Classification of multi-family enzymes by multi-label machine learning and sequence-based descriptors

Abstract

Multi-family enzymes are of great importance in life, disease and other domains. However, in terms of the classification of enzymes, the information of multi-family enzymes is always removed from the dataset to account for the limitation of traditional single-label prediction methods. In order to predict multiple classes of multi-family enzymes, we adopted two multi-label learning algorithms, namely RAkEL-RF and MLKNN, and two types of protein descriptors, namely CTD and PseAAC, to generate four predictors, RAkEL-RF-CTD, RAkEL-RF-PseAAC, MLKNN-CTD and MLKNN-PseAAC. When the four predictors were tested on a training set with 10-fold cross validation, the overall success rates reached 97.99%, 96.07%, 96.01% and 95.31%, respectively. For the independent test set, the corresponding rates reached 97.57%, 95.03%, 95.9% and 93.9%, respectively. In conclusion, it proved the outstanding prediction capability and robustness of our predictors from the extremely small difference between two sets for each predictor and the relatively higher accuracy. In addition, three of seven pairs of homologous enzymes with different functions and eighteen of twenty-three distantly related enzymes with a similar family were correctly classified by the RAkEL-RF-CTD predictor. These results indicated the extensive applicability of our predictors.