Promiscuous peptides that can be presented by multiple human leukocyte antigens (HLAs) have great potential for the development of vaccines with wide population coverage. However, the current available methods for the prediction of peptides that bind to major histocompatibility complex (MHC) are mainly aimed at the rapid or mass screening of potential T cell epitopes from pathogen antigens or proteomics. The current approaches do not allow deciphering the contribution of the residue at each peptide position to the promiscuous binding ability of the peptide or obtaining guidelines for the design of promiscuous peptides. In this study, we re-evaluated and characterized four matrix-based prediction models that have been extensively used for the prediction of HLA-binding peptides and found that the prediction models generated based on the average relative binding (ARB) matrix shared a consistent and conservative threshold for all well-studied HLA class I alleles. Evaluations performed using datasets of HLA supertype-specific peptides with various cross-binding abilities and peptide mutant analogues indicated that the ARB-based binding matrices could be used to decipher and design promiscuous peptides that bind to multiple HLA molecules. A web-based tool called PromPDD was developed using ARB matrix-based models, and this tool enables the prediction, deciphering and design of promiscuous peptides that bind to multiple HLA molecules within or across HLA supertypes in a simpler and more direct manner. Furthermore, we expanded the application of PromPDD to HLA class I alleles with limited experimentally verified data by generating pan-specific matrices using a derived modular method, and 2641 HLA molecules encoded by HLA-A and HLA-B genes are available in PromPDD. PromPDD, which is freely available at http://www.immunoinformatics.net/PromPDD/, is the first tool for the deciphering and design of promiscuous peptides that bind to HLA class I molecules.

中文翻译：

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PromPDD，一种基于Web的工具，用于预测，解密和设计与HLA I类分子结合的混杂肽。

可以由多种人类白细胞抗原（HLA）呈递的混杂肽具有开发具有广泛人群覆盖范围的疫苗的巨大潜力。然而，目前可用于预测与主要组织相容性复合物（MHC）结合的肽的方法主要针对从病原体抗原或蛋白质组学快速或大规模筛选潜在T细胞表位。当前的方法不允许破译每个肽位置上的残基对肽的混杂结合能力的贡献，或者不能获得用于混杂肽设计的指导方针。在这个研究中，我们重新评估并表征了四个已广泛用于HLA结合肽预测的基于矩阵的预测模型，发现基于平均相对结合（ARB）矩阵生成的预测模型对所有模型均具有一致且保守的阈值深入研究的HLA I类等位基因。使用具有各种交叉结合能力的HLA超型特异性肽和肽突变类似物的数据集进行的评估表明，基于ARB的结合矩阵可用于破译和设计与多个HLA分子结合的混杂肽。使用基于ARB矩阵的模型开发了一个称为PromPDD的基于网络的工具，该工具可以进行预测，以更简单和更直接的方式破译和设计与HLA超型内或跨HLA超型结合的多个HLA分子的混杂肽。此外，我们通过使用衍生的模块化方法生成泛特异性矩阵，将PromPDD的应用扩展到HLA I类等位基因，并获得了有限的实验验证数据，并且PromPDD中提供了由HLA-A和HLA-B基因编码的2641个HLA分子。PromPDD可从http://www.immunoinformatics.net/PromPDD/免费获得，它是第一个破译和设计与HLA I类分子结合的混杂肽的工具。PromPDD中提供了由HLA-A和HLA-B基因编码的2641个HLA分子。PromPDD可从http://www.immunoinformatics.net/PromPDD/免费获得，它是第一个破译和设计与HLA I类分子结合的混杂肽的工具。PromPDD中提供了由HLA-A和HLA-B基因编码的2641个HLA分子。PromPDD可从http://www.immunoinformatics.net/PromPDD/免费获得，它是第一个破译和设计与HLA I类分子结合的混杂肽的工具。