Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou’s pseudo amino acid composition

Mycobacterium tuberculosis (MTB) is a pathogenic bacterial species in the genus Mycobacterium and the causative agent of most cases of tuberculosis ( Berman et al., 2000). Knowledge of the localization of Mycobacterial protein may help unravel the normal function of this protein. Automated prediction of Mycobacterial protein subcellular localization is an important tool for genome annotation and drug discovery. In this work, a benchmark data set with 638 non-redundant mycobacterial proteins is constructed and an approach for predicting Mycobacterium subcellular localization is proposed by combining amino acid composition, dipeptide composition, reduced physicochemical property, evolutionary information, pseudo-average chemical shift. The overall prediction accuracy is 87.77% for Mycobacterial subcellular localizations and 85.03% for three membrane protein types in Integral membranes using the algorithm of increment of diversity combined with support vector machine. The performance of pseudo-average chemical shift is excellent. In order to check the performance of our method, the data set constructed by Rashid was also predicted and the accuracy of 98.12% was obtained. This indicates that our approach was better than other existing methods in literature.

► We constructed a benchmark dataset of Mycobacterium proteins, it is listed on our website.
► The better predictive accuracy is obtained for the dataset of Rashid.
► A novel constructed feature PseACS is proposed.
► We established a user-friendly web-server PseACS, which is accessible to the public.