Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac192.
Human leukocyte antigens (HLA) regulate various innate and adaptive immune responses and play a crucial immunomodulatory role. Recent studies revealed that non-classical HLA-(HLA-E & HLA-G) based immunotherapies have many advantages over traditional HLA-based immunotherapy, particularly against cancer and COVID-19 infection. In the last two decades, several methods have been developed to predict the binders of classical HLA alleles. In contrast, limited attempts have been made to develop methods for predicting non-classical HLA binding peptides, due to the scarcity of sufficient experimental data. Of note, in order to facilitate the scientific community, we have developed an artificial intelligence-based method for predicting binders of class-Ib HLA alleles. All the models were trained and tested on experimentally validated data obtained from the recent release of IEDB. The machine learning models achieved more than 0.98 AUC for HLA-G alleles on validation dataset. Similarly, our models achieved the highest AUC of 0.96 and 0.94 on the validation dataset for HLA-E01:01 and HLA-E01:03, respectively. We have summarized the models developed in the past for non-classical HLA and validated the performance with the models developed in this study. Moreover, to facilitate the community, we have utilized our tool for predicting the potential non-classical HLA binding peptides in the spike protein of different variants of virus causing COVID-19, including Omicron (B.1.1.529). One of the major challenges in the field of immunotherapy is to identify the promiscuous binders or antigenic regions that can bind to a large number of HLA alleles. To predict the promiscuous binders for the non-classical HLA alleles, we developed a web server HLAncPred (https://webs.iiitd.edu.in/raghava/hlancpred) and standalone package.
人类白细胞抗原（HLA）调节各种先天和适应性免疫反应，并发挥关键的免疫调节作用。最近的研究表明，基于非经典 HLA（HLA-E 和 HLA-G）的免疫疗法比传统的 HLA 为基础的免疫疗法有许多优势，特别是针对癌症和 COVID-19 感染。在过去的二十年中，已经开发了几种方法来预测经典 HLA 等位基因的结合物。相比之下，由于缺乏足够的实验数据，开发用于预测非经典 HLA 结合肽的方法的尝试有限。值得注意的是，为了方便科学界，我们开发了一种基于人工智能的方法来预测 I 类 b HLA 等位基因的结合物。所有的模型都是在从最近 IEDB 发布中获得的经过实验验证的数据上进行训练和测试的。机器学习模型在验证数据集上对 HLA-G 等位基因的 AUC 超过 0.98。同样，我们的模型在验证数据集上对 HLA-E01:01 和 HLA-E01:03 分别达到了最高的 0.96 和 0.94 AUC。我们总结了过去用于非经典 HLA 的模型，并使用本研究中开发的模型验证了它们的性能。此外，为了方便社区，我们利用我们的工具来预测导致 COVID-19 的不同变体的病毒的刺突蛋白中的潜在非经典 HLA 结合肽，包括奥密克戎（B.1.1.529）。免疫疗法领域的主要挑战之一是识别能够与大量 HLA 等位基因结合的混杂结合物或抗原区域。为了预测非经典 HLA 等位基因的混杂结合物，我们开发了一个 Web 服务器 HLAncPred（https://webs.iiitd.edu.in/raghava/hlancpred）和独立的软件包。
