RESUMO
Mycobacterium is a pathogenic bacterium, which is a causative agent of tuberculosis (TB) and leprosy. These diseases are very crucial and become the cause of death of millions of people every year in the world. So, the characterize structure of membrane proteins of the protozoan play a vital role in the field of drug discovery because, without any knowledge about this Mycobacterium's membrane protein and their types, the scientists are unable to treat this pathogenic protozoan. So, an accurate and competitive computational model is needed to characterize this uncharacterized structure of mycobacterium. Series of attempts were carried out in this connection. Split amino acid compositions, Unbiased-Dipeptide peptide compositions (Unb-DPC), Over-represented tri-peptide compositions, compositions & translation were the few recent encoding techniques followed by different researchers in their publications. Although considerable results have been achieved by these models, still there is a gap which is filled in this study. In this study, an evolutionary feature extraction technique position specific scoring matrix (PSSM) is applied in order to extract evolutionary information from protein sequences. Consequently, 99.6% accuracy was achieved by the learning algorithms. The experimental results demonstrated that the proposed computational model will lead to develop a powerful tool for anti-mycobacterium drugs as well as play a promising rule in proteomic and bioinformatics.