Wang Jiren, Sung Wing-Kin, Krishnan Arun, Li Kuo-Bin
Bioinformatics Institute, Matrix, Singapore 138671.
BMC Bioinformatics. 2005 Jul 13;6:174. doi: 10.1186/1471-2105-6-174.
Predicting the subcellular localization of proteins is important for determining the function of proteins. Previous works focused on predicting protein localization in Gram-negative bacteria obtained good results. However, these methods had relatively low accuracies for the localization of extracellular proteins. This paper studies ways to improve the accuracy for predicting extracellular localization in Gram-negative bacteria.
We have developed a system for predicting the subcellular localization of proteins for Gram-negative bacteria based on amino acid subalphabets and a combination of multiple support vector machines. The recall of the extracellular site and overall recall of our predictor reach 86.0% and 89.8%, respectively, in 5-fold cross-validation. To the best of our knowledge, these are the most accurate results for predicting subcellular localization in Gram-negative bacteria.
Clustering 20 amino acids into a few groups by the proposed greedy algorithm provides a new way to extract features from protein sequences to cover more adjacent amino acids and hence reduce the dimensionality of the input vector of protein features. It was observed that a good amino acid grouping leads to an increase in prediction performance. Furthermore, a proper choice of a subset of complementary support vector machines constructed by different features of proteins maximizes the prediction accuracy.
预测蛋白质的亚细胞定位对于确定蛋白质的功能很重要。先前专注于预测革兰氏阴性菌中蛋白质定位的工作取得了良好成果。然而，这些方法在预测细胞外蛋白质的定位方面准确率相对较低。本文研究提高革兰氏阴性菌中细胞外定位预测准确率的方法。
我们基于氨基酸子字母表和多个支持向量机的组合开发了一个用于预测革兰氏阴性菌蛋白质亚细胞定位的系统。在五折交叉验证中，我们的预测器对细胞外位点的召回率和总体召回率分别达到86.0%和89.8%。据我们所知，这些是预测革兰氏阴性菌亚细胞定位的最准确结果。
通过提出的贪心算法将20种氨基酸聚类为几组，为从蛋白质序列中提取特征提供了一种新方法，以覆盖更多相邻氨基酸，从而降低蛋白质特征输入向量的维度。据观察，良好的氨基酸分组会导致预测性能的提高。此外，由蛋白质不同特征构建的互补支持向量机子集的适当选择可使预测准确率最大化。
