%0 Journal Article
%T PREDICTION OF THE CONTENT OF α-HELIX AND β-STRAND OF NON-HOMOLOGOUS GLOBULAR PROTEINS BASED ON BP NEURAL NETWORK<br>用BP神经网络基于氨基酸特性预测非同源蛋白质二级结构含量
%A QIN Hong-shan
%A YANG Xin-qi
%A <br>秦红珊
%A 杨新岐
%A 胡娅
%J 生物物理学报
%D 2002
%I 
%X The amino acid composition and the biased auto-covariance function are considered as features and multiple layer propagation artificial neural network of BP algorithm is used to synthesize these features. A new method to predict the content of a-helix and b-strand of globular proteins is presented. The prediction accuracy of this method is verified by using the independent non-homologous protein database. We have found that the amino-acid index proposed by Ponnuswamy leads to the optimal predictive result in the case for the database sets studied in this paper. It is shown that the average absolute errors for resubstitution test are 0.069 and 0.065 with the standard deviations 0.044 and 0.047 for the prediction of the content of α-helix and β-strand respectively. The average absolute errors for cross-validation test are 0.077 and 0.070 with the standard deviations 0.051 and 0.049 for the prediction of the content of α-helix and β-strand respectively. Compared with the BP neural network method only using the amino acid composition as features, the average absolute errors for cross-validation test are relatively reduced by 0.024 and 0.016 with the standard deviations reduced by 0.031 and 0.018, respectively. Compared with the multiple linear regression method proposed by Zhang et al (Protein Eng., 11, 971- 979, 1998), the average absolute errors for cross-validation test are relatively reduced by 0.018 and 0.011 with the standard deviations reduced by 0.020 and 0.012. It is shown that the BP neural network method combined with the amino-acid composition and the biased auto-covariance function features could effectively improve the prediction accuracy.
%K BP神经网络
%K 氨基酸组成
%K 有偏自协方差函数
%K 氨基酸特性
%K 二级结构
%K 含量
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=E0C9D9BBED813D6674AC13E942EAC86D&aid=F261732F3A252B34&yid=C3ACC247184A22C1&vid=13553B2D12F347E8&iid=E158A972A605785F&sid=98494933359B55EC&eid=8143FF92EEF26F96&journal_id=1000-6737&journal_name=生物物理学报&referenced_num=1&reference_num=14