scholarly journals Nearest-neighbor parameter for inosine-cytosine pairs through a combined experimental and computational approach

2018 ◽  
Author(s):  
Shun Sakuraba ◽  
Junichi Iwakiri ◽  
Michiaki Hamada ◽  
Tomoshi Kameda ◽  
Genichiro Tsuji ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Nearest Neighbor ◽  
Secondary Structure Prediction ◽  
Free Energy Calculations ◽  
Rna Secondary Structure Prediction ◽  
Adsorption Measurement ◽  
The Stability ◽  
The One ◽  
Dynamics Simulations

AbstractIn RNA secondary structure prediction, nearest-neighbor parameters are used to determine the stability of a given structure. We derived the nearest-neighbor parameters for RNAs containing inosine-cytosine pairs. For parameter derivation, we developed a method that combines UV adsorption measurement experiments with free-energy calculations using molecular dynamics simulations. The method provides fast drop-in parameters for modified bases. Derived parameters were compared and found to be consistent with existing parameters for canonical RNAs. A duplex with an internal inosine-cytosine pair is 0.9 kcal/mol more unstable than the same duplex with an internal guanine-cytosine pair, and is as stable as the one with an internal adenine-uracil pair (only 0.1 kcal/mol more stable) on average.

scholarly journals Predictions and analyses of RNA nearest neighbor parameters for modified nucleotides

2020 ◽  
Vol 48 (16) ◽  
pp. 8901-8913
Author(s):  
Melissa C Hopfinger ◽  
Charles C Kirkpatrick ◽  
Brent M Znosko
Keyword(s):  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Nearest Neighbor ◽  
Secondary Structure Prediction ◽  
Modified Nucleotides ◽  
Base Pairs ◽  
Rna Secondary Structure Prediction ◽  
Experimental Parameters ◽  
Melting Experiments ◽  
Insight Into

Abstract The most popular RNA secondary structure prediction programs utilize free energy (ΔG°37) minimization and rely upon thermodynamic parameters from the nearest neighbor (NN) model. Experimental parameters are derived from a series of optical melting experiments; however, acquiring enough melt data to derive accurate NN parameters with modified base pairs is expensive and time consuming. Given the multitude of known natural modifications and the continuing use and development of unnatural nucleotides, experimentally characterizing all modified NNs is impractical. This dilemma necessitates a computational model that can predict NN thermodynamics where experimental data is scarce or absent. Here, we present a combined molecular dynamics/quantum mechanics protocol that accurately predicts experimental NN ΔG°37 parameters for modified nucleotides with neighboring Watson–Crick base pairs. NN predictions for Watson-Crick and modified base pairs yielded an overall RMSD of 0.32 kcal/mol when compared with experimentally derived parameters. NN predictions involving modified bases without experimental parameters (N6-methyladenosine, 2-aminopurineriboside, and 5-methylcytidine) demonstrated promising agreement with available experimental melt data. This procedure not only yields accurate NN ΔG°37 predictions but also quantifies stacking and hydrogen bonding differences between modified NNs and their canonical counterparts, allowing investigators to identify energetic differences and providing insight into sources of (de)stabilization from nucleotide modifications.

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