BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features

Abstract

BackgroundUnderstanding how biomolecules interact is a major task of systems biology. To model protein-nucleic acid interactions, it is important to identify the DNA or RNA-binding residues in proteins. Protein sequence features, including the biochemical property of amino acids and evolutionary information in terms of position-specific scoring matrix (PSSM), have been used for DNA or RNA-binding site prediction. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modelling DNA or RNA-binding sites in protein sequences.ResultsIn the present study, several new descriptors of evolutionary information have been developed and evaluated for sequence-based prediction of DNA and RNA-binding residues using support vector machines (SVMs). The new descriptors were shown to improve classifier performance. Interestingly, the best classifiers were obtained by combining the new descriptors and PSSM, suggesting that they captured different aspects of evolutionary information for DNA and RNA-binding site prediction. The SVM classifiers achieved 77.3% sensitivity and 79.3% specificity for prediction of DNA-binding residues, and 71.6% sensitivity and 78.7% specificity for RNA-binding site prediction.ConclusionsPredictions at this level of accuracy may provide useful information for modelling protein-nucleic acid interactions in systems biology studies. We have thus developed a web-based tool called BindN+ (http://bioinfo.ggc.org/bindn+/) to make the SVM classifiers accessible to the research community.

Molecular Characterization of an rsmD -Like rRNA Methyltransferase from the Wolbachia Endosymbiont of Brugia malayi and Antifilarial Activity of Specific Inhibitors of the Enzyme

“…Two motifs, viz. the SAM binding motif (FXGXG) as well as the catalytic motif (DPPY), are present in the enzyme sequence for proper catalysis of the methylation reaction (24)(25)(26), with the RNA recognition sequence being located in the stretch of 8 to 26 amino acids at the N terminus, as predicted (54). The latter information conforms to the fact that the naked RNA is copurified along with the enzyme, which gets methylated on addition of SAM-3H (data not shown); however, there is still a possibility that it has methylation activity on the assembled 30S subunit from an rsmD mutant strain, and this possibility should be examined.…”

Use of Bioinformatics Tools in Different Spheres of Life Sciences

“…Understanding how biomolecules interact with other molecules holds numerous implications, for example, for protein folding, drug design and purification techniques [75] and therefore has become one of the mostly pursued research area using either experimental or bioinformatics approaches. Understanding of molecular interactions is also essential to elucidate the biological functions of a molecule.…”

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

“…Until now, several groups have published different studies based on either experimental or computational identification of DNA-binding proteins [1,[6][7][8][9][10][11] as well as residues in these proteins [12][13][14][15][16][17][18][19][20][21][22][23]. However, the usage of experimental approaches for the determination of binding sites is still challenging since they are often demanding, relatively expensive, and time-consuming.…”

“…Using these features, several machine learning techniques have been applied to construct the classifiers for the prediction of binding residues in proteins. To this end, a variety of support vector machine (SVM) classifiers have been developed in recent studies [2,[17][18][19]23,26,28]. For example, Westhof et al have recently used an SVM classifier approach in their study, named RBscore (http://ahsoka.u-strasbg.fr/rbscore/), by using the physicochemical and evolutionary features that are linearly combined with a residue neighboring network [2].…”

“…For example, Westhof et al have recently used an SVM classifier approach in their study, named RBscore (http://ahsoka.u-strasbg.fr/rbscore/), by using the physicochemical and evolutionary features that are linearly combined with a residue neighboring network [2]. Further, SVM algorithms were also applied for the models proposed in BindN [18], DISIS [19], BindN+ [23], DP-Bind [27] using different sequence information features including the biochemical property of amino acids, sequence conservation, evolutionary information in terms of PSSM, the side chain pKa value, hydrophobicity index, molecular mass and BLOSUM62 matrix. In addition, other machine learning classifiers such as neural network models [13,15], naive Bayes classifier [26], Random Forest classifiers (RF) [4,29,30] have been developed based on the features derived from protein sequences.…”