Identification of Secretory Proteins in Sus scrofa Using Machine Learning Method

Abstract

Background: The expression of secretory proteins is involved in each stage of biomass from fetal development to the immune response. As an animal model for the study of human diseases, the study of protein secretion in pigs has strong application prospects. Objective: Although secretory proteins play an important role in cell activities, there are no machine learning-based approaches for the prediction of pig secretory proteins. This study aims to establish a prediction model for identifying the secretory protein in Sus scrofa. Methods: Based on the pseudo composition of k-spaced amino acid pairs feature encoding method and support vector machine algorithm, a prediction model was established for the identification of the secretory protein in Sus scrofa. Results: The model produced the AUROC of 0.885 and 0.728 on the training set and independent testing set, respectively. In addition, we discussed features used for the prediction. Conclusion: In this study, we proposed the first classification model to identify secretory proteins in Sus scrofa. By learning the characteristic of secretory proteins, it may become feasible to design and produce secretory proteins with distinctive properties that are currently unavailable.