Medical BioSciences Department, Radboud University Medical Center, Nijmegen, Netherlands.
Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Front Immunol. 2023 Dec 8;14:1285899. doi: 10.3389/fimmu.2023.1285899. eCollection 2023.
T-cell specificity to differentiate between self and non-self relies on T-cell receptor (TCR) recognition of peptides presented by the Major Histocompatibility Complex (MHC). Investigations into the three-dimensional (3D) structures of peptide:MHC (pMHC) complexes have provided valuable insights of MHC functions. Given the limited availability of experimental pMHC structures and considerable diversity of peptides and MHC alleles, it calls for the development of efficient and reliable computational approaches for modeling pMHC structures. Here we present an update of PANDORA and the systematic evaluation of its performance in modelling 3D structures of pMHC class II complexes (pMHC-II), which play a key role in the cancer immune response. PANDORA is a modelling software that can build low-energy models in a few minutes by restraining peptide residues inside the MHC-II binding groove. We benchmarked PANDORA on 136 experimentally determined pMHC-II structures covering 44 unique αβ chain pairs. Our pipeline achieves a median backbone Ligand-Root Mean Squared Deviation (L-RMSD) of 0.42 Å on the binding core and 0.88 Å on the whole peptide for the benchmark dataset. We incorporated software improvements to make PANDORA a pan-allele framework and improved the user interface and software quality. Its computational efficiency allows enriching the wealth of pMHC binding affinity and mass spectrometry data with 3D models. These models can be used as a starting point for molecular dynamics simulations or structure-boosted deep learning algorithms to identify MHC-binding peptides. PANDORA is available as a Python package through Conda or as a source installation at https://github.com/X-lab-3D/PANDORA.
T 细胞通过其 T 细胞受体（TCR）识别主要组织相容性复合体（MHC）呈递的肽来区分自身和非自身。对肽：MHC（pMHC）复合物的三维（3D）结构的研究为 MHC 功能提供了有价值的见解。鉴于实验性 pMHC 结构的有限可用性以及肽和 MHC 等位基因的相当大的多样性，需要开发有效的和可靠的计算方法来模拟 pMHC 结构。在这里，我们介绍了 PANDORA 的更新版本，并对其在建模 pMHC 类 II 复合物（pMHC-II）3D 结构方面的性能进行了系统评估，pMHC-II 在癌症免疫反应中起着关键作用。PANDORA 是一种建模软件，它可以通过将肽残基限制在 MHC-II 结合槽内，在几分钟内构建低能量模型。我们在涵盖 44 个独特的 αβ 链对的 136 个实验确定的 pMHC-II 结构的基准数据集上对 PANDORA 进行了基准测试。我们的流水线在结合核心上的骨干配体均方根偏差（L-RMSD）中位数为 0.42Å，在整个肽上为 0.88Å。我们对软件进行了改进，使其成为泛等位基因框架，并改进了用户界面和软件质量。其计算效率允许用 3D 模型丰富 pMHC 结合亲和力和质谱数据的财富。这些模型可用作分子动力学模拟或结构增强的深度学习算法的起点，以识别 MHC 结合肽。PANDORA 可通过 Conda 作为 Python 包获得，也可在 https://github.com/X-lab-3D/PANDORA 作为源安装获得。
