Abstract:There are many unannotated proteins with unknown functions in cereals, which are difficult to be verified by experiments. However, computational methods have become one of the mainstream methods to evaluate the functions of the cereal proteins. In this study, maize, wheat, indica rice and japonica rice proteins were studied, and their structural domain interaction information was obtained from the related databases. The protein-protein interaction information was obtained and the protein-protein interaction network was constructed, starting from the relatively stable domain information of protein and combining the AdaBoost algorithm. Combining it with the biomolecular structure similarity network obtained by blast, the functions of the cereal proteins were predicted based on the cooperative classification and multi-layer perceptron algorithms. Results showed that both of cooperative classification algorithm and multi-layer perceptron algorithm could accurately predict the functions of the proteins. Moreover, collaborative classification algorithm showed a better recall rate, whereas multi-layer perceptron algorithm showed a better accuracy. This sudy revealed a new idea and method for the unannotated protein investigation, and provided a strong guarantee for cereal processing and nutrition research.
