Prediction of protein supersecondary structures based on the artificial neural network method
Prediction of protein supersecondary structures based on the artificial neural network method
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DOI：
10.1093/protein/10.7.763
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发表时间：
1997-07-01
期刊：
影响因子：
--
通讯作者：
Xu, D
中科院分区：
文献类型：
--
作者：
Sun, ZR;Rao, XQ;Xu, D
研究了 11 种常见连接肽的序列模式，从而对超二级蛾进行了分类。建立了蛋白质超二级模体数据库，应用人工神经网络方法，即反向传播神经网络对蛋白质序列进行超二级模体的预测，预测正确率高于70%，其中很多在75%~82%之间，这些结果对于进一步研究蛋白质的结构与功能之间的关系有一定的参考价值，也可能为蛋白质设计和蛋白质三级结构的预测提供一些重要信息。
The sequence patterns of 11 types of frequently occurring connecting peptides, which lead to a classification of supersecondary moths, were studied. A database of protein supersecondary motifs was set up, An artificial neural network method, i,e, the back propagation neural network, was applied to the predictions of the supersecondary moths from protein sequences, The prediction correctness ratios are higher than 70%, and many of them vary from 75 to 82%, These results are useful for the further study of the relationship between the structure and function of proteins, It may also provide some important information about protein design and the prediction of protein tertiary structure.