Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance—especially the multidrug-resistant (MDR) and extensively drug-resistance (XDR)—poses a huge threat to the treatment. However, traditional biochemical experimental method to tackle TB is time-consuming and costly. Benefited by the appearance of the enormous genomic and proteomic sequence data, TB can be treated via sequence-based biological computational approach-bioinformatics. Studies on predicting subcellular localization of mycobacterial protein (MBP) with high precision and efficiency may help figure out the biological function of these proteins and then provide useful insights for protein function annotation as well as drug design. In this review, we reported the progress that has been made in computational prediction of subcellular localization of MBP including the following aspects: 1) Construction of benchmark datasets. 2) Methods of feature extraction. 3) Techniques of feature selection. 4) Application of several published prediction algorithms. 5) The published results. 6) The further study on prediction of subcellular localization of MBP.

结核分枝杆菌（MTB）可以导致可怕的结核病（TB），据报道这是最可怕的流行病之一。尽管已经开发出许多生物化学分子药物来应对这种疾病，但耐药性，尤其是耐多药（MDR）和广泛耐药性（XDR），对治疗构成了巨大威胁。然而，解决结核病的传统生化实验方法既费时又昂贵。得益于巨大的基因组和蛋白质组序列数据的出现，结核病可以通过基于序列的生物计算方法-生物信息学进行治疗。以高精度和高效率预测分枝杆菌蛋白（MBP）亚细胞定位的研究可能有助于弄清这些蛋白的生物学功能，然后为蛋白功能注释和药物设计提供有用的见识。在这篇综述中，我们报告了MBP亚细胞定位的计算预测所取得的进展，包括以下几个方面：1）建立基准数据集。2）特征提取方法。3）特征选择技术。4）几种已公开的预测算法的应用。5）公布的结果。6）对MBP亚细胞定位的预测的进一步研究。我们报告了MBP亚细胞定位的计算预测方面取得的进展，包括以下几个方面：1）建立基准数据集。2）特征提取方法。3）特征选择技术。4）几种已公开的预测算法的应用。5）公布的结果。6）对MBP亚细胞定位的预测的进一步研究。我们报告了MBP的亚细胞定位的计算预测方面取得的进展，包括以下几个方面：1）建立基准数据集。2）特征提取方法。3）特征选择技术。4）几种已公开的预测算法的应用。5）公布的结果。6）对MBP亚细胞定位的预测的进一步研究。