Introduction

Re-emergence of multidrug resistant strains of Mycobacterium tuberculosis has caused a serious discussion because tuberculosis continues to be a major cause of morbidity and mortality throughout the world (Aagaard et al.,2003 ) and infects approximately 36 million people worldwide between 2002 to 2020 (WHO, 2005). TB Structural Genomics Consortium (TBSGC) reported 1,238,518 deaths and 5,308,950 new cases in 2008. The rise in number of patients has caused global concern and stringent need to review various therapeutics aspects of tuberculosis (Zhang et al., 2006). Drugs available for the treatment of tuberculosis, First line drugs are isoniazid (INH), rifampin (RIF), pyrazinamide (PZA), ethambutol (EMB) etc., and second line drugs, para amino salicylate (PAS), kanamycin, cycloserine (CS), ethionamide (ETA), amikacin, capreomycin, thiacetazone, fluoroquinolones. Current TB therapy, also known as directly observed treatment short-course (DOTS) consists of an initial phase of treatment with these 4 drugs for 2 months daily, followed by treatment with INH and RIF for another 4 months (WHO 2000). Major drawbacks of current TB therapy are long drugs administration (at least 6-8 months) and very expensive drug combination has to be administered with significant side effects (Chopra et. al., 2003). An innovative, approach is needed for new drug designing against novel drug target to fight tuberculosis.

Computer-aided drug design (CADD)

Computer-aided drug design, often called structure based design involves using the biochemical information of ligand-receptor interaction in order to postulate ligand refinements i.e. improvement in binding affinity to receptor. Identification of new lead compounds for new target depends on the information of the target-ligand system, like target and ligand are well known or Target is known but ligand is not known, or ligand is known etc. A large no. of software’s is available on different information and different strategy for new lead compounds, like Ligbuilder (Wang et al., 2000), ligand scout 2.0. Compatibility of target and ligand could be performed through docking. Docking is a method which predicts the preferred orientation of target to ligand when bound to each other to form a stable complex (Lengauer et. al, 1996). Molecular-docking-based virtual screening is an important tool in drug discovery that is used to significantly reduce the number of possible chemical compounds to be investigated. In addition to the selection of a sound docking strategy with appropriate scoring functions, another technical challenge is to in silico screen millions of compounds in a reasonable time. To meet this challenge, it is necessary to use high performance computing (HPC) platforms and techniques. Several commercial as well as Academics docking programs, Glide (Friesner et. Al., 2004), LigandFit, GLOD, M-ZDOCK (G. Costakes) and Autodock (Vaque M, 2006) are available.

Material and Methods

Identification of unique pathways of by the comparative study of

Identification of non-homologous proteins by performing the BLAST search

Target characterization and ligand library generation

Virtual screening

Results and Discussion

In present study, fourteen unique pathways with 24 new non-homologous targets were identified through in-silico comparative metabolic pathway analysis of Homo sapiens and M. tuberculosis H37Rv. Pathways which are not present in the Homo sapiens but present in the mycobacterium are designated as unique pathways. Total, 119 metabolic pathways have been found in mycobacterium tuberculosis including 14 unique pathway proteins (Table-1).

Figure 1

Table -1 show unique pathways of mycobacterium tuberculosis with reference to

Non-homologous proteins are first preference for effective drug designing to avoid the deceptive targeting and side-effect. Alanine Racemase (Rv3423c) has been considered for drug designing due its role in cell wall synthesis, cell wall organization, alanine metabolic process, alanine racemase activity, and pyridoxal phosphate binding etc. and its structure is available in Protein Data Bank (1XFC). Characterization of structure is very important for rational drug designing.PDB structure of ALR protein is shown in Figure1.

Figure 4

Figure -1 shows the active site residue (LYS- 42) through LIGPLOT software.

Prediction of active site residues (ALA39, LYS42) of target protein were done through comparative analysis of POCKET FINDER, SURFACE RACER-4.0 and LIGPLOT software results (Figure-2). The comparative results of softwares are shown in table-2.

Figure 3

Table-2 Comparative results of softwares and analysis for Active-site prediction

Figure 5

Table-3 shows list of selected ligands from generated library after docking

Ligand library were designed through Ligand Scout 2.0 and ACDLABS 10.0 ChemSketch. Total 50 ligand molecules were generated including Ligand scout generated ligands. Docking was performed by the AutoDock4.0. On the basis of docking energies, a list of top 6 molecules has been proposed which has good compatibility binding affinity with target in table-3.

Figure 6

Table-4 shows binding energy list of selected ligand and their interacting amino-acids

Best confirmations of these results are shown in table-4. Each of these confirmation results calculated from the ligand with the help of Autodock are shown below. Lowest negative energies of these confirmations have shown best binding of ligands to target proteins shown in table-3.

{image:6}

Conclusion

In the present study, comparative metabolic pathway analysis of the host H. sapiens and the pathogen M. tuberculosis has been performed. Fourteen unique pathways of Mycobacterium tuberculosis have been identified with the comparative study to Homo sapiens. Identification of non-homologous proteins has been done by the BLAST similarity search. Alanine Racemase (Rv3423c) has been considered for drug designing due its role in cell wall synthesis, cell wall organization, alanine metabolic process, alanine racemase activity, and pyridoxal phosphate binding etc. Ligand library of 50 molecules were designed through Ligand Scout 2.0 and docking studies were performed by the AutoDock 4.0. On the basis of docking energies, a list of top 6 molecules has been proposed which has good compatibility binding affinity with target. Designed ligand are interacting with ASP (85), LYS (156), ALA (181) residues of protein structure.

Future Direction

Potential target identification of mycobacterium is on high demand due to re-emergence of drug resistance mycobacterium strains. Comparative study of metabolic pathways is a good approach for the identification of mycobacterium tuberculosis but it still need refining with more high level and number of cutoffs. Proposed ligand molecules need further studies like- molecular interaction of ligand with targets, toxicity prediction, drug likeness, etc.

Acknowledgements