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Homology modeling and virtual screening approaches to identify potent inhibitors of VEB-1 β-lactamase



blaVEB-1 is an integron-located extended-spectrum β-lactamase gene initially detected in Escherichia coli and Pseudomonas aeruginosa strains from south-east Asia. Several recent studies have reported that VEB-1-positive strains are highly resistant to ceftazidime, cefotaxime and aztreonam antibiotics. One strategy to overcome resistance involves administering antibiotics together with β-lactamase inhibitors during the treatment of infectious diseases. During this study, four VEB-1 β-lactamase inhibitors were identified using computer-aided drug design.


The SWISS-MODEL tool was utilized to generate three dimensional structures of VEB-1 β-lactamase, and the 3D model VEB-1 was verified using PROCHECK, ERRAT and VERIFY 3D programs. Virtual screening was performed by docking inhibitors obtained from the ZINC Database to the active site of the VEB-1 protein using AutoDock Vina software.

Results and conclusion

Homology modeling studies were performed to obtain a three-dimensional structure of VEB-1 β-lactamase. The generated model was validated, and virtual screening of a large chemical ligand library with docking simulations was performed using AutoDock software with the ZINC database. On the basis of the dock-score, four molecules were subjected to ADME/TOX analysis, with ZINC4085364 emerging as the most potent inhibitor of the VEB-1 β-lactamase.


blaVEB-1 was identified in 1996 from an Escherichia coli strain isolated from a Vietnamese patient. Subsequent analysis demonstrated that blaVEB-1 is both plasmid- and integron-located [1, 2]. Among the different Ambler class A expanded-spectrum β-lactamase (ESBL) genes, blaVEB-1 is considered to be “emerging”; it has been detected in several Gram-negative organisms including Enterobacteriaceae and Pseudomonas aeruginosa[3, 4], and in multiple countries including France, Spain, Algeria, Turkey, Canada, Korea, Thailand and Tunisia [510]. Furthermore, P. aeruginosa isolates producing the VEB-1a variant, which differs from VEB-1 by only a single amino acid located in the leader peptide of the pre-mature protein, have been identified in Kuwait and India [11, 12]. VEB-1 has high amino-acid identity to PER-1 and PER-2 (38%) EBSLs, and confers high-level resistance to ceftazidime, cefotaxime and aztreonam [13]. The blaVEB-1 gene was characterized in an unusual genetic environment in P. aeruginosa isolates from India and Bangladesh, and in P. stuartii from Algeria. Rather than having a typical class 1 integron structure, in these isolates blaVEB-1 is flanked by identical 135-bp sequences, termed repeated elements (Res), which are bracketed by two truncated 3-conserved class 1 integron sequences in direct repeat [14].

There are currently no clinically useful inhibitors of VEB-1 β-lactamase. However, several studies have been undertaken using a variety of experimental inhibitors against other Ambler class A ESBLs. Clavulanic acid, the first β-lactamase inhibitor introduced into clinical medicine, was isolated from Streptomyces clavuligerus during the 1970s [15]. Clavulanate (the salt form of the acid in solution) presented with little antimicrobial activity in isolation, but when combined with amoxicillin, it significantly lowered amoxicillin MICs against Staphylococcus aureus, Klebsiella pneumoniae, Proteus mirabilis and E. coli[16]. Sulbactam and tazobactam are penicillinate sulfones developed as synthetic compounds in 1978 and 1980, respectively [17, 18]. Class A β-lactamase is inhibited to comparable levels by moxalactam, imipenem and cefoxitin.

The crystal structure of VEB-1 β-lactamase has not been described. Determining the three-dimensional (3D) structure of this molecule would assist in the discovery of more potent inhibitors, particularly in the application of structure-based virtual screening to identify lead compounds. To this end, a homology model of the 3D structure of VEB-1 protein was produced and a computational docking process was used to identify a series of potent inhibitors from the ZINC Database to allow VEB-1 to be compared with other Class A β-lactamase complexes.


Template identification and protein homology modeling

Searching the RCSB Protein Data Bank ( confirmed that the tertiary structure of VEB-1 β-lactamase was not publicly available. The complete E. coli VEB-1 β-lactamase protein sequence, which consists of 299 amino acids and has a calculated molecular weight of 33.7 kDa, was retrieved from the UniProtKB database ( (accession number Q7BVU7). BLASTP [19] was used to identify homologs in the RCSB Protein Databank [20]. Accordingly, the crystal structure of PER-1 β-lactamase from P. aeruginosa (PDB ID: 1E25), which has 40% sequence identity to VEB-1, was selected as the template [21]. To analyze sequence conservation, the VEB-1, PER-1, CTX-M and Toho-1 sequences were aligned. Gaps were inserted into the sequences to discover an optimal alignment, as presented in Figure 1A. The 3D structure of VEB-1 was modeled using the SWISS-MODEL tool [22] in the ExPASy Bioinformatics resource portal [23], and viewed using Swiss PDB Viewer v 4.0.1 software [24].

Figure 1
figure 1

Overall structure of VEB-1 and its sequence alignment with its homologue proteins. A. Sequence alignment of VEB-1 with PER-1, Toho-1 and CTX-M-16. The second structure assignment of PER-1 is labeled on the top of the sequences. B. Cartoon representation of the overall structure of VEB-1 is in light orange color. The serine active-site is colored in red, the SDN motif in green.

Model optimization and evaluation

Protein models generated using homology modeling frequently produce unfavorable bond lengths, bond angles, torsion angles and contacts. Therefore, it was essential to minimize the energy to regularize local bond and angle geometry, and to relax close contacts in the geometric chain. Each model of VEB-1 was optimized using the variable target function method (VTFM) with conjugate gradients (CG), followed by further refinement using molecular dynamics (MD) with a simulated annealing (SA) method in Modeller [25]. Energy minimization was performed to minimize stearic collisions and strains without significantly altering the overall structure. Energy computations and minimization were carried out using the GROMOS96 force field [26] and implementing Swiss-PdbViewer. After optimization the 3D model of VEB-1 was verified using the PROCHECK [27], ERRAT [28] and VERIFY 3D [29] programs available from the Structural Analysis and Verification Server (SAVES) ( PROCHECK was used to assess the stereochemical quality of the protein structure, while the Verify3D program analyzed the compatibility of an atomic model (3D) with its own amino acid sequence (1D) to assess the 3D protein structure.

Screening of compounds from the ZINC Database

Ligand-based virtual screening experiments are important during the early stages of drug discovery, as they can screen compound databases using the active sites of proteins with known 3D structure. The ZINC Database [30] is free to use and contains commercially available chemical compounds prepared for virtual screening. It contains more than 21 million compounds in ready-to-dock, 3D formats that can be purchased. During this work the ZINC Database was screened for structurally similar inhibitors of VEB-1 β-lactamases. The compounds identified included clavulanic acid, sulbactam, tazobactam, imipenem, cefoxitin and moxalactam. Furthermore, this study identified 950 compounds that were structurally similar to available Amber class A β-lactamase inhibitors during screening.

Structure-based virtual screening using molecular docking

Virtual screening uses computational methods to identify molecules that are biologically active against a specific protein target [31]. Two types of methodologies can be used during virtual screening: those that search for similarity to validated ligands, and molecular docking methods that require structural information about the target. During this study the first method, which is also known as ligand-based virtual screening, was utilized. Analogs with a minimum of 70% similarity to the known β-lactamase inhibitors (clavulanic acid, sulbactam, tazobactam, imipenem, cefoxitin and moxalactam) were selected from the ZINC database. To remove structural redundancies from the chemical library, structurally similar compounds with a Tanimoto coefficient larger than 0.8 were clustered into a single representative molecule. As a consequence, a docking library consisting of 950 compounds was obtained and downloaded in mol2 format.

Virtual screening was performed by docking the inhibitors obtained from the ZINC database to the active site of the VEB-1 protein using AutoDock Vina software (version 1.0) [32]. This docking allowed a population of possible conformations and orientations for the ligand at the binding site to be obtained. Using the Autodock Tools software [33], polar hydrogen atoms were added to VEB-1 protein, and its non-polar hydrogen atoms were merged. The protein receptor (VEB-1) and inhibitors were converted from PDB format to PDBQT format. All bonds within ligands were set to allow rotation. In the configuration file of the Autodock Vina software, a grid box with dimensions of 20 × 20 × 20 points was used around the active site to cover the entire enzyme binding site and allow ligands to move freely.

The docking simulation of each compound was conducted using an improved empirical AutoDock scoring function, in which a new solvation model for organic molecules was introduced. This modified scoring function can be expressed as follows:


where WvdW, Whbond, Welec, Wtor and Wso l are the weighting factors of van der Waals, hydrogen bond, electrostatic interactions, torsional term and desolvation energy of the inhibitors, respectively. rij represents the interatomic distance, and Aij, Bij, Cij and Dij are related to the depths of the potential energy well and the equilibrium separations between the two atoms. The hydrogen bond term has an additional weighting factor, E(t), representing the angle-dependent directionality. The cubic equation approach was applied to obtain the dielectric constant required for computing the interatomic electrostatic interactions between VEB-1 and a ligand molecule [34]. In the entropic term, Ntor is the number of sp3 bonds in the ligand. In the desolvation term, Si and Vi are the solvation parameter and the fragmental volume of atom i, [35] respectively, while Occimax is the maximum atomic occupancy. In the calculation of the molecular solvation free energy term in Eq. (1), the atomic parameters developed by Kang et al. [36] were used, as only carbon atoms were available. This modification of the solvation free energy term is expected to increase the accuracy of virtual screening, as underestimation of ligand solvation can lead to overestimation of the binding affinity of a ligand with several polar atoms [37].

The best conformation with the lowest docked energy was chosen from the docking search. The interactions of complex VEB-1 protein-ligand conformations including hydrogen bonds and bond lengths were analyzed using Swiss-PdbViewer v4.0 [38], Pymol software [39], UCSF Chimera [40] and Accelrys DS Visualizer software [41]. The commercially available software toxtree (developed by Idea consult Ltd., Sofia, Bulgaria) was used for computer-based estimation of chemical toxicity [42].

Results and Discussion

Protein homology modeling and validation

Multiple sequence alignment of VEB-1 with PER-1, CTX-M-16 and Toho-1 β-lactamases demonstrated that VEB-1 is highly homologous to PER-1 type β-lactamases (38% sequence identity) (Figure 1A). The BLASTP homology search using the E. coli VEB-1 β-lactamase sequence against the PDB database confirmed this result (data not shown). In addition, sequence alignment indicated that VEB-1 contains a serine-valine-methionine-lysine tetrad (SXXK) at positions 70–73, including the conserved serine and lysine amino acid residues that are characteristic of β-lactamases with a serine active site [43]. Several other structural elements characteristic of class A β-lactamases were identified including a serine-aspartate-asparagine (SDN) motif at positions 135–137, and lysine-threonine-arginine (KTG) residues at positions 239–242 (Figure 1A.). Accordingly, the crystal structure of PER-1 β-lactamase from P. aeruginosa (PDB ID: 1E25) was used as the template during homology modeling.

The modeled enzyme is a monomer, folded into an α/β domain consisting of a seven-stranded β-sheet and 11 α-helices (Figure 1B.). The residues in the Ser135-Asp136-Asn137 (SDN) motif are involved in maintaining the structure of the active site cavity, enzyme stability and stabilization of the enzyme transition state, respectively [44]. Ser135, a conserved amino acid among all class A β-lactamases, is occasionally replaced by a Gly residue. The multiple roles of this residue include anchoring β-lactams to the active site, and stabilizing it, through hydrogen bonding with the C-3/C-4 carboxylates of inhibitors and substrates and facilitating proton transfer to the β-lactam nitrogen during acylation, leading to opening of the β-lactam ring [45].

The quality of the 3D model was evaluated via the Ramachandran plot using PROCHECK software (Figure 2). The Ramachandran plot for the predicted model revealed that 88.2% of residues were in the most favorable region, while 10.6% were in the allowed region, confirming that the predicted model is of good quality. ERRAT is a so-called “overall quality factor” for non-bonded atomic interactions, with higher scores indicating higher quality. The generally accepted range is >50 for a high quality model. For the current 3D model, the overall quality factor predicted by the ERRAT server was 80.524 (Figure 3). The Verify 3D server predicted that 88.77% of the residues in VEB-1 β-lactamase had an average 3D-1D score > 0.2, thereby verifying the model.

Figure 2
figure 2

Ramachandran plot of VEB-1 β-lactamase model from Escherichia coli obtained by PROCHECK: 88.2% residues in favorable regions; 10.6% residues in additional allowed regions; 1.2% residues in generously allowed regions; 0% residues in disallowed regions.

Figure 3
figure 3

Errat plot for the VEB-1 β-lactamase model. Black bars identify the misfolded region located distantly from the active site, gray bars demonstrate the error region between 95% and 99%, and white bars indicate the region with a lower error rate for protein folding.

Virtual screening result analysis

Following docking simulations, the four most promising inhibitors were selected on the basis of binding affinity (Figure 4). Among the six categories of Amber class A β-lactamase inhibitors considered during the analysis, the optimal interactions with the highest affinity scores were obtained with sulbactam analogs. This finding was contrary to previous results for SHV-1 Amber class A β-lactamase, and sulbactam is a less potent inhibitor than clavulanate [46]. Sulbactam is more potent against class C β-lactamases than clavulanate, whereas its activity against class D enzymes is less potent than against class A β-lactamases. Similarly, sulbactam does not inhibit OXA-type enzymes as efficiently as TEM-1 and other clinically used inhibitors [47].

Figure 4
figure 4

Estimated Free Energy of Binding of the top four ligands.

The best conformation demonstrated that the free energy of binding (ΔGbind, kcal/mol) for the top four inhibitors was good. The negative and low value of ΔGbind (-6.4) indicated strong bonds between VEB-1 and the ZINC4085364 inhibitor, and demonstrated that the inhibitor was in its most favorable conformation. Analysis of the docked complexes demonstrated that the inhibitor was located close to the active site (Ser68), at a distance of 0.6 Å. The complex was stabilized by four hydrogen bonds through residues Ser68, Lys71, Ser131 and Gln67 (Figure 5B). The residue involved in cavity formation is presented in Figure 5A. Interaction analysis revealed that the cavity involved in the binding site has a volume of 186.6 Å3 and a surface area of 178.2 Å2. Toxtree was used to estimate toxic properties. Finally, four molecules were selected (Table 1).

Figure 5
figure 5

Binding interactions of inhibitors demonstrating maximum binding affinity (ZINC4085364), and the modeled VEB-1 protein. The left presents the structural electrostatic surface, and the right the detailed binding interactions. (hydrogen bond interactions are indicated with blue dotted lines).

Table 1 Physiochemical properties of the most potent ligand (ZINC4085364) obtained from the docking study


Antibiotic resistance is one of the most serious threats to public health. Development of resistance is assisted by the existence of plasmids, which can be transmitted easily between bacteria. The emergent VEB-1 β-lactamase possesses potent hydrolysis activity towards almost all antibiotics and is a significant threat. Virtual screening is an important tool for exploring biologically relevant chemical spaces, and allows studies focused on small molecule libraries to be performed, using up to millions of compounds. During the present study, structural models of a VEB-1/ZINC4085364 inhibitor complex were obtained using homology modeling and molecular docking methods. At present, there are no effective antibiotics against VEB-1-positive pathogens. An appropriate strategy involves identifying drug candidates from existing antibiotics, such as cephalosporin, on the basis of the 3D model of VEB-1 using structure-based virtual screening. This strategy was used successfully in the discovery of Merck’s HIV protease inhibitor [48]. The molecule identified in the current study as a VEB-1 inhibitor could be exploited for drug design. However, further in vivo experimentation is required for complete evaluation.


  1. Poirel L, Naas T, Guibert M, Chaibi EB, Labia R, Nordmann P: Molecular and biochemical characterization of VEB-1, a novel class A extended-spectrum β-lactamase encoded by an Escherichia coli integron gene. Antimicrob Agents Chemother. 1999, 43: 573-581.

    PubMed Central  CAS  PubMed  Google Scholar 

  2. Naas T, Mikami Y, Imai T, Poirel L, Nordmann P: Characterization of In53, a class 1 plasmid and composite transposon-located integron of Escherichia coli which carries an unusual array of gene cassettes. J Bacteriol. 2001, 183: 235-249. 10.1128/JB.183.1.235-249.2001.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. Naas T, Poirel L, Nordmann P: Minor extended-spectrum β-lactamases. Clin Microbiol Infect. 2008, 14 (Suppl.1): 42-52.

    Article  CAS  PubMed  Google Scholar 

  4. Poirel L, Villa L, Bertini A, Pitout JD, Nordmann P, Carattoli A: Expanded-spectrum β-lactamase and plasmid-mediated quinolone resistance. Emerg Infect Dis. 2007, 13: 803-805. 10.3201/eid1305.061293.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Adams MD, Goglin K, Molyneaux N, Hujer KM, Lavender H, Jamison JJ, MacDonald IJ, Martin KM, Russo T, Campagnari AA, Hujer AM, Bonomo RA, Gill SR: Comparative genome sequence analysis of multidrug-resistant Acinetobacter baumannii. J Bacteriol. 2008, 190: 8053-8064. 10.1128/JB.00834-08.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Aragon LM, Mirelis B, Miro E, Mata C, Gomez L, Rivera A, Coll P, Navarro F: Increase in β-lactam-resistant Proteus mirabilis strains due to CTX-M- and CMY-type as well as new VEB-1 and inhibitor-resistant TEM-type β-lactamases. J Antimicrob Chemother. 2008, 61: 1029-1032. 10.1093/jac/dkn056.

    Article  CAS  PubMed  Google Scholar 

  7. Girlich D, Poirel L, Leelaporn A, Karim A, Tribuddharat C, Fennewald M, Nordmann P: Molecular epidemiology of the integronlocated VEB-1 extended-spectrum β-lactamase in nosocomial enterobacterial isolates in Bangkok, Thailand. J Clin Microbiol. 2001, 39: 175-182. 10.1128/JCM.39.1.175-182.2001.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Poirel L, Pitout JD, Calvo L, Rodriguez-Martinez JM, Church D, Nordmann P: In vivo selection of fluoroquinolone-resistant Escherichia coli isolates expressing plasmid-mediated quinolone resistance and expanded-spectrum β-lactamase. Antimicrob Agents Chemother. 2006, 50: 1525-1527. 10.1128/AAC.50.4.1525-1527.2006.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Toleman MA, Bennett PM, Walsh TR: Common regions e.g. orf513 and antibiotic resistance: IS91-like elements evolving complex class 1 integrons. J Antimicrob Chemother. 2006, 58: 1-6. 10.1093/jac/dkl204.

    Article  CAS  PubMed  Google Scholar 

  10. Lahlaoui H, Poirel L, Moussa MB, Ferjani M, Omrane B, Nordmann P: Nosocomial dissemination of extended-spectrum β-lactamase VEB-1a-producing Providencia stuartii isolates in a Tunisian Hospital. Eur J Clin Microbiol Infect Dis. 2011, 30: 1267-1270. 10.1007/s10096-011-1222-1.

    Article  CAS  PubMed  Google Scholar 

  11. Aubert D, Girlich D, Naas T, Nagarajan S, Nordmann P: Functional and structural characterization of the genetic environment of an extended-spectrum β-lactamase blaVEB gene from a Pseudomonas aeruginosa isolate obtained in India. Antimicrob Agents Chemother. 2004, 48: 3284-3290. 10.1128/AAC.48.9.3284-3290.2004.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Poirel L, Rotimi VO, Mokaddas EM, Karim A, Nordmann P: VEB-1-like extended-spectrum β-lactamases in Pseudomonas aeruginosa. Kuwait Emerg Infect Dis. 2001, 7: 468-470.

    Article  CAS  PubMed  Google Scholar 

  13. Nordmann P, Naas T: Sequence analysis of PER-1 extended-spectrum beta-lactamase from Pseudomonas aeruginosa and comparison with class A b-lactamases. Antimicrob Agents Chemother. 1994, 38: 104-114. 10.1128/AAC.38.1.104.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  14. Naas T, Aubert D, Lambert T, Nordmann P: Complex genetic structures with repeated elements, a sul-type class 1 integron, and the blaVEB extended-spectrum b-lactamase gene. Antimicrob Agents Chemother. 2006, 50: 1745-1752. 10.1128/AAC.50.5.1745-1752.2006.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Reading C, Cole M: Clavulanic acid: a β-lactamase-inhibiting β -lactam from Streptomyces clavuligerus. Antimicrob Agents Chemother. 1977, 11: 852-857. 10.1128/AAC.11.5.852.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Brown AG: Clavulanic acid, a novel β-lactamase inhibitor a case study in drug discovery and development. Drug Des Deliv. 1986, 1: 1-21.

    CAS  PubMed  Google Scholar 

  17. English AR, Retsema JA, Girard AE, Lynch JE, Barth WE: CP-45,899, a β-lactamase inhibitor that extends the antibacterial spectrum of β-lactams: initial bacteriological characterization. Antimicrob Agents Chemother. 1978, 14: 414-419. 10.1128/AAC.14.3.414.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Fisher JJ, Belasco G, Charnas RL, Khosla S, Knowles JR: β-Lactamase inactivation by mechanism-based reagents. Philos Trans R Soc Lond. 1980, 289: 309-319. 10.1098/rstb.1980.0048.

    Article  CAS  Google Scholar 

  19. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The protein data bank. Nucleic Acids Res. 2000, 28: 235-242. 10.1093/nar/28.1.235.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25: 3389-3402. 10.1093/nar/25.17.3389.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Tranier S, Bouthors AT, Maveyraud L, Guillet V, Sougakoff W, Samama JP: The high resolution crystal structure for class A beta-lactamase PER-1 reveals the bases for its increase in breadth of activity. J Biol Chem. 2000, 275: 28075-28082.

    CAS  PubMed  Google Scholar 

  22. Schwede T, Kopp J, Guex N, Peitsch MC: SWISS-MODEL: an utomated protein homology-modeling server. Nucleic Acids Res. 2003, 31: 3381-3385. 10.1093/nar/gkg520.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A: ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 2003, 31: 3784-3788. 10.1093/nar/gkg563.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  24. Arnold K, Bordoli L, Kopp J, Schwede T: The SWISS-MODEL Workspace: A web-based environment for protein structure homology modeling. Bioinformatics. 2006, 22: 195-201. 10.1093/bioinformatics/bti770.

    Article  CAS  PubMed  Google Scholar 

  25. Šali A, Blundell TL: Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol. 1993, 234: 779-815. 10.1006/jmbi.1993.1626.

    Article  PubMed  Google Scholar 

  26. Scott W, Huenenberger PH, Tironi IG, Mark AE, Billeter SR, Fennen J, Torda AE, Huber T, Krueger P, van Gunsteren WF: The GROMOS Biomolecular Simulation Program Package. J Phys Chem A. 1999, 103: 3596-3607. 10.1021/jp984217f.

    Article  CAS  Google Scholar 

  27. Laskowski RA, MacArthur MW, Moss DS, Thornton JM: PROCHECK a program to check the stereochemical quality of protein structures. J Appl Crystallogr. 1993, 26: 283-291. 10.1107/S0021889892009944.

    Article  CAS  Google Scholar 

  28. Colovos C, Yeates TO: Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993, 2: 1511-1519. 10.1002/pro.5560020916.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  29. Bowie JU, Lüthy R, Eisenberg D: A method to identify protein sequences that fold into a known three-dimensional structure. Science. 1991, 253: 164-170. 10.1126/science.1853201.

    Article  CAS  PubMed  Google Scholar 

  30. Irwin JJ, Shoichet BK: ZINC: A free database of commercially available compounds for virtual screening. J Chem Inf Model. 2005, 45: 177-182. 10.1021/ci049714+.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Vianna CP, Azevedo WF: Identification of new potential Mycobacterium tuberculosis shikimate kinase inhibitors through molecular docking simulations. J Mol Model. 2012, 18: 755-764. 10.1007/s00894-011-1113-5.

    Article  CAS  PubMed  Google Scholar 

  32. Trott O, Olson AJ: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem. 2010, 31: 455-461.

    PubMed Central  CAS  PubMed  Google Scholar 

  33. Goodsell DS, Olson AJ: Automated docking of substrates to proteins by simulated annealing. Proteins. 1990, 8: 195-202. 10.1002/prot.340080302.

    Article  CAS  PubMed  Google Scholar 

  34. Park H, Jeon JH: Cubic equation governing the outer-region dielectric constant of globular proteins. Phys Rev E. 2007, 75: 021916-

    Article  Google Scholar 

  35. Stouten PFW, Frömmel C, Nakamura H, Sander C: An Effective Solvation Term Based on Atomic Occupancies for Use in Protein Simulations. Mol Simul. 1993, 10: 97-120. 10.1080/08927029308022161.

    Article  CAS  Google Scholar 

  36. Kang H, Choi H, Park H: Prediction of Molecular Solvation Free Energy Based on the Optimization of Atomic Solvation Parameters with Genetic Algorithm. J Chem Inf Model. 2007, 47: 509-514. 10.1021/ci600453b.

    Article  CAS  PubMed  Google Scholar 

  37. Shoichet BK, Leach AR, Kuntz ID: Ligand solvation in molecular docking. Proteins. 1999, 34: 4-16. 10.1002/(SICI)1097-0134(19990101)34:1<4::AID-PROT2>3.0.CO;2-6.

    Article  CAS  PubMed  Google Scholar 

  38. Guex N, Peitsch MC: SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis. 1997, 18: 2714-2723. 10.1002/elps.1150181505.

    Article  CAS  PubMed  Google Scholar 

  39. DeLano WL: The PyMOL molecular graphics system DeLano Scientific LLC. 2002, CA, USA: San Carlos

    Google Scholar 

  40. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Mengb EC, Ferrin TE: UCSF Chimera – a visualization system for exploratory research and analysis. J Comput Chem. 2004, 25: 1605-1612. 10.1002/jcc.20084.

    Article  CAS  PubMed  Google Scholar 

  41. The Accelrys DS Visualizer software.,

  42. Wolber G, Seidel T, Bendix F, Langer T: Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discov Today. 2008, 13: 23-29. 10.1016/j.drudis.2007.09.007.

    Article  CAS  PubMed  Google Scholar 

  43. Joris B, Ledent P, Dideberg O, Fonze E, Lamotte-Brasseur J, Kelly JA, Ghuysen JM, Frere JM: Comparison of the sequences of classA b-lactamases and of the secondary structure elements of penicillin-recognizing proteins. Antimicrob Agents Chemother. 1991, 35: 2294-2301. 10.1128/AAC.35.11.2294.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  44. Jacobs C, Frere JM, Normark S: Cytosolic intermediates for cell wall biosynthesis and degradation control inducible β-lactam resistance in gram-negative bacteria. Cell. 1997, 88: 823-832. 10.1016/S0092-8674(00)81928-5.

    Article  CAS  PubMed  Google Scholar 

  45. Imtiaz U, Billings EM, Knox JR, Manavathu EK, Lerner SA, Mobashery S: Inactivation of class A β-lactamases by clavulanic acid: the role of Arginine 244 in a proposed non certed sequence of events. J Am Chem Soc. 1993, 115: 4435-4442. 10.1021/ja00064a003.

    Article  CAS  Google Scholar 

  46. Bush K, Macalintal C, Rasmussen BA, Lee VJ, Yang Y: Kinetic interactions of tazobactam with b-lactamases from all major structural classes. Antimicrob Agents Chemother. 1993, 7: 851-858.

    Article  Google Scholar 

  47. Akova M: Sulbactam-containing b-lactamase inhibitor combinations. Clin Microbiol Infect. 2008, 14: 185-188. 10.1111/j.1469-0691.2007.01847.x.

    Article  CAS  PubMed  Google Scholar 

  48. Des Jarlais RL, Seibel GL, Kuntz ID, Furth PS, Alvarez JC, Ortiz De Montellano PR, De Camp DL, Babé LM, Craik CS: Structure-based design of non peptide inhibitors specific for the human immunodeficiency virus 1 protease. Proc Natl Acad Sci USA. 1990, 87: 6644-6648. 10.1073/pnas.87.17.6644.

    Article  CAS  Google Scholar 

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Correspondence to Abdelmonaem Messaoudi.

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AM carried out all analyses and drafted the manuscript under the guidance of HB and JBH. All authors read and approved the final manuscript.

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Messaoudi, A., Belguith, H. & Ben Hamida, J. Homology modeling and virtual screening approaches to identify potent inhibitors of VEB-1 β-lactamase. Theor Biol Med Model 10, 22 (2013).

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