Performance of SVM models for different domain binding peptides (6-mer peptides for SH3 and WW and 4-mer peptides for PDZ) on respective unbalanced datasets.

<p>Performance of SVM models for different domain binding peptides (6-mer peptides for SH3 and WW and 4-mer peptides for PDZ) on respective unbalanced datasets.</p

Comparison of sensitivity shown by different prediction methods on the independent datasets.

<p>Comparison of sensitivity shown by different prediction methods on the independent datasets.</p

LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains - Fig 2

<p><b>ROC plots showing prediction performances of the different methods (Green-SVM, Blue-PSSM, Red- Regular Expression Scanning (RES), Black- Motif Instance Matching (MIM)) for (A) SH3 domain ligands, (B) WW domain ligands, and (C) PDZ domain ligands.</b> The respective AUC values are mentioned in the corresponding textboxes.</p

A network of HBX-human protein interactions predicted by our proposed method.

<p>The network visualized by Cytoscape 3.0.2 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112034#pone.0112034-Smoot1" target="_blank">[35]</a>. The HBX protein is represented by cyan node. The significant gene ontology enriched human proteins are representing by salmon node, whereas other human proteins are representing by slate grey node.</p

LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains

<div><p>Protein-peptide interactions form an important subset of the total protein interaction network in the cell and play key roles in signaling and regulatory networks, and in major biological processes like cellular localization, protein degradation, and immune response. In this work, we have described the LMDIPred web server, an online resource for generalized prediction of linear peptide sequences that may bind to three most prevalent and well-studied peptide recognition modules (PRMs)—SH3, WW and PDZ. We have developed support vector machine (SVM)-based prediction models that achieved maximum Matthews Correlation Coefficient (MCC) of 0.85 with an accuracy of 94.55% for SH3, MCC of 0.90 with an accuracy of 95.82% for WW, and MCC of 0.83 with an accuracy of 92.29% for PDZ binding peptides. LMDIPred output combines predictions from these SVM models with predictions using Position-Specific Scoring Matrices (PSSMs) and string-matching methods using known domain-binding motif instances and regular expressions. All of these methods were evaluated using a five-fold cross-validation technique on both balanced and unbalanced datasets, and also validated on independent datasets. LMDIPred aims to provide a preliminary bioinformatics platform for sequence-based prediction of probable binding sites for SH3, WW or PDZ domains.</p></div

SVM based performance on testing dataset (5-fold cross-validation) using parameters t = 2 (RBF kernel), and g = 1, c = 0.1, j = 2.

<p>SVM based performance on testing dataset (5-fold cross-validation) using parameters t = 2 (RBF kernel), and g = 1, c = 0.1, j = 2.</p

Bar graph depicting the average Amino Acid Composition (AAC) of SH3-binding, WW-binding, PDZ-binding and randomly generated peptide sequences.

<p>Bar graph depicting the average Amino Acid Composition (AAC) of SH3-binding, WW-binding, PDZ-binding and randomly generated peptide sequences.</p

Performance of PSSMs for different domain binding peptides (6-mer peptides for SH3 and WW and 4-mer peptides for PDZ) on respective unbalanced datasets.

<p>Performance of PSSMs for different domain binding peptides (6-mer peptides for SH3 and WW and 4-mer peptides for PDZ) on respective unbalanced datasets.</p

Performance of RES method for different domain binding peptide classes on respective unbalanced datasets.

<p>Performance of RES method for different domain binding peptide classes on respective unbalanced datasets.</p

Performance of MIM method for different domain binding peptide classes on respective unbalanced datasets.

<p>Performance of MIM method for different domain binding peptide classes on respective unbalanced datasets.</p