Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Commun Biol. 2023 May 5;6(1):492. doi: 10.1038/s42003-023-04867-2.
The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on separate feature extraction from the peptide and MHC sequences and ignore their pairwise binding information. This paper develops a capsule neural network-based method to efficiently capture the peptide-MHC complex features to predict the peptide-MHC class I binding. Various evaluations confirmed our method outperformance over the alternative methods, while it can provide accurate prediction over less available data. Moreover, for providing precise insights into the results, we explored the essential features that contributed to the prediction. Since the simulation results demonstrated consistency with the experimental studies, we concluded that our method can be utilized for the accurate, rapid, and interpretable peptide-MHC binding prediction to assist biological therapies.
主要组织相容性复合体 (MHC) 与病原体衍生的肽结合，将其呈现在细胞表面的杀伤 T 细胞上。开发准确、快速和可解释的肽-MHC 结合预测的计算方法可以促进免疫疗法和疫苗的开发。各种基于深度学习的方法依赖于从肽和 MHC 序列中分别提取特征，而忽略了它们的成对结合信息。本文开发了一种基于胶囊神经网络的方法，以有效地捕获肽-MHC 复合物的特征，从而预测肽-MHC I 类结合。各种评估证实了我们的方法优于其他方法，同时它可以在数据较少的情况下提供准确的预测。此外，为了更深入地了解结果，我们探索了有助于预测的基本特征。由于模拟结果与实验研究一致，我们得出结论，我们的方法可用于准确、快速和可解释的肽-MHC 结合预测，以辅助生物治疗。
