TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules.
TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules.
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TEPITOPEpan：扩展 TEPITOPE 进行肽结合预测，覆盖 700 多个 HLA-DR 分子
DOI：
10.1371/journal.pone.0030483
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Zhu S
中科院分区：
文献类型：
--
作者：
Zhang L;Chen Y;Wong HS;Zhou S;Mamitsuka H;Zhu S
准确鉴定与特定MHC-II分子结合的肽对于阐明免疫识别的潜在机制，以及开发有效的基于表位的疫苗和针对许多严重疾病的有前途的免疫疗法具有重要意义。由于MHC-II等位基因的极端多态性和生化实验的高成本，开发能够准确预测MHC-II分子结合肽的计算方法，特别是在实验数据很少或没有实验数据的情况下，已经成为人们越来越感兴趣的话题。由于其良好的可解释性和相对较高的性能，TEPITOPE是一种被广泛使用的计算方法。然而，TEPITOPE只能应用于700多个已知HLA DR分子中的51个。方法根据TEPITOPE特征的HLA DR分子与未特征的HLA DR分子的结合特异性进行外推，建立了一种新的方法TEPITOPEpan。首先，每个HLA-DR结合口袋由与相应肽结合核心残基密切接触的氨基酸残基表示。然后计算两个HLA-DR分子之间的口袋相似度作为残基的序列相似度。最后，对于未表征的HLA-DR分子，每个口袋的结合特异性被计算为与TEPITOPE表征的HLA-DR分子的口袋结合特异性的加权平均值。结果TEPITOPEpan的性能已经从不同的角度使用各种数据集进行了广泛的评估：预测MHC结合肽，识别HLA配体和t细胞表位以及识别结合核心。在预测未知HLA-DR分子结合特异性的四种最先进的泛特异性方法中，TEPITOPEpan是仅次于NETMHCIIpan-2.0的第二好的方法。此外，TEPITOPEpan在识别绑定核方面取得了最好的性能。我们进一步分析了TEPITOPEpan检测到的基序，并查阅了相应的免疫学文献。其在线服务器和其中的pssm可在http://www.biokdd.fudan.edu.cn/Service/TEPITOPEpan/上获得。
Motivation Accurate identification of peptides binding to specific Major Histocompatibility Complex Class II (MHC-II) molecules is of great importance for elucidating the underlying mechanism of immune recognition, as well as for developing effective epitope-based vaccines and promising immunotherapies for many severe diseases. Due to extreme polymorphism of MHC-II alleles and the high cost of biochemical experiments, the development of computational methods for accurate prediction of binding peptides of MHC-II molecules, particularly for the ones with few or no experimental data, has become a topic of increasing interest. TEPITOPE is a well-used computational approach because of its good interpretability and relatively high performance. However, TEPITOPE can be applied to only 51 out of over 700 known HLA DR molecules. Method We have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of HLA DR molecules characterized by TEPITOPE to those uncharacterized. First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE. Result The performance of TEPITOPEpan has been extensively evaluated using various data sets from different viewpoints: predicting MHC binding peptides, identifying HLA ligands and T-cell epitopes and recognizing binding cores. Among the four state-of-the-art competing pan-specific methods, for predicting binding specificities of unknown HLA-DR molecules, TEPITOPEpan was roughly the second best method next to NETMHCIIpan-2.0. Additionally, TEPITOPEpan achieved the best performance in recognizing binding cores. We further analyzed the motifs detected by TEPITOPEpan, examining the corresponding literature of immunology. Its online server and PSSMs therein are available at http://www.biokdd.fudan.edu.cn/Service/TEPITOPEpan/.
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影响因子：
4.3
作者：
Nielsen M;Lundegaard C;Blicher T;Peters B;Sette A;Justesen S;Buus S;Lund O
通讯作者：
Lund O
影响因子：
14.9
作者：
Vita R;Zarebski L;Greenbaum JA;Emami H;Hoof I;Salimi N;Damle R;Sette A;Peters B
通讯作者：
Peters B
影响因子：
1.7
作者：
Zaitlen, Noah;Reyes-Gomez, Manuel;Jojic, Nebojsa
通讯作者：
Jojic, Nebojsa
影响因子：
--
作者：
Marsh SG;Albert ED;Bodmer WF;Bontrop RE;Dupont B;Erlich HA;Fernández-Viña M;Geraghty DE;Holdsworth R;Hurley CK;Lau M;Lee KW;Mach B;Maiers M;Mayr WR;Müller CR;Parham P;Petersdorf EW;Sasazuki T;Strominger JL;Svejgaard A;Terasaki PI;Tiercy JM;Trowsdale J
通讯作者：
Trowsdale J
影响因子：
5.8
作者：
Nielsen, M;Lundegaard, C;Lund, O
通讯作者：
Lund, O