Literature DB >> 9125504

Improved parameters for the prediction of RNA hairpin stability.

M J Serra1, T W Barnes, K Betschart, M J Gutierrez, K J Sprouse, C K Riley, L Stewart, R E Temel.   

Abstract

Thermodynamic parameters are reported for hairpin formation in 1 M NaCl by RNA sequences of the type GGXANmAYCC, where XY is the set of four Watson-Crick base pairs and the underlined loop sequences are three to nine nucleotides. A nearest neighbor analysis of the data indicates the free energy of loop formation at 37 degrees C is dependent upon loop size and closing base pair. The model previously developed to predict the stability for RNA hairpin loops (n > 3) includes contributions from the size of the loop, the identity of the closing base pair, the free energy increment (deltaGo(37mm)) for the interaction of the closing base pair with the first mismatch and an additional stabilization term for GA and UU first mismatches [Serra, M. J., Axenson, T. J., & Turner, D. H. (1994) Biochemistry 33, 14289]. The results presented here allow improvements in the parameters used to predict RNA hairpin stability. For hairpin loops of n = 4-9, deltaGo(37iL)(n) is 4.9, 5.0, 5.0, 5.0, 4.9, and 5.5 kcal/mol, respectively, and the penalty for hairpin closure by AU or UA is +0.6 kcal/mol. deltaGo(37iL)(n) is the free energy for initiating a loop of n nucleotides. The model for predicting hairpin loop stability for loops larger than three becomes deltaGo(37L)(n) = deltaGo(37iL)(n) + deltaGo(37mm) + 0.6(if closed by AU or UA) - 0.7(if first mismatch is GA or UU). Hairpin loops of three are modeled as independent of loop sequence with deltaGo(37iL)(3) = 4.8 and the penalty for AU closure of +0.6 kcal/mol. Thermodynamic parameters for hairpin formation in 1 M NaCl for 11 naturally occurring RNA hairpin sequences are reported. The model provides good agreement with the measured values for both T(M) (within 10 degrees C of the measured value) and deltaGo(37) (within 0.8 kcal/mol of the measured value) for hairpin formation. In general, the nearest neighbor model allows prediction of RNA hairpin stability to within 5-10% of the experimentally measured values.

Entities:  

Mesh:

Substances:

Year:  1997        PMID: 9125504     DOI: 10.1021/bi962608j

Source DB:  PubMed          Journal:  Biochemistry        ISSN: 0006-2960            Impact factor:   3.162


  14 in total

1.  A test of the model to predict unusually stable RNA hairpin loop stability.

Authors:  T Dale; R Smith; M J Serra
Journal:  RNA       Date:  2000-04       Impact factor: 4.942

2.  A semiflexible polymer model applied to loop formation in DNA hairpins.

Authors:  S V Kuznetsov; Y Shen; A S Benight; A Ansari
Journal:  Biophys J       Date:  2001-11       Impact factor: 4.033

3.  Computational approaches for RNA energy parameter estimation.

Authors:  Mirela Andronescu; Anne Condon; Holger H Hoos; David H Mathews; Kevin P Murphy
Journal:  RNA       Date:  2010-10-12       Impact factor: 4.942

4.  Predicting RNA folding thermodynamics with a reduced chain representation model.

Authors:  Song Cao; Shi-Jie Chen
Journal:  RNA       Date:  2005-10-26       Impact factor: 4.942

5.  Intrinsic flexibility of snRNA hairpin loops facilitates protein binding.

Authors:  Michael Rau; W Tom Stump; Kathleen B Hall
Journal:  RNA       Date:  2012-09-25       Impact factor: 4.942

6.  Thermodynamic characterization of naturally occurring RNA tetraloops.

Authors:  Justin P Sheehy; Amber R Davis; Brent M Znosko
Journal:  RNA       Date:  2010-01-04       Impact factor: 4.942

7.  Thermodynamic characterization of RNA triloops.

Authors:  Praneetha Thulasi; Lopa K Pandya; Brent M Znosko
Journal:  Biochemistry       Date:  2010-10-26       Impact factor: 3.162

8.  Effects of non-nearest neighbors on the thermodynamic stability of RNA GNRA hairpin tetraloops.

Authors:  Pamela L Vanegas; Teresa S Horwitz; Brent M Znosko
Journal:  Biochemistry       Date:  2012-03-08       Impact factor: 3.162

9.  Reverse transcription of a self-primed retrotransposon requires an RNA structure similar to the U5-IR stem-loop of retroviruses.

Authors:  J H Lin; H L Levin
Journal:  Mol Cell Biol       Date:  1998-11       Impact factor: 4.272

10.  Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure.

Authors:  David H Mathews; Matthew D Disney; Jessica L Childs; Susan J Schroeder; Michael Zuker; Douglas H Turner
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-03       Impact factor: 11.205

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.