Department of Dermatology, General Hospital of Guangzhou Military Command of PLA, Guangzhou, China.
Biopolymers. 2011;96(3):328-39. doi: 10.1002/bip.21564.
Identification of immunodominant epitopes is the first step in the rational design of peptide vaccines aimed at T-cell immunity. To date, however, it is yet a great challenge for accurately predicting the potent epitope peptides from a pool of large-scale candidates with an efficient manner. In this study, a method that we named StepRank has been developed for the reliable and rapid prediction of binding capabilities/affinities between proteins and genome-wide peptides. In this procedure, instead of single strategy used in most traditional epitope identification algorithms, four steps with different purposes and thus different computational demands are employed in turn to screen the large-scale peptide candidates that are normally generated from, for example, pathogenic genome. The steps 1 and 2 aim at qualitative exclusion of typical nonbinders by using empirical rule and linear statistical approach, while the steps 3 and 4 focus on quantitative examination and prediction of the interaction energy profile and binding affinity of peptide to target protein via quantitative structure-activity relationship (QSAR) and structure-based free energy analysis. We exemplify this method through its application to binding predictions of the peptide segments derived from the 76 known open-reading frames (ORFs) of herpes simplex virus type 1 (HSV-1) genome with or without affinity to human major histocompatibility complex class I (MHC I) molecule HLA-A0201, and find that the predictive results are well compatible with the classical anchor residue theory and perfectly match for the extended motif pattern of MHC I-binding peptides. The putative epitopes are further confirmed by comparisons with 11 experimentally measured HLA-A0201-restrcited peptides from the HSV-1 glycoproteins D and K. We expect that this well-designed scheme can be applied in the computational screening of other viral genomes as well.
鉴定免疫优势表位是合理设计针对 T 细胞免疫的肽疫苗的第一步。然而,迄今为止,以有效的方式从大量候选肽中准确预测有效的表位肽仍然是一个巨大的挑战。在这项研究中,我们开发了一种名为 StepRank 的方法,用于可靠且快速地预测蛋白质与全基因组肽之间的结合能力/亲和力。在该过程中,与大多数传统表位鉴定算法中使用的单一策略不同,依次采用四个具有不同目的和因此具有不同计算需求的步骤来筛选通常由致病基因组等生成的大规模肽候选物。步骤 1 和 2 旨在通过经验规则和线性统计方法定性排除典型的非结合物,而步骤 3 和 4 则通过定量结构-活性关系 (QSAR) 和基于结构的自由能分析,重点定量检查和预测肽与靶蛋白的相互作用能量分布和结合亲和力。我们通过将该方法应用于从单纯疱疹病毒 1 (HSV-1) 基因组的 76 个已知开放阅读框 (ORF) 衍生的肽段与人类主要组织相容性复合体 I (MHC I) 分子 HLA-A0201 的结合预测,说明了这种方法,发现预测结果与经典锚定残基理论非常吻合,并且与 MHC I 结合肽的扩展基序模式完全匹配。通过与来自 HSV-1 糖蛋白 D 和 K 的 11 个实验测量的 HLA-A0201 限制肽的比较,进一步证实了这些假定的表位。我们期望这种精心设计的方案可以应用于其他病毒基因组的计算筛选。