logo
CONTENT TYPES

Testing the Nearest Neighbor Model for Canonical RNA Base Pairs: Revision of GU Parameters

View Author Information
Department of Chemistry, University of Rochester, Rochester, New York 14627, United States
Department of Chemistry, Allegheny College, Meadville, Pennsylvania 16335, United States
§ Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, United States
# Center for RNA Biology, University of Rochester, Rochester, New York 14627, United States
*Phone: (585) 275-3207. Fax: (585) 276-0205. E-mail: [email protected]
Cite this: Biochemistry 2012, 51, 16, 3508–3522
Publication Date (Web):April 10, 2012
https://doi.org/10.1021/bi3002709
Copyright © 2012 American Chemical Society
Authors ChoiceACS AuthorChoice
Article Views
1424
Altmetric
-
Citations
LEARN ABOUT THESE METRICS

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

PDF (1 MB)
Supporting Info (1)»

Abstract

Thermodynamic parameters for GU pairs are important for predicting the secondary structures of RNA and for finding genomic sequences that code for structured RNA. Optical melting curves were measured for 29 RNA duplexes with GU pairs to improve nearest neighbor parameters for predicting stabilities of helixes. The updated model eliminates a prior penalty assumed for terminal GU pairs. Six additional duplexes with the 5′GG/3′UU motif were added to the single representation in the previous database. This revises the ΔG°37 for the 5′GG/3′UU motif from an unfavorable 0.5 kcal/mol to a favorable −0.2 kcal/mol. Similarly, the ΔG°37 for the 5′UG/3′GU motif changes from 0.3 to −0.6 kcal/mol. The correlation coefficients between predicted and experimental ΔG°37, ΔH°, and ΔS° for the expanded database are 0.95, 0.89, and 0.87, respectively. The results should improve predictions of RNA secondary structure.

  Funding Statement

This work was supported by NIH Grant GM22939 (D.H.T.)

 Author Present Address

Department of Chemistry, Northwestern University, Evanston, Illinois, 60208, USA.

 Author Present Address

Roswell Park Cancer Institute, Buffalo, New York 14263.

The explosion of biological data in the genomics era has filled databanks with large amounts of genetic information. Understanding of these data and making correlations are vital for maximally advancing the fields of biology and medicine. This necessitates accurate methods in bioinformatics and computational chemistry. One important area that bioinformatics and computational chemistry address is finding, predicting, and determining RNA structure from sequence.(1)
RNA participates in a variety of cellular functions involving gene expression and regulation. RNA typically folds in a hierarchical way.(2, 3) Base pairs form to generate motifs such as helixes and loops. Higher order interactions between these features result in three-dimensional structures. On that basis, knowledge of secondary structure is critical for the prediction of tertiary structure. Secondary structure prediction algorithms utilizing experimental thermodynamic data(4-9) have relied on nearest neighbor models.(10-13) Finding regions of genome sequences that code for structured RNA often also relies on nearest neighbor models.(1, 14-16) Because RNA molecules and their reverse complements can fold similarly, the thermodynamics of GU pairs provides information about the reading direction because their complement, CA, forms less stable base pairs.(17)
Prediction of GU pairs is also important because they are the most common non-Watson–Crick pair and have functions in a wide variety of RNAs. For example, GU pairs are found within two helical regions and at the junction of a helix and multibranch loop in eukaryotic 5S rRNA.(18, 19) A GU pair in the third position of the acceptor stem in tRNAAla(20) distorts helix geometry(21) and is important in Escherichia coli for recognition by alanine aminoacyl tRNA synthetase.(22-24) Local helix geometry due to a conserved GU pair may also be important for binding of a yeast intron with hPrp8 or L32 protein.(25, 26) The U-rich tail of guide RNAs bind to a purine-rich region in unedited pre-mRNA to generate recurring 5′AGA/3′UUU motifs that may help RNA editing proteins bind to the major groove.(27, 28) The 5′ leader of HIV-1 can switch between helixes containing GU pairs to promote translation or packaging of its genome.(29)
GU pairs expose the exocyclic amino group of guanine in the minor groove, presenting a unique site for hydrogen bonding to facilitate function and molecular recognition. For example, a GU amino group at the splice site in the Tetrahymena thermophilia group I intron helps bind and align the splice site(30-32) and stabilize the transition state of the splicing reaction.(33) Šponer et al. reported a common tertiary interaction involving a GU pair, where the exocyclic NH2 of the G and the 2′OH of the U form hydrogen bonds, respectively, with the 2′OH and carbonyl oxygen of a cytidine in a GC pair of another helix.(34)
GU pairs can be metal ion binding sites.(35-39) Colmenarejo and Tinoco observed that Co(NH3)63+ preferably binds to 5′GU/3′UG and 5′GG/3′UU over 5′UG/3′GU pairs, whereas Mg(H2O)62+ binds tightest to 5′UG/3′GU.(40) This preference may explain why 5′UG/3′GU is the most prevalent tandem GU motif in rRNA.(41) The propensity for binding metal ions allows design of sequences that bind heavy metals to facilitate solving of X-ray structures.(37, 38)
Prediction of GU pairs often relies on a nearest neighbor model for folding stability. The database of RNA sequences from which GU nearest neighbor parameters were derived(12) is relatively small, however, compared to that for Watson–Crick nearest neighbors.(11) To expand the database, optical melting experiments were carried out on 29 oligoribonucleotide duplexes. Linear regression analysis on the expanded database provides a revised set of individual nearest neighbor (INN) parameters,(42) which are reported herein. The parameters provide stability increments for internal and single terminal GU pairs. Stability increments for additional terminal GU pairs have been reported by Nguyen and Schroeder.(43)

Materials and Methods

ARTICLE SECTIONS
Jump To

Design of Oligonucleotides

Oligonucleotides were designed to expand the previous database(12) to provide all possible combinations of base pair triplets containing GU pairs flanked by Watson–Crick pairs in different orientations (Table 1) and to have a substantial representation of each nearest neighbor containing a GU pair. An additional six sequences containing the 5′GG/3′UU doublet provided nine new representations for that motif, which had only one representation. Care was taken to select self-complementary sequences that do not favorably form alternative secondary structures, such as hairpins or loops.
Table 1. Occurrence of Each Base Pair Triplet 5′WGY/3′XUZ in the Database of RNA Sequences from Which INN Parameters for GU Pairs Were Derived
WX/YZAUCGGCUAGUUG
AU232416
CG244327
GC823241
UA422222
GU251100
UG455600

Synthesis and Purification of Oligoribonucleotides

Sequences for the following oligoribonucleotide duplexes were purchased from Integrated DNA Technologies (IDT): r(AGGCUU)2, r(AUGCGU)2, r(AGUCGAUU)2, r(CUGGCUAG)2, r(5′CAGAGGAGAC/3′GUCUUUUCUG), r(CAGUCGAUUG)2, r(CCGAAUUUGG)2, r(CGGAAUUUCG)2, r(CGGAUAUUCG)2, r(CGGGCGUUCG)2, r(CUGGAUUCAG)2, r(GAGAGCUUUC)2, r(GAGGAUCUUC)2, r(5′GAGUGGAGAG/3′CUCAUUUCUC), r(GGUUCGGGCC)2, and r(GUGAAUUUAC)2 (the / denotes a nonself-complementary duplex). Purity was checked by NMR except for those forming duplexes with adjacent GU pairs, which were checked by thin layer chromatography. All other sequences were synthesized and purified as previously described.(44) All sequences were desalted with Sep-Pak C18 cartridges (Supporting Information).

UV Melting

RNA duplexes with concentrations from 10–6–10–3 M were melted in 0.5 mM Na2EDTA, 1 M NaCl, and 20 mM sodium cacodylate, pH 7, which maintains a stable pKa over a wide temperature range.(45) Absorbance at 280 nm, typically from 15 to 80 °C, was measured on a Beckman Coulter DU 640 spectrophotometer.

NMR Experiments

Spectra were acquired on a Varian Inova 500 or 600 MHz spectrometer. The buffer for NMR was 80 mM NaCl, 18.8 mM NaH2PO4, 1.16 mM Na2HPO4, 0.02 mM Na2EDTA, pH 6.0, to which 15 μL of D2O was added to provide a lock signal. One-dimensional 1H spectra were acquired with the water 1H signal suppressed with a binomial 1:1 shaped pulse.(46) Two-dimensional 1H–1H NOESY and 1H–1H TOCSY spectra were acquired with the water signal suppressed by a WATERGATE-type pulse sequence with flipback.(47, 48) Two-dimensional 1H–1H NOESY spectra for r(AUGCGU)2 were also measured in D2O.
Spectra were processed with NMRPipe(49) and resonances were assigned with SPARKY.(50) Proton chemical shifts were referenced to a temperature-dependent water chemical shift, δ,(1)where T is temperature in Kelvin.(51) The internal reference standard for water was 2,2-dimethylsilapentate-5-sulfonic acid.

Melting Data Analysis

Melting curves for each duplex were fit to a two-state model with MeltWin 3.5(52) to derive values for ΔH° and ΔS°. The melting temperature, TM, was plotted against ln(CT/a) to provide another measure of ΔH° and ΔS°:(2)Here R is the gas constant (1.987 cal K–1 mol–1), CT is the total concentration of strands, and a is 1 for self-complementary duplexes and 4 for non-self-complementary duplexes. Sequences were added to the database if ΔH° values derived from averaging fits of melting curves agreed within 15% with these derived from eq 2, consistent with the two-state model.

Linear Regression to Fit Nearest Neighbor Parameters

Nearest neighbor thermodynamic parameters were obtained with a regression function reported by Xia et al.(11) Matrix calculations were performed with R(53) and independently verified with Mathematica 8.0(54) and Octave.(55) All three software packages yielded nearly identical results.
Terms representing free energy contributions from non-GU nearest neighbors, that is, helix initiation (ΔG°init), symmetry (ΔG°sym), terminal AU pairs (ΔG°term AU), and Watson–Crick nearest neighbors (ΔG°j (WC NN)),(11) were subtracted from the free energy found from the TM–1 vs ln(CT/a) plots (ΔG°i(duplex)) to provide an experimental free energy attributable to the GU components of each duplex:(3)where i and j are labels for each different duplex and INN parameter, respectively, NN stands for nearest neighbor parameter, and mij is the number of terminal AU pairs. For example,(4)Here, ΔG°37(GU component) contains four 5′GG/3′CU nearest neighbors and two 5′GG/3′UU nearest neighbors. Values for Watson–Crick nearest neighbors from Xia et al.(11) were used because experimental measurements on 22 duplexes not included in the fitting by Xia et al.(11) are predicted within experimental error (Supporting Information). Making the new GU parameters consistent with the Xia et al.(11) parameters provides compatibility with loop parameters derived with Xia et al.(11) nearest neighbor parameters and allows easy adoption by programs using those parameters.
Each experimental duplex ΔG°37 was given an error limit of ±4% to account for systematic errors unless the percent difference between parameters found from TM–1 vs ln(CT/a) and averaged curve fits was greater. For the seven latter cases, this percent difference was doubled to provide an error limit. Error limits for ΔH° were assumed to be 12%.(11) The symmetry contribution, 0.43 kcal/mol in ΔG°37, has no error(56) and was therefore subtracted from ΔG°37 of self-complementary duplexes before calculating the error limit.
The GU component free energies were placed into M × 1 matrix G, where M is the number of duplexes.(5)S is an M × N matrix containing the counts of each nearest neighbor doublet in a duplex, where N is the number of GU nearest neighbor parameters being fit. GNN is an N × 1 matrix that contains the nearest neighbor parameters to be derived from G and S.
The general law of error propagation was used to calculate the variances for each duplex.(57, 58) Multiplication of both sides of eq 5 by an M × M matrix, σ–1, containing the variances in the diagonals yielded error-weighted matrices from which thermodynamic parameters were derived.(6)The values in GNN are thus Sσ–1·Gσ. The variances of each INN parameter are obtained with singular value decomposition (SVD) (ref 11, Supporting Information). Nearest neighbor parameters for ΔH° were found through the same process, and ΔS° parameters were calculated from ΔS° = (ΔH° – ΔG°)/TM.
Nearest neighbor parameters for Watson–Crick pairs were obtained from fitting published data for 112 duplexes, which included the 90 duplexes that Xia et al. previously fit, and 22 additional duplexes (Supporting Information). The symmetry contribution, if present, was subtracted from each thermodynamic parameter derived from the TM–1 vs ln(CT/a) plot. Matrix calculations were carried out as described above to generate ΔG°37 and ΔH° for each nearest neighbor parameter, with all three software packages yielding similar results.
The F-test was used to test the hypothesis that a least-squares model can fit the dependence of Gσ on Sσ and GNN.(59, 60) If the F-value is larger than the critical F-value for N and N v degrees of freedom at the 5% significance level, where N is the number of duplexes and v the number of nearest neighbor parameters, or if the p-value is less than 0.05, then the hypothesis that there is a dependence of Gσ on GNN may be accepted.(60)
The paired t-test was used to evaluate the significance of the differences between predictions of thermodynamic properties with the updated parameters and those reported by Mathews et al.(12) and the difference between experimental values and predictions by each set of nearest neighbor parameters. The difference between each pair of a set with b values of a variable, X, before and after treatment is defined as μ(XD) = μ(X1) – μ(X2), where X2 represents the response of X1 to treatment.(61) The null hypothesis states that μ(XD) = 0. To test this and the alternative hypothesis that μ(XD) ≠ 0, the mean and standard deviation of the difference between each block of values is found.(7)(8)A t-ratio is defined as(9)
If the t-ratio is greater than t-value for (b – 1) degrees of freedom or less than its negative, then the null hypothesis is rejected at the 0.05 significance level.
For example, in using the paired t-test to evaluate how well experimental ΔG°’s are predicted by nearest neighbor parameters, b is the number of duplexes whose ΔG°’s are being tested and XD is the difference between the predicted and experimental ΔG° for each sequence.
The probability density function (PDF), f(t), of the Student’s t-distribution was used as a measure of how significantly a given INN parameter contributes to the model,(11, 59) with smaller values of f(t) indicating greater contribution,(10)where Γ is the gamma function, r = Nv degrees of freedom and t = ΔG°j(NN)/σj(NN), that is, the quotient of the free energy of the INN parameter over the estimate of its error. Calculations were carried out with R(53) using the anova and t-test functions, and the critical t-value was determined with the qt function in R.

Results

ARTICLE SECTIONS
Jump To

Table 2 lists results for duplexes in the database used for determination of nearest neighbor parameters for GU pairs. Most of the duplexes are six to eight base pairs in length and have melting temperatures in the 30–70 °C range. For the 29 new duplexes reported here, the average difference between ΔG°37, ΔH°, and ΔS° derived from TM–1 vs ln(CT/a) plots and averaged curve fits are 2%, 7%, and 8%, respectively. Three duplexes, r(AGGCUU)2, r(AUGCGU)2, and r(GUCGUAC/), with TM’s less than 25 °C that were included in the database of Mathews et al.(12) were omitted from the new database. Determination of thermodynamics from optical melting curves is difficult when the TM is less than 25 °C.
Table 2. Thermodynamic Parameters for Duplex Formation in 1 M NaCla
TM–1 vs log CTaverage of curve fitspredicted 
sequenceb–ΔG°37(kcal/mol)–ΔH° (kcal/mol)–ΔS° (eu)TMc(°C)–ΔG°37(kcal/mol)–ΔH° (kcal/mol)–ΔS° (eu)TMc(°C)–ΔG°37(kcal/mol)–ΔH° (kcal/mol)–ΔS° (eu)TMc(°C)ref
Two State Sequences Used in Regression Analysis
CGGCUG5.5543.20121.435.75.5145.40128.635.94.9441.27117.131.7101
CUGCGG4.3141.40119.626.84.5536.00101.427.44.9441.27117.131.7101
GCCGGUp9.1758.20158.157.09.4460.40164.357.08.6655.99152.754.4102
GCGUGC5.1146.18132.433.25.1549.69143.633.74.1051.10151.527.986
GCUGGC6.4759.10169.741.56.5959.10169.341.96.4357.67165.041.4101
GGCGCU8.4256.40154.752.98.4755.40151.353.18.2255.67152.852.1102
GGCGUC4.6738.10107.829.04.9237.30104.430.25.7443.27120.937.6101
GUGCAU5.1047.50136.933.15.1047.00135.033.44.9246.93135.532.043
UCCGCC/6.7157.00162.238.06.6954.30153.437.97.7348.54131.644.784
UCCGGGp7.4447.70129.848.57.3447.10128.248.77.9647.87128.752.5102
UGGCCGp8.5653.00143.354.78.1146.60124.155.17.9249.17133.051.8102
UUGCAG4.2037.20106.525.34.3035.50100.425.73.8240.07116.923.343
CUCGCUC/7.7864.20181.843.18.0070.30200.843.68.1758.88163.446.0103
GCGGGAC/9.0045.20116.855.09.3050.60133.154.79.8562.22168.754.8this work
AGUCGAUU6.0053.30152.638.96.0358.20168.338.94.1447.17138.827.1104
AUGCGCGUp9.3154.90147.058.69.0553.90144.658.88.0855.25152.151.1101
AUGCGUAUp5.2746.80133.934.45.2942.60120.334.84.2242.45123.326.6101
AUGUGCAUp6.1757.10164.239.56.0851.30145.839.95.8763.41185.737.7101
CAGGGCUC/11.1062.80166.661.411.5065.60174.261.011.5271.58193.660.4this work
CCAGUUGG5.7061.10178.637.15.8060.40176.337.46.2065.80192.339.395
CCAUGUGG7.8070.50202.146.57.8071.20204.346.98.5971.47202.950.095
CCUGUAGG6.8171.10207.342.06.8166.10191.142.46.2259.88173.139.7104
CGGAUUCG6.5672.60213.040.86.5970.30205.441.15.9261.87180.338.3104
CGUUGACG6.9373.50214.642.46.9368.40198.042.86.2773.87217.939.7104
CUCGGCUC/8.2273.90211.844.28.3076.80220.944.38.4276.70220.144.9104
CUGGCUAG7.1060.38171.844.47.0662.56179.044.07.3853.03147.147.5this work
GACGCCAG/10.5063.80171.857.511.0074.60205.156.911.3571.19192.959.5this work
GACGCGUU9.5062.20169.957.49.6062.70171.457.69.1276.11215.951.9this work
GAGGUGAG/7.6378.40228.241.47.6376.10220.841.67.1068.65198.439.7105
GAGUGCUC9.4083.00237.451.69.2077.40220.051.89.2177.05218.752.052
GAGUGGAG/9.6682.30234.149.39.5980.40228.249.39.2675.40213.248.8105
GAUGCAUUp6.8262.90180.842.66.8458.70167.243.26.3471.67210.739.9106
GCAGCUGU10.3072.30199.858.310.4072.00198.458.911.5875.29205.563.3this work
GCAGUUGC5.9064.80190.038.16.0069.30203.938.66.5268.78200.740.985
GCAUGUGC8.4072.40206.149.28.5073.00208.049.48.9174.45211.351.185
GCUGGUGC/7.6069.40199.142.07.7071.80206.642.18.4873.24208.845.585
GGAGCUCU10.5066.57180.961.110.6067.60184.061.311.3075.99208.462.0this work
GGAGUUCC6.4373.10214.940.26.4468.40199.640.56.6869.80203.541.5104
GGAUGUCC8.3973.00208.449.08.5978.00223.449.09.0775.47214.151.6104
GGCGGGGC/13.8076.50202.269.514.0077.40204.470.112.5485.75236.060.444
GGCGUGCC9.7273.40206.955.09.7574.20208.055.010.6277.88216.958.0104
GGCUGGCC13.1087.20238.865.913.3090.30248.465.412.9584.45230.466.452
GGUUGACC8.3078.30225.947.68.3077.40222.847.78.0779.37229.946.752
GUAGCUAU7.3050.30138.747.47.4057.90162.846.77.5262.39176.946.5this work
GUCGGGCC/15.0096.00261.366.914.2083.30222.768.613.1175.61201.466.8this work
GUCGUGAC6.0569.10203.338.76.0864.70188.939.06.4469.02201.740.7104
GUCUAGAU7.7070.00201.046.37.7068.70196.846.37.4764.70184.545.9107
UACCGGUG9.7051.70135.463.110.0054.70144.163.69.6258.53157.759.5this work
UAUGCAUGp6.4462.30180.141.06.5553.10150.141.86.1052.83150.739.5106
UCACGUGG8.4046.90124.256.08.6045.00117.158.710.1464.77176.160.1this work
UGACGUCG10.4063.80172.361.610.6067.30182.761.79.5265.83181.456.5this work
UUACGUAG6.2044.60124.040.56.2049.20138.940.15.6853.13152.937.2this work
CAGAGGAGAC/9.4398.95288.646.49.3995.19276.746.610.2396.23277.349.4this work
CAGCGCGUUG12.3177.02208.666.211.5966.84178.267.112.8483.53227.966.1this work
CAGUCGAUUG8.7092.33269.747.58.5484.54245.047.99.2675.49213.652.4this work
CCAGCGUCCU/11.6087.90246.055.911.7090.10252.755.913.1780.81218.264.6108
CCGAAUUUGG6.9976.76225.042.67.0076.35223.542.68.4878.07224.548.4this work
CGGAAUUUCG7.8890.52266.544.77.7784.47247.344.97.7875.51218.345.9this work
CGGAUAUUCG8.7888.20256.248.38.4980.30231.448.58.3578.94227.548.0this work
CGGGCGUUCG11.55101.66290.556.011.65101.34289.256.410.96100.19287.754.3this work
CGGUGCAUCG14.76102.42282.664.114.1894.72259.764.213.2482.27222.668.4this work
CUGGAUUCAG10.1597.81282.751.89.9792.86267.352.09.5882.43234.952.4this work
GAGAGCUUUC8.8286.57250.648.98.7282.81238.948.99.4885.79245.951.5this work
GAGGAUCUUC9.8393.86270.951.39.4083.92240.351.410.2282.33232.355.4this work
GAGUGGAGAG/9.8796.93280.748.19.7993.52270.048.210.2493.27267.649.9this work
GGUUCGGGCC13.59115.71329.359.813.56113.36321.860.212.76105.69299.759.2this work
GUGAAUUUAC4.7862.63186.432.84.6072.40218.632.54.7264.89193.932.6this work
GUGUGCAUAC8.9058.60160.155.09.2066.70185.654.210.1871.65198.257.9this work
GUUAGCUGAC8.6069.60196.750.48.5066.90188.550.59.3477.71220.352.5this work
UCGCCAGAGG/15.3293.46252.069.215.4294.49254.969.216.3592.38245.273.8109
a

Duplexes with a 5′GGUC/3′CUGG motif are not listed because they were not used in fitting nearest neighbor parameters and no new sequences were measured.

b

Listed in order of length of the oligoribonucleotide and then alphabetically for sequences of the same length. All non-self-complementary sequences have a slash and only one sequence shown. GU base pairs are underlined.

c

Calculated at a total strand concentration of 1 × 10–4 M.

With the Exclusion of the 5′GGUC/3′CUGG Motif, the Experimental Results Can Be Fit to a Nearest Neighbor Model

The results in Table 2 were fit to a nearest neighbor model for GU pairs after subtracting contributions from Watson–Crick nearest neighbors (eq 3). This method avoids conflating thermodynamic parameters for Watson–Crick pairs with the idiosyncrasies of GU pairs. Published thermodynamics for duplexes with all Watson–Crick pairs (Supporting Information) were used to test published parameters for Watson–Crick pairs.(11) Fitting the expanded database of 112 duplexes gave INN parameters within error of the values reported by Xia et al.(11) (Table 3). Most free energy parameters did not change by more than 0.05 kcal/mol at 37 °C. Consequently, the GU component values were calculated from eq 3 by subtracting the previously published Watson–Crick thermodynamic values(11) so that the GU parameters are consistent with the widely used Watson–Crick parameters.
Table 3. INN Parameters for Canonical Base Pairs in 1 M NaCl without a Separate Parameter for Terminal GU Pairs
INNINN countsΔG°37(kcal/mol)ΔG°37 error (kcal/mol)ΔH° (kcal/mol)ΔH° error (kcal/mol)ΔS°a(eu)ΔS° error (eu)
Watson–Crick Nearest Neighbor Doublets
5′AA3′ –0.93 (−0.96)0.03 (0.03)–6.82 (−7.09)0.79 (0.77)–19.0 (−19.8)2.5 (2.4)
3′UU5′
5′AU3′ –1.10 (−1.09)0.08 (0.07)–9.38 (−9.11)1.68 (1.56)–26.7 (−25.8)5.2 (4.8)
3′UA5′
5′UA3′ –1.33 (−1.39)0.09 (0.08)–7.69 (−8.50)2.02 (1.86)–20.5 (−22.9)6.3 (5.7)
3′AU5′
5′CU3′ –2.08 (−2.07)0.06 (0.06)–10.48 (−10.90)1.24 (1.15)–27.1 (−28.5)3.8 (3.5)
3′GA5′
5′CA3′ –2.11 (−2.11)0.07 (0.06)–10.44 (−11.03)1.28 (1.18)–26.9 (−28.8)3.9 (3.6)
3′GU5′
5′GU3′ –2.24 (−2.27)0.06 (0.05)–11.40 (−11.98)1.23 (1.12)–29.5 (−31.3)3.9 (3.5)
3′CA5′
5′GA3′ –2.35 (−2.39)0.06 (0.05)–12.44 (−13.21)1.20 (1.05)–32.5 (−34.9)3.7 (3.2)
3′CU5′
5′CG3′ –2.36 (−2.38)0.09 (0.09)–10.64 (−10.88)1.65 (1.54)–26.7 (−27.4)5.0 (4.7)
3′GC5′
5′GG3′ –3.26 (−3.31)0.07 (0.06)–13.39 (−14.18)1.24 (1.07)–32.7 (−35.0)3.8 (3.3)
3′CC5′
5′GC3′ –3.42 (−3.46)0.08 (0.07)–14.88 (−16.04)1.58 (1.33)–36.9 (−40.6)4.9 (4.0)
3′CG5′
GU Nearest Neighbor Doublets
5′GU3′80.720.19–13.834.21–46.913.0
3′UG5′
5′GG3′9–0.250.16–17.823.75–56.711.6
3′UU5′
5′AG3′22–0.350.08–3.961.73–11.65.3
3′UU5′
5′UG3′18–0.390.09–0.961.80–1.85.5
3′AU5′
5′UU3′26–0.510.08–10.381.79–31.85.5
3′AG5′
5′UG3′10–0.570.19–12.644.01–38.912.3
3′GU5′
5′AU3′24–0.900.08–7.391.65–21.05.1
3′UG5′
5′CG3′26–1.250.09–5.561.68–13.95.1
3′GU5′
5′CU3′21–1.770.09–9.441.76–24.75.4
3′GG5′
5′GG3′24–1.800.09–7.031.75–16.85.4
3′CU5′
5′GU3′25–2.150.10–11.091.78–28.85.4
3′CG5′
5′GGUC3′b3–4.120.54–30.808.87–86.023.7
3′CUGG5′
Other Nearest Neighbor Parameters
initiationc4.09 (4.23)0.22 (0.20)3.61 (6.40)4.12 (3.56)–1.5 (6.99)12.7 (10.9)
terminal AU penaltyc0.45 (0.43)0.04 (0.04)3.72 (3.85)0.83 (0.77)10.5 (11.04)2.6 (2.4)
symmetryc0.43000–1.40
a

Values for ΔS° were derived from ΔS° = (ΔH° – ΔG°37)/310.15.

b

Ref 12.

c

Values for initiation, terminal AU, and nearest neighbors with only Watson–Crick pairs are from ref 11 when not in parentheses and derived from an expanded database when in parentheses. Values for nearest neighbors with GU pairs were derived using the Xia et al. parameters(11) for Watson–Crick nearest neighbors.

For fitting GU parameters, duplexes containing the motif, 5′GGUC/3′CUGG, were excluded from the regression due to its poor fit in the nearest neighbor model.(12) For the other 70 duplexes, 12 GU INN parameters were initially derived by linear regression, which included a penalty term for terminal GU pairs to correct for the fact that two duplexes with the same nearest neighbors can have different numbers of GU pairs and therefore different number of hydrogen bonds. A similar term was required for terminal AU pairs.(11) Fitting of additional parameters would not give a unique fit.(42) This 12 parameter fit gave values of −0.02 ± 0.06 kcal/mol and 2.34 ± 1.17 kcal/mol for the terminal GU penalty ΔG°37 and ΔH°, respectively (Supporting Information). The PDF for the terminal GU penalty ΔG°37 and ΔH° were 0.38 and 5.6 × 10–2, respectively, indicating that the term is not statistically significant. Therefore, the data were fit without a terminal GU term. The resulting nearest neighbor parameters are listed in Table 3.
The free energy parameters at 37 °C for 5′UG/3′AU, 5′UU/3′AG, and 5′AU/3′UG are less favorable than previously reported(12) by at least 0.43 kcal/mol. This corresponds to at least a factor of 2 for an equilibrium constant at 37 °C. The ΔG°37 values for each of the tandem GU motifs are more favorable than previously reported. The 5′UG/3′GU nearest neighbor contributes favorably to helix stability by −0.57 kcal/mol, whereas previous data provided an unfavorable increment of 0.30 kcal/mol.(12) Similarly increased stability from 0.47 to −0.25 kcal/mol at 37 °C was found for 5′GG/3′UU, which was previously represented by a single duplex (Table 3).
Estimated errors of the free energy parameters for most nearest neighbor motifs are less than 0.10 kcal/mol (Table 3). The p-value for the F-test is less than 2.2 × 10–16, indicating that there is a linear dependence of the free energy of a duplex on the occurrence of each nearest neighbor parameter in it at the 5% significance level.(60) The PDF values from the Student t-distribution (Table 4) are small for ΔG°37 except for the 5′GG/3′UU motif. The relatively large PDF for the 5′GG/3′UU motif may be attributed to the small magnitude of its free energy and large error compared to those of most of the other INN parameters.
Table 4. Probability Density Function (PDF) of Student’s t-Distribution for ΔG°37 and ΔH° for Each GU INN Motif without a Separate Parameter for Terminal GU Pairsa
motifPDF, ΔG°37PDF, ΔH°motifPDF, ΔG°37PDF, ΔH°
5′GU3′5.8 × 10–42.6 × 10–35′AU3′4.7 × 10–166.1 × 10–5
3′UG5′3′UG5′
5′GG3′1.2 × 10–12.4 × 10–55′CG3′4.9 × 10–202.4 × 10–3
3′UU5′3′GU5′
5′AG3′8.7 × 10–53.1 × 10–25′CU3′1.8 × 10–272.7 × 10–6
3′UU5′3′GG5′
5′UG3′1.0 × 10–43.4 × 10–15′GG3′7.4 × 10–282.8 × 10–4
3′AU5′3′CU5′
5′UU3′5.9 × 10–85.3 × 10–75′GU3′1.6 × 10–291.0 × 10–7
3′AG5′3′CG5′
5′UG3′5.6 × 10–33.7 × 10–3   
3′GU5′
a

Refs 11 and 59. The 5′GGUC/3′CUGG motif is not included.

Table 5 lists results for apparently two-state duplexes that were omitted from the database fitted because their TM’s are less than 25 °C. The predicted thermodynamic parameters for r(AGGCUU)2 and r(AUGCGU)2 do not agree well with those measured. The NMR spectra of r(AGGCUU)2 and r(AUGCGU)2 have strong H2′-H6/8 cross peaks and a sequential H2′, H1′-H6/8 proton walk in 2D 1H–1H NMR (Supporting Information) that indicate the duplexes adopt a largely A-form conformation.(62) For r(AUGCGU)2, however, the presence of broad on-diagonal peaks and exchange cross peaks in the aromatic region of the 2D spectra and of more imino resonances in a 1D spectrum than the number of imino protons in the sequence indicates the presence of alternate conformations. The presence of broad on-diagonal peaks, particularly for A1H8 and H2, in the aromatic region of the 2D spectra for r(AGGCUU)2 also suggests multiple conformations at 0 °C.
Table 5. Thermodynamic Parameters for Formation of Duplexes Not Included in Fitting Nearest Neighbor Parametersa
 TM–1 vs log CTaverage of curve fitspredicted 
sequenceb–ΔG°37(kcal/mol)–ΔH° (kcal/mol)–ΔS° (eu)TMc (°C)–ΔG°37(kcal/mol)–ΔH° (kcal/mol)–ΔS° (eu)TMc (°C)–ΔG°37(kcal/mol)–ΔH°(kcal/mol)–ΔS° (eu)TMc (°C)ref
Sequences with TM < 25 °C
AGGCUU4.0737.10106.624.24.2434.6097.924.72.2430.6391.55.8104
AUGCGUp4.2224.5065.419.54.3724.1063.621.12.3029.7388.55.2101
GUCGUAC/4.20 ± 0.6547.30 ± 9.01139.1 ± 30.122.23.80 ± 0.4854.20 ± 11.09162.3 ± 36.122.23.6651.98155.721.0this work
Non-Two-State Sequences
UGGCUA5.3649.70143.035.05.5436.1098.535.82.3224.6371.9–0.1104
CAUGUGC/7.70 ± 0.1449.10 ± 4.46133.5 ± 14.244.38.10 ± 0.1460.00 ± 11.18167.4 ± 35.645.15.9259.57173.033.8this work
GUGGUCG/7.93 ± 0.1856.21 ± 4.47155.7 ± 14.444.98.05 ± 0.1869.80 ± 6.64199.1 ± 18.143.97.6755.73154.943.6this work
GGUGUACC5.9465.80193.138.35.9949.60140.639.06.5461.72177.941.4104
GUAGCUGC6.5773.10214.440.96.4150.70142.841.68.1456.33155.351.3104
GAGGCGCGGAG/9.52 ± 0.24136.60 ± 11.90409.7 ± 37.743.98.28 ± 0.5659.27 ± 6.93164.4 ± 21.046.410.88111.46324.349.6this work
GCUUUGCGGAGC13.22 ± 0.35141.60 ± 7.01414.0 ± 21.554.410.39 ± 0.3482.58 ± 14.35232.7 ± 45.355.813.92129.51372.658.1this work
a

Experimental errors are listed for sequences melted in this study.

b

Listed in order of length of the oligoribonucleotide and then alphabetically for sequences of the same length. All non-self-complementary sequences have a slash and only one sequence shown. GU base pairs are underlined.

c

Calculated at a total strand concentration of 1 × 10–4 M.

Table 5 also lists duplexes that do not melt in a two-state manner. There are many possible reasons for this.(56, 63-65) The average difference between experimental and predicted TM for these sequences is 10.0 °C, while the predicted free energy is, on average, within 1.33 kcal/mol of the experimental free energy (Table 5). Evidently, the INN model may provide useful predictions for non-two-state sequences even though ΔH° from the van’t Hoff equation is erroneous.(64)

The Expanded Database Improves Predictions of Duplex Stability

Using the previous parameters,(12) the correlation coefficients between experimental values for ΔG°37, ΔH°, and ΔS° and those predicted for the 70 duplexes in Table 2 are 0.89, 0.86, and 0.85, respectively. Comparisons of the values of ΔG°37, ΔH°, and ΔS° of the 70 duplexes as predicted with the previous parameters,(12) and those in Table 3yielded, respectively, means of the differences of −0.36 kcal/mol, −1.75 kcal/mol, and −4.5 eu. The paired t-test gives t-values of −3.386, −2.528, and −2.257, respectively, which have absolute magnitudes greater than 1.995, indicating that the two sets of parameters differ with a significance level of 0.05 for 69 degrees of freedom.(61) Furthermore, the respective p-values of 1.2 × 10–3, 1.4 × 10–2, and 2.7 × 10–2 are less than 0.05. This again indicates that the new parameters predict the thermodynamics of RNA duplexes significantly differently from those published previously.(12)
Using the set of GU parameters in Table 3, the correlation coefficients between experimental values for ΔG°37, ΔH°, and ΔS° and those predicted for the 70 duplexes in Table 2 are 0.95, 0.89, and 0.87, respectively. Comparison of experimental values of ΔG°37, ΔH°, and ΔS° of the 70 duplexes and those predicted with the set of GU parameters in Table 3 yielded means of differences of −0.05 kcal/mol, 0.58 kcal/mol, and 2.1 eu and t-values of −0.544, 0.590, and 0.686, respectively, which have absolute magnitudes less than 1.995. The corresponding p-values of 0.59, 0.56, and 0.50 are greater than 0.05. These results show that the thermodynamic properties predicted with the new INN parameters are not significantly different from experiment.(61) The same analysis yielded means of differences of −0.41 kcal/mol, −1.17 kcal/mol, and −2.4 eu and t-values of −2.752, −1.096, and −0.736, with corresponding p-values of 7.6 × 10–3, 0.28, and 0.46 for ΔG°37, ΔH°, and ΔS°, respectively, when experimental properties were compared with predicted properties using previous parameters.(12) Evidently, the expanded database provides improved modeling of the thermodynamics of GU pairs.

The Nearest Neighbor Model Is Not Perfect

While the nearest neighbor model predicts well the ΔG°37 for most of the duplexes in Table 2, there are likely to be other terms that partially control stability. For example, there are four duplexes, r(GGCGUC)2, r(AGUCGAUU)2, r(UCACGUGG)2, and r(CCGAAUUUGG)2 with predicted ΔG°37 values not within 1.0 kcal/moland 20% of the 1/TM vs ln(CT/a) experimental values. No pattern is evident for these duplexes. A series of 1D spectra were acquired for r(CCGAAUUUGG)2 at different temperatures (Figure 1) because its predicted free energy is 1.5 kcal/mol more favorable at 37 °C than measured. These spectra show that the imino protons of all but U8, which is in the GU pair, and G10, which is in the terminal base pair disappear with each other, consistent with the duplex melting in a two-state manner. The results suggest that the nearest neighbor model does not include all factors that determine stabilities of duplexes with GU pairs.

Figure 1

Figure 1. 1D 1H imino spectra of r(CCGAAUUUGG)2 from 0 to 45 °C.

The expanded database allows preliminary testing of models beyond the nearest neighbor model. For example, terminal GU pairs could be considered separately(43) and a base pair triplet model used for internal GU pairs. Comparison of measured values of ΔG°37 for terminal GU pairs with those predicted from the parameters in Table 3 give a standard deviation within 0.30 kcal/mol at 37 °C (Supporting Information). For the 16 triplets, 5′WGY/3′XUZ, with WX and YZ as Watson–Crick pairs, 12 measured ΔG°37(GU component) values are within 0.5 kcal/mol of the predicted values and the others within 1.0 kcal/mol (Supporting Information). The nearest neighbor model is apparently a reasonable approximation, and considerably more data would be required to develop a triplet model.
One clear exception to the nearest neighbor model is multiple terminal GU pairs.(43) Thus, the parameters in Table 3 cannot be used beyond the first terminal GU pair at a helix end. Parameters for additional terminal GU pairs have been published by Nguyen and Schroeder.(43)

Imino Proton NMR Spectra of Several Duplexes Are Consistent with the Expected Base Pairing

To check for expected base pairing, NMR imino proton spectra were measured for 12 duplexes. All had chemical shifts from 10 to 15 ppm (Figure 2). Chemical shifts for GH1 and UH3 of GU pairs were relatively upfield (10–12 ppm), consistent with expectations.(66) Chemical shifts for UH3 in AU pairs resonated between 13 and 15 ppm and GH1 in GC pairs resonated from 12 to 13.5 ppm, as expected.(67, 68) The absence of an imino peak for a terminal base pair in r(CUGGCUAG)2 indicates exchange with water. The G3-H1 and U7–H3 resonances of r(CUGGAUUCAG)2 appear to overlap, as evident by the presence of a single large peak. These chemical shift signatures show that the RNA sequences form the expected duplexes.

Figure 2

Figure 2. 1D imino NMR spectra for some RNA duplexes with GU pairs. Spectra were acquired at 0 °C for r(AGGCUU)2 and r(AUGCGU)2; 1 °C for r(AGUCGAUU)2; 5 °C for others.

Discussion

ARTICLE SECTIONS
Jump To

GU pairs are the most common non-Watson–Crick base pairs in RNA structures. Thus, the thermodynamics of GU pairs are important for finding regions of RNA that are structured,(1, 14-16, 69) predicting the secondary structure(12) or determining structure on the basis of chemical modification(8, 70) and/or NMR data.(71)
GU pairs can serve as binding sites for proteins or metal ions and participate in tertiary interactions.(72, 73) Thus, a better characterization of the thermodynamic properties of GU pairs can improve prediction of secondary and tertiary structure and help predict binding sites for metal ions and target sites for therapeutics. For example, GU pairs in group I introns can bind cations, including Mg2+, Co3+, and Os3+.(38, 40, 74, 75) Divalent metal ion binding by GU pairs, which have greater negative potential in the major groove than other base pairs, was postulated as important for activating RNA catalysis.(76) Divalent ions that interact with a GU pair help catalyze splicing by group I and group II introns(77-82) and cleavage by HDV ribozyme.(35) Metal ion binding with RNA neutralizes negative potential, which may promote higher order RNA folding.(75) The 5′GG/3′UU and 5′GU/3′UG motifs particularly contain greater negative potential in the major groove than their Watson–Crick counterparts.(83)

The Database of Sequences for Determining GU Thermodynamic Parameters Was Expanded

Not including sequences containing the 5′GGUC/3′CUGG motif, the database in Table 2 expands from 35 to 70 the duplexes used to fit nearest neighbor parameters for GU pairs. This expansion includes published data not included in the original database(43, 84-86) along with 29 new measurements (Table 2). Two of the original 35 duplexes were removed from the database because their melting temperatures were below 25 °C, which makes it difficult to analyze the melting curves. A third duplex, r(AUCUAGGU)2, was omitted because two-state melting could not be confirmed. The expanded database contains GU pairs flanked by Watson–Crick pairs in all possible orientations (Table 1). The new set of GU INN parameters were obtained with consideration for propagated errors from experiment and from Watson–Crick nearest neighbor parameters. Errors for the free energies of individual nearest neighbors were less than 0.2 kcal/mol for tandem GU pairs and 0.1 kcal/mol for other GU motifs. The 5′GG/3′UU motif, which was previously represented by a single sequence, was added to the fitting. The favorable free energy of −0.25 ± 0.16 kcal/mol for 5′GG/3′UU is in better agreement with the value of −0.5 kcal/mol used by Mathews et al.(12) to optimize secondary structure prediction than with the previous single experimental measurement of 0.47 kcal/mol.

GU Pairs Are Generally Less Stable than GC and AU Pairs

The free energies of formation for many of the duplexes with GU pairs (Table 2) can be compared with the free energies when the U or G of the GU pairs is replaced with a C or A, respectively, to form GC or AU pairs (Table 6). Because many of the latter duplexes terminated with a 3′ phosphate, the comparisons assume that the 3′ phosphate has negligible effect on ΔG°37 at 1 M NaCl.(87, 88) Duplexes containing GC pairs in place of GU pairs are more stable at 37 °C by 1.8 ± 0.8 kcal/mol per GU pair (Table 6). This is presumably due to the presence of an additional hydrogen bond in GC pairs and unfavorable backbone distortion due to GU pairs. Terminal substitutions all have a less than average effect while internal substitutions have a larger than average effect, as expected if backbone distortion is less important for a terminal GU.
Table 6. Free Energy Differences When GU Pairs Are Replaced with AU or GC Pairs
GC duplexrefΔG°37 GC duplexGU duplex.refΔG°37 GU duplexΔΔG°37 per GU pair (kcal/mol)
CCGCGG119.84CUGCGG1014.312.77
CGGCCGp1109.90CGGCUG1015.552.18
CGGCCGp1109.90UGGCCGp1028.560.67
CUGCAGp1117.11UUGCAG434.201.46
GCCGGCp11011.20GCCGGUp1029.171.02
GGCGCCp11211.33GGCGCU1028.421.46
GGCGCCp11211.33GGCGUC1014.673.33
GUGCAC1117.65GUGCAU435.101.28
AUGCGCAUp10110.17AUGCGUAUp1015.272.45
CAUGCAUGp1139.67UAUGCAUGp1066.441.62
GAUGCAUCp11310.12GAUGCAUUp1066.821.65
GCAGCUGC11413.87GCAGCUGUthis work10.301.79
average 1.80 ± 0.76
AU duplexrefΔG°37 AU duplexGU duplex.refΔG°37 GU duplexΔΔG°37 per GU pair (kcal/mol).
ACCGGUp1158.51GCCGGUp1029.17–0.33
AGCGCU1127.99GGCGCU1028.42–0.22
CAGCUGp1116.68CGGCUG1015.550.57
CUGCAGp1117.11CUGCGG1014.311.40
GACGUC1167.35GGCGUC1014.671.34
UCCGGAp887.99UCCGGGp1027.440.28
CUCACUC/119.71CUCGCUC/1177.781.93
AAUGCAUUp1137.18GAUGCAUUp1066.820.18
AUACGUAU1016.53AUGCGUAUp1015.270.63
AUGCGCAUp10110.17AUGCGCGUp1019.310.43
UAUGCAUAp1137.27UAUGCAUGp1066.440.42
Average 0.60 ± 0.70
The effect of replacing GU with GC pairs can be compared to replacing AU pairs with GC pairs (Supporting Information). On average, replacing an AU pair with a GC pair stabilized a duplex by 1.5 ± 0.4 kcal/mol per AU pair. In this case, there was no apparent difference between terminal and internal substitutions.
Duplexes containing AU pairs in place of GU pairs are more stable at 37 °C by 0.6 ± 0.7 kcal/mol per GU pair (Table 6). While the difference is zero within the standard deviation, in only 2 of 11 cases is the GU duplex more stable than the AU duplex and in both cases the difference is within the experimental error of 4%.
Unlike terminal AU pairs, no penalty for terminal GU pairs is required to account for base pair composition. The terminal AU penalty of 0.45 kcal/mol at 37 °C was considered to account for numbers of base pairing hydrogen bonds.(11) Thus, the penalty for terminal GU pairs was assumed to be equal to that of AU pairs,(12) consistent with wobble GU pairs at the end of a helix having two hydrogen bonds.(89) When the terminal GU parameter was included in the reparameterization of GU nearest neighbor thermodynamic parameters, the free energy of each nearest neighbor parameter differed by no more than 0.01 kcal/mol from that calculated without it (Table 2 and Supporting Information). The lack of a terminal GU penalty may arise from the flexibility of a terminal GU pair which allows optimization of hydrogen bonding and stacking interactions without incurring the energetic penalty associated with an interior GU distorting the backbone.(43) For example, even for an internal GU pair, optimal stability may be found with only one hydrogen bond due to stacking energies.(90, 91) Thus, flexibility of terminal GU’s may compensate for the difference between the free energy of formation of two and three hydrogen bonds in GU and GC pairs, respectively.

Tandem GU Pairs Have Structural Features That Correlate with Their Thermodynamic Properties

With the exception of 5′GGUC/3′CUGG, the 5′UG/3′GU motif is more stable than 5′GU/3′UG (Table 3). Available structures show that 5′UG/3′GU contains interstrand stacking between the guanines,(90-93) whereas 5′GU/3′UG does not.(91, 94, 95) The favorability of the 5′UG/3′GU motif relative to the 5′GU/3′UG motif is consistent with molecular dynamics (MD) simulations(91) that predict a one hydrogen bond GU pair(90) predominates in duplexes containing the 5′GU/3′UG motif while a two hydrogen bond model predominates in duplexes containing the 5′UG/3′GU motif. There is also less overlap of negative potentials in 5′UG/3′GU than in 5′GU/3′UG.(95) In two different sequences containing the 5′UG/3′GU motif, there is also intrastrand stacking between each GU pair and its Watson–Crick neighbors.(92, 93) By comparison, the 5′GU/3′UG motif contains less overlap between the GU pairs and Watson–Crick purine neighbors, but has intrastrand stacking between the tandem GU pairs.(94) Furthermore, the 5′UG/3′GU motif preserves the A-form of RNA more than 5′GU/3′UG.(96)
The 5′GGUC/3′CUGG motif is an exception to the above generalizations. NMR spectra and modeling indicate that the GU pairs of r(GAGGUCUC)2 contain two hydrogen bonds,(52) whereas the GU pairs in r(GGCGUGCC)2 contain only one hydrogen bond.(90) This difference would contribute to the favorable free energy of 5′GGUC/3′CUGG compared to that of 5′GU/3′UG in other contexts, such as 5′CGUG/3′GUGC. Pan et al. saw similar hydrogen-bonding scenarios in MD simulations.(91) Additional stability for the 5′GGUC/3′CUGG motif may also be conferred from less overlap of its negative electrostatic potentials between a GC and GU pair than for its related motif, 5′CGUG/3′GUGC.(52) These patterns may explain the poor fit of nearest neighbor parameters for the 5′GU/3′UG motif when duplexes containing the 5′GGUC/3′CUGG motif are included in the fit. Alternatively, the extra stability of the 5′GGUC/3′CUGG motif over 5′CGUG/3′GUGC may arise from poor cross-strand overlap between the U in a GU pair and the C in its neighboring GC pair in 5′CGUG/3′GUGC.(97) Stacking interactions alone do not contribute to the stability of nearest neighbor motifs comprised of the same base pairs, however, as evident from the comparable stability of 5′UG/3′GU and 5′GG/3′UU. This contrasts with the expectation that the free energy of 5′GG/3′UU is between the other tandem GU motifs because its base stacking is intermediate among them.(98) Understanding the interactions responsible for the observed sequence dependence of thermodynamics presents a challenge to computational chemists.

GU Pairs of RNA Are More Stable than GT Pairs of DNA

Comparison of ΔG°37 values for GT nearest neighbors in DNA(74) with those measured for GU nearest neighbors show that GT nearest neighbors are on average 0.84 ± 0.36 kcal/mol less stable than their GU counterparts. The extra stability of GU relative to GT is also evident from comparisons of ΔG°37 (GU or GT component) for duplexes containing comparable triplet motifs (Table 7). This may reflect a possible hydrogen bond between the amino group of guanine and the O2′ of uracil,(99) which is not possible with DNA. MD simulations utilizing residual dipolar coupling (RDC) restraints suggest that the 5′TG/3′GT motif contains a bifurcated hydrogen bond(100) similar to that in the 5′GU/3′UG motif.(90, 91) Another difference between GT and GU is that the 5′GGTC/3′CTGG motif fits the nearest neighbor parameters for the 5′GT/3′TG motif better than their respective uracil-substituted RNA motifs.(74) Consistent with the relative stabilities of GT and GU nearest neighbors, component free energies of GT pairs in duplexes are consistently less favorable than those of GU pairs flanked by the same Watson–Crick pairs (Table 7).
Table 7. Component Free Energies of GU and GT Pairsa
sequencesbΔG°37(1/TM vs ln(CT/a)) (kcal/mol)experimental ΔG°37(component) (kcal/mol)predicted ΔG°37(component)c(kcal/mol)d
GCGUGC–5.11–2.79–1.78
GACCGTGCAC/–7.17–0.400.20
AUGCGUAUc–5.27–3.07–2.54
CCATGCGTAACG/–8.94–0.90–0.30
CTTGCATGTAAGc,e–6.10–0.55–0.15
CUCGGCUC/–8.22–3.45–3.65
GACGTTGGAC/–7.91–1.40–0.30
CUGGCUAG–7.10–4.04–4.32
CTTGGATCTAAG–5.89–1.200.20
GAGUGCUC–9.40–5.06–4.87
GGAGTGCTCC–7.66–2.20–0.70
GCAGUUGC–5.900.640.02
GGAGUUCC–6.430.270.02
GGCAGTTCGC/–6.871.402.60
GCAUGUGC–8.40–1.86–2.37
GGAUGUCC–8.39–1.69–2.37
GCGATGTCGCe–7.980.100.70
CAGUCGAUUGc–8.70–0.97–1.25
GTACAGTGATC/–7.78–1.200.80
CGAGTCGATTCGc,e–7.710.350.80
GAGAGCUUUC–8.82–1.06–1.72
CGAGACGTTTCG–6.961.702.10
GAGGAUCUUCc–9.83–1.93–2.12
CATGAGGCTAC/–8.57–0.900.40
GAGUGGAGAG/–9.87–0.78–1.15
GACTGGAGAG/e–4.610.301.50
GUGAAUUUAC–4.78–1.86–1.80
GUUAGCUGAC–8.60–1.06–1.80
CGTGACGTTACG–8.190.701.50
CGTTACGTGACG–7.861.101.50
GUGUGCAUAC–8.90–1.30–2.58
CGTGTCGATACG–8.420.201.00
CGTGTCTAGATACGe–9.400.201.00
a

Data for DNA duplexes were referenced from ref 74.

b

Listed in order of length of the oligoribonucleotide and then alphabetically for sequences of the same length. All nonself-complementary sequences have a slash and only one sequence shown. GU and GT base pairs are underlined.

c

Component free energies were divided by 2.

d

Calculated with free energies in Table 3.

e

Marginally non-two-state.(74)

Supporting Information

ARTICLE SECTIONS
Jump To

(I) Thermodynamic parameters for duplex formation of Watson–Crick sequences. (II) Experimental thermodynamic parameters and error limits for newly measured sequences. (III) Component free energies and enthalpies of GU pairs. (IV) Free energies of doublets and triplets containing GU pairs calculated as component ΔG°37 of their sequences. (V) Free energy differences between sequences where GC pair(s) were replaced by AU pair(s). (VI) INN parameters for GU pairs calculated with a separate term for terminal GU pairs. (VII) Probability density function of the Student’s t-distribution for each INN motif with a separate parameter for terminal GU pairs. (VIII) 2D NOESY spectra for r(AGGCUU)2 showing H2′, H1′, and H6/H8 regions. (IX) 2D NOESY spectra for r(AUGCGU)2 showing H2′, H1′, and H6/H8 regions. (X) Desalting procedure for oligoribonucleotides. This material is available free of charge via the Internet at http://pubs.acs.org.

The authors declare no competing financial interest.

Terms & Conditions

Electronic Supporting Information files are available without a subscription to ACS Web Editions. The American Chemical Society holds a copyright ownership interest in any copyrightable Supporting Information. Files available from the ACS website may be downloaded for personal use only. Users are not otherwise permitted to reproduce, republish, redistribute, or sell any Supporting Information from the ACS website, either in whole or in part, in either machine-readable form or any other form without permission from the American Chemical Society. For permission to reproduce, republish and redistribute this material, requesters must process their own requests via the RightsLink permission system. Information about how to use the RightsLink permission system can be found at http://pubs.acs.org/page/copyright/permissions.html.

Acknowledgment

ARTICLE SECTIONS
Jump To

The authors thank Zhenjiang Xu for suggesting the paired t-test and Dr. Susan Schroeder for comments on the manuscript.

Abbreviations

1D

one-dimensional

2D

two-dimensional

HIV-1

human immunodeficiency virus-1

INN

individual nearest neighbor

MD

molecular dynamics

NN

nearest neighbor

NOESY

nuclear Overhauser effect spectroscopy

PDF

probability density function

RDC

residual dipolar coupling

SVD

singular value decomposition

TOCSY

total correlation spectroscopy

WC

Watson–Crick

References

ARTICLE SECTIONS
Jump To

This article references 117 other publications.

  1. 1
    Mathews, D. H., Moss, W. N., and Turner, D. H. (2010) Folding and finding RNA secondary structure, in RNA Worlds: From Life’s Origins to Diversity in Gene Regulation (Atkins, J. F., Gesteland, R. F., and Cech, T. R., Eds.) 4th ed., pp 293 308, Cold Spring Harbor Laboratory Press, Cold Spring Harbor.
  2. 2
    Turner, D. H., Sugimoto, N., and Freier, S. M. (1988) RNA structure prediction Annu. Rev. Biophys. Biophys. Biochem. 17, 167 192
  3. 3
    Tinoco, I. and Bustamante, C. (1999) How RNA folds J. Mol. Biol. 293, 271 281
  4. 4
    Andronescu, M., Aguirre-Hernández, R., Condon, A., and Hoos, H. H. (2003) RNAsoft: a suite of RNA secondary structure prediction and design software tools Nucleic Acids Res. 31, 3416 3422
  5. 5
    Hofacker, I. L., Fontana, W., Stadler, P. F., Bonhoeffer, L. S., Tacker, M., and Schuster, P. (1994) Fast folding and comparison of RNA secondary structures Monatsh. Chem. 125, 167 188
  6. 6
    Lück, R., Gräf, S., and Steger, G. (1999) ConStruct: a tool for thermodynamic controlled prediction of conserved secondary structure Nucleic Acids Res. 27, 4208 4217
  7. 7
    Mathews, D. H. and Turner, D. H. (2002) Dynalign: An algorithm for finding the secondary structure common to two RNA sequences J. Mol. Biol. 317, 191 203
  8. 8
    Mathews, D. H., Disney, M. D., Childs, J. L., Schroeder, S. J., Zuker, M., and Turner, D. H. (2004) Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure Proc. Natl. Acad. Sci. U.S.A. 101, 7287 7292
  9. 9
    Mathews, D. H. (2004) Using an RNA secondary structure partition function to determine confidence in base pairs predicted by free energy minimization RNA 10, 1178 1190
  10. 10
    Borer, P. N., Dengler, B., Tinoco, I., and Uhlenbeck, O. C. (1974) Stability of ribonucleic acid double-stranded helices J. Mol. Biol. 86, 843 853
  11. 11
    Xia, T. B., SantaLucia, J., Burkard, M. E., Kierzek, R., Schroeder, S. J., Jiao, X. Q., Cox, C., and Turner, D. H. (1998) Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs Biochemistry 37, 14719 14735
  12. 12
    Mathews, D. H., Sabina, J., Zuker, M., and Turner, D. H. (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure J. Mol. Biol. 288, 911 940
  13. 13
    Turner, D. H. (2000) Conformational changes, in Nucleic Acids: Structures, Properties, and Functions (Bloomfield, V. A., Crothers, D. M., and Tinoco, J., I., Eds.) pp 259 334, University Science Books, Herndon, VA.
  14. 14
    Washietl, S., Hofacker, I. L., and Stadler, P. F. (2005) Fast and reliable prediction of noncoding RNAs Proc. Natl. Acad. Sci. U.S.A. 102, 2454 2459
  15. 15
    Uzilov, A., Keegan, J., and Mathews, D. H. (2006) Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change BMC Bioinformatics 7, 173
  16. 16
    Gruber, A. R., Neuböck, R., Hofacker, I. L., and Washietl, S. (2007) The RNAz web server: prediction of thermodynamically stable and evolutionarily conserved RNA structures Nucleic Acids Res. 35, W335 W338
  17. 17
    Reiche, K. and Stadler, P. F. (2007) RNAstrand: reading direction of structured RNAs in multiple sequence alignments Algorithm. Mol. Biol. 2, 6
  18. 18
    White, S. A., Nilges, M., Huang, A., Brunger, A. T., and Moore, P. B. (1992) NMR analysis of helix-I from the 5S RNA of Escherichia coli Biochemistry 31, 1610 1621
  19. 19
    Szymański, M., Barciszewska, M. Z., Erdmann, V. A., and Barciszewski, J. (2000) An analysis of G-U base pair occurrence in eukaryotic 5S rRNAs Mol. Biol. Evol. 17, 1194 1198
  20. 20
    Sprinzl, M. and Vassilenko, K. S. (2005) Compilation of tRNA sequences and sequences of tRNA genes Nucleic Acids Res. 33, D139 D140
  21. 21
    Limmer, S., Reif, B., Ott, G., Arnold, L., and Sprinzl, M. (1996) NMR evidence for helix geometry modifications by a G-U wobble base pair in the acceptor arm of E-coli tRNA(Ala) FEBS Lett. 385, 15 20
  22. 22
    Hou, Y. M. and Schimmel, P. (1988) A simple structural feature is a major determinant of the identity of a transfer RNA Nature 333, 140 145
  23. 23
    McClain, W. H. and Foss, K. (1988) Changing the identity of a transfer RNA by introducing a G-U wobble pair near the 3′ acceptor end Science 240, 793 796
  24. 24
    Mueller, U., Schubel, H., Sprinzl, M., and Heinemann, U. (1999) Crystal structure of acceptor stem of tRNA(Ala) from Escherichia coli shows unique G·U wobble base pair at 1.16 angstrom resolution RNA 5, 670 677
  25. 25
    White, S. A. and Li, H. (1996) Yeast ribosomal protein L32 recognizes an RNA G:U juxtaposition RNA 2, 226 234
  26. 26
    Reyes, J. L., Gustafson, E. H., Luo, H. R., Moore, M. J., and Konarska, M. M. (1999) The C-terminal region of hPrp8 interacts with the conserved GU dinucleotide at the 5′ splice site RNA 5, 167 179
  27. 27
    Leung, S. S. and Koslowsky, D. J. (2001) Interactions of mRNAs and gRNAs involved in trypanosome mitochondrial RNA editing: structure probing of an mRNA bound to its cognate gRNA RNA 7, 1803 1816
  28. 28
    Mooers, B. H. M. and Singh, A. (2011) The crystal structure of an oligo(U):pre-mRNA duplex from a trypanosome RNA editing substrate RNA 17, 1870 1883
  29. 29
    Lu, K., Heng, X., Garyu, L., Monti, S., Garcia, E. L., Kharytonchyk, S., Dorjsuren, B., Kulandaivel, G., Jones, S., Hiremath, A., Divakaruni, S. S., LaCotti, C., Barton, S., Tummillo, D., Hosic, A., Edme, K., Albrecht, S., Telesnitsky, A., and Summers, M. F. (2011) NMR detection of structures in the HIV-1 5′-leader RNA that regulate genome packaging Science 334, 242 245
  30. 30
    Knitt, D. S., Narlikar, G. J., and Herschlag, D. (1994) Dissection of the role of the conserved G·U pair in group I RNA self-splicing Biochemistry 33, 13864 13879
  31. 31
    Pyle, A. M., Moran, S., Strobel, S. A., Chapman, T., Turner, D. H., and Cech, T. R. (1994) Replacement of the conserved G·U with a G-C pair at the cleavage site of the tetrahymena ribozyme decreases binding, reactivity, and fidelity Biochemistry 33, 13856 13863
  32. 32
    Strobel, S. A. and Cech, T. R. (1995) Minor groove recognition of the conserved G·U pair at the tetrahymena ribozyme reaction site Science 267, 675 679
  33. 33
    Strobel, S. A. and Cech, T. R. (1996) Exocyclic amine of the conserved G·U pair at the cleavage site of the Tetrahymena ribozyme contributes to 5′-splice site selection and transition state stabilization Biochemistry 35, 1201 1211
  34. 34
    Šponer, J., Šponer, J. E., Petrov, A. I., and Leontis, N. B. (2010) Quantum chemical studies of nucleic acids: Can we construct a bridge to the RNA structural biology and bioinformatics communities? J. Phys. Chem. B 114, 15723 15741
  35. 35
    Chen, J.-H., Gong, B., Bevilacqua, P. C., Carey, P. R., and Golden, B. L. (2009) A catalytic metal ion interacts with the cleavage site G·U wobble in the HDV ribozyme Biochemistry 48, 1498 1507
  36. 36
    Chen, J.-H., Yajima, R., Chadalavada, D. M., Chase, E., Bevilacqua, P. C., and Golden, B. L. (2010) A 1.9 Å crystal structure of the HDV ribozyme precleavage suggests both Lewis acid and general acid mechanisms contribute to phosphodiester cleavage Biochemistry 49, 6508 6518
  37. 37
    Keel, A. Y., Rambo, R. P., Batey, R. T., and Kieft, J. S. (2007) A general strategy to solve the phase problem in RNA crystallography Structure 15, 761 772
  38. 38
    Kieft, J. S. and Tinoco, I. (1997) Solution structure of a metal-binding site in the major groove of RNA complexed with cobalt (III) hexammine Structure 5, 713 721
  39. 39
    Wang, W. M., Zhao, J. W., Han, Q. W., Wang, G., Yang, G. C., Shallop, A. J., Liu, J., Gaffney, B. L., and Jones, R. A. (2009) Modulation of RNA metal binding by flanking bases: N-15 NMR evaluation of GC, tandem GU, and tandem GA sites Nucleosides Nucleotides Nucleic Acids 28, 424 434
  40. 40
    Colmenarejo, G. and Tinoco, I., Jr. (1999) Structure and thermodynamics of metal binding in the P5 helix of a group I intron ribozyme J. Mol. Biol. 290, 119 135
  41. 41
    Gautheret, D., Konings, D., and Gutell, R. R. (1995) G·U base pairing motifs in ribosomal RNA RNA 1, 807 814
  42. 42
    Gray, D. M. (1997) Derivation of nearest-neighbor properties from data on nucleic acid oligomers. 1. Simple sets of independent sequences and the influence of absent nearest neighbors Biopolymers 42, 783 793
  43. 43
    Nguyen, M.-T. and Schroeder, S. J. (2010) Consecutive terminal GU pairs stabilize RNA helices Biochemistry 49, 10574 10581
  44. 44
    Serra, M. J., Smolter, P. E., and Westhof, E. (2004) Pronouced instability of tandem IU base pairs in RNA Nucleic Acids Res. 32, 1824 1828
  45. 45
    Fukada, H. and Takahashi, K. (1998) Enthalpy and heat capacity changes for the proton dissociation of various buffer components in 0.1 M potassium chloride Proteins 33, 159 166
  46. 46
    Smallcombe, S. H. (1993) Solvent suppression with symmetrically-shifted pulses J. Am. Chem. Soc. 115, 4776 4785
  47. 47
    Grzesiek, S. and Bax, A. (1993) The importance of not saturating H2O in protein NMR - application to sensitivity enhancement and NOE measurements J. Am. Chem. Soc. 115, 12593 12594
  48. 48
    Piotto, M., Saudek, V., and Sklenář, V. (1992) Gradient-tailored excitation for single-quantum NMR spectroscopy of aqueous solutions J. Biomol. NMR 2, 661 665
  49. 49
    Delaglio, F., Grzesiek, S., Vuister, G. W., Zhu, G., Pfeifer, J., and Bax, A. (1995) NMRPipe: A multidimensional spectral processing system based on UNIX pipes J. Biomol. NMR 6, 277 293
  50. 50
    Goddard, T. D. and Kneller, D. G. (2004) SPARKY, NMR Assignment and Integration Software, 3rd ed., University of California, San Francisco.
  51. 51
    Cavanagh, J., Fairbrother, W. J., Palmer, A. G. I., and Skelton, N. J. (1996) Protein NMR Spectroscopy: Principles and Practice, Academic Press, San Diego.
  52. 52
    McDowell, J. A. and Turner, D. H. (1996) Investigation of the structural basis for thermodynamic stabilities of tandem GU mismatches: Solution structure of (rGAGGUCUC)2 by two-dimensional NMR and simulated annealing Biochemistry 35, 14077 14089
  53. 53
    R Development Core Team (2010) R: A Language and Environment for Statistical Computing, x64 2.11.1 ed., R Foundation for Statistical Computing, Vienna, Austria.
  54. 54
    Wolfram Research (2010) Mathematica Edition: Version 8.0, Champaign, Illinois.
  55. 55
    Eaton, J. W. (2002) GNU Octave Manual.
  56. 56
    Cantor, C. R. and Schimmel, P. R. (1980) Biophysical Chemistry, Part III: The Behavior of Biological Macromolecules, pp. 1197 1198, W. H. Freeman and Company, San Francisco.
  57. 57
    Bevington, P. R. and Robinson, D. K. (2002) Data Reduction and Error Analysis for the Physical Sciences, 3rd ed., McGraw-Hill, New York.
  58. 58
    Drosg, M. (2007) Dealing with Uncertainties: A Guide to Error Analysis, Springer-Verlag, Heidelberg.
  59. 59
    Crawley, M. J. (2007) The R Book, 1st ed., John Wiley & Sons, West Sussex.
  60. 60
    Kinney, J. J. (2002) Statistics for Science and Engineering, 1st ed., Addison-Wesley, Boston.
  61. 61
    Devore, J. and Peck, R. (2005) Statistics: the Exploration and Analysis of Data, 5th ed., Brooks/Cole - Thomson Learning, Belmont, CA.
  62. 62
    Varani, G., Aboulela, F., and Allain, F. H. T. (1996) NMR investigation of RNA structure Prog. Nucl. Mag. Res. Spectrosc. 29, 51 127
  63. 63
    Chaires, J. B. (1997) Possible origin of differences between van’t Hoff and calorimetric enthalpy estimates Biophys. Chem. 64, 15 23
  64. 64
    Mergny, J.-L. and Lacroix, L. (2003) Analysis of thermal melting curves Oligonucleotides 13, 515 537
  65. 65
    SantaLucia, J. and Turner, D. H. (1997) Measuring the thermodynamics of RNA secondary structure formation Biopolymers 44, 309 319
  66. 66
    Fürtig, B., Richter, C., Wohnert, J., and Schwalbe, H. (2003) NMR spectroscopy of RNA ChemBioChem 4, 936 962
  67. 67
    Reid, B. R., McCollumn, L., Ribeiro, N. S., Abbate, J., and Hurd, R. E. (1979) Identification of tertiary base pair resonances in the nuclear magnetic resonance spectra of transfer ribonucleic acid Biochemistry 18, 3996 4005
  68. 68
    Johnston, P. D. and Redfield, A. G. (1981) Nuclear magnetic resonance and nuclear Overhauser effect study of yeast phenylalanine transfer ribonucleic acid imino protons Biochemistry 20, 1147 1156
  69. 69
    Cockerill, M. (1993) Not much to malign - Multalin 4.0 Trends Biochem. Sci. 18, 106 107
  70. 70
    Deigan, K. E., Li, T. W., Mathews, D. H., and Weeks, K. M. (2009) Accurate SHAPE-directed RNA structure determination Proc. Natl. Acad. Sci. U.S.A. 106, 97 102
  71. 71
    Hart, J. M., Kennedy, S. D., Mathews, D. H., and Turner, D. H. (2008) NMR-assisted prediction of RNA secondary structure: Identification of a probable pseudoknot in the coding region of an R2 Retrotransposon J. Am. Chem. Soc. 130, 10233 10239
  72. 72
    Batey, R. T., Rambo, R. P., and Doudna, J. A. (1999) Tertiary motifs in RNA structure and folding Angew. Chem., Int. Ed. 38, 2327 2343
  73. 73
    Varani, G. and McClain, W. H. (2000) The G·U wobble base pair: a fundamental building block of RNA structure crucial to RNA function in diverse biological systems EMBO Rep. 1, 18 23
  74. 74
    Allawi, H. T. and SantaLucia, J. (1997) Thermodynamics and NMR of internal G·T mismatches in DNA Biochemistry 36, 10581 10594
  75. 75
    Cate, J. H. and Doudna, J. A. (1996) Metal-binding sites in the major groove of a large ribozyme domain Structure 4, 1221 1229
  76. 76
    Konforti, B. B., Abramovitz, D. L., Duarte, C. M., Karpeisky, A., Beigelman, L., and Pyle, A. M. (1998) Ribozyme catalysis from the major groove of group II intron domain 5 Mol. Cell 1, 433 441
  77. 77
    Adams, P. L., Stahley, M. R., Kosek, A. B., Wang, J., and Strobel, S. A. (2004) Crystal structure of a self-splicing group I intron with both exons Nature 430, 45 50
  78. 78
    Forconi, M., Sengupta, R. N., Piccirilli, J. A., and Herschlag, D. (2010) A rearrangement of the guanosine-binding site establishes an extended network of functional interactions in the tetrahymena group I ribozyme active site Biochemistry 49, 2753 2762
  79. 79
    Lipchock, S. V. and Strobel, S. A. (2008) A relaxed active site after exon ligation by the group I intron Proc. Natl. Acad. Sci. U.S.A 105, 5699 5704
  80. 80
    Stahley, M. R., Adams, P. L., Wang, J., and Strobel, S. A. (2007) Structural metals in the group I intron: A ribozyme with a multiple metal ion core J. Mol. Biol. 372, 89 102
  81. 81
    Strobel, S. A. and Ortoleva-Donnelly, L. (1999) A hydrogen-bonding triad stabilizes the chemical transition state of a group I ribozyme Chem. Biol. 6, 153 165
  82. 82
    Toor, N., Keating, K. S., Taylor, S. D., and Pyle, A. M. (2008) Crystal structure of a self-spliced group II intron Science 320, 77 82
  83. 83
    Xu, D., Landon, T., Greenbaum, N. L., and Fenley, M. O. (2007) The electrostatic characteristics of G·U wobble base pairs Nucleic Acids Res. 35, 3836 3847
  84. 84
    Chen, G., Znosko, B. M., Jiao, X. Q., and Turner, D. H. (2004) Factors affecting thermodynamic stabilities of RNA 3 × 3 internal loops Biochemistry 43, 12865 12876
  85. 85
    Serra, M. J., Baird, J. D., Dale, T., Fey, B. L., Retatagos, K., and Westhof, E. (2002) Effects of magnesium ions on the stabilization of RNA oligomers of defined structures RNA 8, 307 323
  86. 86
    Walter, A. E., Wu, M., and Turner, D. H. (1994) The stability and structure of tandem GA mismatches in RNA depend on closing base-pairs Biochemistry 33, 11349 11354
  87. 87
    Freier, S. M., Burger, B. J., Alkema, D., Neilson, T., and Turner, D. H. (1983) Effects of 3′ dangling end stacking on the stability of GGCC and CCGG double helixes Biochemistry 22, 6198 6206
  88. 88
    Freier, S. M., Alkema, D., Sinclair, A., Neilson, T., and Turner, D. H. (1985) Contributions of dangling end stacking and terminal base-pair formation to the stabilities of XGGCCp, XCCGGp, XGGCCYp, and XCCGGYp helixes Biochemistry 24, 4533 4539
  89. 89
    Crick, F. H. C. (1966) Codon-anticodon pairing: the wobble hypothesis J. Mol. Biol. 19, 548 555
  90. 90
    Chen, X. Y., McDowell, J. A., Kierzek, R., Krugh, T. R., and Turner, D. H. (2000) Nuclear magnetic resonance spectroscopy and molecular modeling reveal that different hydrogen bonding patterns are possible for G·U pairs: One hydrogen bond for each G·U pair in r(GGCGUGCC)2 and two for each G·U pair in r(GAGUGCUC)2 Biochemistry 39, 8970 8982
  91. 91
    Pan, Y. P., Priyakumar, U. D., and MacKerell, A. D. (2005) Conformational determinants of tandem GU mismatches in RNA: Insights from molecular dynamics simulations and quantum mechanical calculations Biochemistry 44, 1433 1443
  92. 92
    Biswas, R., Wahl, M. C., Ban, C., and Sundaralingam, M. (1997) Crystal structure of an alternating octamer r(GUAUGUA)dC with adjacent G·U wobble pairs J. Mol. Biol. 267, 1149 1156
  93. 93
    Utsunomiya, R., Suto, K., Balasundaresan, D., Fukamizu, A., Kumar, P. K. R., and Mizuno, H. (2006) Structure of an RNA duplex r(GGCG(Br)UGCGCU)2 with terminal and internal tandem G·U base pairs Acta Crystallogr. D 62, 331 338
  94. 94
    Biswas, R. and Sundaralingam, M. (1997) Crystal structure of r(GUGUGUA)dC with tandem G·U/U·G wobble pairs with strand slippage J. Mol. Biol. 270, 511 519
  95. 95
    McDowell, J. A., He, L. Y., Chen, X. Y., and Turner, D. H. (1997) Investigation of the structural basis for thermodynamic stabilities of tandem GU wobble pairs: NMR structures of (rGGAGUUCC)2 and (rGGAUGUCC)2 Biochemistry 36, 8030 8038
  96. 96
    Masquida, B. and Westhof, E. (2000) On the wobble G·U and related pairs RNA 6, 9 15
  97. 97
    Jang, S. B., Hung, L. W., Jeong, M. S., Holbrook, E. L., Chen, X. Y., Turner, D. H., and Holbrook, S. R. (2006) The crystal structure at 1.5 Å resolution of an RNA octamer duplex containing tandem G·U basepairs Biophys. J. 90, 4530 4537
  98. 98
    Deng, J. P. and Sundaralingam, M. (2000) Synthesis and crystal structure of an octamer RNA r(guguuuac)/r(guaggcac) with G·G/U·U tandem wobble base pairs: comparison with other tandem G·U pairs Nucleic Acids Res. 28, 4376 4381
  99. 99
    Shi, K., Wahl, M. C., and Sundaralingam, M. (1999) Crystal structure of an RNA duplex r(GGGCGCUCC)2 with non-adjacent G·U base pairs Nucleic Acids Res. 27, 2196 2201
  100. 100
    Alvarez-Salgado, F., Desvaux, H., and Boulard, Y. (2006) NMR assessment of the global shape of a non-labelled DNA dodecamer containing a tandem of G·T mismatches Magn. Reson. Chem. 44, 1081 1089
  101. 101
    Sugimoto, N., Kierzek, R., Freier, S. M., and Turner, D. H. (1986) Energetics of internal GU mismatches in ribooligonucleotide helixes Biochemistry 25, 5755 5759
  102. 102
    Freier, S. M., Kierzek, R., Caruthers, M. H., Neilson, T., and Turner, D. H. (1986) Free energy contributions of G·U and other terminal mismatches to helix stability Biochemistry 25, 3209 3213
  103. 103
    Testa, S. M., Disney, M. D., Turner, D. H., and Kierzek, R. (1999) Thermodynamics of RNA-RNA duplexes with 2-or 4-thiouridines: Implications for antisense design and targeting a group I intron Biochemistry 38, 16655 16662
  104. 104
    He, L., Kierzek, R., SantaLucia, J., Walter, A. E., and Turner, D. H. (1991) Nearest-neighbor parameters for G·U mismatches - 5′GU3′/3′UG5′ is destabilizing in the contexts CGUG/GUGC, UGUA/AUGU, and AGUU/UUGU but stabilizing in GGUC/CUGG Biochemistry 30, 11124 11132
  105. 105
    Xia, T. B., McDowell, J. A., and Turner, D. H. (1997) Thermodynamics of nonsymmetric tandem mismatches adjacent to G·C base pairs in RNA Biochemistry 36, 12486 12497
  106. 106
    Sugimoto, N., Kierzek, R., and Turner, D. H. (1987) Sequence dependence for the energetics of terminal mismatches in ribonucleic acid Biochemistry 26, 4559 4562
  107. 107
    Ziomek, K., Kierzek, E., Biala, E., and Kierzek, R. (2002) The thermal stability of RNA duplexes containing modified base pairs placed at internal and terminal positions of the oligoribonucleotides Biophys. Chem. 97, 233 241
  108. 108
    Schroeder, S. J. and Turner, D. H. (2001) Thermodynamic stabilities of internal loops with GU closing pairs in RNA Biochemistry 40, 11509 11517
  109. 109
    Schroeder, S. J. and Turner, D. H. (2000) Factors affecting the thermodynamic stability of small asymmetric internal loops in RNA Biochemistry 39, 9257 9274
  110. 110
    Freier, S. M., Sinclair, A., Neilson, T., and Turner, D. H. (1985) Improved free energies for G·C base-pairs J. Mol. Biol. 185, 645 647
  111. 111
    Freier, S. M., Kierzek, R., Jaeger, J. A., Sugimoto, N., Caruthers, M. H., Neilson, T., and Turner, D. H. (1986) Improved free-energy parameters for predictions of RNA duplex stability Proc. Natl. Acad. Sci. U.S.A. 83, 9373 9377
  112. 112
    Freier, S. M., Sugimoto, N., Sinclair, A., Alkema, D., Neilson, T., Kierzek, R., Caruthers, M. H., and Turner, D. H. (1986) Stability of XGCGCp, GCGCYp, and XGCGCYp helixes: an empirical estimate of the energetics of hydrogen bonds in nucleic acids Biochemistry 25, 3214 3219
  113. 113
    Sugimoto, N., Kierzek, R., and Turner, D. H. (1987) Sequence dependence for the energetics of dangling ends and terminal base pairs in ribooligonucleotides Biochemistry 26, 4554 4558
  114. 114
    Burkard, M. E. and Turner, D. H. (2000) NMR structures of r(GCAGGCGUGC)2 and determinants of stability for single guanosine-guanosine base pairs Biochemistry 39, 11748 11762
  115. 115
    Petersheim, M. and Turner, D. H. (1983) Base-stacking and base-pairing contributions to helix stability: thermodynamics of double-helix formation with CCGG, CCGGp, CCGGAp, ACCGGp, CCGGUp, and ACCGGUp Biochemistry 22, 256 263
  116. 116
    Kierzek, R., Caruthers, M. H., Longfellow, C. E., Swinton, D., Turner, D. H., and Freier, S. M. (1986) Polymer-supported RNA synthesis and its application to test the nearest-neighbor model for duplex stability Biochemistry 25, 7840 7846
  117. 117
    Kierzek, R., Burkard, M. E., and Turner, D. H. (1999) Thermodynamics of single mismatches in RNA duplexes Biochemistry 38, 14214 14223

Cited By


This article is cited by 54 publications.

  1. Alan Ann Lerk Ong, Desiree-Faye Kaixin Toh, Kiran M. Patil, Zhenyu Meng, Zhen Yuan, Manchugondanahalli S. Krishna, Gitali Devi, Phensinee Haruehanroengra, Yunpeng Lu, Kelin Xia, Katsutomo Okamura, Jia Sheng, Gang Chen. General Recognition of U-G, U-A, and C-G Pairs by Double-Stranded RNA-Binding PNAs Incorporated with an Artificial Nucleobase. Biochemistry 2019, 58 (10) , 1319-1331. https://doi.org/10.1021/acs.biochem.8b01313
  2. Kyle D. Berger, Scott D. Kennedy, Douglas H. Turner. Nuclear Magnetic Resonance Reveals That GU Base Pairs Flanking Internal Loops Can Adopt Diverse Structures. Biochemistry 2019, 58 (8) , 1094-1108. https://doi.org/10.1021/acs.biochem.8b01027
  3. Lixia Yang, Zhensheng Zhong, Cailing Tong, Huan Jia, Yiran Liu, Gang Chen. Single-Molecule Mechanical Folding and Unfolding of RNA Hairpins: Effects of Single A-U to A·C Pair Substitutions and Single Proton Binding and Implications for mRNA Structure-Induced −1 Ribosomal Frameshifting. Journal of the American Chemical Society 2018, 140 (26) , 8172-8184. https://doi.org/10.1021/jacs.8b02970
  4. Saeed K. Amini. Relative Populations of Some Tautomeric Forms of 2′-Deoxyguanosine-5-Fluorouridine Mismatch. The Journal of Physical Chemistry B 2018, 122 (16) , 4433-4444. https://doi.org/10.1021/acs.jpcb.8b00818
  5. Kyle D. Berger, Scott D. Kennedy, Susan J. Schroeder, Brent M. Znosko, Hongying Sun, David H. Mathews, Douglas H. Turner. Surprising Sequence Effects on GU Closure of Symmetric 2 × 2 Nucleotide RNA Internal Loops. Biochemistry 2018, 57 (14) , 2121-2131. https://doi.org/10.1021/acs.biochem.7b01306
  6. Tauanne D. Amarante and Gerald Weber . Evaluating Hydrogen Bonds and Base Stacking of Single, Tandem and Terminal GU Mismatches in RNA with a Mesoscopic Model. Journal of Chemical Information and Modeling 2016, 56 (1) , 101-109. https://doi.org/10.1021/acs.jcim.5b00571
  7. Amber R. Truitt, Bok-Eum Choi, Jenny Li, and Ana Maria Soto . Effect of Mutations on the Binding of Kanamycin-B to RNA Hairpins Derived from the Mycobacterium tuberculosis Ribosomal A-Site. Biochemistry 2015, 54 (51) , 7425-7437. https://doi.org/10.1021/acs.biochem.5b00710
  8. Jonathan L. Chen, Stanislav Bellaousov, Jason D. Tubbs, Scott D. Kennedy, Michael J. Lopez, David H. Mathews, and Douglas H. Turner . Nuclear Magnetic Resonance-Assisted Prediction of Secondary Structure for RNA: Incorporation of Direction-Dependent Chemical Shift Constraints. Biochemistry 2015, 54 (45) , 6769-6782. https://doi.org/10.1021/acs.biochem.5b00833
  9. Xiaobo Gu, Blaine H. M. Mooers, Leonard M. Thomas, Joshua Malone, Steven Harris, and Susan J. Schroeder . Structures and Energetics of Four Adjacent G·U Pairs That Stabilize an RNA Helix. The Journal of Physical Chemistry B 2015, 119 (42) , 13252-13261. https://doi.org/10.1021/acs.jpcb.5b06970
  10. Enver Cagri Izgu, Albert C. Fahrenbach, Na Zhang, Li Li, Wen Zhang, Aaron T. Larsen, J. Craig Blain, and Jack W. Szostak . Uncovering the Thermodynamics of Monomer Binding for RNA Replication. Journal of the American Chemical Society 2015, 137 (19) , 6373-6382. https://doi.org/10.1021/jacs.5b02707
  11. Meghan H. Murray, Jessicah A. Hard, and Brent M. Znosko . Improved Model to Predict the Free Energy Contribution of Trinucleotide Bulges to RNA Duplex Stability. Biochemistry 2014, 53 (21) , 3502-3508. https://doi.org/10.1021/bi500204e
  12. Miroslav Krepl, Michal Otyepka, Pavel Banáš, and Jiří Šponer . Effect of Guanine to Inosine Substitution on Stability of Canonical DNA and RNA Duplexes: Molecular Dynamics Thermodynamics Integration Study. The Journal of Physical Chemistry B 2013, 117 (6) , 1872-1879. https://doi.org/10.1021/jp311180u
  13. Melissa C Hopfinger, Charles C Kirkpatrick, Brent M Znosko. Predictions and analyses of RNA nearest neighbor parameters for modified nucleotides. Nucleic Acids Research 2020, 48 (16) , 8901-8913. https://doi.org/10.1093/nar/gkaa654
  14. Dmitri N. Ermolenko, David H. Mathews. Making ends meet: New functions of mRNA secondary structure. WIREs RNA 2020, 6 https://doi.org/10.1002/wrna.1611
  15. Ketty Pernod, Laure Schaeffer, Johana Chicher, Eveline Hok, Christian Rick, Renaud Geslain, Gilbert Eriani, Eric Westhof, Michael Ryckelynck, Franck Martin. The nature of the purine at position 34 in tRNAs of 4-codon boxes is correlated with nucleotides at positions 32 and 38 to maintain decoding fidelity. Nucleic Acids Research 2020, 48 (11) , 6170-6183. https://doi.org/10.1093/nar/gkaa221
  16. Debapratim Dutta, Joseph E. Wedekind. Nucleobase mutants of a bacterial preQ 1 -II riboswitch that uncouple metabolite sensing from gene regulation. Journal of Biological Chemistry 2020, 295 (9) , 2555-2567. https://doi.org/10.1074/jbc.RA119.010755
  17. Shen Tian, Goro Terai, Yoshiaki Kobayashi, Yasuaki Kimura, Hiroshi Abe, Kiyoshi Asai, Kumiko Ui-Tei. A robust model for quantitative prediction of the silencing efficacy of wild-type and A-to-I edited miRNAs. RNA Biology 2020, 17 (2) , 264-280. https://doi.org/10.1080/15476286.2019.1678364
  18. Allison A. O'Connell, Jared A. Hanson, Darryl C. McCaskill, Ethan T. Moore, Daniel C. Lewis, Neena Grover. Thermodynamic examination of pH and magnesium effect on U6 RNA internal loop. RNA 2019, 25 (12) , 1779-1792. https://doi.org/10.1261/rna.070466.119
  19. Michał Gładysz, Witold Andrałojć, Tomasz Czapik, Zofia Gdaniec, Ryszard Kierzek. Thermodynamic and structural contributions of the 6-thioguanosine residue to helical properties of RNA. Scientific Reports 2019, 9 (1) https://doi.org/10.1038/s41598-019-40715-2
  20. Janvier, Despons, Schaeffer, Tidu, Martin, Eriani. A tRNA-mimic Strategy to Explore the Role of G34 of tRNAGly in Translation and Codon Frameshifting. International Journal of Molecular Sciences 2019, 20 (16) , 3911. https://doi.org/10.3390/ijms20163911
  21. Eric Westhof, Marat Yusupov, Gulnara Yusupova. The multiple flavors of GoU pairs in RNA. Journal of Molecular Recognition 2019, 32 (8) https://doi.org/10.1002/jmr.2782
  22. Jeffrey Zuber, David H. Mathews. Estimating uncertainty in predicted folding free energy changes of RNA secondary structures. RNA 2019, 25 (6) , 747-754. https://doi.org/10.1261/rna.069203.118
  23. Jolanta Lisowiec-Wachnicka, Brent M. Znosko, Anna Pasternak. Contribution of 3′T and 3′TT overhangs to the thermodynamic stability of model siRNA duplexes. Biophysical Chemistry 2019, 246 , 35-39. https://doi.org/10.1016/j.bpc.2018.12.006
  24. Daniel J Wright, Christopher R Force, Brent M Znosko. Stability of RNA duplexes containing inosine·cytosine pairs. Nucleic Acids Research 2018, 46 (22) , 12099-12108. https://doi.org/10.1093/nar/gky907
  25. Wan-Jung C. Lai, Mohammad Kayedkhordeh, Erica V. Cornell, Elie Farah, Stanislav Bellaousov, Robert Rietmeijer, Enea Salsi, David H. Mathews, Dmitri N. Ermolenko. mRNAs and lncRNAs intrinsically form secondary structures with short end-to-end distances. Nature Communications 2018, 9 (1) https://doi.org/10.1038/s41467-018-06792-z
  26. Jeffrey Zuber, B. Joseph Cabral, Iain McFadyen, David M. Mauger, David H. Mathews. Analysis of RNA nearest neighbor parameters reveals interdependencies and quantifies the uncertainty in RNA secondary structure prediction. RNA 2018, 24 (11) , 1568-1582. https://doi.org/10.1261/rna.065102.117
  27. Stanislav Bellaousov, Mohammad Kayedkhordeh, Raymond J. Peterson, David H. Mathews. Accelerated RNA secondary structure design using preselected sequences for helices and loops. RNA 2018, 24 (11) , 1555-1567. https://doi.org/10.1261/rna.066324.118
  28. Maryam Hosseini, Poorna Roy, Marie Sissler, Craig L Zirbel, Eric Westhof, Neocles Leontis. How to fold and protect mitochondrial ribosomal RNA with fewer guanines. Nucleic Acids Research 2018, 1819 https://doi.org/10.1093/nar/gky762
  29. Alexey Rozov, Philippe Wolff, Henri Grosjean, Marat Yusupov, Gulnara Yusupova, Eric Westhof. Tautomeric G•U pairs within the molecular ribosomal grip and fidelity of decoding in bacteria. Nucleic Acids Research 2018, 46 (14) , 7425-7435. https://doi.org/10.1093/nar/gky547
  30. Norio Kitadai, Shigenori Maruyama. Origins of building blocks of life: A review. Geoscience Frontiers 2018, 9 (4) , 1117-1153. https://doi.org/10.1016/j.gsf.2017.07.007
  31. Aleksandar Spasic, Kyle D Berger, Jonathan L Chen, Matthew G Seetin, Douglas H Turner, David H Mathews. Improving RNA nearest neighbor parameters for helices by going beyond the two-state model. Nucleic Acids Research 2018, 46 (10) , 4883-4892. https://doi.org/10.1093/nar/gky270
  32. Aleksandar Spasic, Scott D. Kennedy, Laura Needham, Muthiah Manoharan, Ryszard Kierzek, Douglas H. Turner, David H. Mathews. Molecular dynamics correctly models the unusual major conformation of the GAGU RNA internal loop and with NMR reveals an unusual minor conformation. RNA 2018, 24 (5) , 656-672. https://doi.org/10.1261/rna.064527.117
  33. Matthew J. Payea, Michael F. Sloma, Yoshiko Kon, David L. Young, Michael P. Guy, Xiaoju Zhang, Thareendra De Zoysa, Stanley Fields, David H. Mathews, Eric M. Phizicky. Widespread temperature sensitivity and tRNA decay due to mutations in a yeast tRNA. RNA 2018, 24 (3) , 410-422. https://doi.org/10.1261/rna.064642.117
  34. Marek C. Milewski, Karol Kamel, Anna Kurzynska-Kokorniak, Marcin K. Chmielewski, Marek Figlerowicz. EvOligo: A Novel Software to Design and Group Libraries of Oligonucleotides Applicable for Nucleic Acid-Based Experiments. Journal of Computational Biology 2017, 24 (10) , 1014-1028. https://doi.org/10.1089/cmb.2016.0154
  35. Louis G. Smith, Jianbo Zhao, David H. Mathews, Douglas H. Turner. Physics-based all-atom modeling of RNA energetics and structure. Wiley Interdisciplinary Reviews: RNA 2017, 8 (5) , e1422. https://doi.org/10.1002/wrna.1422
  36. Jeffrey Zuber, Hongying Sun, Xiaoju Zhang, Iain McFadyen, David H. Mathews. A sensitivity analysis of RNA folding nearest neighbor parameters identifies a subset of free energy parameters with the greatest impact on RNA secondary structure prediction. Nucleic Acids Research 2017, 45 (10) , 6168-6176. https://doi.org/10.1093/nar/gkx170
  37. Andy Phan, Katherine Mailey, Jessica Saeki, Xiaobo Gu, Susan J. Schroeder. Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops. RNA 2017, 23 (5) , 770-781. https://doi.org/10.1261/rna.059865.116
  38. Claire V. Crowther, Laura E. Jones, Jessica N. Morelli, Eric M. Mastrogiacomo, Claire Porterfield, Jessica L. Kent, Martin J. Serra. Influence of two bulge loops on the stability of RNA duplexes. RNA 2017, 23 (2) , 217-228. https://doi.org/10.1261/rna.056168.116
  39. Oleksandr Plashkevych, Qing Li, Jyoti Chattopadhyaya. How RNase HI (Escherichia coli) promoted site-selective hydrolysis works on RNA in duplex with carba-LNA and LNA substituted antisense strands in an antisense strategy context?. Molecular BioSystems 2017, 13 (5) , 921-938. https://doi.org/10.1039/C6MB00762G
  40. Henri Grosjean, Eric Westhof. An integrated, structure- and energy-based view of the genetic code. Nucleic Acids Research 2016, 44 (17) , 8020-8040. https://doi.org/10.1093/nar/gkw608
  41. Fang-Chieh Chou, Wipapat Kladwang, Kalli Kappel, Rhiju Das. Blind tests of RNA nearest-neighbor energy prediction. Proceedings of the National Academy of Sciences 2016, 113 (30) , 8430-8435. https://doi.org/10.1073/pnas.1523335113
  42. Tian Jiang, Aitor Nogales, Steven F Baker, Luis Martinez-Sobrido, Douglas H Turner, . Mutations Designed by Ensemble Defect to Misfold Conserved RNA Structures of Influenza A Segments 7 and 8 Affect Splicing and Attenuate Viral Replication in Cell Culture. PLOS ONE 2016, 11 (6) , e0156906. https://doi.org/10.1371/journal.pone.0156906
  43. Nandini Manickam, Kartikeya Joshi, Monika J. Bhatt, Philip J. Farabaugh. Effects of tRNA modification on translational accuracy depend on intrinsic codon–anticodon strength. Nucleic Acids Research 2016, 44 (4) , 1871-1881. https://doi.org/10.1093/nar/gkv1506
  44. Ivan Guerra, Susan J. Schroeder. Crumple: An Efficient Tool to Explore Thoroughly the RNA Folding Landscape. 2016,,, 1-14. https://doi.org/10.1007/978-1-4939-6433-8_1
  45. Zhensheng Zhong, Lai Huat Soh, Ming Hui Lim, Gang Chen. A U⋅U Pair-to-U⋅C Pair Mutation-Induced RNA Native Structure Destabilisation and Stretching-Force-Induced RNA Misfolding. ChemPlusChem 2015, 80 (8) , 1267-1278. https://doi.org/10.1002/cplu.201500144
  46. Ryszard Kierzek, Douglas H. Turner, Elzbieta Kierzek. Microarrays for identifying binding sites and probing structure of RNAs. Nucleic Acids Research 2015, 43 (1) , 1-12. https://doi.org/10.1093/nar/gku1303
  47. Ouala Abdelhadi Ep Souki, Luke Day, Andreas A. Albrecht, Kathleen Steinhöfel. MicroRNA Target Prediction Based Upon Metastable RNA Secondary Structures. 2015,,, 456-467. https://doi.org/10.1007/978-3-319-16480-9_45
  48. J. Craig Blain, Jack W. Szostak. Progress Toward Synthetic Cells. Annual Review of Biochemistry 2014, 83 (1) , 615-640. https://doi.org/10.1146/annurev-biochem-080411-124036
  49. Elzbieta Kierzek, Magdalena Malgowska, Jolanta Lisowiec, Douglas H. Turner, Zofia Gdaniec, Ryszard Kierzek. The contribution of pseudouridine to stabilities and structure of RNAs. Nucleic Acids Research 2014, 42 (5) , 3492-3501. https://doi.org/10.1093/nar/gkt1330
  50. Luke Day, Ouala Abdelhadi Ep Souki, Andreas A. Albrecht, Kathleen Steinhöfel. Accessibility of microRNA binding sites in metastable RNA secondary structures in the presence of SNPs. Bioinformatics 2014, 30 (3) , 343-352. https://doi.org/10.1093/bioinformatics/btt695
  51. Walter N Moss, Nara Lee, Genaro Pimienta, Joan A Steitz. RNA families in Epstein–Barr virus. RNA Biology 2014, 11 (1) , 10-17. https://doi.org/10.4161/rna.27488
  52. J. K. Grohman, R. J. Gorelick, C. R. Lickwar, J. D. Lieb, B. D. Bower, B. M. Znosko, K. M. Weeks. A Guanosine-Centric Mechanism for RNA Chaperone Function. Science 2013, 340 (6129) , 190-195. https://doi.org/10.1126/science.1230715
  53. Kathryn D. Mouzakis, Andrew L. Lang, Kirk A. Vander Meulen, Preston D. Easterday, Samuel E. Butcher. HIV-1 frameshift efficiency is primarily determined by the stability of base pairs positioned at the mRNA entrance channel of the ribosome. Nucleic Acids Research 2013, 41 (3) , 1901-1913. https://doi.org/10.1093/nar/gks1254
  54. Walter N. Moss, Lumbini I. Dela-Moss, Elzbieta Kierzek, Ryszard Kierzek, Salvatore F. Priore, Douglas H. Turner, . The 3′ Splice Site of Influenza A Segment 7 mRNA Can Exist in Two Conformations: A Pseudoknot and a Hairpin. PLoS ONE 2012, 7 (6) , e38323. https://doi.org/10.1371/journal.pone.0038323
  • Abstract

    Figure 1

    Figure 1. 1D 1H imino spectra of r(CCGAAUUUGG)2 from 0 to 45 °C.

    Figure 2

    Figure 2. 1D imino NMR spectra for some RNA duplexes with GU pairs. Spectra were acquired at 0 °C for r(AGGCUU)2 and r(AUGCGU)2; 1 °C for r(AGUCGAUU)2; 5 °C for others.

  • References

    ARTICLE SECTIONS
    Jump To

    This article references 117 other publications.

    1. 1
      Mathews, D. H., Moss, W. N., and Turner, D. H. (2010) Folding and finding RNA secondary structure, in RNA Worlds: From Life’s Origins to Diversity in Gene Regulation (Atkins, J. F., Gesteland, R. F., and Cech, T. R., Eds.) 4th ed., pp 293 308, Cold Spring Harbor Laboratory Press, Cold Spring Harbor.
    2. 2
      Turner, D. H., Sugimoto, N., and Freier, S. M. (1988) RNA structure prediction Annu. Rev. Biophys. Biophys. Biochem. 17, 167 192
    3. 3
      Tinoco, I. and Bustamante, C. (1999) How RNA folds J. Mol. Biol. 293, 271 281
    4. 4
      Andronescu, M., Aguirre-Hernández, R., Condon, A., and Hoos, H. H. (2003) RNAsoft: a suite of RNA secondary structure prediction and design software tools Nucleic Acids Res. 31, 3416 3422
    5. 5
      Hofacker, I. L., Fontana, W., Stadler, P. F., Bonhoeffer, L. S., Tacker, M., and Schuster, P. (1994) Fast folding and comparison of RNA secondary structures Monatsh. Chem. 125, 167 188
    6. 6
      Lück, R., Gräf, S., and Steger, G. (1999) ConStruct: a tool for thermodynamic controlled prediction of conserved secondary structure Nucleic Acids Res. 27, 4208 4217
    7. 7
      Mathews, D. H. and Turner, D. H. (2002) Dynalign: An algorithm for finding the secondary structure common to two RNA sequences J. Mol. Biol. 317, 191 203
    8. 8
      Mathews, D. H., Disney, M. D., Childs, J. L., Schroeder, S. J., Zuker, M., and Turner, D. H. (2004) Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure Proc. Natl. Acad. Sci. U.S.A. 101, 7287 7292
    9. 9
      Mathews, D. H. (2004) Using an RNA secondary structure partition function to determine confidence in base pairs predicted by free energy minimization RNA 10, 1178 1190
    10. 10
      Borer, P. N., Dengler, B., Tinoco, I., and Uhlenbeck, O. C. (1974) Stability of ribonucleic acid double-stranded helices J. Mol. Biol. 86, 843 853
    11. 11
      Xia, T. B., SantaLucia, J., Burkard, M. E., Kierzek, R., Schroeder, S. J., Jiao, X. Q., Cox, C., and Turner, D. H. (1998) Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs Biochemistry 37, 14719 14735
    12. 12
      Mathews, D. H., Sabina, J., Zuker, M., and Turner, D. H. (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure J. Mol. Biol. 288, 911 940
    13. 13
      Turner, D. H. (2000) Conformational changes, in Nucleic Acids: Structures, Properties, and Functions (Bloomfield, V. A., Crothers, D. M., and Tinoco, J., I., Eds.) pp 259 334, University Science Books, Herndon, VA.
    14. 14
      Washietl, S., Hofacker, I. L., and Stadler, P. F. (2005) Fast and reliable prediction of noncoding RNAs Proc. Natl. Acad. Sci. U.S.A. 102, 2454 2459
    15. 15
      Uzilov, A., Keegan, J., and Mathews, D. H. (2006) Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change BMC Bioinformatics 7, 173
    16. 16
      Gruber, A. R., Neuböck, R., Hofacker, I. L., and Washietl, S. (2007) The RNAz web server: prediction of thermodynamically stable and evolutionarily conserved RNA structures Nucleic Acids Res. 35, W335 W338
    17. 17
      Reiche, K. and Stadler, P. F. (2007) RNAstrand: reading direction of structured RNAs in multiple sequence alignments Algorithm. Mol. Biol. 2, 6
    18. 18
      White, S. A., Nilges, M., Huang, A., Brunger, A. T., and Moore, P. B. (1992) NMR analysis of helix-I from the 5S RNA of Escherichia coli Biochemistry 31, 1610 1621
    19. 19
      Szymański, M., Barciszewska, M. Z., Erdmann, V. A., and Barciszewski, J. (2000) An analysis of G-U base pair occurrence in eukaryotic 5S rRNAs Mol. Biol. Evol. 17, 1194 1198
    20. 20
      Sprinzl, M. and Vassilenko, K. S. (2005) Compilation of tRNA sequences and sequences of tRNA genes Nucleic Acids Res. 33, D139 D140
    21. 21
      Limmer, S., Reif, B., Ott, G., Arnold, L., and Sprinzl, M. (1996) NMR evidence for helix geometry modifications by a G-U wobble base pair in the acceptor arm of E-coli tRNA(Ala) FEBS Lett. 385, 15 20
    22. 22
      Hou, Y. M. and Schimmel, P. (1988) A simple structural feature is a major determinant of the identity of a transfer RNA Nature 333, 140 145
    23. 23
      McClain, W. H. and Foss, K. (1988) Changing the identity of a transfer RNA by introducing a G-U wobble pair near the 3′ acceptor end Science 240, 793 796
    24. 24
      Mueller, U., Schubel, H., Sprinzl, M., and Heinemann, U. (1999) Crystal structure of acceptor stem of tRNA(Ala) from Escherichia coli shows unique G·U wobble base pair at 1.16 angstrom resolution RNA 5, 670 677
    25. 25
      White, S. A. and Li, H. (1996) Yeast ribosomal protein L32 recognizes an RNA G:U juxtaposition RNA 2, 226 234
    26. 26
      Reyes, J. L., Gustafson, E. H., Luo, H. R., Moore, M. J., and Konarska, M. M. (1999) The C-terminal region of hPrp8 interacts with the conserved GU dinucleotide at the 5′ splice site RNA 5, 167 179
    27. 27
      Leung, S. S. and Koslowsky, D. J. (2001) Interactions of mRNAs and gRNAs involved in trypanosome mitochondrial RNA editing: structure probing of an mRNA bound to its cognate gRNA RNA 7, 1803 1816
    28. 28
      Mooers, B. H. M. and Singh, A. (2011) The crystal structure of an oligo(U):pre-mRNA duplex from a trypanosome RNA editing substrate RNA 17, 1870 1883
    29. 29
      Lu, K., Heng, X., Garyu, L., Monti, S., Garcia, E. L., Kharytonchyk, S., Dorjsuren, B., Kulandaivel, G., Jones, S., Hiremath, A., Divakaruni, S. S., LaCotti, C., Barton, S., Tummillo, D., Hosic, A., Edme, K., Albrecht, S., Telesnitsky, A., and Summers, M. F. (2011) NMR detection of structures in the HIV-1 5′-leader RNA that regulate genome packaging Science 334, 242 245
    30. 30
      Knitt, D. S., Narlikar, G. J., and Herschlag, D. (1994) Dissection of the role of the conserved G·U pair in group I RNA self-splicing Biochemistry 33, 13864 13879
    31. 31
      Pyle, A. M., Moran, S., Strobel, S. A., Chapman, T., Turner, D. H., and Cech, T. R. (1994) Replacement of the conserved G·U with a G-C pair at the cleavage site of the tetrahymena ribozyme decreases binding, reactivity, and fidelity Biochemistry 33, 13856 13863
    32. 32
      Strobel, S. A. and Cech, T. R. (1995) Minor groove recognition of the conserved G·U pair at the tetrahymena ribozyme reaction site Science 267, 675 679
    33. 33
      Strobel, S. A. and Cech, T. R. (1996) Exocyclic amine of the conserved G·U pair at the cleavage site of the Tetrahymena ribozyme contributes to 5′-splice site selection and transition state stabilization Biochemistry 35, 1201 1211
    34. 34
      Šponer, J., Šponer, J. E., Petrov, A. I., and Leontis, N. B. (2010) Quantum chemical studies of nucleic acids: Can we construct a bridge to the RNA structural biology and bioinformatics communities? J. Phys. Chem. B 114, 15723 15741
    35. 35
      Chen, J.-H., Gong, B., Bevilacqua, P. C., Carey, P. R., and Golden, B. L. (2009) A catalytic metal ion interacts with the cleavage site G·U wobble in the HDV ribozyme Biochemistry 48, 1498 1507
    36. 36
      Chen, J.-H., Yajima, R., Chadalavada, D. M., Chase, E., Bevilacqua, P. C., and Golden, B. L. (2010) A 1.9 Å crystal structure of the HDV ribozyme precleavage suggests both Lewis acid and general acid mechanisms contribute to phosphodiester cleavage Biochemistry 49, 6508 6518
    37. 37
      Keel, A. Y., Rambo, R. P., Batey, R. T., and Kieft, J. S. (2007) A general strategy to solve the phase problem in RNA crystallography Structure 15, 761 772
    38. 38
      Kieft, J. S. and Tinoco, I. (1997) Solution structure of a metal-binding site in the major groove of RNA complexed with cobalt (III) hexammine Structure 5, 713 721
    39. 39
      Wang, W. M., Zhao, J. W., Han, Q. W., Wang, G., Yang, G. C., Shallop, A. J., Liu, J., Gaffney, B. L., and Jones, R. A. (2009) Modulation of RNA metal binding by flanking bases: N-15 NMR evaluation of GC, tandem GU, and tandem GA sites Nucleosides Nucleotides Nucleic Acids 28, 424 434
    40. 40
      Colmenarejo, G. and Tinoco, I., Jr. (1999) Structure and thermodynamics of metal binding in the P5 helix of a group I intron ribozyme J. Mol. Biol. 290, 119 135
    41. 41
      Gautheret, D., Konings, D., and Gutell, R. R. (1995) G·U base pairing motifs in ribosomal RNA RNA 1, 807 814
    42. 42
      Gray, D. M. (1997) Derivation of nearest-neighbor properties from data on nucleic acid oligomers. 1. Simple sets of independent sequences and the influence of absent nearest neighbors Biopolymers 42, 783 793
    43. 43
      Nguyen, M.-T. and Schroeder, S. J. (2010) Consecutive terminal GU pairs stabilize RNA helices Biochemistry 49, 10574 10581
    44. 44
      Serra, M. J., Smolter, P. E., and Westhof, E. (2004) Pronouced instability of tandem IU base pairs in RNA Nucleic Acids Res. 32, 1824 1828
    45. 45
      Fukada, H. and Takahashi, K. (1998) Enthalpy and heat capacity changes for the proton dissociation of various buffer components in 0.1 M potassium chloride Proteins 33, 159 166
    46. 46
      Smallcombe, S. H. (1993) Solvent suppression with symmetrically-shifted pulses J. Am. Chem. Soc. 115, 4776 4785
    47. 47
      Grzesiek, S. and Bax, A. (1993) The importance of not saturating H2O in protein NMR - application to sensitivity enhancement and NOE measurements J. Am. Chem. Soc. 115, 12593 12594
    48. 48
      Piotto, M., Saudek, V., and Sklenář, V. (1992) Gradient-tailored excitation for single-quantum NMR spectroscopy of aqueous solutions J. Biomol. NMR 2, 661 665
    49. 49
      Delaglio, F., Grzesiek, S., Vuister, G. W., Zhu, G., Pfeifer, J., and Bax, A. (1995) NMRPipe: A multidimensional spectral processing system based on UNIX pipes J. Biomol. NMR 6, 277 293
    50. 50
      Goddard, T. D. and Kneller, D. G. (2004) SPARKY, NMR Assignment and Integration Software, 3rd ed., University of California, San Francisco.
    51. 51
      Cavanagh, J., Fairbrother, W. J., Palmer, A. G. I., and Skelton, N. J. (1996) Protein NMR Spectroscopy: Principles and Practice, Academic Press, San Diego.
    52. 52
      McDowell, J. A. and Turner, D. H. (1996) Investigation of the structural basis for thermodynamic stabilities of tandem GU mismatches: Solution structure of (rGAGGUCUC)2 by two-dimensional NMR and simulated annealing Biochemistry 35, 14077 14089
    53. 53
      R Development Core Team (2010) R: A Language and Environment for Statistical Computing, x64 2.11.1 ed., R Foundation for Statistical Computing, Vienna, Austria.
    54. 54
      Wolfram Research (2010) Mathematica Edition: Version 8.0, Champaign, Illinois.
    55. 55
      Eaton, J. W. (2002) GNU Octave Manual.
    56. 56
      Cantor, C. R. and Schimmel, P. R. (1980) Biophysical Chemistry, Part III: The Behavior of Biological Macromolecules, pp. 1197 1198, W. H. Freeman and Company, San Francisco.
    57. 57
      Bevington, P. R. and Robinson, D. K. (2002) Data Reduction and Error Analysis for the Physical Sciences, 3rd ed., McGraw-Hill, New York.
    58. 58
      Drosg, M. (2007) Dealing with Uncertainties: A Guide to Error Analysis, Springer-Verlag, Heidelberg.
    59. 59
      Crawley, M. J. (2007) The R Book, 1st ed., John Wiley & Sons, West Sussex.
    60. 60
      Kinney, J. J. (2002) Statistics for Science and Engineering, 1st ed., Addison-Wesley, Boston.
    61. 61
      Devore, J. and Peck, R. (2005) Statistics: the Exploration and Analysis of Data, 5th ed., Brooks/Cole - Thomson Learning, Belmont, CA.
    62. 62
      Varani, G., Aboulela, F., and Allain, F. H. T. (1996) NMR investigation of RNA structure Prog. Nucl. Mag. Res. Spectrosc. 29, 51 127
    63. 63
      Chaires, J. B. (1997) Possible origin of differences between van’t Hoff and calorimetric enthalpy estimates Biophys. Chem. 64, 15 23
    64. 64
      Mergny, J.-L. and Lacroix, L. (2003) Analysis of thermal melting curves Oligonucleotides 13, 515 537
    65. 65
      SantaLucia, J. and Turner, D. H. (1997) Measuring the thermodynamics of RNA secondary structure formation Biopolymers 44, 309 319
    66. 66
      Fürtig, B., Richter, C., Wohnert, J., and Schwalbe, H. (2003) NMR spectroscopy of RNA ChemBioChem 4, 936 962
    67. 67
      Reid, B. R., McCollumn, L., Ribeiro, N. S., Abbate, J., and Hurd, R. E. (1979) Identification of tertiary base pair resonances in the nuclear magnetic resonance spectra of transfer ribonucleic acid Biochemistry 18, 3996 4005
    68. 68
      Johnston, P. D. and Redfield, A. G. (1981) Nuclear magnetic resonance and nuclear Overhauser effect study of yeast phenylalanine transfer ribonucleic acid imino protons Biochemistry 20, 1147 1156
    69. 69
      Cockerill, M. (1993) Not much to malign - Multalin 4.0 Trends Biochem. Sci. 18, 106 107
    70. 70
      Deigan, K. E., Li, T. W., Mathews, D. H., and Weeks, K. M. (2009) Accurate SHAPE-directed RNA structure determination Proc. Natl. Acad. Sci. U.S.A. 106, 97 102
    71. 71
      Hart, J. M., Kennedy, S. D., Mathews, D. H., and Turner, D. H. (2008) NMR-assisted prediction of RNA secondary structure: Identification of a probable pseudoknot in the coding region of an R2 Retrotransposon J. Am. Chem. Soc. 130, 10233 10239
    72. 72
      Batey, R. T., Rambo, R. P., and Doudna, J. A. (1999) Tertiary motifs in RNA structure and folding Angew. Chem., Int. Ed. 38, 2327 2343
    73. 73
      Varani, G. and McClain, W. H. (2000) The G·U wobble base pair: a fundamental building block of RNA structure crucial to RNA function in diverse biological systems EMBO Rep. 1, 18 23
    74. 74
      Allawi, H. T. and SantaLucia, J. (1997) Thermodynamics and NMR of internal G·T mismatches in DNA Biochemistry 36, 10581 10594
    75. 75
      Cate, J. H. and Doudna, J. A. (1996) Metal-binding sites in the major groove of a large ribozyme domain Structure 4, 1221 1229
    76. 76
      Konforti, B. B., Abramovitz, D. L., Duarte, C. M., Karpeisky, A., Beigelman, L., and Pyle, A. M. (1998) Ribozyme catalysis from the major groove of group II intron domain 5 Mol. Cell 1, 433 441
    77. 77
      Adams, P. L., Stahley, M. R., Kosek, A. B., Wang, J., and Strobel, S. A. (2004) Crystal structure of a self-splicing group I intron with both exons Nature 430, 45 50
    78. 78
      Forconi, M., Sengupta, R. N., Piccirilli, J. A., and Herschlag, D. (2010) A rearrangement of the guanosine-binding site establishes an extended network of functional interactions in the tetrahymena group I ribozyme active site Biochemistry 49, 2753 2762
    79. 79
      Lipchock, S. V. and Strobel, S. A. (2008) A relaxed active site after exon ligation by the group I intron Proc. Natl. Acad. Sci. U.S.A 105, 5699 5704
    80. 80
      Stahley, M. R., Adams, P. L., Wang, J., and Strobel, S. A. (2007) Structural metals in the group I intron: A ribozyme with a multiple metal ion core J. Mol. Biol. 372, 89 102
    81. 81
      Strobel, S. A. and Ortoleva-Donnelly, L. (1999) A hydrogen-bonding triad stabilizes the chemical transition state of a group I ribozyme Chem. Biol. 6, 153 165
    82. 82
      Toor, N., Keating, K. S., Taylor, S. D., and Pyle, A. M. (2008) Crystal structure of a self-spliced group II intron Science 320, 77 82
    83. 83
      Xu, D., Landon, T., Greenbaum, N. L., and Fenley, M. O. (2007) The electrostatic characteristics of G·U wobble base pairs Nucleic Acids Res. 35, 3836 3847
    84. 84
      Chen, G., Znosko, B. M., Jiao, X. Q., and Turner, D. H. (2004) Factors affecting thermodynamic stabilities of RNA 3 × 3 internal loops Biochemistry 43, 12865 12876
    85. 85
      Serra, M. J., Baird, J. D., Dale, T., Fey, B. L., Retatagos, K., and Westhof, E. (2002) Effects of magnesium ions on the stabilization of RNA oligomers of defined structures RNA 8, 307 323
    86. 86
      Walter, A. E., Wu, M., and Turner, D. H. (1994) The stability and structure of tandem GA mismatches in RNA depend on closing base-pairs Biochemistry 33, 11349 11354
    87. 87
      Freier, S. M., Burger, B. J., Alkema, D., Neilson, T., and Turner, D. H. (1983) Effects of 3′ dangling end stacking on the stability of GGCC and CCGG double helixes Biochemistry 22, 6198 6206
    88. 88
      Freier, S. M., Alkema, D., Sinclair, A., Neilson, T., and Turner, D. H. (1985) Contributions of dangling end stacking and terminal base-pair formation to the stabilities of XGGCCp, XCCGGp, XGGCCYp, and XCCGGYp helixes Biochemistry 24, 4533 4539
    89. 89
      Crick, F. H. C. (1966) Codon-anticodon pairing: the wobble hypothesis J. Mol. Biol. 19, 548 555
    90. 90
      Chen, X. Y., McDowell, J. A., Kierzek, R., Krugh, T. R., and Turner, D. H. (2000) Nuclear magnetic resonance spectroscopy and molecular modeling reveal that different hydrogen bonding patterns are possible for G·U pairs: One hydrogen bond for each G·U pair in r(GGCGUGCC)2 and two for each G·U pair in r(GAGUGCUC)2 Biochemistry 39, 8970 8982
    91. 91
      Pan, Y. P., Priyakumar, U. D., and MacKerell, A. D. (2005) Conformational determinants of tandem GU mismatches in RNA: Insights from molecular dynamics simulations and quantum mechanical calculations Biochemistry 44, 1433 1443
    92. 92
      Biswas, R., Wahl, M. C., Ban, C., and Sundaralingam, M. (1997) Crystal structure of an alternating octamer r(GUAUGUA)dC with adjacent G·U wobble pairs J. Mol. Biol. 267, 1149 1156
    93. 93
      Utsunomiya, R., Suto, K., Balasundaresan, D., Fukamizu, A., Kumar, P. K. R., and Mizuno, H. (2006) Structure of an RNA duplex r(GGCG(Br)UGCGCU)2 with terminal and internal tandem G·U base pairs Acta Crystallogr. D 62, 331 338
    94. 94
      Biswas, R. and Sundaralingam, M. (1997) Crystal structure of r(GUGUGUA)dC with tandem G·U/U·G wobble pairs with strand slippage J. Mol. Biol. 270, 511 519
    95. 95
      McDowell, J. A., He, L. Y., Chen, X. Y., and Turner, D. H. (1997) Investigation of the structural basis for thermodynamic stabilities of tandem GU wobble pairs: NMR structures of (rGGAGUUCC)2 and (rGGAUGUCC)2 Biochemistry 36, 8030 8038
    96. 96
      Masquida, B. and Westhof, E. (2000) On the wobble G·U and related pairs RNA 6, 9 15
    97. 97
      Jang, S. B., Hung, L. W., Jeong, M. S., Holbrook, E. L., Chen, X. Y., Turner, D. H., and Holbrook, S. R. (2006) The crystal structure at 1.5 Å resolution of an RNA octamer duplex containing tandem G·U basepairs Biophys. J. 90, 4530 4537
    98. 98
      Deng, J. P. and Sundaralingam, M. (2000) Synthesis and crystal structure of an octamer RNA r(guguuuac)/r(guaggcac) with G·G/U·U tandem wobble base pairs: comparison with other tandem G·U pairs Nucleic Acids Res. 28, 4376 4381
    99. 99
      Shi, K., Wahl, M. C., and Sundaralingam, M. (1999) Crystal structure of an RNA duplex r(GGGCGCUCC)2 with non-adjacent G·U base pairs Nucleic Acids Res. 27, 2196 2201
    100. 100
      Alvarez-Salgado, F., Desvaux, H., and Boulard, Y. (2006) NMR assessment of the global shape of a non-labelled DNA dodecamer containing a tandem of G·T mismatches Magn. Reson. Chem. 44, 1081 1089
    101. 101
      Sugimoto, N., Kierzek, R., Freier, S. M., and Turner, D. H. (1986) Energetics of internal GU mismatches in ribooligonucleotide helixes Biochemistry 25, 5755 5759
    102. 102
      Freier, S. M., Kierzek, R., Caruthers, M. H., Neilson, T., and Turner, D. H. (1986) Free energy contributions of G·U and other terminal mismatches to helix stability Biochemistry 25, 3209 3213
    103. 103
      Testa, S. M., Disney, M. D., Turner, D. H., and Kierzek, R. (1999) Thermodynamics of RNA-RNA duplexes with 2-or 4-thiouridines: Implications for antisense design and targeting a group I intron Biochemistry 38, 16655 16662
    104. 104
      He, L., Kierzek, R., SantaLucia, J., Walter, A. E., and Turner, D. H. (1991) Nearest-neighbor parameters for G·U mismatches - 5′GU3′/3′UG5′ is destabilizing in the contexts CGUG/GUGC, UGUA/AUGU, and AGUU/UUGU but stabilizing in GGUC/CUGG Biochemistry 30, 11124 11132
    105. 105
      Xia, T. B., McDowell, J. A., and Turner, D. H. (1997) Thermodynamics of nonsymmetric tandem mismatches adjacent to G·C base pairs in RNA Biochemistry 36, 12486 12497
    106. 106
      Sugimoto, N., Kierzek, R., and Turner, D. H. (1987) Sequence dependence for the energetics of terminal mismatches in ribonucleic acid Biochemistry 26, 4559 4562
    107. 107
      Ziomek, K., Kierzek, E., Biala, E., and Kierzek, R. (2002) The thermal stability of RNA duplexes containing modified base pairs placed at internal and terminal positions of the oligoribonucleotides Biophys. Chem. 97, 233 241
    108. 108
      Schroeder, S. J. and Turner, D. H. (2001) Thermodynamic stabilities of internal loops with GU closing pairs in RNA Biochemistry 40, 11509 11517
    109. 109
      Schroeder, S. J. and Turner, D. H. (2000) Factors affecting the thermodynamic stability of small asymmetric internal loops in RNA Biochemistry 39, 9257 9274
    110. 110
      Freier, S. M., Sinclair, A., Neilson, T., and Turner, D. H. (1985) Improved free energies for G·C base-pairs J. Mol. Biol. 185, 645 647
    111. 111
      Freier, S. M., Kierzek, R., Jaeger, J. A., Sugimoto, N., Caruthers, M. H., Neilson, T., and Turner, D. H. (1986) Improved free-energy parameters for predictions of RNA duplex stability Proc. Natl. Acad. Sci. U.S.A. 83, 9373 9377
    112. 112
      Freier, S. M., Sugimoto, N., Sinclair, A., Alkema, D., Neilson, T., Kierzek, R., Caruthers, M. H., and Turner, D. H. (1986) Stability of XGCGCp, GCGCYp, and XGCGCYp helixes: an empirical estimate of the energetics of hydrogen bonds in nucleic acids Biochemistry 25, 3214 3219
    113. 113
      Sugimoto, N., Kierzek, R., and Turner, D. H. (1987) Sequence dependence for the energetics of dangling ends and terminal base pairs in ribooligonucleotides Biochemistry 26, 4554 4558
    114. 114
      Burkard, M. E. and Turner, D. H. (2000) NMR structures of r(GCAGGCGUGC)2 and determinants of stability for single guanosine-guanosine base pairs Biochemistry 39, 11748 11762
    115. 115
      Petersheim, M. and Turner, D. H. (1983) Base-stacking and base-pairing contributions to helix stability: thermodynamics of double-helix formation with CCGG, CCGGp, CCGGAp, ACCGGp, CCGGUp, and ACCGGUp Biochemistry 22, 256 263
    116. 116
      Kierzek, R., Caruthers, M. H., Longfellow, C. E., Swinton, D., Turner, D. H., and Freier, S. M. (1986) Polymer-supported RNA synthesis and its application to test the nearest-neighbor model for duplex stability Biochemistry 25, 7840 7846
    117. 117
      Kierzek, R., Burkard, M. E., and Turner, D. H. (1999) Thermodynamics of single mismatches in RNA duplexes Biochemistry 38, 14214 14223
  • Supporting Information

    Supporting Information

    ARTICLE SECTIONS
    Jump To

    (I) Thermodynamic parameters for duplex formation of Watson–Crick sequences. (II) Experimental thermodynamic parameters and error limits for newly measured sequences. (III) Component free energies and enthalpies of GU pairs. (IV) Free energies of doublets and triplets containing GU pairs calculated as component ΔG°37 of their sequences. (V) Free energy differences between sequences where GC pair(s) were replaced by AU pair(s). (VI) INN parameters for GU pairs calculated with a separate term for terminal GU pairs. (VII) Probability density function of the Student’s t-distribution for each INN motif with a separate parameter for terminal GU pairs. (VIII) 2D NOESY spectra for r(AGGCUU)2 showing H2′, H1′, and H6/H8 regions. (IX) 2D NOESY spectra for r(AUGCGU)2 showing H2′, H1′, and H6/H8 regions. (X) Desalting procedure for oligoribonucleotides. This material is available free of charge via the Internet at http://pubs.acs.org.


    Terms & Conditions

    Electronic Supporting Information files are available without a subscription to ACS Web Editions. The American Chemical Society holds a copyright ownership interest in any copyrightable Supporting Information. Files available from the ACS website may be downloaded for personal use only. Users are not otherwise permitted to reproduce, republish, redistribute, or sell any Supporting Information from the ACS website, either in whole or in part, in either machine-readable form or any other form without permission from the American Chemical Society. For permission to reproduce, republish and redistribute this material, requesters must process their own requests via the RightsLink permission system. Information about how to use the RightsLink permission system can be found at http://pubs.acs.org/page/copyright/permissions.html.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

OOPS

You have to login with your ACS ID befor you can login with your Mendeley account.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect

This website uses cookies to improve your user experience. By continuing to use the site, you are accepting our use of cookies. Read the ACS privacy policy.

CONTINUE