title: Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC.
abstract: 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.
authors.affiliation: Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
authors.forename: Muslim
authors.initials: M
authors.lastname: Khan
authors.affiliation: Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan. Electronic address: m.hayat@awkum.edu.pk.
authors.forename: Maqsood
authors.initials: M
authors.lastname: Hayat
authors.affiliation: Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan; Faculty of Computing and Information Technology in Rabigh, King Abdul Aziz University, Saudi Arabia.
authors.forename: Sher Afzal
authors.initials: SA
authors.lastname: Khan
authors.affiliation: Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
authors.forename: Nadeem
authors.initials: N
authors.lastname: Iqbal
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D015982:Bias; D019295:Computational Biology / Q000379:methods; D004151:Dipeptides / Q000737:chemistry*; D008565:Membrane Proteins / Q000737:chemistry* / Q000145:classification; D008962:Models, Theoretical; D009159:Mycobacteriaceae / Q000737:chemistry* / Q000648:ultrastructure
chemical_list: D004151:Dipeptides; D008565:Membrane Proteins
keywords: Mycobacterium; Oversampled features; Support vector machine; Un-biasness