Molecular Diversity

, Volume 23, Issue 2, pp 263–273 | Cite as

New insights into the selective inhibition of the β-carbonic anhydrases of pathogenic bacteria Burkholderia pseudomallei and Francisella tularensis: a proteochemometrics study

  • Behnam RastiEmail author
  • Sargol Mazraedoost
  • Hanieh Panahi
  • Mojtaba Falahati
  • Farnoosh Attar
Original Article


Nowadays, antibiotic resistance has turned into one of the most important worldwide health problems. Biological end point of critical enzymes induced by potent inhibitors is recently being considered as a highly effective and popular strategy to defeat antibiotic-resistant pathogens. For instance, the simple but critical β-carbonic anhydrase has recently been in the center of attention for anti-pathogen drug discoveries. However, no β-carbonic anhydrase selective inhibitor has yet been developed. Available β-carbonic anhydrase inhibitors are also highly potent with regard to human carbonic anhydrases, leading to severe inevitable side effects in case of usage. Therefore, developing novel inhibitors with high selectivity against pathogenic β-carbonic anhydrases is of great essence. Herein, for the first time, we have conducted a proteochemometric study to explore the structural and the chemical aspects of the interactions governed by bacterial β-carbonic anhydrases and their inhibitors. We have found valuable information which can lead to designing novel inhibitors with better selectivity for bacterial β-carbonic anhydrases.


β-carbonic anhydrase Proteochemometrics Selectivity Burkholderia pseudomallei Francisella tularensis 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Supplementary material

11030_2018_9869_MOESM1_ESM.csv (60 kb)
Supplementary material 1 (CSV 59 kb)
11030_2018_9869_MOESM2_ESM.csv (5 kb)
Supplementary material 2 (CSV 4 kb)


  1. 1.
    Capasso C, Supuran CT (2016) An overview of the carbonic anhydrases from two pathogens of the oral cavity: streptococcus mutans and Porphyromonas gingivalis. Curr Top Med Chem 16:2359–2368CrossRefGoogle Scholar
  2. 2.
    Ozensoy Guler O, Capasso C, Supuran CT (2016) A magnificent enzyme superfamily: carbonic anhydrases, their purification and characterization. J Enzyme Inhib Med Chem 31:689–694Google Scholar
  3. 3.
    Capasso C, Supuran CT (2015) Bacterial, fungal and protozoan carbonic anhydrases as drug targets. Expert Opin Ther Targets 19:1689–1704CrossRefGoogle Scholar
  4. 4.
    Del Prete S, De Luca V, De Simone G, Supuran CT, Capasso C (2016) Cloning, expression and purification of the complete domain of the η-carbonic anhydrase from Plasmodium falciparum. J Enzyme Inhib Med Chem 31:54–59CrossRefGoogle Scholar
  5. 5.
    Del Prete S, Vullo D, Fisher GM, Andrews KT, Poulsen SA, Capasso C et al (2014) Discovery of a new family of carbonic anhydrases in the malaria pathogen Plasmodium falciparum—The η-carbonic anhydrases. Bioorg Med Chem Lett 24:4389–4396CrossRefGoogle Scholar
  6. 6.
    Supuran CT, Capasso C (2015) The η-class carbonic anhydrases as drug targets for antimalarial agents. Expert Opin Ther Targets 19:551–563CrossRefGoogle Scholar
  7. 7.
    Supuran CT (2013) Carbonic anhydrases: from biomedical applications of the inhibitors and activators to biotechnological use for CO2 capture. Med Chem 28:229–230Google Scholar
  8. 8.
    Bejaoui M, Pantazi E, De Luca V, Panisello A, Folch-Puy E, Hotter G et al (2015) Carbonic anhydrase protects fatty liver grafts against ischemic reperfusion damage. PLoS One 10:1–16Google Scholar
  9. 9.
    Del Prete S, Vullo D, Osman SM, AlOthman Z, Supuran CT (2017) Sulfonamide inhibition profiles of the β-carbonic anhydrase from the pathogenic bacterium Francisella tularensis responsible of the febrile illness tularemia. Bioorg Med Chem 25:3555–3561CrossRefGoogle Scholar
  10. 10.
    Vullo D, Del Prete S, Di Fonzo P, Carginale V, Donald WA, Supuran CT et al (2017) Comparison of the sulfonamide inhibition profiles of the β-and γ-carbonic anhydrases from the pathogenic bacterium Burkholderia pseudomallei. Molecules 22:421–435CrossRefGoogle Scholar
  11. 11.
    Gillard JJ, Laws TR, Lythe G, Molina-París C (2014) Modeling early events in Francisella tularensis pathogenesis. Front Cell Infect Microbiol 11:169–178Google Scholar
  12. 12.
    Saslaw S, Eigelsbach HT, Prior JA, Wilson HE, Carhart S (1961) Tularemia vaccine study: II. Respiratory challenge. Arch Intern Med 107:702–714CrossRefGoogle Scholar
  13. 13.
    Celli J, Zahrt TC (2013) Mechanisms of Francisella tularensis intracellular pathogenesis. Cold Spring Harb Perspect Med 3:a010314–a010327CrossRefGoogle Scholar
  14. 14.
    Oyston PC (2008) Francisella tularensis: unravelling the secrets of an intracellular pathogen. J Med Microbiol 57:921–930CrossRefGoogle Scholar
  15. 15.
    Conlan JW (2011) Francisella tularensis: a red-blooded pathogen. J Infect Dis 204:6–8CrossRefGoogle Scholar
  16. 16.
    Currie BJ (2010) Burkholderia pseudomallei and Burkholderia mallei: melioidosis and glanders. In: Mandell, Douglas and Bennett’s Principles and Practice of Infectious Diseases. Churchill Livingstone Elsevier, Philadelphia, pp 2869–2885Google Scholar
  17. 17.
    Stephens DP, Thomas JH, Ward LM, Currie BJ (2016) Melioidosis causing critical illness: a review of 24 years of experience from the Royal Darwin Hospital ICU. Crit Care Med 44:1500–1505CrossRefGoogle Scholar
  18. 18.
    Cheng AC, Limmathurotsakul D, Chierakul W, Getchalarat N, Wuthiekanun V, Stephens DP et al (2007) A randomized controlled trial of granulocyte colonystimulating factor for the treatment of severe sepsis due to melioidosis in Thailand. Clin Infect Dis 45:308–314CrossRefGoogle Scholar
  19. 19.
    Prusis P, Muceniece R, Andersson P, Post C, Lundstedt T, Wikberg JE (2001) PLS modeling of chimeric MS04/MSH-peptide and MC 1/MC 3-receptor interactions reveals a novel method for the analysis of ligand–receptor interactions. Biochim Biophys Acta 1544:350–357CrossRefGoogle Scholar
  20. 20.
    Lapinsh M, Prusis P, Lundstedt T, Wikberg JE (2002) Proteochemometrics modeling of the interaction of amine G-protein coupled receptors with a diverse set of ligands. Mol Pharmacol 61:1465–1475CrossRefGoogle Scholar
  21. 21.
    Lapinsh M, Prusis P, Uhlén S, Wikberg JE (2005) Improved approach for proteochemometrics modeling: application to organic compound-amine G protein-coupled receptor interactions. Bioinformatics 21:4289–4296CrossRefGoogle Scholar
  22. 22.
    Prusis P, Lapins M, Yahorava S, Petrovska R, Niyomrattanakit P, Katzenmeier G et al (2008) Proteochemometrics analysis of substrate interactions with dengue virus NS3 proteases. Bioorg Med Chem 16:9369–9377CrossRefGoogle Scholar
  23. 23.
    Lapins M, Eklund M, Spjuth O, Prusis P, Wikberg JE (2008) Proteochemometric modeling of HIV protease susceptibility. BMC Bioinf 9:181–191CrossRefGoogle Scholar
  24. 24.
    Lapins M, Wikberg JE (2010) Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques. BMC Bioinf 11:339–353CrossRefGoogle Scholar
  25. 25.
    Subramanian V, Prusis P, Pietilä LO, Xhaard H, Wohlfahrt G (2013) Visually interpretable models of kinase selectivity related features derived from field-based proteochemometrics. J Chem Inf Model 53:3021–3030CrossRefGoogle Scholar
  26. 26.
    Rasti B, Karimi-Jafari MH, Ghasemi JB (2016) Quantitative Characterization of the Interaction Space of the Mammalian Carbonic Anhydrase Isoforms I, II, VII, IX, XII, and XIV and their Inhibitors. Using the Proteochemometric Approach. Chem Biol Drug Des 88:341–353CrossRefGoogle Scholar
  27. 27.
    Rasti B, Namazi M, Karimi-Jafari MH, Ghasemi JB (2017) Proteochemometric modeling of the interaction space of carbonic anhydrase and its inhibitors: an assessment of structure-based and sequence-based descriptors. Mol Info 36:1600102–1600113CrossRefGoogle Scholar
  28. 28.
    Rasti B, Entezari Heravi Y (2018) Probing the chemical interaction space governed by 4-aminosubstituted benzenesulfonamides and carbonic anhydrase isoforms. Res Pharm Sci 13:192–204CrossRefGoogle Scholar
  29. 29.
    Simeon S, Spjuth O, Lapins M, Nabu S, Anuwongcharoen N, Prachayasittikul V et al (2016) Origin of aromatase inhibitory activity via proteochemometric modeling. PeerJ 4:e1979–e2006CrossRefGoogle Scholar
  30. 30.
    Rasti B, Shahangian SS (2018) Proteochemometric modeling of the origin of thymidylate synthase inhibition. Chem Biol Drug Des 91:1007–1016CrossRefGoogle Scholar
  31. 31.
    Rasti B, Schaduangrat N, Shahangian SS, Nantasenamat C (2017) Exploring the origin of phosphodiesterase inhibition via proteochemometric modeling. RSC Adv 7:28056–28068CrossRefGoogle Scholar
  32. 32.
    Version, S., 6.9, Tripos Associates, St. Louis, Mo, 2001Google Scholar
  33. 33.
    Pastor M, Cruciani G, McLay I, Pickett S, Clementi S (2000) GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. J Med Chem 43:3233–3243CrossRefGoogle Scholar
  34. 34.
    Duran A, Martinez GC, Pastor M (2008) Development and validation of AMANDA, a new algorithm for selecting highly relevant regions in molecular interaction fields. J Chem Inf Model 48:1813–1823CrossRefGoogle Scholar
  35. 35.
    Wold S, Jonsson J, Sjörström M, Sandberg M, Rännar S (1993) DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Ana Chim Acta 277:239–253CrossRefGoogle Scholar
  36. 36.
    Hellberg S, Sjoestroem M, Skagerberg B, Wold S (1987) Peptide quantitative structure-activity relationships, a multivariate approach. J Med Chem 30:1126–1135CrossRefGoogle Scholar
  37. 37.
    Sandberg M, Eriksson L, Jonsson J, Sjöström M, Wold S (1998) New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J Med Chem 41:2481–2491CrossRefGoogle Scholar
  38. 38.
    Beasley D, Bull DR, Martin RR (1993) An overview of genetic algorithms: part 1, fundamentals. University computing 15:56–69Google Scholar
  39. 39.
    Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34:854–866CrossRefGoogle Scholar
  40. 40.
    Hou TJ, Wang JM, Liao N, Xu XJ (1999) Applications of genetic algorithms on the structure—activity relationship analysis of some cinnamamides. J Chem Inf Comput Sci 39:775–781CrossRefGoogle Scholar
  41. 41.
    Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137–148CrossRefGoogle Scholar
  42. 42.
    Gramatica P (2007) Principles of QSAR models validation: internal and external. Mol Inf 26:694–701Google Scholar
  43. 43.
    Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Mol Inf 22:69–77Google Scholar
  44. 44.
    Eriksson L, Jaworska J, Worth AP, Cronin MT, McDowell RM, Gramatica P (2003) Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs. Environ Health Perspect 111:1361–1375CrossRefGoogle Scholar
  45. 45.
    Alhanout K, M Rolain J, M Brunel J (2010) Squalamine as an example of a new potent antimicrobial agents class: a critical review. Curr Med Chem 17:3909–3917CrossRefGoogle Scholar
  46. 46.
    Gaynor M, Mankin AS (2003) Macrolide antibiotics: binding site, mechanism of action, resistance. Curr Top Med Chem 3:949–960CrossRefGoogle Scholar
  47. 47.
    Khelaifia S, Drancourt M (2012) Susceptibility of archaea to antimicrobial agents: applications to clinical microbiology. Clin Microbiol Infect 18:841–848CrossRefGoogle Scholar
  48. 48.
    Supuran CT (2017) Advances in structure-based drug discovery of carbonic anhydrase inhibitors. Expert Opin Drug Discov 12:61–88CrossRefGoogle Scholar
  49. 49.
    Supuran CT (2016) Drug interaction considerations in the therapeutic use of carbonic anhydrase inhibitors. Expert Opin Drug Metab Toxicol 12:423–431CrossRefGoogle Scholar
  50. 50.
    Capasso C, Supuran CT (2015) An overview of the selectivity and efficiency of the bacterial carbonic anhydrase inhibitors. Curr Med Chem 22:2130–2139CrossRefGoogle Scholar
  51. 51.
    Capasso C, Supuran CT (2014) Sulfa and trimethoprim-like drugs–antimetabolites acting as carbonic anhydrase, dihydropteroate synthase and dihydrofolate reductase inhibitors. J Enzyme Inhib Med Chem 29:379–387CrossRefGoogle Scholar
  52. 52.
    Capasso C, Supuran CT (2013) Anti-infective carbonic anhydrase inhibitors: a patent and literature review. Expert Opin Ther Pat 23:693–704CrossRefGoogle Scholar
  53. 53.
    Nishimori I, Onishi S, Takeuchi H, Supuran CT (2008) The α and β classes carbonic anhydrases from Helicobacter pylori as novel drug targets. Curr Pharm Des 14:622–630CrossRefGoogle Scholar
  54. 54.
    Morishita S, Nishimori I, Minakuchi T, Onishi S, Takeuchi H, Sugiura T et al (2008) Cloning, polymorphism, and inhibition of β-carbonic anhydrase of Helicobacter pylori. J Gastroenterol 43:849–857CrossRefGoogle Scholar
  55. 55.
    Abuaita BH, Withey JH (2009) Bicarbonate induces Vibrio cholerae virulence gene expression by enhancing ToxT activity. Infect Immun 77:4111–4120CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Microbiology, Faculty of Basic Sciences, Lahijan BranchIslamic Azad University (IAU)LahijanIran
  2. 2.Department of Mathematics and Statistics, Lahijan BranchIslamic Azad UniversityLahijanIran
  3. 3.Department of Nanotechnology, Faculty of Advance Science and Technology, Pharmaceutical Sciences BranchIslamic Azad University (IAUPS)TehranIran
  4. 4.Department of Biology, Faculty of Food Industry and AgricultureStandard Research Institute (SRI)KarajIran

Personalised recommendations