Huang K, Andrec M, Heald S, Blake P, Prestegard J H
Department of Chemistry, Yale University, New Haven, CT 06511, USA.
J Biomol NMR. 1997 Jul;10(1):45-52. doi: 10.1023/a:1018340603528.
A neural network which can determine both amino acid class and secondary structure using NMR data from 15N-labeled proteins is described. We have included nitrogen chemical shifts, 3JHNH alpha coupling constants, alpha-proton chemical shifts, and side-chain proton chemical shifts as input to a three-layer feed-forward network. The network was trained with 456 spin systems from several proteins containing various types of secondary structure, and tested on human ubiquitin, which has no sequence homology with any of the proteins in the training set. A very limited set of data, representative of those from a TOCSY-HSQC and HNHA experiment, was used. Nevertheless, in 60% of the spin systems the correct amino acid class was among the top two choices given by the network, while in 96% of the spin systems the secondary structure was correctly identified. The performance of this network clearly shows the potential of the neural network algorithm in the automation of NMR spectral analysis.
描述了一种神经网络，它可以利用来自15N标记蛋白质的核磁共振（NMR）数据确定氨基酸类别和二级结构。我们将氮化学位移、3JHNHα耦合常数、α-质子化学位移和侧链质子化学位移作为输入，输入到一个三层前馈网络中。该网络用来自几种含有不同类型二级结构的蛋白质的456个自旋系统进行训练，并在人泛素上进行测试，人泛素与训练集中的任何蛋白质都没有序列同源性。使用了一组非常有限的数据，这些数据代表了来自TOCSY-HSQC和HNHA实验的数据。然而，在60%的自旋系统中，正确的氨基酸类别在网络给出的前两个选择之中，而在96%的自旋系统中，二级结构被正确识别。该网络的性能清楚地显示了神经网络算法在NMR光谱分析自动化中的潜力。
