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

Abstract

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 http://app.aporc.org/iSNO-PseAAC/, 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.

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

“…The individual sensitivity S n , the individual specificity S p and the overall accuracy OA over the entire data set, as well as Matthew's correlation coefficient MCC (Xu et al, 2013) are used to evaluate performance.…”

Using Chou’s Pseudo Amino Acid Composition and Machine Learning Method to Predict the Antiviral Peptides

“…1-4 are not quite intuitive and easy to understand, particularly the equation for MCC. Here we adopt the formulation proposed recently in [30,41,79] based on the symbols introduced by Chou [80,81] in predicting signal peptides. According to the Chou's formulation, the same four metrics can be expressed as (5) Where is the total number of the AVPs investigated white ubiquitination peptides incorrectly predicted as the non-AVPs; the total number of the non-AVPs investigated while the number of the non-AVPs incorrectly predicted as the AVPs [82].…”

“…In fact these methods employ several of protein features for instance amino acid sequence [14,15], template [16][17][18] and amino acid composition (AAC) [19,20]. On the other hand one of the most important and [25], Discriminating protein structure classes [26], Predicting anticancer peptides [27], Prediction of bacterial protein subcellular localization [28], predict membrane protein types [29], Predict cysteine Snitrosylation sites in proteins [30], Identifying the heat shock protein families [31] and Predicting hydroxyproline and hydroxylysine in proteins [32], more example and the like [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. Recently, the concept of PseAAC was further extended to represent the feature vectors of DNA and nucleotides [41,[48][49][50][51][52][53], as well as other biological samples (see, e.g., [54]).…”

pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

“…According to the Chou's 5-step rule [32] that has been widely used by many recent investigators (see, e.g., [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]) for developing a statistical predictor, the first important and foremost thing is to construct or select a valid benchmark dataset to train and test the model [1,42,48]. In literature, the benchmark dataset usually consists of a training dataset and a testing dataset: the former is for the purpose of training a proposed model, while the latter for the purpose of testing it.…”