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The chemical structure of the bacterial cell wall imparts rigidity to the cell, protects it against osmotic lysis, and determines the bacterial cell shape. The major structural element of the cell wall is the polymeric peptidoglycan murein. In Escherichia coli it consists of alternating units of N-acetylglucosamine and N-acetylmurein with an attached pentapeptide. MurA (UDP-N-acetylglucosamine enolpyruvyl transferase, EC 220.127.116.11) catalyzes the first committed step in the synthesis of the bacterial cell wall. It is the target of the naturally occurring, broad-spectrum antibiotic fosfomycin. Fosfomycin, an epoxide, is a relatively poor drug because an ever-increasing number of bacteria have developed resistance to fosfomycin. Thus, there is a critical need for the development of novel drugs that target MurA by a different molecular mode of action. Survival of bacteria depends on the activity of the enzyme MurA (UDP-N-acetylglucosamine enolpyruvyl transferase, EC 18.104.22.168) (Brown, E. D. et al, 1995; Du, W. et al 2000). MurA catalyzes the first committed step in the biosynthesis of the bacterial cell wall (van Heijenoort, J. Et al, 1994; van Heijenoort, J. 2001). Because this pathway is absent from mammals, MurA is an attractive target for the development of novel antibacterial agents (El Zoeiby, A. et al, 2003; Green, D. W. et al 2002).
The reaction catalyzed by MurA proceeds through the chemically unusual transfer of the enolpyruvyl moiety of phosphoenolpyruvate (PEP) to UDP-N-acetylglucosamine (UNAG) (Fig. 2.1). Unlike most PEP-dependent enzymes, which use PEP as a phosphoryl donor through cleavage of the high-energy P-O bond, in this reaction the C-O bond of PEP is cleaved to transfer the enolpyruvyl moiety to a second substrate. The only other enzyme known to catalyze a similar reaction is 5-enolpyruvyl shikimate-3-phosphate synthase (EC 22.214.171.124), also known as EPSPS or AroA. 5-Enolpyruvyl shikimate-3-phosphate synthase is the sixth enzyme in the shikimate pathway toward the synthesis of aromatic amino acids in microorganisms and plants (Roberts, C. W. et al 2002). Both enzymes exist in an open, substrate-free state and a closed, liganded state, indicating that their reactions follow an induced-fit mechanism (Schonbrunn, E. et al. 2001).
Widespread antibiotic resistance in many common bacterial pathogens has prompted extensive research toward the development of novel antibiotics that act by new inhibitory mechanisms. The enzymes involved in bacterial cell wall biosynthesis are ideal targets for the design of new inhibitors. The only known antibacterial drug targeting MurA is fosfomycin, the active ingredient of Monurolâ„¢. This epoxide forms a covalent adduct with a cysteine residue, Cys115 (numbering according to Escherichia coli MurA). Cys115 is located in a solvent-exposed loop that undergoes a large conformational change upon UNAG binding; this in turn allows inactivation by fosfomycin to occur.
There are three ways in which pathogenic bacteria can develop resistance to fosfomycin: (i) the resistance of Mycobacterium tuberculosis and Chlamydia to fosfomycin has been primarily attributed to the lack of Cys115 (McCoy, A. J. et al 2003). Sequencing alignments of the murA gene from other organisms suggest that the enzymes from Actinomycetales, Actinomyces, Nocardia, Streptomyces, and Borrelia also have a Cys to Asp change and are therefore suspected to be resistant to fosfomycin, too. (ii) A second mechanism of resistance is due to chromosome-encoded changes in the organisms that result in a decrease in transport of fosfomycin into the cell. (iii) The plasmid-encoded fosfomycin resistance protein (FosA) is a glutathione S-transferase that inactivates fosfomycin by forming an adduct of glutathione with the epoxide (Rife, C. L. et al 2002).
A small number of novel inhibitors of MurA were discovered recently by various high-throughput screening efforts in the pharmaceutical industry. Procter & Gamble compound PGE-553828 was reported to have an IC50 value of 38 Î¼m (Dai, H. J. et al 2001). Bristol-Myers Squibb Co. identified four compounds from an array of target-specific screening strains that inhibit MurA with IC50 values between 1.4 and 6.2 Î¼m (DeVito, J. A. et al 2002). R. W. Johnson Pharmaceutical Research Institute identified three inhibitors of MurA (RWJ-3981, RWJ-110192, and RWJ-140998) with IC50 values of 0.2, 0.3, and 0.9 Î¼m, respectively (Baum, E. Z. et al 2001). All three compounds showed unspecific inhibition DNA, RNA, and protein synthesis and apparently were not developed further.
The E.coli K-12 genomic DNA was isolated and amplification of murA region was done using specific primers in a PCR set up. Amplified murA was then inserted into a plasmid vector (pET 28c). Plasmid vector with the insert was used to transform a fresh culture of E.coli DH5Î±, known to be a good strain for transformation. Plasmids from successful transformants were isolated and a fresh culture of E.coli BL21 was transformed with it. Bl21strain is good for production. Expression of plasmid insert was induced using IPTG and the same was observed in electrophoresis. The culture was preserved for future uses.
On having made a culture of E.coli which is capable of over-expression of murA gene, we can produce a large quantity of the murA gene product and hence it can be used for further studies with special concern to drug designing. Also, drugs acting on murA can be tested against these cells to check the potency of drug. This will help the future studies on drug designing targeting murA gene.
2.1 Structure based drug designing
A new approach of structure based drug designing is very much usable. Drug discovery referred to, as 'rational' started when first structures came into being. In 1897, Ehrlich proposed that there are specific groups on the cells that combine with the toxin; this theory was known as the side chain theory. Ehrlich coined these side chains as receptors. Structure-based drug design of protein ligands has emerged as a new tool in medicinal chemistry. (Klebe G. 2000). The central assumption of structure-based drug design (Anderson A.A. 2003) is shown in Figure 2.2 and there are multiple cycles before a lead goes in for clinical trials.The first cycle includes the cloning, purification and structure determination of the target protein or nucleic acid by one of three principal methods: X-ray crystallography, NMR or comparative modeling. Using bioinformatics tools, compounds or fragments of compounds from a database are matched into a selected region of the structure.
Based on their steric and electrostatic interactions with the target site, the compounds are scored and ranked. The best compounds are further subjected to biochemical assays. In the second cycle, target structure determination in complex with a best lead from the first cycle, reveals sites on the compound that can be modified to increase potency. Additional cycles include synthesis of the optimized lead, determination of structure of the new target, and further optimization of the lead compound.
After the multiple cycles of drug designing process is over, we usually get compounds that are improved in binding and target specificity.
2.2 Evaluating a structure for structure based drug design
After the target identification, next step is to obtain precise structural information. There are three primary methods for structure determination that are useful for drug-design: X-ray crystallography, NMR, and homology modeling.
The most common desired source of structural information related to drug design is the high-resolution crystal structures, especially for proteins that range from few amino acids to 998kD (Davis AM, et al. 2003). Visibility of ordered water molecules is another advantage of the method as it is very useful in drug designing process. A crystal structure needs to be evaluated for resolution of the diffracted amplitudes, R-factors, temperature factors, coordinate errors, and chemical correctness. R-factor and Rfree are the measures of correlation between model and experimental data. In order to use the structure in drug design, the Rfree value of the structure should be below 28% and ideally below 25% and the R-factor should be well below 25%. If the structure available for a specific target does not meet the criteria of R-factor, drug design can still be considered but it should be judged carefully.
Another valuable source of drug design is the structure determined by nuclear magnetic resonance using concentrated protein or nucleic acid in solution (Pellecchia M, et al. 2002). It is sometimes possible to determine the dynamics of the target since the target is in solution. In case there is no experimentally determined structure of target, a homology model can be used for drug design (Enyedy I., et al. 2001). SWISS MODEL is a versatile program to determine amount of correctness of model based on confidence factor per residue that tells us about the amount of structural information used to create that portion of model.
Using the structural information obtained through the above techniques, the structure is then prepared for drug design programs.
2.3 Present state of the art: Computer-aided drug design
Given the vast size of organic chemical space, drug discovery cannot be reduced to a simple "synthesize and test" drudgery. There is an urgent need to identify and/ or design drug-like molecules from the vast expanse of what could be synthesized. In silico methods have the potential to reduce both cost and time in developing suggestions on drug/ lead-like molecules. Computational tools have the advantage for delivering new lead candidate faster and cheaper. Drug discovery in the 21st century is expected to be different in at least two distinct ways: development of individualized medicine departing from genomic information and extensive use of in silico simulations to facilitate target identification, structure prediction and lead/drug discovery. The expectations from computational methods for reliable and expeditious protocols for developing suggestions on potential leads are continuously on the increase. Several conceptual and methodological concerns remain before an automation of drug design in silico could be contemplated.
Computational methods are needed to exploit the structural information to understand specific molecular recognition events and to elucidate the function of the target macromolecule (Figure 2.3). This information should ultimately lead to the design of small molecule ligands for the target, which will block/activate its normal function and thereby act as improved drugs.