Predicting Kinase-specific Phosphorylation Sites Using a Multitask Classification Framework

Identification of phosphorylation sites by computational methods is becoming increasingly important as it may reduce labor-intensive and costly experiments and improve our understanding in the common properties and underlying mechanisms of protein phosphorylation. A multitask learning framework for learning phosphorylation sites from four kinase families simultaneously, instead of studying each one of them separately, is presented in the study. Our strategy successfully selects 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods. Those features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation.