Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan.
Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan.
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DOI：
10.1371/journal.pcbi.1000107
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发表时间：
2008-07-04
影响因子：
4.3
通讯作者：
Lund O
中科院分区：
文献类型：
--
作者：
Nielsen M;Lundegaard C;Blicher T;Peters B;Sette A;Justesen S;Buus S;Lund O
CD4 阳性 T 辅助细胞控制特异性免疫的许多方面。这些细胞对源自蛋白质抗原的肽具有特异性，并由极其多态性的主要组织相容性复合物 (MHC) II 类系统的分子呈递。因此，鉴定与 MHC II 类分子结合的肽对于合理发现免疫表位至关重要。 HLA-DR 是人类 MHC II 类的一个突出例子。在这里，我们提出了一种方法 NetMHCIIpan，该方法允许对肽与任何已知序列的 HLA-DR 分子的结合进行泛特异性预测。该方法源自大量定量 HLA-DR 结合事件的汇编，涵盖 500 多个已知 HLA-DR 等位基因中的 14 个。考虑到肽和 HLA 序列信息，该方法还可以在缺乏实验数据的情况下概括和预测 HLA-DR 分子的肽结合。该方法的验证包括内源性 HLA II 类配体的鉴定、交叉验证、留一分子排除以及迄今为止尚未表征的 HLA-DR 分子的结合基序鉴定。验证表明，该方法可以成功预测 HLA-DR 分子的结合——即使没有相关特定分子的具体数据。此外，与目前唯一一种旨在提供广泛的 HLA-DR 等位基因覆盖的公开预测方法 TEPITOPE 相比，NetMHCIIpan 对于 TEPITOPE 训练中包含的等位基因表现相当，而在新等位基因方面优于 TEPITOPE。我们建议该方法可用于识别那些迄今未表征的等位基因，这些等位基因应在该方法的未来更新中通过实验解决，以最有效地覆盖 HLA-DR 的多态性。因此，我们得出的结论是，所提出的方法满足了跟上 MHC 多态性发现率的挑战，并且它可用于对 MHC“空间”进行采样，从而实现高效的迭代过程来改进 MHC II 类结合预测。 CD4 阳性 T 辅助细胞为刺激细胞和体液免疫反应提供必要的帮助。 T 辅助细胞识别主要组织相容性复合物 (MHC) II 类系统分子呈递的肽。 HLA-DR 是人类 MHC II 类基因座的一个突出例子。 HLA 分子具有极其多态性，目前已知超过 500 种不同的 HLA-DR 蛋白序列。每个 HLA-DR 分子都可能结合一组独特的抗原肽，并且对每个分子的结合特异性进行实验表征将是一项艰巨且成本高昂的任务。仅对非常有限的 MHC 分子进行了实验表征。我们之前已经证明，可以通过插入邻近分子的信息来准确预测 MHC I 类蛋白质。采用类似的方法来得出泛特异性 HLA-DR II 类预测并不简单，因为 HLA II 类分子可以结合长度非常不同的肽。尽管如此，我们在这里证明这确实是可能的。我们开发了一种 HLA-DR 泛特异性方法，即使在没有相关特定分子的具体数据的情况下，也可以预测与已知序列的任何 HLA-DR 分子的结合。
CD4 positive T helper cells control many aspects of specific immunity. These cells are specific for peptides derived from protein antigens and presented by molecules of the extremely polymorphic major histocompatibility complex (MHC) class II system. The identification of peptides that bind to MHC class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method is derived from a large compilation of quantitative HLA-DR binding events covering 14 of the more than 500 known HLA-DR alleles. Taking both peptide and HLA sequence information into account, the method can generalize and predict peptide binding also for HLA-DR molecules where experimental data is absent. Validation of the method includes identification of endogenously derived HLA class II ligands, cross-validation, leave-one-molecule-out, and binding motif identification for hitherto uncharacterized HLA-DR molecules. The validation shows that the method can successfully predict binding for HLA-DR molecules—even in the absence of specific data for the particular molecule in question. Moreover, when compared to TEPITOPE, currently the only other publicly available prediction method aiming at providing broad HLA-DR allelic coverage, NetMHCIIpan performs equivalently for alleles included in the training of TEPITOPE while outperforming TEPITOPE on novel alleles. We propose that the method can be used to identify those hitherto uncharacterized alleles, which should be addressed experimentally in future updates of the method to cover the polymorphism of HLA-DR most efficiently. We thus conclude that the presented method meets the challenge of keeping up with the MHC polymorphism discovery rate and that it can be used to sample the MHC “space,” enabling a highly efficient iterative process for improving MHC class II binding predictions. CD4 positive T helper cells provide essential help for stimulation of both cellular and humoral immune reactions. T helper cells recognize peptides presented by molecules of the major histocompatibility complex (MHC) class II system. HLA-DR is a prominent example of a human MHC class II locus. The HLA molecules are extremely polymorphic, and more than 500 different HLA-DR protein sequences are known today. Each HLA-DR molecule potentially binds a unique set of antigenic peptides, and experimental characterization of the binding specificity for each molecule would be an immense and highly costly task. Only a very limited set of MHC molecules has been characterized experimentally. We have demonstrated earlier that it is possible to derive accurate predictions for MHC class I proteins by interpolating information from neighboring molecules. It is not straightforward to take a similar approach to derive pan-specific HLA-DR class II predictions because the HLA class II molecules can bind peptides of very different lengths. Here, we nonetheless show that this is indeed possible. We develop an HLA-DR pan-specific method that allows for prediction of binding to any HLA-DR molecule of known sequence—even in the absence of specific data for the particular molecule in question.
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DOI：
10.1073/pnas.89.22.10915
发表时间：
1992-11-15
影响因子：
11.1
作者：
HENIKOFF, S;HENIKOFF, JG
通讯作者：
HENIKOFF, JG
影响因子：
3.7
作者：
Nielsen M;Lundegaard C;Blicher T;Lamberth K;Harndahl M;Justesen S;Røder G;Peters B;Sette A;Lund O;Buus S
通讯作者：
Buus S
影响因子：
5.8
作者：
Chang, Stewart T.;Ghosh, Debashis;Linderman, Jennifer J.
通讯作者：
Linderman, Jennifer J.
DOI：
10.1186/1745-7580-1-6
发表时间：
2005-12-13
期刊：
Immunome research
影响因子：
--
作者：
Murugan N;Dai Y
通讯作者：
Dai Y
影响因子：
5.8
作者：
Nielsen, M;Lundegaard, C;Lund, O
通讯作者：
Lund, O