Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
J Biomol Struct Dyn. 2012;29(6):643-9. doi: 10.1080/07391102.2011.672628.
Knowledge of protein structural class can provide important information about its folding patterns. Many approaches have been developed for the prediction of protein structural classes. However, the information used by these approaches is primarily based on amino acid sequences. In this study, a novel method is presented to predict protein structural classes by use of chemical shift (CS) information derived from nuclear magnetic resonance spectra. Firstly, 399 non-homologue (about 15% identity) proteins were constructed to investigate the distribution of averaged CS values of six nuclei ((13)CO, (13)Cα, (13)Cβ, (1)HN, (1)Hα and (15)N) in three protein structural classes. Subsequently, support vector machine was proposed to predict three protein structural classes by using averaged CS information of six nuclei. Overall accuracy of jackknife cross-validation achieves 87.0%. Finally, the feature selection technique is applied to exclude redundant information and find out an optimized feature set. Results show that the overall accuracy increased to 88.0% by using the averaged CSs of (13)CO, (1)Hα and (15)N. The proposed approach outperformed other state-of-the-art methods in terms of predictive accuracy in particular for low-similarity protein data. We expect that our proposed approach will be an excellent alternative to traditional methods for protein structural class prediction.
蛋白质结构类别的知识可以提供关于其折叠模式的重要信息。已经开发了许多方法来预测蛋白质结构类别。然而，这些方法所使用的信息主要基于氨基酸序列。在这项研究中，提出了一种新的方法，通过使用从核磁共振谱中得出的化学位移（CS）信息来预测蛋白质结构类别。首先，构建了 399 个非同源（约 15%的同一性）蛋白质，以研究三种蛋白质结构类别中六个核（(13)CO、(13)Cα、(13)Cβ、(1)HN、(1)Hα 和 (15)N）的平均 CS 值的分布。随后，提出了支持向量机来使用六个核的平均 CS 信息来预测三种蛋白质结构类别。Jackknife 交叉验证的总体准确率达到 87.0%。最后，应用特征选择技术排除冗余信息并找到优化的特征集。结果表明，通过使用 (13)CO、(1)Hα 和 (15)N 的平均 CS，整体准确率提高到 88.0%。与其他最先进的方法相比，该方法在预测准确性方面表现更好，特别是对于低相似度的蛋白质数据。我们期望我们提出的方法将成为蛋白质结构类别预测的传统方法的极好替代方法。
