BMC Genomics. 2014;15 Suppl 1(Suppl 1):S2. doi: 10.1186/1471-2164-15-S1-S2. Epub 2014 Jan 24.
Prediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology.
In this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins. By applying SVM to our extracted features, for the first time we enhance the protein structural class prediction accuracy to over 90% and 85% for two popular low-homology benchmarks that have been widely used in the literature. We report 92.2% and 86.3% prediction accuracies for 25PDB and 1189 benchmarks which are respectively up to 7.9% and 2.8% better than previously reported results for these two benchmarks.
By proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.
预测蛋白质的结构类别可以提供关于其功能以及主要三级结构的重要信息。它也被认为是解决蛋白质结构预测问题的重要步骤。尽管迄今为止已经做出了所有努力，但寻找一种快速而准确的计算方法来解决蛋白质结构类别预测问题仍然是生物信息学和计算生物学中的一个具有挑战性的问题。
在这项研究中，我们提出了分段分布和分段自协方差特征提取方法，从进化谱和预测的蛋白质二级结构中捕获局部和全局判别信息。通过将 SVM 应用于我们提取的特征，我们首次将蛋白质结构类别预测精度提高到 90%以上，对于两个广泛应用于文献中的流行低同源性基准，分别提高了 92.2%和 86.3%。对于 25PDB 和 1189 基准，我们分别报告了 86.3%和 7.9%的预测精度，比这两个基准的先前报告结果分别提高了 2.8%和 7.9%。
通过提出分段分布和分段自协方差特征提取方法，从进化谱和预测的蛋白质二级结构中捕获局部和全局判别信息，我们能够显著提高蛋白质结构类别预测性能。
