatically converted to SBML. The original model was: razumova, bukatina, campbell. (2000) - version03 The original CellML model was created by: Nunns, Geoffrey, Rogan gnunns1@jhem.jhu.edu The University of Auckland Auckland Bioengineering Institute This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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In vivo-like nearest neighbor parameters accurately predict fractional RNA base-pairing in cells

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