In Silico Pharmacology

, 6:9 | Cite as

High throughput screening against pantothenate synthetase identifies amide inhibitors against Mycobacterium tuberculosis and Staphylococcus aureus

  • Sayantan Pradhan
  • Chittaranjan SinhaEmail author
Original Research


Pantothenate is a crucial enzyme for the synthesis of coenzyme A and acyl carrier protein in Mycobacterium tuberculosis and Staphylococcus aureus. It is indispensable for the growth and survival of these bacteria. Amides analogs are designed and have been used as inhibitors of pantothenate synthetase. Molecular docking approach has been used to design and predict the drug activity of molecule to the specific disease. In this work, more than hundred amides have been screened by Discovery Studio molecular docking programme to search best suitable molecule for the treatment of Mycobacterium tuberculosis. Pharmacophore generation has been done to recognize the binding modes of inhibitors in the receptor active site. To observe the stability and flexibility of inhibitors molecular dynamics (MD) simulation has been done; Lipinski’s rule of five protocols is followed to screen drug likeness and ADMET (absorption, distribution, metabolism, excretion and toxicity) filtration is also used to value toxicity. DFT computation of optimized geometry and derivation of MOs has been used to correlate the drug likeness. The small difference in energy between HOMO and LUMO may help to activate the drug in the protein environment quickly. 2-Hydroxy-5-[(E)-2-{4-[(prop-2-enamido)sulfonyl]phenyl}diazen-1-yl]benzoic acid (M1) shows best theoretical efficiency against Mycobacterium tuberculosis (MTB) pantothenate synthetase and so does 2-hydroxy-5-[(E)-2-{4-[(2-phenylacetamido)sulfonyl]phenyl}diazen-1-yl]benzoic acid (M2) against Staphylococcus aureus pantothenate synthetase. These compounds also bind to Adenine–Thymine region of tuberculosis DNA.

Graphical abstract


Pantothenate synthetase inhibitors Heterocyclic amide compounds Structure based drug design Molecular docking ADMET MD simulation 



  1. Ames BN, Gurney EG, Miller JA, Bartsch H (1972) Carcinogens as frameshift mutagens: metabolites and derivatives of 2-acetylaminofluorene and other aromatic amine carcinogens. Proc Natl Acad Sci USA 69(11):3128–3132PubMedGoogle Scholar
  2. Bartzatt R, Cirillo SL, Cirillo JD (2010) Sulfonamide agents for treatment of Staphylococcus MRSA and MSSA infections of the central nervous system. Cent Nerv Syst Agents Med Chem 10(1):84–90PubMedGoogle Scholar
  3. Beard DA, Qian H (2010) Chemical biophysics: quantitative analysis of cellular systems. Cambridge University Press, CambridgeGoogle Scholar
  4. Becke AD (1993) Density-functional thermochemistry. The role of exact exchange. J Chem Phys 98(7):5648–5652Google Scholar
  5. Berg JM, Tymoczko JL, Stryer L (2002) Biochemistry, 5th edn. W. H. Freeman, New YorkGoogle Scholar
  6. Böhm H-J (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6(1):61–78PubMedGoogle Scholar
  7. Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4(2):187–217Google Scholar
  8. Brownell LV, Robins KA, Jeong Y, Lee Y, Lee D-C (2013) Highly systematic and efficient HOMO–LUMO energy gap control of thiophene-pyrazine-acenes. J Phys Chem C 117(48):25236–25247Google Scholar
  9. Chaffey N, Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P (2003) Molecular biology of the cell, 4th edn. Ann Bot 91(3):401PubMedCentralGoogle Scholar
  10. Chaires JB (1998) Drug–DNA interactions. Curr Opin Struct Biol 8(3):314–320PubMedGoogle Scholar
  11. Chen AY, Yu C, Gatto B, Liu LF (1993) DNA minor groove-binding ligands: a different class of mammalian DNA topoisomerase I inhibitors. Proc Natl Acad Sci USA 90(17):8131–8135PubMedGoogle Scholar
  12. Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, Lee PW, Tang Y (2012) admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 52(11):3099–3105PubMedGoogle Scholar
  13. Chenna R, Sugawara H, Koike T, Lopez R, Gibson TJ, Higgins DG, Thompson JD (2003) Multiple sequence alignment with the clustal series of programs. Nucl Acid Res 31(13):3497–3500Google Scholar
  14. Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, Gordon SV, Eiglmeier K, Gas S, Barry CE 3rd, Tekaia F, Badcock K, Basham D, Brown D, Chillingworth T, Connor R, Davies R, Devlin K, Feltwell T, Gentles S, Hamlin N, Holroyd S, Hornsby T, Jagels K, Krogh A, McLean J, Moule S, Murphy L, Oliver K, Osborne J, Quail MA, Rajandream MA, Rogers J, Rutter S, Seeger K, Skelton J, Squares R, Squares S, Sulston JE, Taylor K, Whitehead S, Barrell BG (1998) Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 393(6685):537–544PubMedGoogle Scholar
  15. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J Chem Phys 98(12):10089–10092Google Scholar
  16. Database Resources of the National Center for Biotechnology Information (2013) Nucl Acids Res 41(Database issue):D8–D20Google Scholar
  17. Devlin FJ, Finley JW, Stephens PJ, Frisch MJ (1995) Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields: a comparison of local, nonlocal, and hybrid density functionals. J Phys Chem 99(46):16883–16902Google Scholar
  18. El-Henawy AA, Khowdiary MM, Badawi AB, Soliman HM (2013) In vivo anti-leukemia, quantum chemical calculations and ADMET investigations of some quaternary and isothiouronium surfactants. Pharmaceuticals 6(5):634–649PubMedPubMedCentralGoogle Scholar
  19. Fleming I (2011) Molecular orbitals and organic chemical reactions, Reference edn. Wiley, AmsterdamGoogle Scholar
  20. Frisch M, Trucks G, Schlegel H, Scuseria G, Robb M, Cheeseman J, Scalmani G, Barone V, Mennucci B, Petersson G (2009) Gaussian 09. Gaussian. Inc, WallingfordGoogle Scholar
  21. Fukui K, Yonezawa T, Shingu H (1952) A molecular orbital theory of reactivity in aromatic hydrocarbons. J Chem Phys 20(4):722–725Google Scholar
  22. Gill PMW, Johnson BG, Pople JA, Frisch MJ (1992) The performance of the Becke–Lee–Yang–Parr (B–LYP) density functional theory with various basis sets. Chem Phys Lett 197(4):499–505Google Scholar
  23. Grassl SM (1992) Human placental brush-border membrane Na(+)-pantothenate cotransport. J Biol Chem 267(32):22902–22906PubMedGoogle Scholar
  24. Hestenes MR, Stiefel E (1952) Methods of conjugate gradients for solving linear systems, vol 49Google Scholar
  25. Holloway KA, Rosella L, Henry D (2016) The impact of WHO essential medicines policies on inappropriate use of antibiotics. PLoS One 11(3):e0152020PubMedPubMedCentralGoogle Scholar
  26. Hou T, Wang J (2008) Structure – ADME relationship: still a long way to go? Exp Opin Drug Metab Toxicol 4(6):759–770Google Scholar
  27. Hou TJ, Xia K, Zhang W, Xu XJ (2004) ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach. J Chem Inf Comput Sci 44(1):266–275PubMedGoogle Scholar
  28. Jagessar RC, Rampersaud D (2007) Amides as antimicrobial agents. Life Sci J 4(4):46–49Google Scholar
  29. Kumar A, Casey A, Odingo J, Kesicki EA, Abrahams G, Vieth M, Masquelin T, Mizrahi V, Hipskind PA, Sherman DR, Parish T (2013) A high-throughput screen against pantothenate synthetase (PanC) identifies 3-biphenyl-4-cyanopyrrole-2-carboxylic acids as a new class of inhibitor with activity against Mycobacterium tuberculosis. PLoS One 8(11):e72786PubMedPubMedCentralGoogle Scholar
  30. Leonardi R, Jackowski S (2007) Biosynthesis of pantothenic acid and coenzyme A. EcoSal Plus 2(2)Google Scholar
  31. Lin JH, Yamazaki M (2003) Role of P-glycoprotein in pharmacokinetics: clinical implications. Clin Pharmacokinet 42(1):59–98PubMedGoogle Scholar
  32. Lin K, Tibbitts J, Shen BQ (2013) Pharmacokinetics and ADME characterizations of antibody-drug conjugates. Methods Mol Biol (Clifton NJ) 1045:117–131Google Scholar
  33. Lionta E, Spyrou G, Vassilatis DK, Cournia Z (2014) Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem 14(16):1923–1938PubMedPubMedCentralGoogle Scholar
  34. Lipinski CA (2004) Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1(4):337–341PubMedGoogle Scholar
  35. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46(1–3):3–26PubMedGoogle Scholar
  36. McMurry JE, Ballantine DS, Hoeger CA, Peterson VE (2017) Fundamentals of general, organic and biological chemistry. Pearson Education, Limited, LondonGoogle Scholar
  37. Meduru H, Wang Y-T, Tsai JJP, Chen Y-C (2016) Finding a potential dipeptidyl peptidase-4 (DPP-4) inhibitor for type-2 diabetes treatment based on molecular docking, pharmacophore generation, and molecular dynamics simulation. Int J Mol Sci 17(6):920PubMedCentralGoogle Scholar
  38. Mortelmans K, Zeiger E (2000) The Ames salmonella/microsome mutagenicity assay. Mutat Res 455(1–2):29–60PubMedGoogle Scholar
  39. Nguyen KD, Pan Y (2013) A knowledge-based multiple-sequence alignment algorithm. IEEE/ACM Trans Comput Biol Bioinf 10(4):884–896Google Scholar
  40. Onyango R (2011) State of the globe: tracking tuberculosis is the test of time. J Glob Infect Dis 3(1):1–3PubMedPubMedCentralGoogle Scholar
  41. Overington JP, Al-Lazikani B, Hopkins AL (2006) How many drug targets are there? Nat Rev Drug Discov 5(12):993–996PubMedGoogle Scholar
  42. Petrova SS, Solov’ev AD (1997) The origin of the method of steepest descent. Hist Math 24(4):361–375Google Scholar
  43. Pradhan S, Sinha C (2017) Combating prostate cancer by sulfonamide compounds: Theoretical prediction. J Indian Chem Soc 94(10):1113–1122Google Scholar
  44. Rauk A (1994) Orbital interaction theory of organic chemistry. Wiley, AmsterdamGoogle Scholar
  45. Roncaglioni A, Toropov AA, Toropova AP, Benfenati E (2013) In silico methods to predict drug toxicity. Curr Opin Pharmacol 13(5):802–806PubMedGoogle Scholar
  46. Rozhenko AB (2014) Density functional theory calculations of enzyme-inhibitor interactions in medicinal chemistry and drug design. In: Gorb L, Kuz’min V, Muratov E (eds) Application of computational techniques in pharmacy and medicine. Springer, Dordrecht, pp 207–240Google Scholar
  47. Sangeetha Gowda KR, Mathew BB, Sudhamani CN, Naik HSB (2014) Mechanism of DNA binding and cleavage. Biomed Biotechnol 2(1):1–9Google Scholar
  48. Sanguinetti MC, Tristani-Firouzi M (2006) hERG potassium channels and cardiac arrhythmia. Nature 440(7083):463–469PubMedGoogle Scholar
  49. Sievers F, Higgins DG (2014) Clustal omega, accurate alignment of very large numbers of sequences. Method Mol Biol (Clifton NJ) 1079:105–116Google Scholar
  50. Sirajuddin M, Ali S, Badshah A (2013) Drug–DNA interactions and their study by UV-Visible, fluorescence spectroscopies and cyclic voltametry. J Photochem Photobiol B 124:1–19PubMedGoogle Scholar
  51. Soga S, Shirai H, Kobori M, Hirayama N (2007) Use of amino acid composition to predict ligand-binding sites. J Chem Inf Model 47(2):400–406PubMedGoogle Scholar
  52. Stefańska J, Antoszczak M, Stępień K, Bartoszcze M, Mirski T, Huczyński A (2015) Tertiary amides of Salinomycin: a new group of antibacterial agents against Bacillus anthracis and methicillin-resistant Staphylococcus epidermidis. Bioorg Med Chem Lett 25(10):2082–2088PubMedGoogle Scholar
  53. Strom ET, Wilson AK (2013) Pioneers of quantum chemistry. Am Chem Soc vol 1122Google Scholar
  54. Szymański P, Markowicz M, Mikiciuk-Olasik E (2012) Adaptation of high-throughput screening in drug discovery—toxicological screening tests. Int J Mol Sci 13(1):427–452PubMedGoogle Scholar
  55. The sulfa derivatives in the treatment of tuberculosis (1944) Can Med Assoc J 51(5):467Google Scholar
  56. 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):455–461PubMedPubMedCentralGoogle Scholar
  57. Uttamsingh V, Lu C, Miwa G, Gan LS (2005) Relative contributions of the five major human cytochromes P450, 1A2, 2C9, 2C19, 2D6, and 3A4, to the hepatic metabolism of the proteasome inhibitor bortezomib. Drug Metab Dispos Biol Fate Chem 33(11):1723–1728PubMedGoogle Scholar
  58. Vallari DS, Rock CO (1985) Isolation and characterization of Escherichia coli pantothenate permease (panF) mutants. J Bacteriol 164(1):136–142PubMedPubMedCentralGoogle Scholar
  59. von Delft F, Lewendon A, Dhanaraj V, Blundell TL, Abell C, Smith AG (2001) The crystal structure of E. coli pantothenate synthetase confirms it as a member of the cytidylyltransferase superfamily. Structure 9(5):439–450Google Scholar
  60. Vyas VK, Ghate M, Goel A (2013) Pharmacophore modeling, virtual screening, docking and in silico ADMET analysis of protein kinase B (PKB beta) inhibitors. J Mol Graph Model 42:17–25PubMedGoogle Scholar
  61. Wang S, Eisenberg D (2003) Crystal structures of a pantothenate synthetase from M. tuberculosis and its complexes with substrates and a reaction intermediate. Protein Sci A Publ Protein Soc 12(5):1097–1108Google Scholar
  62. Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA (1998) Glossary of terms used in medicinal chemistry (IUPAC recommendations 1998). Pure Appl Chem 70:1129Google Scholar
  63. White EL, Southworth K, Ross L, Cooley S, Gill RB, Sosa MI, Manouvakhova A, Rasmussen L, Goulding C, Eisenberg D, Fletcher TM 3rd (2007) A novel inhibitor of Mycobacterium tuberculosis pantothenate synthetase. J Biomol Screen 12(1):100–105PubMedGoogle Scholar
  64. Wu G, Robertson DH, Brooks CL 3rd, Vieth M (2003) Detailed analysis of grid-based molecular docking: a case study of CDOCKER-A CHARMm-based MD docking algorithm. J Comput Chem 24(13):1549–1562PubMedGoogle Scholar
  65. Yang SY (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15(11–12):444–450PubMedGoogle Scholar
  66. Yildiz I, Ertan T, Bolelli K, Temiz-Arpaci O, Yalcin I, Aki E (2008) QSAR and pharmacophore analysis on amides against drug-resistant S. aureus. SAR QSAR Environ Res 19(1–2):101–113PubMedGoogle Scholar
  67. Zhang L, Brett CM, Giacomini KM (1998) Role of organic cation transporters in drug absorption and elimination. Annu Rev Pharmacol Toxicol 38:431–460PubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ChemistryJadavpur UniversityKolkataIndia

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