The binding of peptides to class-I Major Histocompability Complex (MHC) receptors and their subsequent recognition downstream by T-cell receptors are crucial processes for most multicellular organisms to be able to fight various diseases. Thus, the identification of peptide antigens that can elicit an immune response is of immense importance for developing successful therapies for bacterial and viral infections, even cancer. Recently, studies have demonstrated the importance of peptide-MHC (pMHC) structural analysis, with pMHC structural modeling methods gradually becoming more popular in peptide antigen identification workflows. Most of the pMHC structural modeling tools provide an ensemble of candidate peptide poses in the MHC-I cleft, each associated with a score stemming from a scoring function, with the top scoring pose assumed to be the most representative of the ensemble. However, identifying the binding mode, that is, the peptide pose from the ensemble that is closer to an unavailable native structure, is not trivial. Oftentimes, the peptide poses characterized as best by a protein-ligand scoring function are not the ones that are the most representative of the actual structure. In this work, we frame the peptide binding pose identification problem as a Learning-to-Rank (LTR) problem. We present RankMHC, an LTR-based pMHC binding mode identification predictor, which is specifically trained to predict the most accurate ranking of an ensemble of pMHC conformations. RankMHC outperforms classical peptide-ligand scoring functions, as well as previous Machine Learning (ML)-based binding pose predictors. We further demonstrate that RankMHC can be used with many pMHC structural modeling tools that use different structural modeling protocols.

中文翻译：

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RankMHC：学习对 I 类肽-MHC 结构模型进行排名。


肽与 I 类主要组织兼容复合物 （MHC） 受体的结合以及随后被 T 细胞受体在下游识别是大多数多细胞生物能够对抗各种疾病的关键过程。因此，鉴定可以引发免疫反应的肽抗原对于开发成功的细菌和病毒感染甚至癌症疗法非常重要。最近，研究表明了肽-MHC （pMHC） 结构分析的重要性，pMHC 结构建模方法在肽抗原鉴定工作流程中逐渐变得越来越流行。大多数 pMHC 结构建模工具在 MHC-I 裂隙中提供了一组候选肽姿势，每个姿势都与源自评分函数的分数相关联，其中得分最高的姿势被认为是最能代表集合的姿势。然而，识别结合模式，即来自更接近不可用天然结构的系综中的肽姿势，并非易事。通常，以蛋白质-配体评分函数为特征的最佳肽位姿并不是最能代表实际结构的位姿。在这项工作中，我们将肽结合姿势识别问题定义为 Learning-to-Rank （LTR） 问题。我们提出了 RankMHC，一种基于 LTR 的 pMHC 结合模式识别预测器，它经过专门训练，可以预测 pMHC 构象集合的最准确排名。RankMHC 优于经典的肽配体评分函数，以及以前基于机器学习 （ML） 的结合姿势预测器。我们进一步证明 RankMHC 可以与许多使用不同结构建模协议的 pMHC 结构建模工具一起使用。