Holley L H, Karplus M
Department of Chemistry, Harvard University, Cambridge, MA 02138.
Proc Natl Acad Sci U S A. 1989 Jan;86(1):152-6. doi: 10.1073/pnas.86.1.152.
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%.
提出了一种基于神经网络的蛋白质二级结构预测方法。在一个由48个已知结构蛋白质组成的样本集上，使用训练阶段来训练网络识别二级结构与氨基酸序列之间的关系。在一个由14个已知结构蛋白质组成的单独测试集上，该方法对于螺旋、折叠和卷曲三种状态的总体预测准确率最高达到了63%。通过计算得出了每个残基的螺旋和折叠倾向的数值度量。当预测结果仅筛选出最强的31%的预测时，预测准确率提高到了79%。
