Using evolutionary and structural information to predict DNA‐binding sites on DNA‐binding proteins

Abstract

Proteins that interact with DNA are involved in a number of fundamental biological activities such as DNA replication, transcription, and repair. A reliable identification of DNA-binding sites in DNA-binding proteins is important for functional annotation, site-directed mutagenesis, and modeling protein-DNA interactions. We apply Support Vector Machine (SVM), a supervised pattern recognition method, to predict DNA-binding sites in DNA-binding proteins using the following features: amino acid sequence, profile of evolutionary conservation of sequence positions, and low-resolution structural information. We use a rigorous statistical approach to study the performance of predictors that utilize different combinations of features and how this performance is affected by structural and sequence properties of proteins. Our results indicate that an SVM predictor based on a properly scaled profile of evolutionary conservation in the form of a position specific scoring matrix (PSSM) significantly outperforms a PSSM-based neural network predictor. The highest accuracy is achieved by SVM predictor that combines the profile of evolutionary conservation with low-resolution structural information. Our results also show that knowledge-based predictors of DNA-binding sites perform significantly better on proteins from mainly-alpha structural class and that the performance of these predictors is significantly correlated with certain structural and sequence properties of proteins. These observations suggest that it may be possible to assign a reliability index to the overall accuracy of the prediction of DNA-binding sites in any given protein using its sequence and structural properties. A web-server implementation of the predictors is freely available online at http://lcg.rit.albany.edu/dp-bind/.

Learning to Translate Sequence and Structure to Function: Identifying DNA Binding and Membrane Binding Proteins

“…26,42,43 In binding site prediction, a support vector machines (SVM) classifier was employed using evolutionary and structural features to predict the binding site of specific structural motifs with 78% accuracy. 28 Likewise, a Naı¨ve Bayes classifier and amino acid sequence have been utilized to achieve an accuracy of 78% with homology information. 47 In our FIGURE 1.…”

Transcriptional activation of endoplasmic reticulum chaperone GRP78 by HCMV IE1-72 protein

“…IE1-72 protein sequence analysis using DP bind, a web server for sequence-based predictions of DNA-binding residues in DNA-binding proteins [29,30], identified at least four Given that exon 4 of IE1-72 and the ERSE of grp78 promoter were found to be required for IE1-72-mediated grp78 promoter activation (Figures 3 and 4), we examined, using the electrophoretic mobility shift assay (EMSA), whether IE1-72 binds directly to the ERSE. As shown in Figure 5A, the His-tagged IE1-72 .…”

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.…”