Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs

Abstract

Prokaryotic proteins are regulated by pupylation, a type of post-translational modification that contributes to cellular function in bacterial organisms. In pupylation process, the prokaryotic ubiquitin-like protein (Pup) tagging is functionally analogous to ubiquitination in order to tag target proteins for proteasomal degradation. To date, several experimental methods have been developed to identify pupylated proteins and their pupylation sites, but these experimental methods are generally laborious and costly. Therefore, computational methods that can accurately predict potential pupylation sites based on protein sequence information are highly desirable. In this paper, a novel predictor termed as pbPUP has been developed for accurate prediction of pupylation sites. In particular, a sophisticated sequence encoding scheme [i.e. the profile-based composition of k-spaced amino acid pairs (pbCKSAAP)] is used to represent the sequence patterns and evolutionary information of the sequence fragments surrounding pupylation sites. Then, a Support Vector Machine (SVM) classifier is trained using the pbCKSAAP encoding scheme. The final pbPUP predictor achieves an AUC value of 0.849 in10-fold cross-validation tests and outperforms other existing predictors on a comprehensive independent test dataset. The proposed method is anticipated to be a helpful computational resource for the prediction of pupylation sites. The web server and curated datasets in this study are freely available at http://protein.cau.edu.cn/pbPUP/.

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

“…The composition of amino acid pairs (AAPC) [20], transforms a sequence fragment into a 441-dimensional vector, which includes 441 elements specifying the numbers of occurrences of 441 amino acid pairs divided by the total number of amino acid pairs in a fragmented sequence [21]. CKSAAP [22] is a widely used sequence encoding method that has been applied with great success to many PTM prediction problems, such as O-glycosylation [23], palmitoylation [24], ubiqutination [8], phosphorylation [25], pupylation [26], methylation [27], N-formylation [28] and crotonylation [29]. In this study, we also employed CKSAAP to classify lysine residues into glutarylation and non-glutarylation sites.…”

A systematic identification of species-specific protein succinylation sites using joint element features information

“…[22][23][24] To construct the PSSM of candidate sequences, the e-value cutoff and iteration times were set as 1. To make a robust predictor, the orthogonal binary encoding was adopted in this study.…”

“…Randomly selected nonsuccinylated sites were considered as negative samples based on an intuitive assumption. 22,27 To construct a robust generic predictor, the training and independent dataset was compiled using the same methods described in our previous publication. 18 A total of 124 proteins with 254 succinylated sites and 2,977 nonsuccinylated sites were obtained as an independent dataset in this study.…”

Computational Modeling of Lysine Post-Translational Modification: An Overview

“…In contrast with the traditional experimental methods, computational analysis of lysine PTMs has also been an attractive and alternative approach due to its accuracy, cost-effective and high-speed [31,32]. The computational methods are more efficient for identifying large-scale novel lysine PTM substrates.…”