An ensemble method approach to investigate kinase-specific phosphorylation sites

Abstract

Protein phosphorylation is one of the most significant and well-studied post-translational modifications, and it plays an important role in various cellular processes. It has made a considerable impact in understanding the protein functions which are involved in revealing signal transductions and various diseases. The identification of kinase-specific phosphorylation sites has an important role in elucidating the mechanism of phosphorylation; however, experimental techniques for identifying phosphorylation sites are labor intensive and expensive. An exponentially increasing number of protein sequences generated by various laboratories across the globe require computer-aided procedures for reliably and quickly identifying the phosphorylation sites, opening a new horizon for in silico analysis. In this regard, we have introduced a novel ensemble method where we have selected three classifiers (least square support vector machine, multilayer perceptron, and k-Nearest Neighbor) and three different feature encoding parameters (dipeptide composition, physicochemical properties of amino acids, and protein–protein similarity score). Each of these classifiers is trained on each of the three different parameter systems. The final results of the ensemble method are obtained by fusing the results of all the classifiers by a weighted voting algorithm. Extensive experiments reveal that our proposed method can successfully predict phosphorylation sites in a kinase-specific manner and performs significantly better when compared with other existing phosphorylation site prediction methods.

A Grammar Inference Approach for Predicting Kinase Specific Phosphorylation Sites

“…In most of the previous studies, these five methods have been used because of their high performance and free availability in the public domain. The six methods are PPSP [ 10 ], KinasePhos2.0 [ 8 ], GPS2.1 [ 11 , 48 ], Scansite [ 7 ], NetphosK 1.0 [ 9 ] and our previous method [ 17 ]. PPSP is based on Bayesian decision theory (BDT).…”

“…Also, ever increasing number of protein sequences in the data banks necessitates the development of computational methods for reliably predicting phosphorylation sites in the protein sequences as fast as possible. Many in silico methods have been proposed to predict kinase specific phosphorylation sites, such as Scansite [ 7 ], KinasePhos [ 8 ], NetPhosK [ 9 ], PPSP [ 10 ], GPS [ 11 , 12 ], Postmod [ 13 ], BAE [ 14 ], AMS 4.0Server [ 15 ], Metapred [ 16 ] and a method that we have developed earlier [ 17 ]. These methods predict the phosphorylation sites in a kinase specific manner.…”

“…Most of these methods are based on machine learning techniques using a single classifier. In the recent years, researchers are concentrating upon using ensemble mechanism instead of a single classifier for predicting protein phosphorylation sites [ 14 – 15 , 17 ]. Another method Musite is considered for large scale predictions of both non-kinase and kinase-specific phosphorylation sites [ 19 ].…”