Comparison of the success rates and <i>MCC</i> values obtained by the current <b>iNR-PhysChem</b> and <b>NR-2L</b>[16] in identifying the subfamilies of NRs by the jackknife test on the benchmark dataset (cf. <b>Eq. 1</b>).

<p>Comparison of the success rates and <i>MCC</i> values obtained by the current <b>iNR-PhysChem</b> and <b>NR-2L</b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030869#pone.0030869-Wang1" target="_blank">[16]</a> in identifying the subfamilies of NRs by the jackknife test on the benchmark dataset (cf. <b>Eq. 1</b>).</p

Comparison of the success rates and <i>MCC</i> values obtained by the current iNR-PhysChem and NR-2L [16] in identifying NRs and non-NRs by the jackknife test on the benchmark dataset (cf. <b>Eq. 1</b>).

<p>Comparison of the success rates and <i>MCC</i> values obtained by the current iNR-PhysChem and NR-2L <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030869#pone.0030869-Wang1" target="_blank">[16]</a> in identifying NRs and non-NRs by the jackknife test on the benchmark dataset (cf. <b>Eq. 1</b>).</p

An illustration to show two types of covariance.

<p>(<b>a</b>) The auto-covariance refers to the coupling between two subsequences from a same sequence when they are separated by unit. (<b>b</b>) The cross-covariance refers to the coupling between two subsequences from two different sequences as indicated by two open curly braces.</p

An illustration to show the predicted results fallen into four different quadrants.

<p>(I) TP, the true positive quadrant (green) for correct prediction of positive dataset, (II) FP, the false positive quadrant (red) for incorrect prediction of negative dataset; (III) TN, the true negative quadrant (blue) for correct prediction of negative dataset; and (IV) FN, the false negative quadrant (pink) for incorrect prediction of positive dataset.</p

A semi-screenshot to see the top page of iNR-PhysChem.

<p>The web-server is at either <a href="http://www.jci-bioinfo.cn/iNR-PhysChem" target="_blank">http://www.jci-bioinfo.cn/iNR-PhysChem</a> or <a href="http://icpr.jci.edu.cn/bioinfo/iNR-PhysChem" target="_blank">http://icpr.jci.edu.cn/bioinfo/iNR-PhysChem</a>.</p

List of the values of the ten physical-chemical properties for each of the 20 native amino acids.

<p>List of the values of the ten physical-chemical properties for each of the 20 native amino acids.</p

The 3D graph to show the success rates by the 5-fold cross-validation with different values of <i>C</i> and in the SVM engine.

<p>(<b>a</b>) The results obtained for the 1^st-level prediction. (b) The results obtained for the 2^nd-level prediction.</p

Breakdown of the benchmark dataset (cf. <b>Eq. 1</b>) used in this study.

<p>Breakdown of the benchmark dataset (cf. <b>Eq. 1</b>) used in this study.</p

iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking

<div><p>Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called “<b>iGPCR-drug</b>”, was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for <b>iGPCR-drug</b> was established at <a href="http://www.jci-bioinfo.cn/iGPCR-Drug/" target="_blank">http://www.jci-bioinfo.cn/iGPCR-Drug/</a>. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by <b>iGPCR-drug</b> via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that <b>iGPCR-Drug</b> may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug – target interaction networks.</p></div

iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model

<div><p>As one of the most important posttranslational modifications (PTMs), ubiquitination plays an important role in regulating varieties of biological processes, such as signal transduction, cell division, apoptosis, and immune response. Ubiquitination is also named “lysine ubiquitination” because it occurs when an ubiquitin is covalently attached to lysine (K) residues of targeting proteins. Given an uncharacterized protein sequence that contains many lysine residues, which one of them is the ubiquitination site, and which one is of non-ubiquitination site? With the avalanche of protein sequences generated in the postgenomic age, it is highly desired for both basic research and drug development to develop an automated method for rapidly and accurately annotating the ubiquitination sites in proteins. In view of this, a new predictor called “iUbiq-Lys” was developed based on the evolutionary information, gray system model, as well as the general form of pseudo-amino acid composition. It was demonstrated via the rigorous cross-validations that the new predictor remarkably outperformed all its counterparts. As a web-server, iUbiq-Lys is accessible to the public at <a href="http://www.jci-bioinfo.cn/iUbiq-Lys" target="_blank">http://www.jci-bioinfo.cn/iUbiq-Lys</a>. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process.</p></div