摘要
The amino acid composition and the biased auto-correlation function are considered as features, BP neural network algorithm is used to synthesize these features. The prediction accuracy of this method is verified by using the independent non-homologous protein database. It is shown that the average absolute errors for resubstitution test are 0.070 and 0.068 with the standard deviations 0.049 and 0.047 for the prediction of the content of α-helix and β-sheet respectively. For cross-validation test, the average absolute errors are 0.075 and 0.070 with the standard deviations 0.050 and 0.049 for the prediction of the content of α-helix and β-sheet respectively. Compared with the other methods currently available, the BP neural network method combined with the amino acid composition and the biased auto-correlation function features can effectively improve the prediction accuracy.
基于氨基酸组成和有偏自相关函数的特征参量 ,利用BP神经网络 ,提出了一种预测蛋白质二级结构中α螺旋和 β折叠含量的计算方法 .采用相互独立的非同源蛋白质数据库对该方法的准确性进行检验 ,对蛋白质二级结构α螺旋和 β折叠含量的预测的结果为 :自检验的平均绝对误差分别为 0 .0 70和 0 .0 6 8,相应的标准偏差分别为 0 .0 49和 0 .0 47;他检验的平均绝对误差分别为 0 .0 75和 0 .0 70 ,相应的标准偏差分别为 0 .0 5 0和 0 .0 49.与常用方法相比 ,利用此方法预测蛋白质二级结构含量可有效提高预测精度 .
