In silico approaches for the identification of virulence candidates amongst hypothetical proteins of Mycoplasma pneumoniae 309. (December 2015)

• Record Type: Journal Article
• Authors: Shahbaaz, Mohd.; Bisetty, Krishna; Ahmad, Faizan; Hassan, Md. Imtaiyaz
• Is Part Of: Computational biology and chemistry. Volume 59:Part A(2015)
• Page Start: 67
• Page End: 80
• Publication Date: 2015-12
• Subjects: Hypothetical proteins -- Mycoplasma pneumoniae -- Function predictions -- Sequence analyses -- Virulence factors -- Molecular dynamics Simulations; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2015.09.007
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Showing computational workflow of adopted pipeline used for annotating function of HPs. Highlights: Genome sequence of Mycoplasma pneumoniae was extensively analyzed. Functions of 83HPs were successfully predicted with high accuracy. We found 23 enzymes, 12 transporter, 27 lipoproteins and 10 binding proteins among HPs. Many new virulent factors and adhesions were identified and characterized. Abstract: Mycoplasma pneumoniae type 2a strain 309 is a simplest known bacterium and is the primary cause of community acquired pneumonia in the children . It mainly causes severe atypical pneumonia as well as several other non-pulmonary manifestations such as neurological, hepatic, hemolytic anemia, cardiac diseases and polyarthritis. The size of M. pneumoniae genome (Accession number:NC_016807.1 ) is relatively smaller as compared to other bacteria and contains 707 functional proteins, in which 204 are classified as hypothetical proteins (HPs) because of the unavailability of experimentally validated functions. The functions of the HPs were predicted by integrating a variety of protein classification systems, motif discovery tools as well as methods that are based on characteristic features obtained from the protein sequence and metabolic pathways. The probable functions of 83HPs were predicted successfully. The accuracy of the diverse tools used in the adopted pipeline was evaluated on the basis of statistical techniques of Receiver Operating Characteristic (ROC), which indicated the reliability of the functional predictions. Furthermore, the virulent HPs present in the set of 83 functionally annotated proteins were predicted by using the Bioinformatics tools and the conformational behaviours of the proteins with highest virulence scores were studied by using the molecular dynamics (MD) simulations. This study will facilitate in the better understanding of various drug resistance and pathogenesis mechanisms present in the M. pneumoniae and can be utilized in designing of better therapeutic agents.