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Advances in Natural and Applied Sciences, 5(1): 58-63, 2011
ISSN 1995-0772
This is a refereed journal and all articles are professionally screened and reviewed
ORIGINAL ARTICLE
Identification of Novel and Potent Inhibitors Against Inha Reductase of Mycobacterium
Tuberculosis Through a Ligand-based Virtual Screening Approach
Uday Chandra Kumar and Shaik Mahmood
Bioinformatics Division, Environmental Microbiology Lab, Department of Botany, Osmania University,
Hyderabad 500 007, A.P., India.
Uday Chandra Kumar and Shaik Mahmood; Identification of Novel and Potent Inhibitors Against Inha
Reductase of Mycobacterium Tuberculosis Through a Ligand-based Virtual Screening Approach
ABSTRACT
InhA, the enoyl reductase from Mycobacterium tuberculosis and a member of the short-chain
dehydrogenase/reductase (SDR) family, catalyzes the NADH-dependent reduction of long chain trans-2-enoylACP fatty acids in the type II fatty acid biosynthesis pathway of M. tuberculosis. In the current study, we have
generated shape and rocs based query for highly active compounds of INHA (oxopyrrolidine derivative), which
is complexed with (PDB: 2H7M). Generated query was validated for goodness of hits and the predictive power
of the query was determined. This query was further used for virtual screening. The top 10% screening hits were
analyzed and docked. Based on the protein ligand interactions few final hits were selected for enzymatic
screening studies.
Key words: Inha reductase, Mycobacterium tuberculosis, Ligand-Based Virtual Screening.
Introduction
Tuberculosis (TB) is the leading cause of morbidity and mortality among the infectious diseases. The World
Health Organization (WHO) has estimated that one-third of the world’s population, nearly 2 billion people,
mostly in the developing countries [1] have been infected with Mycobacterium tuberculosis, the causative agent
of TB. Among the infected individuals 8 million develop active TB and nearly 2 million people die from the
disease annually [2]. In recent years, the pandemic of AIDS has had a major impact on the worldwide TB
problem. On one hand, HIV infection is the most potent risk factor for converting latent TB into the active,
transmissible form, thus fueling the spread of TB; on the other hand, TB bacteria can accelerate the progress of
AIDS infection. One-third of the increase in the incidence of TB in the past 5 years can be attributed to coinfection with HIV [2]. This situation has been further exacerbated by the emergence of multidrug-resistant
tuberculosis (MDR-TB) strains that are resistant to some or most current anti-TB drugs [3]. Over the decade, it
is estimated that as many as 50 million people worldwide have been infected with MDR-TB strains. According
to WHO, from 2002 to 2020, there will be about 1 billion more people newly infected with TB and
approximately 36 million deaths if the worldwide ravage of tuberculosis is left unchecked [3]. Despite the
increasing worldwide incidence of TB and its alarming threat toward the public health, no novel antituberculosis
drugs have been introduced into clinical practice over the past 4 decades.
The TB-specific drugs isoniazid (isonicotinic acid hydrazide (INH)) and ethionamide (Figure 1) have been
shown to target the synthesis of these mycolic acids, which are central constituents of the mycobacterial cell wall.
The biosynthesis of mycolic acids is achieved by the FAS in M. tuberculosis. Unlike other bacteria, M.
tuberculosis is unique in that it possesses both type I and type II fatty acid biosynthetic pathways. FASI in M.
tuberculosis is responsible for generation of the shorter saturated alkyl chain fatty acids, including the 24-carbon
R-branch of mycolic acids. Some of the products from the FASI system, such as the C16-C26 fatty acid products,
are later transferred to the FASII system, where they are further elongated to up to C56, forming the
meromycolate chain that serves as the precursor for the final mycolic acids.
Corresponding Author: Uday Chandra Kumar, Bioinformatics Division, Environmental Microbiology Lab, Department
of Botany, Osmania University, Hyderabad 500 007, A.P., India.
E-mail: [email protected]
Adv. in Nat. Appl. Sci., 5(1): 58-63, 2011
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Fig. 1: Chemical structures of InhA inhibitions (R represent various substituents).
The NADH-dependent enoyl-ACP reductase encoded by the Mycobacterium gene inhA has been validated
as the primary molecular target of the frontline antitubercular drug isoniazid (INH) [4]. Recent studies
demonstrated that InhA is also the target for the second line antitubercular drug ethionamide (ETA) [5]. InhA
catalyzes the reduction of long-chain trans-2-enoyl-ACP in the type II fatty acid biosynthesis pathway of M.
tuberculosis. Inhibition of InhA disrupts the biosynthesis of the mycolic acids that are central constituents of the
mycobacterial cell wall [6]. As a prodrug, INH must first be activated by the mycobacterial catalase-peroxidase
KatG into its acyl radical active form. The adduct resulting from covalent binding of the activated INH to the
InhA cosubstrate NADH, or its oxidation product NAD+, functions as a potent InhA inhibitor [7]. Similarly, a
comparable NAD adduct of ETA has been identified and shown to be an effective InhA inhibitor, although ETA
is activated by EtaA, a flavoprotein monooxygenase, rather than by KatG [5]. INH has been widely applied as
the frontline agent for the treatment of tuberculosis for the past 40 years. Clinical studies indicate that the
majority of these prodrug (INH, ETA)-resistant clinical isolates arise from KatG or EtaA-associated mutations
[8, 9]. Therefore, inhibitors targeting InhA directly without a requirement for activation would be promising
candidates for the development of agents against the ever increasing threat from drug-resistant Mycobacterium
tuberculosis strains. Several series of direct InhA inhibitors, including pyrazole derivatives, indole-5-amides [10]
and alkyl diphenyl ethers [11] have been identified recently that show both in vivo and in vitro activity.
Dataset:
Several series of InhA inhibitors reported so far, including piperazines, pyrrolidines, piperidines, N-benzylformamides, alkyl diphenyl ethers derivatives. High and least active compounds of these derivatives are listed
in Figure 3 & 4.
Fig. 3:
Fig. 4:
Adv. in Nat. Appl. Sci., 5(1): 58-63, 2011
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A set of 139 inhibitors with IC50 (μM) were collected from literature, on the basis of scaffold diversity and
same assay conditions.
An inhibitory activity of these compounds (IC50) ranges from 0.005 μM to 100 μM.
3D structures of all the molecules were constructed using Discovery Studio 2.5. The molecules are then
subjected to energy minimization using steepest descent algorithm.
Reported pdb ids of Inha complexed with different ligands shown in figure 5.
Fig. 5:
Generation of Shape and Feature Based (Rocs) Models:
In the current study, we have used ROCS (Rapid Overlay of Chemical Structures) [12-14] to identify the
INHA inhibitors through virtual screening of the database using a ligand-based approach. ROCS is a 3D method
that aligns compounds based on shape and/or chemical similarity. ROCS can be used for virtual screening [15,
16] and ligand based optimization during later stages of a project. The activities involved in running a ROCS
virtual screen or performing a ROCS-based alignment for design always involve choosing a reference or bioactive
conformation [17-23]. Finding good reference conformations for virtual screening is not always easy to do,
especially for a diverse set of ligands. In this case, multiple queries or reference conformations are necessary in
order to cover the various structural classes of ligands. In addition, for ligand-based design, an alignment is
necessary for each class.
As a starting point for the ROCS search, we selected INHA high active compound 1 (shown in Figure 4)
with IC50 0.09 uM. Shape and feature model was prepared using the highest active compound of INHA
inhibitors. Query features were further edited based on the importance of groups. Generation of ROCS Models
were shown in Figure 6.
Validation of Models:
Compound with an activity values in range of 0.005 to 1 uM were considered as actives and compounds with
an activity values > 1 uM were considered as decoys.
Query was further validated based on the Enrichment Factor (EF), is the fraction of actives seen along with
the top 1% and 2% of known decoys (multiplied by 100).
Docking Studies:
For our docking studies, we have selected protein Inha (PDB entry 2H7M, resolution 1.62 A°) with cocrystal (3s)-1-Cyclohexyl-N-(3,5-dichlorophenyl)-5-oxopyrrolidine-3-carboxamide. Glide, version 3.0
(Schrodinger, Inc.) [24] in Extra Precision docking mode (Glide XP) used for docking studies. During the
docking process, initially Glide performs a complete systematic search of the conformational, orientational, and
positional space of the docked ligand and eliminating unwanted conformations using scoring and followed by
energy optimization. Finally the conformations are further refined via a Monte Carlo sampling of pose
conformation. Predicting the binding affinity and rank-ordering ligands in database screens was implemented by
modified and expanded version of the ChemScore18 scoring function, Glide Score, for use in. The binding
Adv. in Nat. Appl. Sci., 5(1): 58-63, 2011
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Fig. 6: Generation of ROCS Models.
Fig. 7:
region was defined by a 12 Å _ 12 Å _ 12 Å box centered on the centroid of the crystallographic ligand to
confine the centroid of the docked ligand. Default settings were used for all the remaining parameters. Best pose
was selected on the basis of Glide score and the interactions formed between the ligands and important amino
acids.
Fig. 8: Docked conformation of Highly Active molecule.
Adv. in Nat. Appl. Sci., 5(1): 58-63, 2011
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Virtual Screening:
Validated model was used as a query for virtual screening of the 2 Million database. Top 10% screening hits
were selected for the further analysis based on Rocs Tanimoto score. The selected hits further compared for
electrostatic (EON) similarity of highly active compound. On basis of structure diversity, Lipinski rules of 5, top
2000 hits were selected for docking analysis. Flowchart of virtual screening steps was elucidated in Figure 9.
Fig. 9:
Conclusion:
We have successfully used ROCS and DISCOVERY STUDIO 2.5 to develop inhibitors of InhA reductase.
Docking and ROCS results provide useful information in understanding the structural features of the target and
chemical features of the ligands. A series of novel InhA inhibitors screened from the 2 million database using
ROCS Tanimoto score and were send for MTB InhA screening. We are currently working to lower the IC50 and
MIC of our most active compounds. We hope to develop a series of lead molecules to move forward into hit to
lead.
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