IHBPs-VWDC: variable-length window-based dynamic connectivity approach for identifying hormone-binding proteins

Abstract

Hormone-binding proteins (HBPs) are soluble carrier proteins that play a vital role in the growth and development of living organisms. Identifying HBPs accurately is crucial for understanding their functions. However, traditional wet lab experimental methods are labor intensive and cost ineffective. Therefore, there is a need for computational methods to efficiently identify HBPs. In this study, a machine learning method based on support vector machine (SVM) was proposed for the accurate and efficient identification of HBPs. The encoding of protein sequences involved using fifty different physicochemical (PC) properties. A variable-length window-based dynamic connectivity method was applied to capture the connection information between two different PC properties through two distinct strategies. The canonical correlation analysis algorithm was then used to fuse features obtained from these approaches. Feature selection was performed using the F-score approach to choose the most discriminative features. Finally, these selected features were fed into the SVM to discriminate between HBPs and non-HBPs. The proposed method achieved high classification accuracies of 99.19%, 96.77%, and 94.57% on the main dataset and two independent datasets, respectively, as demonstrated in the jackknife test. Comparative results showed that our proposed method outperforms existing approaches on the same datasets, indicating its potential as a useful tool for identifying HBPs. The Matlab codes and datasets used in the current study are freely available at https://figshare.com/articles/online_resource/iHBPs-VWDC/23559834. Communicated by Ramaswamy H. Sarma