Abstract
The explosive global spreading of multidrug resistant Mycobacterium tuberculosis (Mtb) has provoked an urgent need to discover novel anti-TB agents. Enoyl-acyl carrier protein reductase from Mtb is a well-known and thoroughly studied target for anti-tuberculosis therapy. In the present analysis, virtual screening techniques performed from Drug bank database by utilizing INH-NAD adduct as query for the discovery of potent anti-TB agents. About 100 molecules sharing similar scaffold with INH-NAD adduct were analyzed for their binding effectiveness. The initial screening based on number of rotatable bonds gave 42 hit molecules. Subsequently, physiochemical properties such as toxicity, solubility, drug-likeness and drug score were analyzed for the filtered set of compounds. Final data reduction was performed by means of molecular docking and normal mode docking analysis. The result indicates that DB04362, adenosine diphosphate 5-(beta-ethyl)-4-methyl-thiazole-2-carboxylic acid could be a promising lead compound and be effective in treating sensitive as well as drug-resistant strains of Mtb. We believe that this novel scaffolds might be the good starting point for lead compounds and certainly aid the experimental designing of anti-tuberculosis drug in a short time.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Tuberculosis (TB) is a chronic infectious disease caused by mycobacteria such as Mycobacterium bovis, Mycobacterium africanum and mainly Mycobacterium tuberculosis (Aziz et al. 2006). One-third of the world’s population is infected with Mycobacterium tuberculosis, the etiological agent of TB, resulting in 9.2 million new cases and 1.7 million deaths in 2006 (Floyd and Pantoja 2008). Globally in 2007, there were a predictable 13.7 million chronic active cases. In 2010, there were 8.8 million new cases and 1.5 million associated deaths occurred in developing countries. Active TB is usually treated with isoniazid (INH) in association with one or more other anti-TB drugs but multidrug-resistant TB (MDR-TB) and very recently extensively drug-resistant TB (XDR-TB) have become a serious and unsolved public health problem (Aziz et al. 2006; Pasqualoto et al. 2004; Wei et al. 2003; Morlock et al. 2003; Ormerod 2005). INH is a prodrug and must be activated by the catalase–peroxidase KatG (Zhang et al. 1992; Johnsson and Schultz 1994; Marcinkeviciene et al. 1995; Johnsson et al. 1997; Wang et al. 1998) and the isoniazid-activated intermediate forms an isonicotinoyl-NAD adduct (INH-NAD), through addition of either an isonicotinic acyl anion to NAD+ or an isonicotinic acyl radical to an NAD· radical (Rozwarski et al. 1998). After activation, it inhibits enoyl-acyl carrier protein reductase (InhA). Inhibition of this activity by INH blocks the biosynthesis of mycolic acids, which are major lipids of the mycobacterial envelope (Quemard et al. 1996; Marrakchi et al. 2000). INH-NAD adduct is a slow tight binding competitive inhibitor of InhA that binds with an overall dissociation constant of 0.75 nM (Rawat et al. 2003). Missense mutations in the inhA structural gene have been identified in clinical isolates of Mycobacterium tuberculosis resistant to INH. More studies have also demonstrated that the mutations within katG in Mtb are common in INH-resistant strains (Heym et al. 1995). The mutations within inhA have been reported up to 32 % in INH resistant strains (Telenti et al. 1997; Morris et al. 1995; Lee et al. 1999; Kiepiela et al. 2000). Mutations in katG and inhA account for up to 80 % of INH-resistant strains (Morris et al. 1995; Musser et al. 1996), whereas the mutations in katG alone account for the majority of INH resistant strains (Cynamon et al. 1999; Torres et al. 2000). This indicates that high prevalence of resistance to INH was observed, mainly due to emerging KatG mutants that do not activate or poorly activate INH. Therefore, it has been suggested that compounds that inhibit the ultimate target of INH but do not require activation by KatG have tremendous promise as novel drugs for combating MDR-TB and XDR-TB (Rawat et al. 2003; Basso et al. 1998). Keeping in view, the importance of enoyl-acyl carrier protein reductase (InhA), an enzyme involved in the biosynthesis of mycolic acids and low outcome of inhibitors using experimental procedures, we have made an attempt to screen inhibitors of InhA by virtual screening procedures.
Virtual screening (VS) is a widely used method that has been shown to be successful in a variety of studies, although it also has many shortcomings (Oprea and Matter 2004; Chen 2008). In the past few years, many reports indicated that virtual screening techniques proved to be effective in making qualitative predictions that discriminated active from inactive compounds (Kitchen et al. 2004). The use of experimentally derived protein structures and a hybrid computational method that combines the advantages of docking algorithms with dynamic structural information provided by normal mode analysis certainly provide improved library enrichments virtual screening process. Furthermore, this is the first report of virtual screening for InhA inhibitors and the results could aid experimental studies and the rational development of novel drugs against Mtb.
Materials and methods
Data set preparation
The native and mutant (I21V) type’s coordinates of INH-NAD adduct complexed to InhA were taken from the Brookhaven Protein Data Bank (PDB) (Berman et al. 2000). The corresponding PDB codes were 2IDZ and 2IE0, respectively. The adduct was extracted from the PubChem, a database maintained in NCBI (Wishart et al. 2008; Feldman et al. 2006) and SMILES strings was collected and submitted to CORINA for constructing the 3D structure of the INH-NAD adduct (Gasteiger et al. 1990). All the water molecules and the hetero atoms were removed. Energy minimization for native and mutant proteins was carried out using GROMACS package 4.5.3 (Hess et al. 2008; Spoel et al. 2005) adopting the GROMOS43a1 force field parameters before performing molecular docking experiment. The screening performed with the aid of PubChem and Drug bank database.
Virtual screening
VS (Shoichet 2004) is the computational analogue of biological screening. The approach has become increasingly popular in the pharmaceutical research for lead identification. The basic goal of the VS is the reduction of the massive virtual chemical space of small organic molecules, to screen against a specific target protein, to a manageable number of the compounds that inhibit a highest chance to lead to a drug candidate. Two different databases such as PubChem and Drug bank were used for searching new lead compounds by employing the INH-NAD adduct as query (Bolton et al. 2011; Wishart et al. 2008). The numbers of molecules in each of the database are 85 million and 140,000, respectively. Screening was carried out by restricting the number of rotatable bonds to a maximum of 12 (Muegge 2003; Oprea 2000). Several hits were obtained from each of the databases, which were further screened using molecular docking studies. The SMILES strings were used for constructing three-dimensional structures of lead compounds.
Identification of binding site residues for enoyl-acyl carrier protein reductase
It was a challenging task to extrapolate a mechanism of action from the view of 3D structures. Detailed biochemical information about the enzyme can be used to design substrate or transition state analogues, which can then be bound into the enzyme for structure determination. These can reveal binding site locations and identify residues, which are likely to take part in the receptor–ligand interaction. From this, a catalytic mechanism can be proposed. In order to identify the binding residues in the structure of enoyl-acyl carrier protein reductase, we submitted the native and mutant complex structure (PDB ID: 2IDZ and 2IE0) into the ligand contact tool (LCT) program (Lopez et al. 2007). This program calculates contacts between the binding residues of enoyl-acyl carrier protein reductase receptor with INHNAD using default parameters.
Molecular docking
Docking was performed with the help of the PatchDock (Duhovny et al. 2005). It is a geometry-based molecular docking algorithm. The PatchDock algorithm divides the Connolly dot surface representation (Connolly 1983a, b) of the molecules into concave, convex and flat patches. Then, complementary patches are matched in order to generate candidate transformations. Each candidate transformation is further evaluated by a scoring function that considers both geometric fit and atomic desolvation energy (Zhang et al. 1997). Finally, root mean square deviation (RMSD) clustering is applied to the candidate solutions to discard redundant solutions. The input parameters for the docking are the PDB coordinate file of the protein and ligand molecule. This algorithm has three major stages: (1) molecular shape representation, (2) surface patch matching, and (3) filtering and scoring. Furthermore, the steric clashes, introduced by PatchDock, algorithm are removed with the aid of FireDock algorithm (Andrusier et al. 2007; Mashiach et al. 2008). FireDock refines side chain positions and relative protein orientations. After steric clashes are removed, an energy-like function is used to rank the docking models. This interface energy score is a weighted combination of softened van der Waals, desolvation, electrostatics, hydrogen bonding, disulfide bonding, π-stacking, aliphatic interactions, and rotamer preferences (Andrusier et al. 2007); docking results were screened by means of FireDock algorithm.
Prediction of physiochemical properties
Successful drug discovery requires high-quality lead structures which may need to be more drug-like than commonly accepted (Proudfoot 2002). The hits were screened using drug-likeness, drug score and toxicity characteristics. These physicochemical properties were therefore calculated for the filtered set of hits using the program OSIRIS (Sander 2001). The OSIRIS program calculates the drug-likeness based on a list of about 5,300 distinct sub-structure fragments created by 3,300 traded drugs as well as 15,000 commercially available chemicals yielding a complete list of all available fragments with associated drug-likeness. The drug score combines drug-likeness, cLogP, logS, molecular weight, and toxicity risks as a total value which may be used to judge the compound’s overall potential to qualify for a drug.
Normal mode analysis
The exploration of molecular motions of biological molecules and their assemblies by simulation approaches such as molecular dynamics has provided significant insights into structure–function relationships in small biological systems. Normal mode analysis (NMA) provides an alternative to molecular dynamics for studying the motions of macromolecules. The time scale accessible to theoretical work is extended with normal mode analysis, and this approach has been proven extremely useful for studying collective motions of biological systems. (Noguti and Nishikawa 1983; Levitt et al. 1985) Normal mode analysis is a powerful tool for predicting the possible movements of a given macromolecule. It has been shown recently that half of the known protein movements can be modeled by using at most two low-frequency normal modes (Tama and Sanejouand 2001). Applications of NMA cover wide areas of structural biology, such as the study of protein conformational changes upon ligand binding, membrane channel opening and closure, potential movements of the ribosome, and viral capsid maturation. Another newly emerging field of NMA is related to protein structure determination by X-ray crystallography, where normal mode perturbed models are used as templates for diffraction data phasing through molecular replacement. Elnemo is a web interface to the elastic network model that provides a fast and simple tool to compute, visualize and analyze low-frequency normal modes of large macro-molecules and to generate a large number of different starting models for use in molecular replacement (Suhre and Sanejouand 2004). Using this interface, each docked complex was analyzed with default parameters to investigate the active site residues by normal mode analysis.
Results and discussion
Virtual screening
Virtual screening is the computational analogue of biological screening. It uses computer-based methods to discover new ligands on the basis of biological structures. This technique mainly focuses on comparing molecular similarity analyses of compounds with known and unknown moiety. Here, we have performed the virtual screening analysis by using INH-NAD adduct, an active intermediate molecule. The result indicates that 332 hits from PubChem and 100 hits from Drug Bank were identified similar to the INH-NAD adduct. Successful drug discovery requires high-quality lead structures which may need to be more drug-like than is commonly accepted (Proudfoot 2002). The initial screening was carried out by restricting the number of rotatable bonds to a maximum of 12, which reduced the number of hits to 42 from drug bank whereas PubChem database did not show the reasonable hit compounds. The results are shown in Table 1. Therefore, the subsequent analysis was carried out with the aid of 42 molecules screened from drug bank database.
Binding site residues analysis
The binding site residues in the structure of enoyl-acyl carrier protein reductase were obtained from LCT program by using the complex structure of enoyl-acyl carrier protein reductase bound with INH-NAD adduct (PDB ID: 2IE0). The results indicate for 2IE0 are a total of 10 amino acid residues, viz, G-14, S-20, V-21, D-64, V-65, I-95, G-96, K-165, I-194 and T-196. The LIGPLOT (Wallace et al. 1995) tool was used to illustrate the contacts between mutant protein binding residues and INH-NAD adduct shown in Fig. 1.
Docking studies of mutant enoyl-acyl carrier protein reductase with inhibitor
In order to gain insight into possible binding modes of the inhibitor, INH-NAD adduct, was docked into the ligand-binding domain of the native and mutant type of InhA using the program PatchDock. It is well known that the scores calculated by docking programs do not usually permit the exact reproduction of the binding mode of assayed compounds. Hence, we have further screened the PatchDock results with the aid of FireDock program. This will be of immense importance in obtaining the binding affinity of INH-NAD adducts with the target structures. The binding affinity between INH-NAD adducts and the target structures determined by fireDock program were −43.70 and −48.27 kcal/mol for the native and mutant structures, respectively. This clearly indicates that mutation at the position I21V in the target structure leads resistance to drug molecule, isoniazid, but not to the INH-NAD adduct. Subsequently, all 42 selected hits were docked into the InhA active site in the same way in order to understand the binding affinity of the lead compounds against the native and mutant type proteins. We understand that 9 hits showed greater binding affinity with the mutant type (I21V) compared to INH-NAD adduct. The result was shown in Table 2. In particular, five compounds such as DB04362, DB03893, DB00157, DB02498 and DB04418 showed greater binding affinity with both native and mutant type structure. The compound DB04362 showed greatest binding affinity than other lead compounds considered in our study. The docked complex structure of INH-NAD adduct and DB04362 with native and mutant type structure shown in Figs. 2 and 3.
In silico toxicities, solubility, drug-likeness and drug score profiles
Many drug candidates fail in the clinical trials, reasons are unrelated in the potency against the intended drug target. Pharmacokinetic and toxicity issues are blamed for more than half of all failure in the clinical trials. Therefore, it is essential to evaluates Pharmacokinetic and toxicity of small molecules. Drug solubility (log S) is an important factor that affects the movement of a drug from the site of administration into the blood. It is known that insufficient solubility of drug can lead to poor absorption (Muegge 2003). Our estimated log S value is a unit stripped logarithm (base 10) of a compound’s solubility measured in mol/liter. There are more than 80 % of the drugs on the market that have an (estimated) log S value greater than −4. Table 3 shows solubility of the seven compounds that were found in the comparable zone with that of standard drugs to fulfill the requirements of solubility and could be considered as a candidate drug for oral absorption.
Drug-likeness
Currently, there are many approaches to assess a compound drug-likeness based on topological descriptors, fingerprints of molecular drug-likeness structure keys or other properties such as C log P and molecular weight. In this work, Osiris program (Sander 2001) was used for calculating the fragment-based drug-likeness of the most active compounds and comparing them with INH-NAD adduct. Seven compounds showed a little improvement of drug-likeness values than INH-NAD adduct and it is shown in Table 3. The drug scores of the potent compounds have also been determined in the present study.
Drug score
We have calculated overall drug score (DS) for the lead compounds as compared with that of standard drugs INH-NAD adduct. The drug score combines drug-likeness, miLogP, log S, molecular weight and toxicity risks in one handy value that may be used to judge the compound’s overall potential to qualify for a drug. The result is shown in Table 3. The reported lead compounds showed moderate to good drug score as compared with standard drug used. The drug score of the seven compounds showed a good score which is of significantly higher value than that of the INH-NAD adduct.
The toxicity risk predictor locates fragments within a molecule, which indicate a potential toxicity risk. Toxicity risk alerts are an indication that the drawn structure may be harmful concerning the risk category specified. Data evaluated in Table 3 indicate that all the seven lead compounds were supposed to be non-mutagenic, non-irritating with no tumorigenic effects when run through the mutagenicity assessment system comparable with standard drugs used.
Normal mode docking analysis
It has been recently shown that half of the known protein movements can be modeled by using at most too low-frequency normal modes for explaining collective large amplitude motions of proteins in different conformational states (Delarue and Dumas 2004). These motions typically describe conformational changes which are essential for the functioning of proteins (Alexandrov et al. 2005). Hence, the lowest frequency mode (mode 7) (Choudhury et al. 2010) was used for our docking study. The normal mode analysis generates 11 possible confirmations between DQMIN of −100 and DQMAX of 100 with DQSTEP step size of 20 (Suhre and Sanejouand 2004). It is to be noted that understanding the binding affinity between the target and the drug based on relevant normal modes will authorize the strength of docking process (Cavasotto et al. 2005). Hence, entire trajectory files from the lowest frequency mode were used as the input for docking analysis. Each harmonic vibrational mode derived from NMA simulates a state of the system in which all particles are oscillating with the same characteristic frequency and, therefore, the method is often referred to as collective motion analysis. Unfortunately, the protein structure deposited in the PDB corresponds to single conformation. Therefore, NMA, particularly with a simple elastic network model, can be helpful for simulation of an active site motion. The three-dimensional structure of INH-NAD adduct was generated by using the tool CORINA (Gasteiger et al. 1990). The normal mode-based docking result is shown in Fig. 4. We observed that free energy of binding for the lead compound, DB04362, was significantly higher than INH-NAD adduct in all the 11 conformations generated by means of normal mode analysis. This clearly indicates the effective binding of DB04362 than INH-NAD adduct with native and mutant structure of InhA.
Conclusion
Despite the availability of effective treatments, tuberculosis still persists as epidemiological entities that cause chronic, crippling illness and death on a large scale. The main reason for this is the emergence of drug-resistant strains due to poor compliance among the patients to their lengthy treatment regimens. Therefore, in the present study, we identified novel drugs that are active against the drug-resistant as well as non-resistant strains, and can shorten their treatment durations using virtual screening protocols. Initially, virtual compounds were subjected to molinspiration program and screening was carried out by restricting the number of rotatable bonds to a maximum of 12. Subsequently the screened molecules were docked at the active site of Mtb InhA to select inhibitors establishing favorable interactions. Finally, toxicity and drug-likeness were evaluated in order to screen the high-quality lead structure. Several potential drug-like inhibitors have been screened out showing strong binding affinity to Mtb InhA. Furthermore, normal mode analysis indicates that the compound, adenosine diphosphate 5-(beta-ethyl)-4-methyl-thiazole-2-carboxylic acid (DB04362) displayed strong binding affinity with both the native and mutant type InhA. Hence, we believed that DB04362 represents promising starting point as a lead compound for Mtb. Though experimental studies are indispensable to mark them as lead compound for the development of novel drugs against Mtb, however, screened out inhibitors would undoubtedly aid the experimental designing of anti-tubercular agents expeditiously.
References
Alexandrov V, Lehnert U, Echols N, Milburn D, Engelman D, Gerstein M (2005) Normal modes for predicting protein motions: a comprehensive database assessment and associated Web tool. Protein Sci 14:633–643
Andrusier N, Nussinov R, Wolfson HJ (2007) FireDock: fast interaction refinement in molecular docking. Proteins 69:139–159
Aziz MA, Wright A, De Laszlo A, Muynck A, Portaels F, Van Deun A, Wells C, Nunn P, Blanc L, Raviglione M (2006) Epidemiology of antituberculosis drug resistance (the Global Project on Anti-tuberculosis Drug Resistance Surveillance): an updated analysis. Lancet 368:2142–2154
Basso LA, Zheng R, Musser JM, Jacobs WR Jr, Blanchard JS (1998) Mechanisms of isoniazid resistance in Mycobacterium tuberculosis: enzymatic characterization of enoyl reductase mutants identified in isoniazid-resistant clinical isolates. J Infect Dis 178:769–775
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242
Bolton EE, Chen J, Kim S, Han L, He S, Shi W, Simonyan V, Sun Y, Thiessen PA, Wang J, Yu B, Zhang J, Bryant SH (2011) PubChem3D: a new resource for scientists. J Cheminform 3:32
Cavasotto CN, Kovacs JA, Abagyan RA (2005) Representing receptor flexibility in ligand docking through relevant normal modes. J Am Chem Soc 127:9632–9640
Chen CYC (2008) Discovery of novel inhibitors for c-met by virtual screening and pharmacophore analysis. J Chin Inst Chem Eng 39:617–624
Choudhury D, Biswas S, Roy S, Dattagupta JK (2010) Improving thermostability of papain through structure-based protein engineering. Protein Eng Des Sel 23:457–467
Connolly ML (1983a) Solvent-accessible surfaces of proteins and nucleic acids. Science 221:709–713
Connolly ML (1983b) Analytical molecular surface calculation. J Appl Crystallogr 16:548–558
Cynamon MH, Zhang Y, Harpster T, Cheng S, DeStefano MS (1999) High-dose isoniazid therapy for isoniazid-resistant murine Mycobacterium tuberculosis infection. Antimicrob Agents Chemother 43:2922–2924
Delarue M, Dumas P (2004) On the use of low-frequency normal modes to enforce collective movements in refining macromolecular structural models. Proc Natl Acad Sci USA 101:6957–6962
Duhovny SD, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: server for rigid and symmetric docking. Nucleic Acids Res 33:363–367
Feldman J, Snyder KA, Ticoll A, Pintilie G, Hogue CW (2006) A complete small molecule dataset from the protein data bank. FEBS Lett 580:1649–1653
Floyd K, Pantoja A (2008) Financial resources required for tuberculosis control to achieve global targets set for 2015. Bull World Health Organ 86:568–576
Gasteiger J, Rudolph C, Sadowski J (1990) Automatic generation of 3D-atomic coordinates for organic molecules. Tetrahedron Comput Meth 3:537–547
Hess B, Kutzner C, Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447
Heym B, Alzari PM, Honoré N, Cole ST (1995) Missense mutations in the catalase–peroxidase gene, KatG, are associated with isoniazid resistance in Mycobacterium tuberculosis. Mol Microbiol 15:235–245
Johnsson K, Schultz PG (1994) Mechanistic studies of the oxidation of isoniazid by the catalase peroxidase from Mycobacterium tuberculosis. J Am Chem Soc 116:7425–7426
Johnsson K, Froland WA, Schultz PG (1997) Over expression, purification, and characterization of the catalase–peroxidase KatG form Mycobacterium tuberculosis. J Biol Chem 272:2834–2840
Kiepiela P, Bishop KS, Smith AN, Roux L, York DF (2000) Genomic mutations in the KatG, inhA and aphC genes are useful for the prediction of isoniazid resistance in Mycobacterium tuberculosis isolates from Kwazulu Natal, south Africa. Tuber Lung Dis 80:47–56
Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949
Lee AS, Lim IH, Tang LL, Telenti A, Wong SY (1999) Contribution of KasA analysis to detection of isoniazid-resistant Mycobacterium tuberculosis in Singapore. Antimicrob Agents Chemother 43:2087–2089
Levitt M, Sander C, Stern PS (1985) Protein normal-mode dynamics: trypsin inhibitor crambin, ribonuclease and lysozyme. J Mol Biol 181:423–447
Lopez G, Valencia A, Tress ML (2007) Firestar-prediction of functionally important residues using structural templates and alignment reliability. Nucleic Acids Res 35:573–577
Marcinkeviciene JA, Magliozzo RS, Blanchard JS (1995) Purification and characterization of the Mycobacterium smegmatis catalase–peroxidase involved in isoniazid activation. J Biol Chem 270:22290–22295
Marrakchi H, Laneelle G, Que′mard A (2000) InhA, a target of the antituberculous drug isoniazid, is involved in a mycobacterial fatty acid elongation system FAS-II. Microbiology 146:289–296
Mashiach E, Schneidman-Duhovny D, Andrusier N, Nussinov R, Wolfson HJ (2008) FireDock: a web server for fast interaction refinement in molecular docking. Nucleic Acids Res 36:229–232
Morlock GP, Metchock B, Sikes D, Crawford JT, Cooksey RC (2003) ethA, inhA, and katG loci of ethionamide-resistant clinical Mycobacterium tuberculosis isolates. Antimicrob Agents Chemother 47:3799–3805
Morris S, Bai GH, Suffys P, Portillo-Gomez L, Fairchok M, Rouse D (1995) Molecular mechanisms of multiple drug resistance in clinical isolates of Mycobacterium tuberculosis. J Infect Dis 171:954–960
Muegge I (2003) Selection criteria for drug-like compounds. Med Res Rev 23:302–321
Musser JM, Kapur V, Williams DL, Kreiswirth BN, van Soolingen D, van Embden JD (1996) Characterization of the catalase–peroxidase gene (KatG) and inhA locus in isoniazid-resistant and susceptible strains of Mycobacterium tuberculosis by automated DNA sequencing: restricted array of mutations associated with drug resistance. J Infect Dis 173:196–202
Noguti GN, Nishikawa T (1983) Dynamics of a small globular protein in terms of low-frequency vibrational modes. Proc Natl Acad Sci USA 80:3696–3700
Oprea TI (2000) Property distribution of drug-related chemical databases. J Comput Aided Mol Des 14:64–251
Oprea TI, Matter H (2004) Integrating virtual screening in lead discovery. Curr Opin Chem Biol 8:349–358
Ormerod LP (2005) Multidrug-resistant tuberculosis (MDR-TB): epidemiology, prevention and treatment. Br Med Bull 14:73–74
Pasqualoto KF, Ferreira EI, Santos-Filho OA (2004) Rational design of new antituberculosis agents: receptor-independent four-dimensional quantitation structure–activity relationship analysis of a set of isoniazid derivatives. J Med Chem 47:3755–3764
Proudfoot JR (2002) Drugs, leads, and drug-likeness: an analysis of some recently launched drugs. Bioorg Med Chem Lett 12:1647–1650
Quemard A, Dessen A, Sugantino M, Jacobs WR, Sacchettini JC, Blanchard JS (1996) Binding of catalase–peroxidase-activated isoniazid to native-type and mutant Mycobacterium tuberculosis enoyl-ACP reductases. J Am Chem Soc 118:1561–1562
Rawat R, Whitty A, Tonge PJ (2003) The isoniazid-NAD adduct is a slow, tight-binding inhibitor of InhA, the Mycobacterium tuberculosis enoyl reductase: adduct affinity and drug resistance. Proc Natl Acad Sci USA 100:13881–13886
Rozwarski DA, Grant GA, Barton DH, Jacobs WR Jr, Sacchettini JC (1998) Modification of the NADH of the isoniazid target (InhA) from Mycobacterium tuberculosis. Science 279:98–102
Sander T (2001) OSIRIS Property Explorer. Actelion Pharmaceuticals Ltd 81:6–16. http://www.organicchemistry.org/prog/peo/
Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432:862–865
Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718
Suhre K, Sanejouand YH (2004) ElNemo: a normal mode web-server for protein movement analysis and the generation of templates for molecular replacement. Nucl Acids Res 32:610–614
Tama F, Sanejouand YH (2001) Conformational change of proteins arising from normal mode calculations. Protein Eng 14:1–6
Telenti A, Honoré N, Bernasconi C, March J, Ortega A, Heym B, Takiff HE, Cole ST (1997) Genotypic assessment of isoniazid and rifampin resistance in Mycobacterium tuberculosis: a blind study at reference laboratory level. J Clin Microbiol 35:719–723
Torres MJ, Criado A, Palomares JC, Aznar J (2000) Use of real-time PCR and fluorimetry for rapid detection of rifampin and isoniazid resistance-associated mutations in Mycobacterium tuberculosis. J Clin Microbiol 38:3194–3199
Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein–ligand interactions. Protein Eng 8:127–134
Wang JY, Burger RM, Drlica K (1998) Role of superoxide in catalase–peroxidase-mediated isoniazid action against mycobacteria. Antimicrob Agents Chemother 42:709–711
Wei CJ, Lei B, Musser JM, Tu SC (2003) Isoniazid activation defects in recombinant Mycobacterium tuberculosis catalase–peroxidase (KatG) mutants evident in InhA inhibitor production. Antimicrob Agents Chemother 47:670–675
Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucl Acids Res 36:901–906
Zhang Y, Heym B, Allen B, Young D, Cole S (1992) The catalase–peroxidase gene and isoniazid resistance of Mycobacterium tuberculosis. Nature 358:591–593
Zhang C, Vasmatzis G, Cornette JL, DeLisi C (1997) Determination of atomic desolvation energies from the structures of crystallized proteins. J Mol Biol 267:707–726
Acknowledgments
The authors express deep sense of gratitude to the management of Vellore Institute of Technology for all the support, assistance and constant encouragements to carry out this work.
Conflict of interest
The authors declare that they have no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
This article is published under license to BioMed Central Ltd.Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
About this article
Cite this article
Shanthi, V., Ramanathan, K. Identification of potential inhibitor targeting enoyl-acyl carrier protein reductase (InhA) in Mycobacterium tuberculosis: a computational approach. 3 Biotech 4, 253–261 (2014). https://doi.org/10.1007/s13205-013-0146-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13205-013-0146-0