Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor
Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor
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DOI：
10.1007/s00894-013-1827-7
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发表时间：
2013-04
影响因子：
2.2
通讯作者：
Yuanqiang Wang;Yong Lin;M. Shu;Rui Wang;Yong Hu;Zhihua Lin
中科院分区：
文献类型：
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作者：
Yuanqiang Wang;Yong Lin;M. Shu;Rui Wang;Yong Hu;Zhihua Lin
细胞毒性T淋巴细胞表位的准确鉴定在肽疫苗设计中变得越来越重要。泛素-蛋白酶体系统通过降解抗原蛋白，在加工和呈递主要组织相容性复合物I类限制性表位中起关键作用。为了提高表位预测和识别的特异性和效率，必须考虑泛素-蛋白酶体复合物与蛋白质抗原之间的识别模式。因此，必须建立一个准确预测蛋白酶体切割的模型。本研究提出了一组新的参数来表征切割窗口，并使用反向传播神经网络算法来建立准确预测蛋白酶体切割的模型。预测模型的准确性取决于切割窗口的大小，对N端和C端的预测准确率分别达到95.454%和95.011%。结果表明，蛋白酶体切割位点的识别依赖于邻近的序列，且C端的预测性能平均优于N端。因此，基于氨基酸性质的模型可以是高度可靠的，并反映蛋白酶体和肽序列之间相互作用的结构特征。
The accurate identification of cytotoxic T lymphocyte epitopes is becoming increasingly important in peptide vaccine design. The ubiquitin–proteasome system plays a key role in processing and presenting major histocompatibility complex class I restricted epitopes by degrading the antigenic protein. To enhance the specificity and efficiency of epitope prediction and identification, the recognition mode between the ubiquitin–proteasome complex and the protein antigen must be considered. Hence, a model that accurately predicts proteasomal cleavage must be established. This study proposes a new set of parameters to characterize the cleavage window and uses a backpropagation neural network algorithm to build a model that accurately predicts proteasomal cleavage. The accuracy of the prediction model, which depends on the window sizes of the cleavage, reaches 95.454 % for theN-terminus and 95.011 % for theC-terminus. The results show that the identification of proteasomal cleavage sites depends on the sequence next to it and that the prediction performance of theC-terminus is better than that of theN-terminus on average. Thus, models based on the properties of amino acids can be highly reliable and reflect the structural features of interactions between proteasomes and peptide sequences.