iSulf-Cys: Prediction of S-sulfenylation Sites in Proteins with Physicochemical Properties of Amino Acids

<div><p>Cysteine S-sulfenylation is an important post-translational modification (PTM) in proteins, and provides redox regulation of protein functions. Bioinformatics and structural analyses indicated that S-sulfenylation could impact many biological and functional categories and had distinct structural features. However, major limitations for identifying cysteine S-sulfenylation were expensive and low-throughout. In view of this situation, the establishment of a useful computational method and the development of an efficient predictor are highly desired. In this study, a predictor iSulf-Cys which incorporated 14 kinds of physicochemical properties of amino acids was proposed. With the 10-fold cross-validation, the value of area under the curve (AUC) was 0.7155 ± 0.0085, MCC 0.3122 ± 0.0144 on the training dataset for 20 times. iSulf-Cys also showed satisfying performance in the independent testing dataset with AUC 0.7343 and MCC 0.3315. Features which were constructed from physicochemical properties and position were carefully analyzed. Meanwhile, a user-friendly web-server for iSulf-Cys is accessible at <a href="http://app.aporc.org/iSulf-Cys/" target="_blank">http://app.aporc.org/iSulf-Cys/</a>.</p></div

The predictive IBS results of the online webserver.

<p>The predictive IBS results of the online webserver.</p

The 10-fold cross-validation results of three different feature constructions on the balanced training dataset.

<p>The results have been run 20 times for every feature construction by SVM algorithm with g = 0.005 and cutoff = 0.5. The values are mean ± standard variance. The results of MDD-SOH were obtained in 5-fold cross-validation.</p

The number of positive and negative peptides in training and independent test dataset.

<p>The number of positive and negative peptides in training and independent test dataset.</p

The number of dimensions of three feature constructions.

<p>The number of dimensions of three feature constructions.</p

The TwoSampleLogo between sulfenylation and non-sulfenylation peptides (p<0.01).

<p>The TwoSampleLogo between sulfenylation and non-sulfenylation peptides (p<0.01).</p

A diagram flow to illustrate the predicting procedure.

<p>A diagram flow to illustrate the predicting procedure.</p

The 10-fold cross-validation results of independent test by SVM algorithm with g = 0.005 and cutoff = 0.5.

<p>The 10-fold cross-validation results of independent test by SVM algorithm with g = 0.005 and cutoff = 0.5.</p

iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition

<div><p>Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at <a href="http://app.aporc.org/iSNO-PseAAC/">http://app.aporc.org/iSNO-PseAAC/</a>, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.</p> </div

A schematic illustration to show the S-nitrosylation (SNO) site of a protein segment.

<p>The protein segment contains residues, where C (cysteine) is located at the center of the peptide and all the other amino acids are depicted as an open circle with a number to indicate their sequential positions, respectively.</p