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.

中文翻译：

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EnIL：预测多种白细胞介素诱导肽的通用集成计算方法。

白细胞介素（IL）是一组多功能细胞因子，在免疫调节和炎症反应中发挥重要作用。最近，发现IL-6会影响COVID-19的发展，据报道，重症COVID-19患者的IL-6细胞因子水平显着升高。IL-10和IL-17分别是抗炎和促炎细胞因子，在宿主防御病原体中发挥多种保护作用。目前，已经提出了多种机器学习方法来预测ILs诱导肽，但其预测性能有待进一步提高，并且不同ILs的诱导肽是分开预测的，而不是使用通用的方法。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 EnILs 的通用集成模型来预测不同 IL 的诱导肽，其中随机森林、极限梯度提升和神经网络的预测概率集成在一个模型中。平均方式。与最先进的机器学习方法相比，EnILs 在预测 IL-6、IL-10 和 IL-17 诱导肽方面显示出相当可观的性能。此外，我们在案例研究中预测了严重急性呼吸综合征冠状病毒2刺突蛋白中最有前途的IL-6诱导肽，以供进一步的实验验证。