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Identification of potent L,D-transpeptidase 5 inhibitors for Mycobacterium tuberculosis as potential anti-TB leads: virtual screening and molecular dynamics simulations

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Abstract

Virtual screening is a useful in silico approach to identify potential leads against various targets. It is known that carbapenems (doripenem and faropenem) do not show any reasonable inhibitory activities against L,D-transpeptidase 5 (LdtMt5) and also an adduct of meropenem exhibited slow acylation. Since these drugs are active against L,D-transpeptidase 2 (LdtMt2), understanding the differences between these two enzymes is essential. In this study, a ligand-based virtual screening of 12,766 compounds followed by molecular dynamics (MD) simulations was applied to identify potential leads against LdtMt5. To further validate the obtained virtual screening ranking for LdtMt5, we screened the same libraries of compounds against LdtMt2 which had more experimetal and calculated binding energies reported. The observed consistency between the binding affinities of LdtMt2 validates the obtained virtual screening binding scores for LdtMt5. We subjected 37 compounds with docking scores ranging from − 7.2 to − 9.9 kcal mol−1 obtained from virtual screening for further MD analysis. A set of compounds (n = 12) from four antibiotic classes with ≤ − 30 kcal mol−1 molecular mechanics/generalized born surface area (MM-GBSA) binding free energies (ΔGbind) was characterized. A final set of that, all β-lactams (n = 4), was considered. The outcome of this study provides insight into the design of potential novel leads for LdtMt5.

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References

  1. Seung KJ, Keshavjee S, Rich ML (2015) Multidrug-resistant tuberculosis and extensively drug-resistant tuberculosis. Cold Spring Harbor perspectives in medicine:a017863

  2. Billones JB, Carrillo MCO, Organo VG, Macalino SJY, Sy JBA, Emnacen IA, Clavio NAB, Concepcion GP (2016) Toward antituberculosis drugs: in silico screening of synthetic compounds against Mycobacterium tuberculosis L, D-transpeptidase 2. Drug Des Devel Ther 10:1147

  3. Adewumi OA (2012) Treatment outcomes in patients infected with multidrug resistant tuberculosis and in patients with multidrug resistant tuberculosis coinfected with human immunodeficiency virus at Brewelskloof Hospital

  4. Basta LAB, Ghosh A, Pan Y, Jakoncic J, Lloyd EP, Townsend CA, Lamichhane G, Bianchet MA (2015) Loss of a functionally and structurally distinct ld-transpeptidase, LdtMt5, compromises cell wall integrity in mycobacterium tuberculosis. J Biol Chem 290(42):25670–25685

    Google Scholar 

  5. Lavollay M, Arthur M, Fourgeaud M, Dubost L, Marie A, Veziris N, Blanot D, Gutmann L, Mainardi J-L (2008) The peptidoglycan of stationary-phase Mycobacterium tuberculosis predominantly contains cross-links generated by L, D-transpeptidation. J Bacteriol 190(12):4360–4366

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Dubée V, Triboulet S, Mainardi J-L, Ethève-Quelquejeu M, Gutmann L, Marie A, Dubost L, Hugonnet J-E, Arthur M (2012) Inactivation of mycobacterium tuberculosis L, D-transpeptidase LdtMt1 by carbapenems and cephalosporins. Antimicrob Agents Chemother 56(8):4189–4195

    PubMed  PubMed Central  Google Scholar 

  7. Cordillot M, Dubée V, Triboulet S, Dubost L, Marie A, Hugonnet J-E, Arthur M, Mainardi J-L (2013) In vitro cross-linking of Mycobacterium tuberculosis peptidoglycan by l, d-transpeptidases and inactivation of these enzymes by carbapenems. Antimicrob Agents Chemother 57(12):5940–5945

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Lecoq L, Dubée V, Sb T, Bougault C, Hugonnet J-E, Arthur M, Simorre J-P (2013) Structure of enterococcus faecium L, D-transpeptidase acylated by ertapenem provides insight into the inactivation mechanism. ACS Chem Biol 8(6):1140–1146

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Gupta R, Lavollay M, Mainardi J-L, Arthur M, Bishai WR, Lamichhane G (2010) The mycobacterium tuberculosis protein LdtMt2 is a nonclassical transpeptidase required for virulence and resistance to amoxicillin. Nat Med 16(4):466–469

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Biarrotte-Sorin S, Hugonnet J-E, Delfosse V, Mainardi J-L, Gutmann L, Arthur M, Mayer C (2006) Crystal structure of a novel β-lactam-insensitive peptidoglycan transpeptidase. J Mol Biol 359(3):533–538

    CAS  PubMed  Google Scholar 

  11. Mainardi J-L, Fourgeaud M, Hugonnet J-E, Dubost L, Brouard J-P, Ouazzani J, Rice LB, Gutmann L, Arthur M (2005) A novel peptidoglycan cross-linking enzyme for a β-lactam-resistant transpeptidation pathway. J Biol Chem 280(46):38146–38152

    CAS  PubMed  Google Scholar 

  12. Mainardi J-L, Villet R, Bugg TD, Mayer C, Arthur M (2008) Evolution of peptidoglycan biosynthesis under the selective pressure of antibiotics in Gram-positive bacteria. FEMS Microbiol Rev 32(2):386–408

    CAS  PubMed  Google Scholar 

  13. Tolufashe GF, Sabe VT, Ibeji CU, Ntombela T, Govender T, Maguire GE, Kruger HG, Lamichhane G, Honarparvar B (2019) Structure and function of L, D-and D, D-transpeptidase family enzymes from Mycobacterium tuberculosis. Curr Med Chem

  14. Honarparvar B, Govender T, Maguire GE, Soliman ME, Kruger HG (2013) Integrated approach to structure-based enzymatic drug design: molecular modeling, spectroscopy, and experimental bioactivity. Chem Rev 114(1):493–537

    PubMed  Google Scholar 

  15. Bradley J, Garau J, Lode H, Rolston K, Wilson S, Quinn J (1999) Carbapenems in clinical practice: a guide to their use in serious infection. Int J Antimicrob Agents 11(2):93–100

    CAS  PubMed  Google Scholar 

  16. Paterson D (2000) Recommendation for treatment of severe infections caused by Enterobacteriaceae producing extended-spectrum β-lactamases (ESBLs). Clin Microbiol Infect 6(9):460–463

    CAS  PubMed  Google Scholar 

  17. Paterson DL Serious infections caused by enteric gram-negative bacilli--mechanisms of antibiotic resistance and implications for therapy of gram-negative sepsis in the transplanted patient. In: Seminars in respiratory infections, 2002. vol 4. pp 260–264

    PubMed  Google Scholar 

  18. Paterson DL, Bonomo RA (2005) Extended-spectrum β-lactamases: a clinical update. Clin Microbiol Rev 18(4):657–686

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Torres JA, Villegas MV, Quinn JP (2007) Current concepts in antibiotic-resistant gram-negative bacteria. Expert Rev Anti-Infect Ther 5(5):833–843

    CAS  PubMed  Google Scholar 

  20. Meletis G (2016) Carbapenem resistance: overview of the problem and future perspectives. Ther Adv Infect Dis 3(1):15–21

    Google Scholar 

  21. Kattan J, Villegas M, Quinn J (2008) New developments in carbapenems. Clin Microbiol Infect 14(12):1102–1111

    CAS  PubMed  Google Scholar 

  22. El-Gamal MI, Brahim I, Hisham N, Aladdin R, Mohammed H, Bahaaeldin A (2017) Recent updates of carbapenem antibiotics. Eur J Med Chem 131:185–195

    CAS  PubMed  Google Scholar 

  23. Tolufashe GF, Sabe VT, Ibeji CU, Lawal MM, Govender T, Maguire GE, Lamichhane G, Kruger HG, Honarparvar B (2019) Inhibition mechanism of L, D-transpeptidase 5 in presence of the β-lactams using ONIOM method. J Mol Graph Model 87:204–210

    CAS  PubMed  Google Scholar 

  24. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Schrödinger Release 2018–1: Maestro S, LLC, New York, NY, 2018

  26. Reddy AS, Pati SP, Kumar PP, Pradeep H, Sastry GN (2007) Virtual screening in drug discovery-a computational perspective. Curr Protein Pept Sci 8(4):329–351

    CAS  PubMed  Google Scholar 

  27. Roe DR, Cheatham III TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095

    CAS  PubMed  Google Scholar 

  28. Pearlman DA, Case DA, Caldwell JW, Ross WS, Cheatham III TE, DeBolt S, Ferguson D, Seibel G, Kollman P (1995) AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput Phys Commun 91(1–3):1–41

    CAS  Google Scholar 

  29. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2006) The protein data bank, 1999–. In: International tables for crystallography Volume F: Crystallography of biological macromolecules. Springer, pp 675–684

  30. Sali A (1994) Modeller. A program for protein structure modeling by satisfaction of spatial restraints. http://guitar.rockefeller.edu/modiller/modeller.html

  31. Li H, Robertson AD, Jensen JH (2005) Very fast empirical prediction and rationalization of protein pKa values. Proteins 61(4):704–721

    CAS  Google Scholar 

  32. Fakhar Z, Govender T, Maguire GE, Lamichhane G, Walker RC, Kruger HG, Honarparvar B (2017) Differential flap dynamics in l, d-transpeptidase2 from mycobacterium tuberculosis revealed by molecular dynamics. Mol BioSyst 13(6):1223–1234

    CAS  PubMed  Google Scholar 

  33. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52(7):1757–1768

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings1. Adv Drug Deliv Rev 46(1–3):3–26

    CAS  PubMed  Google Scholar 

  35. Veber DF, Johnson SR, Cheng H-Y, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45(12):2615–2623

    CAS  PubMed  Google Scholar 

  36. Singh UC, Kollman PA (1984) An approach to computing electrostatic charges for molecules. J Comput Chem 5(2):129–145

    CAS  Google Scholar 

  37. Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 36(22):3219–3228

    CAS  Google Scholar 

  38. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791

    CAS  PubMed  PubMed Central  Google Scholar 

  39. BIOVIA DS (2017) BIOVIA Discovery Studio 2017 R2: A comprehensive predictive science application for the Life Sciences. San Diego, CA, USA http://accelrys.com/products/collaborative-science/biovia-discovery-studio

  40. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234

    PubMed  Google Scholar 

  41. Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M (2007) Epik: a software program for pK a prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 21(12):681–691

    CAS  PubMed  Google Scholar 

  42. Harder E, Damm W, Maple J, Wu C, Reboul M, Xiang JY, Wang L, Lupyan D, Dahlgren MK, Knight JL (2015) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12(1):281–296

    PubMed  Google Scholar 

  43. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748

    CAS  PubMed  Google Scholar 

  44. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein− ligand complexes. J Med Chem 49(21):6177–6196

    CAS  PubMed  Google Scholar 

  45. Enyedy IJ, Egan WJ (2008) Can we use docking and scoring for hit-to-lead optimization? J Comput Aided Mol Des 22(3–4):161–168

    CAS  PubMed  Google Scholar 

  46. Repasky MP, Shelley M, Friesner RA (2007) Flexible ligand docking with glide. Current protocols in bioinformatics:8.12. 11-18.12. 36

  47. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092

    CAS  Google Scholar 

  48. Taylor RD, Jewsbury PJ, Essex JW (2002) A review of protein-small molecule docking methods. J Comput Aided Mol Des 16(3):151–166

    CAS  PubMed  Google Scholar 

  49. Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65(3):712–725

    CAS  Google Scholar 

  50. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174

    CAS  PubMed  Google Scholar 

  51. Harvey M, De Fabritiis G (2009) An implementation of the smooth particle mesh Ewald method on GPU hardware. J Chem Theory Comput 5(9):2371–2377

    CAS  PubMed  Google Scholar 

  52. Kräutler V, Van Gunsteren WF, Hünenberger PH (2001) A fast SHAKE algorithm to solve distance constraint equations for small molecules in molecular dynamics simulations. J Comput Chem 22(5):501–508

    Google Scholar 

  53. John A, Sivashanmugam M, Umashankar V, Natarajan SK (2017) Virtual screening, molecular dynamics, and binding free energy calculations on human carbonic anhydrase IX catalytic domain for deciphering potential leads. J Biomol Struct Dyn 35(10):2155–2168

    CAS  PubMed  Google Scholar 

  54. Tolufashe GF, Halder AK, Ibeji CU, Lawal MM, Ntombela T, Govender T, Maguire GE, Lamichhane G, Kruger HG, Honarparvar B (2018) Inhibition of mycobacterium tuberculosis L, D-transpeptidase 5 by carbapenems: MD and QM/MM mechanistic studies. ChemistrySelect 3(48):13603–13612

    CAS  Google Scholar 

  55. Silva JRA, Bishai WR, Govender T, Lamichhane G, Maguire GE, Kruger HG, Lameira J, Alves CN (2016) Targeting the cell wall of mycobacterium tuberculosis: a molecular modeling investigation of the interaction of imipenem and meropenem with L, D-transpeptidase 2. J Biomol Struct Dyn 34(2):304–317

    CAS  PubMed  Google Scholar 

  56. Sun H, Li Y, Tian S, Xu L, Hou T (2014) Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys Chem Chem Phys 16(31):16719–16729

    CAS  PubMed  Google Scholar 

  57. Miller III BR, McGee Jr TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE (2012) MMPBSA. py: an efficient program for end-state free energy calculations. J Chem Theory Comput 8(9):3314–3321

    CAS  PubMed  Google Scholar 

  58. Islam MA, Pillay TS (2017) Identification of promising DNA GyrB inhibitors for tuberculosis using pharmacophore-based virtual screening, molecular docking and molecular dynamics studies. Chem Biol Drug Des 90(2):282–296

    CAS  PubMed  Google Scholar 

  59. Martin YC (2005) A bioavailability score. J Med Chem 48(9):3164–3170

    CAS  PubMed  Google Scholar 

  60. Bianchet MA, Pan YH, Basta LAB, Saavedra H, Lloyd EP, Kumar P, Mattoo R, Townsend CA, Lamichhane G (2017) Structural insight into the inactivation of Mycobacterium tuberculosis non-classical transpeptidase Ldt Mt2 by biapenem and tebipenem. BMC Biochem 18(1):8

    PubMed  PubMed Central  Google Scholar 

  61. Erdemli SB, Gupta R, Bishai WR, Lamichhane G, Amzel LM, Bianchet MA (2012) Targeting the cell wall of mycobacterium tuberculosis: structure and mechanism of L, D-transpeptidase 2. Structure 20(12):2103–2115

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688

    Google Scholar 

  63. Lobanov MY, Bogatyreva N, Galzitskaya O (2008) Radius of gyration as an indicator of protein structure compactness. Mol Biol 42(4):623–628

    CAS  Google Scholar 

  64. Peterson K, Zimmt M, Linse S, Domingue R, Fayer M (1987) Quantitative determination of the radius of gyration of poly (methyl methacrylate) in the amorphous solid state by time-resolved fluorescence depolarization measurements of excitation transport. Macromolecules 20(1):168–175

    CAS  Google Scholar 

  65. Kassem S, Ahmed M, El-Sheikh S, Barakat KH (2015) Entropy in bimolecular simulations: a comprehensive review of atomic fluctuations-based methods. J Mol Graph Model 62:105–117. https://doi.org/10.1016/j.jmgm.2015.09.010

    Article  CAS  PubMed  Google Scholar 

  66. Chiba S, Harano Y, Roth R, Kinoshita M, Sakurai M (2012) Evaluation of protein-ligand binding free energy focused on its entropic components. J Comput Chem 33(5):550–560. https://doi.org/10.1002/jcc.22891

    Article  CAS  PubMed  Google Scholar 

  67. Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng Des Sel 8(2):127–134

    CAS  Google Scholar 

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Funding

Our gratitude goes to Aspen Pharmacare, National Research Foundation (NRF), and the University of KwaZulu-Natal (UKZN) for the financial support.

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Authors and Affiliations

  1. Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, 4001, South Africa

    Victor T. Sabe, Gideon F. Tolufashe, Collins U. Ibeji, Sibusiso B. Maseko, Thavendran Govender, Glenn E. M. Maguire, Bahareh Honarparvar & Hendrik G. Kruger

  2. School of Chemistry and Physics, University of KwaZulu-Natal, Durban, 4001, South Africa

    Glenn E. M. Maguire

  3. Center for Tuberculosis Research, Division of Infectious Diseases, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA

    Gyanu Lamichhane

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  1. Victor T. Sabe
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  2. Gideon F. Tolufashe
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Sabe, V.T., Tolufashe, G.F., Ibeji, C.U. et al. Identification of potent L,D-transpeptidase 5 inhibitors for Mycobacterium tuberculosis as potential anti-TB leads: virtual screening and molecular dynamics simulations. J Mol Model 25, 328 (2019). https://doi.org/10.1007/s00894-019-4196-z

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