Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix

Abel Chandra,Daichi Shigemizu,Abdollah Dehzangi,Alok Sharma,Tatsuhiko Tsunoda

doi:10.1186/s12860-019-0240-1

Abstract

BackgroundThe biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and hence the pathogenesis. A number of PTMs have been discovered in the recent years and lysine phosphoglycerylation is one of the fairly recent developments. Even with a large number of proteins being sequenced in the post-genomic era, the identification of phosphoglycerylation remains a big challenge due to factors such as cost, time consumption and inefficiency involved in the experimental efforts. To overcome this issue, computational techniques have emerged to accurately identify phosphoglycerylated lysine residues. However, the computational techniques proposed so far hold limitations to correctly predict this covalent modification.ResultsWe propose a new predictor in this paper called Bigram-PGK which uses evolutionary information of amino acids to try and predict phosphoglycerylated sites. The benchmark dataset which contains experimentally labelled sites is employed for this purpose and profile bigram occurrences is calculated from position specific scoring matrices of amino acids in the protein sequences. The statistical measures of this work, such as sensitivity, specificity, precision, accuracy, Mathews correlation coefficient and area under ROC curve have been reported to be 0.9642, 0.8973, 0.8253, 0.9193, 0.8330, 0.9306, respectively.ConclusionsThe proposed predictor, based on the feature of evolutionary information and support vector machine classifier, has shown great potential to effectively predict phosphoglycerylated and non-phosphoglycerylated lysine residues when compared against the existing predictors. The data and software of this work can be acquired from https://github.com/abelavit/Bigram-PGK.

Highlights

The biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and the pathogenesis
The biological process of enzymatic change in proteins brought about after the translation in the ribosome is known as post-translational modification (PTM)
Cardiovascular disease, such as heart failure, is a highly probable condition caused by phosphoglycerylation since this chemical modification is associated with glycolytic pathways and glucose metabolism [18, 19]

Summary

Introduction

The biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and the pathogenesis. Even with a large number of proteins being sequenced in the post-genomic era, the identification of phosphoglycerylation remains a big challenge due to factors such as cost, time consumption and inefficiency involved in the experimental efforts To overcome this issue, computational techniques have emerged to accurately identify phosphoglycerylated lysine residues. Phosphoglycerylation, which is a non-enzymatic lysine modification, is a type of PTM that has been recently discovered in human cells and mouse liver [16, 17] Cardiovascular disease, such as heart failure, is a highly probable condition caused by phosphoglycerylation since this chemical modification is associated with glycolytic pathways and glucose metabolism [18, 19]. As this PTM is relatively new to the field, it is important to identify and analyze its functional aspects to be able to understand the selectivity mechanism and its regulatory roles for better diagnosis and treatment of affected persons

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