Background: Prediction of protein subcellular location is a meaningful task which attracts much attention in recent years. Particularly, the number of new protein sequences yielded by the highthroughput sequencing technology in the post genomic era has increased explosively.

Objective: Protein subcellular localization prediction based solely on sequence data remains to be a challenging problem of computational biology.

Methods: In this paper, three sets of evolutionary features are derived from the position-specific scoring matrix, which has shown great potential in other bioinformatics problems. A fusion model is built up by the optimal parameters combination. Finally, principal component analysis and support vector machine classifier is applied to predict protein subcellular localization on NNPSL dataset and Cell- PLoc 2.0 dataset.

Results: Our experimental results show that the proposed method remarkably improved the prediction accuracy, and the features derived from PSI-BLAST profile only are appropriate for protein subcellular localization prediction.

背景：预测蛋白质亚细胞定位是一项有意义的工作，近年来引起了很多关注。特别地，在后基因组时代，通过高通量测序技术产生的新蛋白质序列的数量爆炸性地增加。

目的：仅基于序列数据的蛋白质亚细胞定位预测仍然是计算生物学的一个难题。

方法：本文从特定位置的得分矩阵中得出了三组进化特征，这些特征在其他生物信息学问题中显示出巨大的潜力。通过最佳参数组合建立融合模型。最后，将主成分分析和支持向量机分类器用于预测NNPSL数据集和Cell-PLoc 2.0数据集上的蛋白质亚细胞定位。

结果：我们的实验结果表明，该方法显着提高了预测准确性，并且从PSI-BLAST谱图得出的特征仅适用于蛋白质亚细胞定位预测。