Roberts C G, Meister G E, Jesdale B M, Lieberman J, Berzofsky J A, De Groot A S
TB/HIV Research Laboratory, Brown University School of Medicine, Providence, Rhode Island 02912, USA.
AIDS Res Hum Retroviruses. 1996 May 1;12(7):593-610. doi: 10.1089/aid.1996.12.593.
Identification of promiscuous or multideterminant T cell epitopes is essential for HIV vaccine development, however, current methods for T cell epitope identification are both cost intensive and labor intensive. We have developed a computer-driven algorithm, named EpiMer, which searches protein amino acid sequences for putative MHC class I- and/or class II-restricted T cell epitopes. This algorithm identifies peptides that contain multiple MHC-binding motifs from protein sequences. To evaluate the predictive power of EpiMer, the amino acid sequences of the HIV-1 proteins nef, gp160, gag p55, and tat were searched for regions of MHC-binding motif clustering. We assessed the algorithm's predictive power by comparing the EpiMer-predicted peptide epitopes to T cell epitopes that have been published in the literature. The EpiMer method of T cell epitope identification was compared to the standard method of synthesizing short, overlapping peptides and testing them for immunogenicity (overlapping peptide method), and to an alternate algorithm that has been used to identify putative T cell epitopes from primary structure (AMPHI). For the four HIV-1 proteins analyzed, the in vitro testing of EpiMer peptides for immunogenicity would have required the synthesis of fewer total peptides than either AMPHI or the overlapping peptide method. The EpiMer algorithm proved to be more efficient and more sensitive per amino acid than both the overlapping peptide method and AMPHI. The EpiMer predictions for these four HIV proteins are described. Since EpiMer-predicted peptides have the potential to bind to multiple MHC alleles, they are strong candidates for inclusion in a synthetic HIV vaccine.
识别多特异性或多决定簇的T细胞表位对于HIV疫苗的研发至关重要，然而，目前用于T细胞表位识别的方法既耗费成本又需要大量人力。我们开发了一种名为EpiMer的计算机驱动算法，该算法可在蛋白质氨基酸序列中搜索假定的MHC I类和/或II类限制性T细胞表位。此算法可从蛋白质序列中识别包含多个MHC结合基序的肽段。为了评估EpiMer的预测能力，我们在HIV-1蛋白nef、gp160、gag p55和tat的氨基酸序列中搜索MHC结合基序聚集区域。我们通过将EpiMer预测的肽表位与文献中已发表的T细胞表位进行比较，来评估该算法的预测能力。将EpiMer识别T细胞表位的方法与合成短的重叠肽并测试其免疫原性的标准方法（重叠肽法）以及用于从一级结构中识别假定T细胞表位的另一种算法（AMPHI）进行比较。对于所分析的四种HIV-1蛋白，对EpiMer肽进行免疫原性的体外测试所需合成的总肽数比AMPHI或重叠肽法都要少。事实证明，EpiMer算法在每个氨基酸上比重叠肽法和AMPHI都更高效、更灵敏。本文描述了针对这四种HIV蛋白的EpiMer预测结果。由于EpiMer预测的肽有可能与多个MHC等位基因结合，因此它们是合成HIV疫苗的有力候选物。
