Using a neural network to identify potential HLA-DR1 binding sites within proteins
Using a neural network to identify potential HLA-DR1 binding sites within proteins
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DOI：
10.1002/jmr.300060105
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发表时间：
1993-01-01
影响因子：
2.7
通讯作者：
Fierz, Walter
中科院分区：
文献类型：
--
作者：
Bisset, Leslie R.;Fierz, Walter
由抗原呈递细胞呈递与主要组织相容性复合体（MHC）编码的蛋白质相关的免疫显性肽段是特异性免疫应答功效的基础。一种用于鉴定蛋白质内的免疫显性区段的方法涉及开发预测算法，其利用氨基酸序列数据来鉴定与体内抗原性相关的结构特征或基序。最近被称为“神经网络”的并行计算技术已被证明是非常有效的解决问题的模式识别，并可以应用于预测蛋白质二级结构属性直接从氨基酸序列数据。为了检查神经网络概括与II类MHC编码的蛋白质内的结合相关的肽结构特征的潜力，我们训练了神经网络以确定蛋白质的任何给定氨基酸是否是能够结合HLA-DR 1的肽段的一部分。我们报告说，一个神经网络训练的数据库组成的肽段已知结合HLA-DR 1能够概括有关HLA-DR 1结合能力的功能（r = 0.17和p = 0.0001）。
The presentation by antigen-presenting cells of immunodominant peptide segments in association with major histocompatibility complex (MHC) encoded proteins is fundamental to the efficacy of a specific immune response. One approach used to identify immunodominant segments within proteins has involved the development of predictive algorithms which utilize amino acid sequence data to identify structural characteristics or motifs associated with in vivo antigenicity. The parallel-computing technique termed 'neural networking' has recently been shown to be remarkably efficient at addressing the problem of pattern recognition and can be applied to predict protein secondary structure attributes directly from amino acid sequence data. In order to examine the potential of a neural network to generalize peptide structural features related to binding within class II MHC-encoded proteins, we have trained a neural network to determine whether or not any given amino acid of a protein is part of a peptide segment capable of binding to HLA-DR1. We report that a neural network trained on a data base consisting of peptide segments known to bind to HLA-DR1 is able to generalize features relating to HLA-DR1-binding capacity (r = 0.17 and p = 0.0001).