Using multi-label algorithm to predict the post-translation modification types of proteins

Post-translational modifications (PTMs) play vital roles in most of the protein maturation, structural stabilization and function. How to predict protein´ PTMs types is an important and challenging problem. Most of the existing approaches can only be used to recognize single-label PTMs type. By introducing the multi-labeled K-Nearest-Neighbor algorithm, a new predictor has been proposed which can be used to dispose of the proteins containing both single and multi-label PTMs type. As a result that the 10-fold crosses validation was implemented on a benchmark data set of proteins which were divided into the following 4 types: (1) methylation, (2) nitrosylation, (3) acetylation, (4) phosphorylation, where many proteins belong to two or more types. For such a complex system, the outcomes achieved by our predictor for the six indices were quite promising, anticipated the predictor may become a complementary tool in this area.