Khan Muslim, Hayat Maqsood, Khan Sher Afzal, Iqbal Nadeem
Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
J Theor Biol. 2017 Feb 21;415:13-19. doi: 10.1016/j.jtbi.2016.12.004. Epub 2016 Dec 8.
This study investigates an efficient and accurate computational method for predicating mycobacterial membrane protein. Mycobacterium is a pathogenic bacterium which is the causative agent of tuberculosis and leprosy. The existing feature encoding algorithms for protein sequence representation such as composition and translation, and split amino acid composition cannot suitably express the mycobacterium membrane protein and their types due to biasness among different types. Therefore, in this study a novel un-biased dipeptide composition (Unb-DPC) method is proposed. The proposed encoding scheme has two advantages, first it avoid the biasness among the different mycobacterium membrane protein and their types. Secondly, the method is fast and preserves protein sequence structure information. The experimental results yield SVM based classification accurately of 97.1% for membrane protein types and 95.0% for discriminating mycobacterium membrane and non-membrane proteins by using jackknife cross validation test. The results exhibit that proposed model achieved significant predictive performance compared to the existing algorithms and will lead to develop a powerful tool for anti-mycobacterium drugs.
本研究探讨了一种用于预测分枝杆菌膜蛋白的高效且准确的计算方法。分枝杆菌是一种致病细菌，是结核病和麻风病的病原体。现有的用于蛋白质序列表示的特征编码算法，如组成和翻译以及分割氨基酸组成，由于不同类型之间的偏差，无法适当地表达分枝杆菌膜蛋白及其类型。因此，本研究提出了一种新颖的无偏差二肽组成（Unb-DPC）方法。所提出的编码方案有两个优点，首先它避免了不同分枝杆菌膜蛋白及其类型之间的偏差。其次，该方法速度快且保留了蛋白质序列结构信息。通过留一法交叉验证测试，实验结果表明基于支持向量机的分类对于膜蛋白类型的准确率为97.1%，对于区分分枝杆菌膜蛋白和非膜蛋白的准确率为95.0%。结果表明，与现有算法相比，所提出的模型具有显著的预测性能，并将有助于开发一种强大的抗分枝杆菌药物工具。
