USP Electronic Research Repository

PhoglyStruct: prediction of phosphoglycerylated lysine residues using structural properties of amino acids

Chandra, Abel and Sharma, Alokanand and Dehzangi, Abdollah and Ranganathan, Shoba and Jokhan, Anjeela D. and Chou, Kuo-Chen and Tsunoda, Tatsuhiko (2018) PhoglyStruct: prediction of phosphoglycerylated lysine residues using structural properties of amino acids. Scientific Reports, 8 (17923). pp. 1-11. ISSN 2045-2322

PDF - Published Version
Download (2MB) | Preview


The biological process known as post-translational modification (PTM) contributes to diversifying the proteome hence affecting many aspects of normal cell biology and pathogenesis. There have been many recently reported PTMs, but lysine phosphoglycerylation has emerged as the most recent subject of interest. Despite a large number of proteins being sequenced, the experimental method for detection of phosphoglycerylated residues remains an expensive, time-consuming and inefficient endeavor in the post-genomic era. Instead, the computational methods are being proposed for accurately predicting phosphoglycerylated lysines. Though a number of predictors are available, performance in detecting phosphoglycerylated lysine residues is still limited. In this paper, we propose a new predictor called PhoglyStruct that utilizes structural information of amino acids alongside a multilayer perceptron classifier for predicting phosphoglycerylated and non-phosphoglycerylated lysine residues. For the experiment, we located phosphoglycerylated and non-phosphoglycerylated lysines in our employed benchmark. We then derived and integrated properties such as accessible surface area, backbone torsion angles, and local structure conformations. PhoglyStruct showed significant improvement in the ability to detect phosphoglycerylated residues from non-phosphoglycerylated ones when compared to previous predictors. The sensitivity, specificity, accuracy, Mathews correlation coefficient and AUC were 0.8542, 0.7597, 0.7834, 0.5468 and 0.8077, respectively. The data and Matlab/Octave software packages are available at

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User: Alokanand Sharma
Date Deposited: 20 Feb 2019 03:08
Last Modified: 04 Oct 2019 03:30

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...