Examining the independent binding assumption for binding of peptide epitopes to MHC-1 molecules
Examining the independent binding assumption for binding of peptide epitopes to MHC-1 molecules
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DOI：
10.1093/bioinformatics/btg247
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发表时间：
2003-09-22
期刊：
影响因子：
5.8
通讯作者：
Weng, ZP
中科院分区：
文献类型：
--
作者：
Peters, B;Tong, WW;Weng, ZP
动机：已经提出了各种方法来基于实验结合数据预测肽与主要组织相容性复合物I类（MHC-I）分子的结合亲和力。它们可以分为两组：（1）AIB方法，其假设所有肽位置对与MHC-I分子的结合的独立贡献（例如评分矩阵）和（2）可以考虑不同位置之间的相互作用的一般方法（例如人工神经网络）。我们的目标是比较这些方法的预测精度，并量化肽位置之间的相互作用的影响，结果：我们比较了几个以前发表的和广泛使用的方法，发现最好的AIB方法给出了显着更好的预测比以前发表的三个一般方法，可能是由于缺乏足够的训练数据的一般方法。然而，最好的结果，实现了与我们新开发的一般方法，它结合了一个矩阵描述独立的结合对系数描述成对肽位置之间的相互作用。配对系数一致，但仅略微提高了预测精度，并且比矩阵条目小得多。这就解释了为什么忽略它们（如AIB方法所做的那样）仍然可以得到好的预测。
Motivation: Various methods have been proposed to predict the binding affinities of peptides to Major Histocompatibility Complex class I (MHC-I) molecules based on experimental binding data. They can be classified into two groups: (1) AIB methods that assume independent contributions of all peptide positions to the binding to MHC-I molecule (e.g. scoring matrices) and (2) general methods which can take into account interactions between different positions (e.g. artificial neural networks). We aim to compare the prediction accuracies of these methods, and quantify the impact of interactions between peptide positions.Results: We compared several previously published and widely used methods and discovered that the best AIB methods gave significantly better predictions than three previously published general methods, possibly due to the lack of a sufficient training data for the general methods. The best results, however, were achieved with our newly developed general method, which combined a matrix describing independent binding with pair coefficients describing pair-wise interactions between peptide positions. The pair coefficients consistently but only slightly improved prediction accuracy, and were much smaller than the matrix entries. This explains why neglecting them-as is done in AIB methods-can still lead to good predictions.