RNA base-amino acid interaction strengths derived from structures and sequences

Lustig, Brooke; Arora, Shalini; Jernigan, Robert L.

doi:10.1093/nar/25.13.2562

Cited by 26 publications

(29 citation statements)

References 32 publications

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“…Other strong biases for different types of AAs present at the RNA-protein interfaces have also been reported in prior studies [16,17,19,23,51,52]. The above motivates the inclusion of the sequence-based features as inputs for prediction of RBRs.…”

Section: Sequence-based Featuressupporting

confidence: 66%

Analysis and Prediction of RNA-Binding Residues Using Sequence, Evolutionary Conservation, and Predicted Secondary Structure and Solvent Accessibility

Zhang

Zhang²,

Chen³

et al. 2010

CPPS

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Identification and prediction of RNA-binding residues (RBRs) provides valuable insights into the mechanisms of protein-RNA interactions. We analyzed the contributions of a wide range of factors including amino acid sequence, evolutionary conservation, secondary structure and solvent accessibility, to the prediction/characterization of RBRs. Five feature sets were designed and feature selection was performed to find and investigate relevant features. We demonstrate that (1) interactions with positively charged amino acids Arg and Lys are preferred by the egatively charged nucleotides; (2) Gly provides flexibility for the RNA binding sites; (3) Glu with negatively charged side chain and several hydrophobic residues such as Leu, Val, Ala and Phe are disfavored in the RNA-binding sites; (4) coil residues, especially in long segments, are more flexible (than other secondary structures) and more likely to interact with RNA; (5) helical residues are more rigid and consequently they are less likely to bind RNA; and (6) residues partially exposed to the solvent are more likely to form RNA-binding sites. We introduce a novel sequence-based predictor of RBRs, RBRpred, which utilizes the selected features. RBRpred is comprehensively tested on three datasets with varied atom distance cutoffs by performing both five-fold cross validation and jackknife tests and achieves Matthew's correlation coefficient (MCC) of 0.51, 0.48 and 0.42, respectively. The quality is comparable to or better than that for state-of-the-art predictors that apply the distancebased cutoff definition. We show that the most important factor for RBRs prediction is evolutionary conservation, followed by the amino acid sequence, predicted secondary structure and predicted solvent accessibility. We also investigate the impact of using native vs. predicted secondary structure and solvent accessibility. The predictions are sufficient for the RBR prediction and the knowledge of the actual solvent accessibility helps in predictions for lower distance cutoffs.

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Section: Sequence-based Featuressupporting

confidence: 66%

Analysis and Prediction of RNA-Binding Residues Using Sequence, Evolutionary Conservation, and Predicted Secondary Structure and Solvent Accessibility

Zhang

Zhang²,

Chen³

et al. 2010

CPPS

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“…Arginine-rich motifs (Weiss and Narayana 1998) are abundant in RNA binding sites, and other strong biases in the types of amino acids present in RNA-protein interfaces have been reported in several previous studies (Lustig et al 1997;Jones et al 2001;Kim et al 2003;Jeong et al 2004;Jeong and Miyano 2006). To evaluate whether these primary sequence biases can be effectively exploited in a machine learning approach to identify amino acid sequence correlates of RNA binding sites, we generated a nonredundant data set of 109 RNA binding proteins (see Materials and Methods) to estimate the interface propensity for each amino acid type as follows:…”

Section: Sequence Characteristics Of Rna Binding Sitesmentioning

confidence: 53%

Prediction of RNA binding sites in proteins from amino acid sequence

Terribilini¹,

Lee²,

Yan³

et al. 2006

RNA

165

196

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RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-oneout cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.)

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“…Guanine bases above and below guanidinium (G72 and G88) are predicted to form cation-π interactions. In protein-RNA interactions, the guanidino group of arginine commonly interacts with RNA bases via cation-π interactions, suggesting that the guanidinium ion alone could use the same mechanism of binding to RNA (Gallivan and Dougherty, 1999; Lustig et al, 1997; Zhang et al, 2014). …”

Section: Discussionmentioning

confidence: 99%

Structural Basis for Ligand Binding to the Guanidine-I Riboswitch

2017

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SUMMARY The guanidine-I riboswitch is a conserved RNA element with approximately 2000 known examples across four phyla of bacteria. It exists upstream of nitrogen metabolism and multidrug resistance transporter genes and alters expression through the specific recognition of free guanidinium cation. Here we report the structure of a guanidine riboswitch aptamer from Sulfobacillus acidophilus at 2.7Å resolution. Helices P1, P1a, P1b, and P2 form a coaxial stack that acts as a scaffold for ligand binding. A previously unidentified P3 helix docks into P1a to form the guanidinium binding pocket, which is completely enclosed. Every functional group of the ligand is recognized through hydrogen bonding to guanine bases and phosphate oxygens. Guanidinium binding is further stabilized through cation-π interactions with guanine bases. This allows the riboswitch to recognize guanidinium while excluding other bacterial metabolites with a guanidino group, including the amino acid arginine.

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RNA base-amino acid interaction strengths derived from structures and sequences

Cited by 26 publications

References 32 publications

Analysis and Prediction of RNA-Binding Residues Using Sequence, Evolutionary Conservation, and Predicted Secondary Structure and Solvent Accessibility

Analysis and Prediction of RNA-Binding Residues Using Sequence, Evolutionary Conservation, and Predicted Secondary Structure and Solvent Accessibility

Prediction of RNA binding sites in proteins from amino acid sequence

Structural Basis for Ligand Binding to the Guanidine-I Riboswitch

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