Guided by Prof.R.Nayak, of Microbilogy and Cell Biology in Indian Institute of Science, Bangalore, India.
Few notes about my project....
OVERVIEW OF THE STUDY 1. Mycobacterium tuberculosis – bacterium responsible for tuberculosis.
2. Infects mainly lungs and occasionally CNS, lymphatic system, circulatory system, genitourinary system, bones and joints.
3. 2 billion people are affected per year.
4. Treatment is given by certain drugs like Isoniazid, Rifampicin, Ethambutol and Pyrazinamide and vaccination is made possible by BCG (Bacillus Calmette Guerin).
Pathogenesis includes 5 major stages
1. Inhalation of the droplet nuclei
2. Initial infection
3. Tubercle formation
4. Secondary lesion
5. Extracellular Multiplication
GENOMIC AND PROTEOMIC INFORMATIONS
• MTB H37Rv strain was first isolated in 1905
• It has a total of 4047 genes with 3999 genes which encodes for proteins (Cole et al., 1998).
• Total number of possible annotated proteins from M.tuberculosis genome is 3945 of which 1659 proteins has been analysed for MHC peptide binding against human as well as mouse alleles.
IMMUNOINFORMATICS
1. Since immune system is recognised as the most complex among the biological systems, informatics can be the key to explore the complexity.
2. The application of immunoinformatics is to predict the peptides binding to different MHC regions which are consistent with experimental results.
3. In this context – BIMAS.
MHC BINDING PREDICTIONS
1. Helps in predicting T-Cell epitopes from proteins
2. Several methods like EPIPREDICT, PROPRED, PREDICT, EPIMATRIX, MHCPEP, JENPEP have been developed for predicting MHC binders in protein sequence on the basis of rules that govern the binding of a peptide to MHC molecule.
3. Binding Motifs
4. Quantitative Matrices
5. Artificial Neural Networks (ANN)
6. Abinitio or Structural Information.
AIM AND OBJECTIVE
1. Define T-cell epitopes of Mycobacterium tuberculosis H37Rv strain through insilico methods using the proteome of the bacterium.
2. Define MHC class I binding motifs of the Mycobacterial proteins.
3. Generate all promiscuous HLA class I binding peptides from tuberculosis proteome, which is also repeated for MHC class I mouse alleles.
4. Select high affinity binding peptides and promiscuous HLA binding peptides and to evaluate their ability to bind with HLA experimentally.
5. Based on the leads obtained synthetic haplotype specific protein vaccine suitable for use in a given population or a personalized vaccine is prepared.
6. 522 proteins of Mycobacterium tuberculosis from class I.G, I.I, I.J and II.I were taken from Sanger centre and the binding affinity of nonameric peptides were analysed insilico by HLA peptide binding prediction tool BIMAS.
7. Validation of analysis is done by molecular modeling of the chosen peptide complex (having high score from BIMAS analysis) with the corresponding HLA molecule by replacing the original peptide crystal structure present in PDB. So the model was built by energy minimization using Insight II (Discoverer module).
8. A comprehensive database known as “ TUBERCULOSIS VACCINOGENOMICS DATABASE was developed using MySQL (A database module), PHP (A server site scripting language) and IIS/Apache (A local web server in order to run the PHP scripts).
METHODS AND MATERIALS
1. Sequence retrieval from the Sanger centre.
2. BIMAS – BioInformatics and Molecular Analysis Section.
3. Principles of calculation
4. MySQL
5. PHP
RESULTS OBTAINED
1. 522 proteins from the classes I.G Biosynthesis of Cofactors, Prosthetic groups and carriers, I.I Polyketide and non-ribosomal peptide synthesis, I.J Broad regulatory functions, II.I Cell processes were analysed
2. Rv2027c and Rv2040c were found to show the highest percentage of binding.
3. Rv1528 which is a PKS-associated protein, belonging to the class polyketide and non-ribosomal proteins has minimum energy at binding affinity 100.
4. The peptide taken for the replacement is “AATMKSYGR” and it shows minimum energy at lower affinity.
REFERENCES
1. Stewart et al., 2003: Stewart GR, Robertson BD, Young DB Tuberculosis: a problem with persistence. Nat Rev Microbiol . 2003; 1(2): 97 - 105. [PubMed: 15035039 ].
2. Camus et al., 2002: Camus Jean-Christophe, Melinda J. Pryor, Claudine Mdigue, and Stewart T. Cole Re-annotation of the genome sequence of Mycobacterium tuberculosis H37Rv. Microbiology . 2002; 148: 2967 - 2973. [PubMed: 12368430 ].
3. Cole et al
4. http://pathport.vbi.vt.edu/pathinfo/pathogens/Tuberculosis /
5. http://www-micro.msb.le.ac.uk/index.html
6. http://www.jgi.doe.gov/
7. http://www- medlib.med.utah.edu/WebPath/TUTORIAL/TUTORIAL.html
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