Abstract:

Knowing which types of functionality that human membrane proteins belong to is very helpful for understanding their functions. However, most existing online prediction methods have some disadvantages, including: 1) they obtain very low prediction accuracy, and 2) they can only predict single-functional classes of cytomembrane proteins in humans. To overcome the drawbacks, a new multi-label predictor, namely mMem-Hum, is proposed. In addition to predicting types of single-function membrane proteins, it can also predict multi-functional types. Specifically, discriminative features of membrane proteins are generated by using amino acid sequence information and evolutionary information, and then they are classified by a new multi-label classifier that utilizes label correlations. Experimental results reveal that the performance of mMem-Hum is significantly better than other existing forecasting methods. This indicates that mMem-Hum may become a promising prediction tool for classifying functional classes of cytomembrane proteins in humans.