Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research.
Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research.
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DOI：
10.1186/1471-2105-9-s12-s22
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发表时间：
2008-12-12
影响因子：
3
通讯作者：
Brusic, Vladimir
中科院分区：
文献类型：
--
作者：
Lin, Hong Huang;Zhang, Guang Lan;Tongchusak, Songsak;Reinherz, Ellis L.;Brusic, Vladimir
人类免疫应答的启动和调节涉及识别由人类白细胞抗原II类（HLA-II）分子呈递的肽。这些肽（HLA-II T细胞表位）作为疫苗和免疫疗法开发的研究靶点越来越重要。HLA-II肽结合研究涉及跨越单个抗原的多个重叠肽，以及完整的病毒蛋白质组。病原体和肿瘤抗原中的抗原变异以及HLA分子的广泛多态性增加了筛选研究的靶点数量。实验筛选方法昂贵且耗时，并且对于许多HLA II类分子而言试剂不容易获得。计算预测方法补充了实验研究，最大限度地减少了验证实验的数量，并显着加快了表位定位过程。我们从四项独立研究中收集了721项肽结合试验的测试数据。四种抗原的完全重叠研究鉴定了103种肽与七种常见HLA-DR分子（DRB 1 *0101、0301、0401、0701、1101、1301和1501）的结合亲和力。我们使用这些数据来分析21个HLA-II结合预测服务器的性能，这些服务器可通过WWW访问。因为不是所有的服务器都有所有测试的HLA-II分子的预测因子，我们总共评估了113个预测因子。测试肽的长度范围为15至19个氨基酸。我们尝试了三种预测策略-较长肽内的最佳9-mer预测，最佳三个9-mer预测的平均值，以及较长肽内所有9-mer预测的平均值。最好的策略是在较长的肽内鉴定单个最好的9聚体。总体而言，通过受试者工作特征法（AROC）测量，17个预测因子表现良好（AROC > 0.8），41个表现边缘（AROC > 0.7），55个表现较差（AROC < 0.7）。良好的性能预测因子包括HLA-DRB 1 *0101（7）、1101（6）、0401（3）和0701（1）。最佳个体预测因子是NETMHCIIPAN，紧随其后的是PROPRED、IEDB（共识）和MULTIPRED（SVM）。没有一个单独的预测因子被证明适合于预测混杂肽。目前的预测能力允许使用实际阈值预测仅50%的实际T细胞表位。可用的HLA-II服务器与HLA-I预测器的预测能力不匹配。目前可用的HLA-II预测服务器仅提供有限的预测准确性，并且需要开发改进的预测器用于大规模研究，例如蛋白质组范围的表位作图。HLA-II结合预测的准确性要求是严格的，因为假阳性的实质性影响。
Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW. Because not all servers have predictors for all tested HLA-II molecules, we assessed a total of 113 predictors. The length of test peptides ranged from 15 to 19 amino acids. We tried three prediction strategies – the best 9-mer within the longer peptide, the average of best three 9-mer predictions, and the average of all 9-mer predictions within the longer peptide. The best strategy was the identification of a single best 9-mer within the longer peptide. Overall, measured by the receiver operating characteristic method (AROC), 17 predictors showed good (AROC > 0.8), 41 showed marginal (AROC > 0.7), and 55 showed poor performance (AROC < 0.7). Good performance predictors included HLA-DRB1*0101 (seven), 1101 (six), 0401 (three), and 0701 (one). The best individual predictor was NETMHCIIPAN, closely followed by PROPRED, IEDB (Consensus), and MULTIPRED (SVM). None of the individual predictors was shown to be suitable for prediction of promiscuous peptides. Current predictive capabilities allow prediction of only 50% of actual T-cell epitopes using practical thresholds. The available HLA-II servers do not match prediction capabilities of HLA-I predictors. Currently available HLA-II prediction servers offer only a limited prediction accuracy and the development of improved predictors is needed for large-scale studies, such as proteome-wide epitope mapping. The requirements for accuracy of HLA-II binding predictions are stringent because of the substantial effect of false positives.