Recent studies reported that ion binding proteins (IBPs) in phage play a key role in developing drugs to treat diseases caused by drug-resistant bacteria. Therefore, correct recognition of IBPs is an urgent task, which is beneficial for understanding their biological functions. To explore this issue, a new computational model was developed to identify IBPs in this study. First, we used the physicochemical (PC) property and Pearson's correlation coefficient (PCC) to denote protein sequences, and the temporal and spatial variabilities were employed to extract features. Next, a similarity network fusion algorithm was employed to capture the correlation characteristics between these two different kinds of features. Then, a feature selection method called F-score was utilized to remove the influence of redundant and irrelative information. Finally, these reserved features were fed into support vector machine (SVM) to discriminate IBPs from non-IBPs. Experimental results showed that the proposed method has significant improvement in the classification performance, as compared with the state-of-the-art approach. The Matlab codes and dataset used in this study are available at https://figshare.com/articles/online_resource/iIBP-TSV/21779567 for academic use.
Keywords: F-score; Ion binding proteins; Pearson’s correlation coefficient; physicochemical property; support vector machine.