Using ensemble SVM to identify human GPCRs N-linked glycosylation sites based on the general form of Chou's PseAAC

Abstract

As the most frequent drug target, G-protein coupled receptors (GPCRs) are a large family of seven transmembrane receptors that sense molecules outside the cell and activate inside signal transduction pathways. Glycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. It plays important roles in a variety of cellular functions, including protein folding, protein trafficking and localization, cell-cell interactions and epitope recognition. Therefore, investigating the exact position of glycosylation site in GPCR sequence can provide useful clues for drug design and other biotechnology applications. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is a significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences. In this article, we presented an effective method to recognize the sites of human GPCRs by combining amino acid hydrophobicity with ensemble support vector machine. The prediction accuracy, sensitivity, specificity, Matthews correlation coefficient and area under the curve values were 94.4, 89.7, 98.9%, 0.895 and 0.989, respectively. The establishment of such a fast and accurate prediction method will speed up the pace of identifying proper GPCRs functional sites to facilitate drug discovery.

iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking

“…To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96,97] or Chou’s PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96], it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99], predicting protein subcellular location [100–106], predicting membrane protein types [107,108], predicting protein submitochondria locations [109–112], predicting GABA(A) receptor proteins [113], predicting enzyme subfamily classes [114], predicting antibacterial peptides [115], predicting supersecondary structure [116], predicting bacterial virulent proteins [117], predicting protein structural class [118], predicting the cofactors of oxidoreductases [119], predicting metalloproteinase family [120], identifying cysteine S -nitrosylation sites in proteins [66], identifying bacterial secreted proteins [121], identifying antibacterial peptides [115], identifying allergenic proteins [122], identifying protein quaternary structural attributes [123,124], identifying risk type of human papillomaviruses [125], identifying cyclin proteins [126], identifying GPCRs and their types [15,16], discriminating outer membrane proteins [127], classifying amino acids [128], detecting remote homologous proteins [129], among many others (see a long list of papers cited in the References section of [60]). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65], as well as other biological samples (see, e.g., [130–132]).…”

“…Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73,74,156,166–168]. Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14,15,68,99,106,107,124,169,170]). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.…”

iSS-PseDNC: Identifying Splicing Sites Using Pseudo Dinucleotide Composition

“…Since the concept of PseAAC was proposed in 2001 [29], it has been penetrating into almost all the fields of protein attribute predictions (see, e.g., [31–73]). Because it has been widely used, recently two types of open access software, called “PseAAC-Builder” [51] and “propy” [74], were established for generating various modes of PseAAC.…”

Prediction of β-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine

“…It is considered to be the most rigorous test for evaluation of performance (Chou and Zhang, 1995;Kumar and Raghava, 2009). Although it is time consuming but gives better results than other cross-validation methods (Feng et al, 2013;Xie et al, 2013;Xu et al, 2013;Zhou and Assa-Munt, 2001). During the LOOCV, each protein in the dataset was, in turn, used for testing by the classifier trained with the remaining proteins.…”