EnILs: A General Ensemble Computational Approach for Predicting Inducing Peptides of Multiple Interleukins

Abstract

Interleukins (ILs) are a group of multifunctional cytokines, which play important roles in immune regulations and inflammatory responses. Recently, IL-6 has been found to affect the development of COVID-19, and significantly elevated levels of IL-6 cytokines have been reported in patients with severe COVID-19. IL-10 and IL-17 are anti-inflammatory and proinflammatory cytokines, respectively, which play multiple protective roles in host defense against pathogens. At present, a number of machine learning methods have been proposed to predict ILs inducing peptides, but their predictive performance needs to be further improved, and the inducing peptides of different ILs are predicted separately, rather than using a general approach. In our work, we combine the statistical features of peptide sequence with word embedding to design a general ensemble model named EnILs to predict inducing peptides of different ILs, in which the predictive probabilities of random forest, eXtreme Gradient Boosting and neural network are integrated in an average way. Compared with the state-of-the-art machine learning methods, EnILs shows considerable performance in the prediction of IL-6, IL-10, and IL-17 inducing peptides. In addition, we predict the most promising IL-6 inducing peptides in Severe Acute Respiratory Syndrome Coronavirus 2 spike protein in the case study for further experimental verification.