Application of Machine Learning Techniques to Predict Protein Phosphorylation Sites

Protein phosphorylation is one of the most important post-translational modifications of proteins. Almost all processes that regulate the life activities of an organism as well as almost all physiological and pathological processes are involved in protein phosphorylation. In this paper, we summarize specific implementation and application of the methods used in protein phosphorylation site prediction such as the support vector machine algorithm, random forest, Jensen-Shannon divergence combined with quadratic discriminant analysis, Adaboost algorithm, increment of diversity with quadratic discriminant analysis, modified CKSAAP algorithm, Bayes classifier combined with phosphorylation sequences enrichment analysis, least absolute shrinkage and selection operator, stochastic search variable selection, partial least squares and deep learning. On the basis of this prediction, we use k-nearest neighbor algorithm with BLOSUM80 matrix method to predict phosphorylation sites. Firstly, we construct dataset and remove the redundant set of positive and negative samples, that is, removal of protein sequences with similarity of more than 30%. Next, the proposed method is evaluated by sensitivity (Sn), specificity (Sp), accuracy (ACC) and Mathew’s correlation coefficient (MCC) these four metrics. Finally, tenfold cross-validation is employed to evaluate this method. The result, which is verified by tenfold cross-validation, shows that the average values of Sn, Sp, ACC and MCC of three types of amino acid (serine, threonine, and tyrosine) are 90.44%, 86.95%, 88.74% and 0.7742, respectively. A comparison with the predictive performance of PhosphoSVM and Musite reveals that the prediction performance of the proposed method is better, and it has the advantages of simplicity, practicality and low time complexity in classification.