Tumor Necrosis Factor alpha (TNF-α) is a pleiotropic pro-inflammatory cytokine that is crucial in controlling the signaling pathways within the immune cells. Recent studies reported that higher expression levels of TNF-α are associated with the progression of several diseases, including cancers, cytokine release syndrome in COVID-19, and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF-α progression in various disease conditions. In the pilot study, we proposed a host-specific in-silico tool for predicting, designing, and scanning TNF-α inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF-α inducing/non-inducing epitopes from human and mouse hosts. Firstly, we developed alignment-free (machine learning based models using composition-based features of peptides) methods for predicting TNF-α inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, an alignment-based (using BLAST) method has been used for predicting TNF-α inducing epitopes. Finally, a hybrid method (combination of alignment-free and alignment-based method) has been developed for predicting epitopes. Hybrid approach achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified potential TNF-α inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2, and human insulin. The best models developed in this study has been incorporated in the webserver TNFepitope (https://webs.iiitd.edu.in/raghava/tnfepitope/), standalone package and GitLab (https://gitlab.com/raghavalab/tnfepitope).

肿瘤坏死因子 α (TNF-α) 是一种多效性促炎细胞因子，对于控制免疫细胞内的信号通路至关重要。最近的研究报告称，TNF-α 的高表达水平与多种疾病的进展有关，包括癌症、COVID-19 中的细胞因子释放综合征和自身免疫性疾病。因此，现在需要开发免疫疗法或亚单位疫苗来管理各种疾病条件下的 TNF-α 进展。在试点研究中，我们提出了一种用于预测、设计和扫描 TNF-α 诱导表位的宿主特异性计算机内工具。预测模型在来自人类和小鼠宿主的实验验证的 TNF-α 诱导/非诱导表位上进行了训练和验证。首先，我们开发了用于预测 TNF-α 诱导肽的无对齐（基于机器学习的模型，使用基于肽的成分特征）方法，并分别为人类和小鼠宿主实现了 0.79 和 0.74 的最大 AUROC。其次，基于比对（使用 BLAST）的方法已被用于预测 TNF-α 诱导表位。最后，已经开发了一种混合方法（无对齐和基于对齐的方法的组合）来预测表位。混合方法在人类和小鼠宿主的独立数据集上分别实现了 0.83 和 0.77 的最大 AUROC。我们还在 HIV-1、HIV-2、SARS-CoV-2 和人胰岛素的不同蛋白质中发现了潜在的 TNF-α 诱导肽。本研究中开发的最佳模型已被纳入网络服务器 TNFepitope（https://webs.iiitd.edu.in/raghava/tnfepitope/ )，独立包和 GitLab ( https://gitlab.com/raghavalab/tnfepitope )。