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.
摘要：
白细胞介素（IL）是一组多功能的细胞因子，在免疫调节和炎症反应中起重要作用。最近,已发现IL-6会影响COVID-19的发展，据报道，重度COVID-19患者的IL-6细胞因子水平显着升高。IL-10和IL-17是抗炎和促炎细胞因子，分别,在宿主防御病原体中发挥多重保护作用。目前,已经提出了许多机器学习方法来预测IL诱导肽，但是它们的预测性能需要进一步提高，并分别预测不同IL的诱导肽，而不是使用一般的方法。在我们的工作中,我们将肽序列的统计特征与词嵌入相结合，设计了一个名为EnIL的通用集成模型来预测不同IL的诱导肽。其中随机森林的预测概率，极限梯度提升和神经网络以平均方式集成。与最先进的机器学习方法相比，EnIL在IL-6、IL-10和IL-17诱导肽的预测中显示出相当大的性能。此外,我们预测最有希望的IL-6诱导肽在严重急性呼吸综合征冠状病毒2刺突蛋白的案例研究中进行进一步的实验验证。
