Peptide length-based prediction of peptide-MHC class II binding
Peptide length-based prediction of peptide-MHC class II binding
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DOI：
10.1093/bioinformatics/btl479
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发表时间：
2006-11-15
期刊：
影响因子：
5.8
通讯作者：
Linderman, Jennifer J.
中科院分区：
文献类型：
--
作者：
Chang, Stewart T.;Ghosh, Debashis;Linderman, Jennifer J.
动机：用于预测肽-MHC II类结合的算法通常与用于预测肽-MHC I类结合的方法相似（如果不相同的话），尽管这两种情况之间存在已知差异。我们调查是否代表这些差异之一，结合MHC II类的肽长度的更大范围，提高了这些algorithms.Results的性能：肽长度和肽-MHC II类结合亲和力之间的非线性关系被确定在几个MHC II类等位基因的数据。使用以下几种修改之一来将肽长度并入现有预测算法中：使用回归来预处理数据，使用肽长度作为算法内的附加变量，或表示较长肽中的寄存器移位。对于几个数据集和至少两个算法，这些修改一致地提高了预测精度。可用性：http://malthus.micro.med.umich.edu/BioinformaticsContact：linderma@umich.edu。
Motivation: Algorithms for predicting peptide-MHC class II binding are typically similar, if not identical, to methods for predicting peptide-MHC class I binding despite known differences between the two scenarios. We investigate whether representing one of these differences, the greater range of peptide lengths binding MHC class II, improves the performance of these algorithms.Results: A non-linear relationship between peptide length and peptide-MHC class II binding affinity was identified in the data available for several MHC class II alleles. Peptide length was incorporated into existing prediction algorithms using one of several modifications: using regression to pre-process the data, using peptide length as an additional variable within the algorithm, or representing register shifting in longer peptides. For several datasets and at least two algorithms these modifications consistently improved prediction accuracy.Availability: http://malthus.micro.med.umich.edu/BioinformaticsContact: linderma@umich.edu.