Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins.
J Theor Biol
; 435: 116-124, 2017 12 21.
Article
en En
| MEDLINE
| ID: mdl-28927812
ABSTRACT
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Proteínas Bacterianas
/
Inteligencia Artificial
/
Posición Específica de Matrices de Puntuación
/
Proteínas de la Membrana
/
Mycobacterium
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
J Theor Biol
Año:
2017
Tipo del documento:
Article
País de afiliación:
Pakistán