Improved sequence-based prediction of protein secondary structures by combining vacuum-ultraviolet circular dichroism spectroscopy with neural network
Improved sequence-based prediction of protein secondary structures by combining vacuum-ultraviolet circular dichroism spectroscopy with neural network
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DOI：
10.1002/prot.22055
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发表时间：
2008-10-01
影响因子：
2.9
通讯作者：
Gekko, Kunihiko
中科院分区：
文献类型：
--
作者：
Matsuo, Koichi;Watanabe, Hidenori;Gekko, Kunihiko
同步辐射真空紫外圆二色光谱（VUVCD）技术通过扩展光谱的短波长范围，可以显著提高蛋白质二级结构含量和片段数的预测精度。在本研究中，我们结合VUVCD光谱下降到160 nm的神经网络（NN）的方法，以提高基于序列的蛋白质二级结构的预测。通过DSSP程序基于其X射线晶体结构将30个靶蛋白（测试集）的二级结构分配为α-螺旋、β-链等。结合从靶蛋白的VUVCD光谱估计的x-螺旋和β-链含量，将三个二级结构组分的总体基于序列的预测准确度Q（3）从59.5%提高到60.7%。在神经网络方法中，当结合二级结构内容时，对位置特定的评分矩阵进行分类时，预测准确率从70.9%提高到72.1%，当结合片段的文件编号时，预测准确率提高到72.5%，最后当过滤VUVCD数据时，预测准确率提高到74.9%。基于序列的二级结构预测的改进在总体性能的另外两个指标中也是明显的：轮胎相关系数（C）和片段重叠值（SOV）。这些结果表明，VUVCD数据可以提高预测精度到80%以上，结合目前最好的序列预测算法，大大扩展了VUVCD光谱的蛋白质结构生物学的适用性。
Synchrotron-radiation vacuum-ultraviolet circular dichroism (VUVCD) spectroscopy can significantly improve the predictive accuracy of the contents and segment numbers of protein secondary structures by extending the short-wavelength limit of the spectra. In the present study, we combined VUVCD spectra down to 160 nm with neural-network (NN) method to improve the sequence-based prediction of protein secondary structures. The secondary structures of 30 target proteins (test set) were assigned into alpha-helices, beta-strands, and others by the DSSP program based on their X-ray crystal structures. Combining the x-helix and beta-strand contents estimated from the VUVCD spectra of the target proteins improved the overall sequence-based predictive accuracy Q(3) for three secondary-structure components from 59.5 to 60.7%. Incorporating the position-specific scoring matrix in the NN method improved the predictive accuracy from 70.9 to 72.1% when combining the secondary-structure contents, to 72.5% when combining file numbers of segments, and finally to 74.9% when filtering the VUVCD data. Improvement in the sequence-based prediction of secondary structures was also apparent in two other indices of the overall performance: tire correlation coefficient (C) and the segment overlap value (SOV). These results suggest that VUVCD data could enhance the predictive accuracy to over 80% when combined with the currently best sequence-prediction algorithms, greatly expanding the applicability of VUVCD spectroscopy to protein structural biology.