PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection.

A newly invented post-translational modification (PTM), phosphoglycerylation, has shown its essential role in the construction and functional properties of proteins and dangerous human diseases. Hence, it is very urgent to know about the molecular mechanism behind the phosphoglycerylation process to develop the drugs for related diseases. But accurately identifying of phosphoglycerylation site from a protein sequence in a laboratory is a very difficult and challenging task. Hence, the construction of an efficient computation model is greatly sought for this purpose. A little number of computational models are currently available for identifying the phosphoglycerylation sites, which are not able to reach their prediction capability at a satisfactory level. Therefore, an effective predictor named PLP_FS has been designed and constructed to identify phosphoglycerylation sites in this study. For the training purpose, an optimal number of feature sets was obtained by fusion of multiple F_Score feature selection techniques from the features generated by three types of sequence-based feature extraction methods and fitted with the support vector machine classification technique to the prediction model. On the other hand, the k-neighbor near cleaning and SMOTE methods were also implemented to balance the benchmark dataset. The suggested model in 10-fold cross-validation obtained an accuracy of 99.22%, a sensitivity of 98.17% and a specificity of 99.75% according to the experimental findings, which are better than other currently available predictors for accurately identifying the phosphoglycerylation sites.