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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Subcellular Localization Prediction with New Protein Encoding Schemes
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Subcellular Localization Prediction with New Protein Encoding Schemes

机译：新蛋白编码方案的亚细胞定位预测。

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摘要

Subcellular localization is one of the key properties in functional annotation of proteins. Support vector machines (SVMs) have been widely used for automated prediction of subcellular localizations. Existing methods differ in the protein encoding schemes used. In this study, we present two methods for protein encoding to be used for SVM-based subcellular localization prediction: n-peptide compositions with reduced amino acid alphabets for larger values of n and pairwise sequence similarity scores based on whole sequence and N-terminal sequence. We tested the methods on a common benchmarking data set that consists of 2,427 eukaryotic proteins with four localization sites. As a result of 5-fold cross-validation tests, the encoding with n-peptide compositions provided the accuracies of 84.5, 88.9, 66.3, and 94.3 percent for cytoplasmic, extracellular, mitochondrial, and nuclear proteins, where the overall accuracy was 87.1 percent. The second method provided 83.6, 87.7, 87.9, and 90.5 percent accuracies for individual locations and 87.8 percent overall accuracy. A hybrid system, which we called PredLOC, makes a final decision based on the results of the two presented methods which achieved an overall accuracy of 91.3 percent, which is better than the achievements of many of the existing methods. The new system also outperformed the recent methods in the experiments conducted on a new-unique SWISSPROT test set
机译：亚细胞定位是蛋白质功能注释中的关键特性之一。支持向量机（SVM）已被广泛用于自动预测亚细胞定位。现有方法在所使用的蛋白质编码方案上有所不同。在这项研究中，我们提出了两种用于基于SVM的亚细胞定位预测的蛋白质编码方法：具有较大n值的氨基酸字母的n肽组成和基于全序列和N末端序列的成对序列相似性评分。我们在一个共同的基准数据集上测试了这些方法，该数据集由2,427个具有四个定位位点的真核蛋白质组成。作为5倍交叉验证测试的结果，使用n肽组合物编码的细胞质，细胞外，线粒体和核蛋白的准确度为84.5％，88.9％，66.3和94.3％，而总准确性为87.1％ 。第二种方法为各个位置提供了83.6％，87.7％，87.9％和90.5％的准确度，以及87.8％的整体准确度。混合系统，我们称为PredLOC，根据所提出的两种方法的结果做出最终决定，该方法的总体准确度达到91.3％，优于许多现有方法的成果。在新的独特SWISSPROT测试仪上进行的实验中，新系统的性能也优于最新方法

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