Shift-invariant adaptive double threading: Learning MHC II-peptide binding
Shift-invariant adaptive double threading: Learning MHC II-peptide binding
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DOI：
10.1089/cmb.2007.0183
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发表时间：
2008-09-01
影响因子：
1.7
通讯作者：
Jojic, Nebojsa
中科院分区：
文献类型：
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作者：
Zaitlen, Noah;Reyes-Gomez, Manuel;Jojic, Nebojsa
主要组织相容性复合体（MHC）在人体免疫系统的工作中起着重要作用。已经发现MHC与细胞和病原体蛋白质的肽片段结合的特异性与疾病结局和病原体或癌症进化相关。在本文中，我们提出了一种预测MHC II类分子结合构型和能量的新方法，MHC II类分子的表位通常比MHC I类分子的表位预测得更差，部分原因是结合肽长度的差异更大。我们将肽的相对位置作为一个隐藏变量，并对不同结合构型的集合进行建模，而不是使用单独的比对程序将其缩小到一个。因此，我们的预测器从MHC II和肽序列中推断出肽位置的分布，并计算出总结合亲和力。训练过程通过重新估计绑定槽模型的参数来迭代预测。对于给定的相对肽位置，任何MHC I类预测模型都可以使用。这里我们选择Jojic等人（2006）的基于物理的模型。我们表明，结合模型的参数可以有效地从训练数据中学习，然后用于估计先前未测试的肽的结合能。我们的技术与先前的MHC II表位预测方法相当。此外，我们的模型选择允许泛化到新的MHC II类等位基因，这些等位基因不属于训练集的一部分。
The major histocompatibility complex (MHC) plays important roles in the workings of the human immune system. Specificity of MHC binding to peptide fragments from cellular and pathogens' proteins has been found to correlate with disease outcome and pathogen or cancer evolution. In this paper we propose a novel approach to predicting binding configurations and energies for MHC class II molecules, whose epitopes are generally predicted less well than the MHC I epitopes due in part to larger variation in bound peptide length. We treat the relative position of the peptide as a hidden variable, and model the ensemble of different binding configurations, rather than use a separate alignment procedure to narrow it down to one. Thus, our predictor infers a distribution over peptide positions from the MHC II and peptide sequences, and computes the total binding affinity. The training procedure iterates the predictions with re-estimation of the parameters of the binding groove model. For a given relative peptide position, any MHC class I prediction model can be used. Here we choose the physics based model of Jojic et al. (2006). We show that the parameters of the binding model can be learned efficiently from the training data and then used to estimate binding energies for previously untested peptides. Our technique performs on par with previous approaches to MHC II epitope prediction. Furthermore, our model choice allows generalization to new MHC class II alleles, which were not a part of the training set.