PROTEIN SECONDARY STRUCTURE PREDICTION WITH A NEURAL NETWORK
PROTEIN SECONDARY STRUCTURE PREDICTION WITH A NEURAL NETWORK
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DOI：
10.1073/pnas.86.1.152
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发表时间：
1989-01-01
影响因子：
11.1
通讯作者：
KARPLUS, M
中科院分区：
文献类型：
--
作者：
HOLLEY, LH;KARPLUS, M
提出了一种基于神经网络的蛋白质二级结构预测方法。一个训练阶段被用来教网络识别一个样本集的48个已知结构的蛋白质的二级结构和氨基酸序列之间的关系。在已知结构的14种蛋白质的单独测试集上，该方法对三种状态（螺旋、片和卷曲）的最大总体预测准确率为63%。从计算中获得每个残基的螺旋和折叠趋势的数值测量。当预测被过滤，只包括最强的31%的预测时，预测准确率上升到79%。
A method is presented for protein secondary structure prediction based on a neural network. A training phase was used to teach the network to recognize the relation between secondary structure and amino acid sequences on a sample set of 48 proteins of known structure. On a separate test set of 14 proteins of known structure, the method achieved a maximum overall predictive accuracy of 63% for three states: helix, sheet, and coil. A numerical measure of helix and sheet tendency for each residue was obtained from the calculations. When predictions were filtered to include only the strongest 31% of predictions, the predictive accuracy rose to 79%.