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In silico Screening of Flavones and its Derivatives as Potential Inhibitors of Quorum-Sensing Regulator LasR of Pseudomonas aeruginosa

  • STRUCTURAL-FUNCTIONAL ANALYSIS OF BIOPOLYMERS AND THEIR COMPLEXES
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Abstract

Antibiotic resistance is a global problem nowadays and in 2017 the World Health Organization published the list of bacteria for which treatment are urgently needed, where Pseudomonas aeruginosa is of critical priority. Current therapies lack efficacy because this organism creates biofilms conferring increased resistance to antibiotics and host immune responses. The strategy is to “not kill, but disarm” the pathogen and resistance will be developed slowly. It has been shown that LasI/LasR system is the main component of the quorum sensing system in P. aeruginosa. LasR is activated by the interaction with its native autoinducer. A lot flavones and their derivatives are used as antibacterial drug compounds. The purpose is to search compounds that will inhibit LasR. This leads to the inhibition of the synthesis of virulence factors thus the bacteria will be vulnerable and not virulent. We performed virtual screening using AutoDock Vina, rDock, LeDock for obtaining consensus predictions. The results of virtual screening suggest benzamides which are synthetical derivatives of flavones as potential inhibitors of transcriptional regulator LasR. These are consistent with recently published experimental data, which demonstrate the high antibacterial activity of benzamides. The compounds interact with the ligand binding domain of LasR with higher binding affinity than with DNA binding domain. Among the selected compounds, by conformational analysis, it was found that there are compounds that bind to the same amino acids of ligand binding domain as the native autoinducer. This could indicate the possibility of competitive interaction of these compounds. A number of compounds that bind to other conservative amino acids ligand binding domain have also been discovered, which will be of interest for further study. Selected compounds meet the criteria necessary for their consideration as drugs and can serve as a basis for conducting further in vitro/in vivo experiments. It could be used for the development of modern anti-infective therapy based on the quorum sensing system of P. aeruginosa.

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REFERENCES

  1. Hampton T. 2013. Report reveals scope of US antibiotic resistance threat. J. Am. Med. Assoc. 310, 1661–1663. https://doi.org/10.1001/jama.2013.280695

    Article  CAS  Google Scholar 

  2. Tacconelli E., Carrara E., Savoldi A., Harbarth S., Mendelson M., Monnet D.L., Pulcini C., Kahlmeter G., Kluytmans J., Carmeli Y., Ouellette M., Outterson K., Patel J., Cavaleri M., Cox E.M., et al. 2018. Discovery, research, and development of new antibiotics: The WHO priority list of antibiotic-resistant bacteria and tuberculosis. Lancet Infect. Dis.18, 318–327. https://doi.org/10.1016/S1473-3099(17)30753-3

    Article  PubMed  Google Scholar 

  3. Lee J., Zhang L. 2015. The hierarchy quorum sensing network in Pseudomonas aeruginosa.Protein Cell.6, 26–41. https://doi.org/10.1007/s13238-014-0100-x

    Article  CAS  PubMed  Google Scholar 

  4. Pérez-Pérez M., Jorge P., Pérez Rodríguez G., Pereira M.O., Lourenço A. 2017. Quorum sensing inhibition in Pseudomonas aeruginosa biofilms: New insights through network mining. Biofouling. 33, 128–142. https://doi.org/10.1080/08927014.2016.1272104

    Article  PubMed  Google Scholar 

  5. Williams P., Cámara M. 2009. Quorum sensing and environmental adaptation in Pseudomonas aeruginosa: A tale of regulatory networks and multifunctional signal molecules. Curr. Opin. Microbiol.12, 182–191. https://doi.org/10.1016/j.mib.2009.01.005

    Article  CAS  PubMed  Google Scholar 

  6. Rutherford S.T., Bassler B.L. 2012. Bacterial quorum sensing: its role in virulence and possibilities for its control. Cold Spring Harb. Perspect. Med. 2, https://doi.org/10.1101/cshperspect.a012427

  7. Moré M.I., Finger L.D., Stryker J.L., Fuqua C., Eberhard A., Winans S.C. 1996. Enzymatic synthesis of a quorum-sensing autoinducer through use of defined substrates. Science.272, 1655–1658. https://doi.org/10.1126/science.272.5268.1655

    Article  PubMed  Google Scholar 

  8. Parsek M.R., Val D.L., Hanzelka B.L., Cronan J.E., Greenberg E.P. 1999. Acyl homoserine-lactone quorum-sensing signal generation. Proc. Nat. Acad. Sci. U. S. A.96, 4360–4365. https://doi.org/10.1073/pnas.96.8.4360

    Article  CAS  Google Scholar 

  9. Ochsner U.A., Fiechter A., Reiser J. 1994. Isolation, characterization, and expression in Escherichia coli of the Pseudomonas aeruginosarhlAB genes encoding a rhamnosyltransferase involved in rhamnolipid biosurfactant synthesis. J. Biol. Chem.269, 19787–19795.

    CAS  PubMed  Google Scholar 

  10. Pearson J.P., Passador L., Iglewski B.H., Greenberg E.P. 1995. A second N-acylhomoserine lactone signal produced by Pseudomonas aeruginosa.Proc. Nat. Acad. Sci. U. S. A.92, 1490–1494. https://doi.org/10.1073/pnas.92.5.1490

    Article  CAS  Google Scholar 

  11. McGrath S., Wade D.S., Pesci E.C. 2004. Dueling quorum sensing systems in Pseudomonas aeruginosa control the production of the Pseudomonas quinolone signal (PQS). FEMS Microbiol. Lett.230, 27–34. https://doi.org/10.1016/S0378-1097(03)00849-8

    Article  CAS  PubMed  Google Scholar 

  12. Moradali M.F., Ghods S., Rehm B.H. 2017. Pseudomonas aeruginosa lifestyle: A paradigm for adaptation, survival, and persistence. Front Cell. Infect. Microbiol.7, 39. https://doi.org/10.3389/fcimb.2017.00039

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jensen P.Ø., Bjarnsholt T., Phipps R., Rasmussen T.B., Calum H., Christoffersen L., Moser C., Williams P., Pressler T., Givskov M., Høiby N. 2007. Rapid necrotic killing of polymorphonuclear leukocytes is caused by quorum-sensing-controlled production of rhamnolipid by Pseudomonas aeruginosa.Microbiology. 153, 1329–1338. https://doi.org/10.1099/mic.0.2006/003863-0

    Article  CAS  PubMed  Google Scholar 

  14. Allen L., Dockrell D.H., Pattery T., Lee D.G., Cornelis P., Hellewell P.G., Whyte M.K. 2005. Pyocyanin production by Pseudomonas aeruginosa induces neutrophil apoptosis and impairs neutrophil-mediated host defenses in vivo. J. Immunol.174, 3643–3649. https://doi.org/10.4049/jimmunol.174.6.3643

    Article  CAS  PubMed  Google Scholar 

  15. Bylund J., Burgess L.A., Cescutti P., Ernst R.K., Speert D.P. 2006. Exopolysaccharides from Burkholderia cenocepacia inhibit neutrophil chemotaxis and scavenge reactive oxygen species. J. Biol. Chem.281, 2526–2532. https://doi.org/10.1074/jbc.M510692200

    Article  CAS  PubMed  Google Scholar 

  16. Grabski H., Hunanyan L., Tiratsuyan S., Vardapetyan H. 2019. Interaction of N-3-oxododecanoyl homoserine lactone with transcriptional regulator LasR of Pseudomonas aeruginosa: Insights from molecular docking and dynamics simulations. F1000Research.8, 324. https://doi.org/10.12688/f1000research.18435.1

    Article  Google Scholar 

  17. Huang B. 2009. MetaPocket: A meta approach to improve protein ligand binding site prediction. Omics.13, 325–330. https://doi.org/10.1089/omi.2009.0045

    Article  CAS  PubMed  Google Scholar 

  18. Kumar S., Pandey A.K. 2013. Chemistry and biological activities of flavonoids: An overview. Sci. World J. 2013, 162750. https://doi.org/10.1155/2013/162750

  19. Paczkowski J.E., Mukherjee S., McCready A.R., Cong J.P., Aquino C.J., Kim H., Henke‖ B.R., Smith Ch.D., Bassler B.L. 2017. Flavonoids suppress Pseudomonas aeruginosa virulence through allosteric inhibition of quorum-sensing receptors. J. Biol. Chem.292, 4064–4076.https://doi.org/10.1074/jbc.M116.770552

  20. Pawson A.J., Sharman J.L., Benson H.E., Faccenda E., Alexander S.P., Buneman O.P., Davenport A.P., McGrath J.C., Peters J.A., Southan Ch., Spedding M., Yu W., Harmar A.J., NC-IUPHAR. 2014. The IUPHAR/ BPS Guide to PHARMACOLOGY: An expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res.42, D1098–D1106. https://doi.org/10.1093/nar/gkt1143

    Article  CAS  PubMed  Google Scholar 

  21. Lipinski C.A. 2004. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov. Today Technol.1, 337–341. https://doi.org/10.1016/j.ddtec.2004.11.007

    Article  CAS  PubMed  Google Scholar 

  22. O’Boyle N.M., Banck M., James C.A., Morley C., Vandermeersch T., Hutchison G.R. 2011. Open Babel: An open chemical toolbox. J. Cheminform.3, 33. https://doi.org/10.1186/1758-2946-3-33

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Trott O., Olson A.J. 2010. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem.31, 455–461. https://doi.org/10.1002/jcc.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Li L., Chen R., Weng Z. 2003. RDOCK: Refinement of rigid-body protein docking predictions. Proteins. 53, 693–707. https://doi.org/10.1002/prot.10460

    Article  CAS  PubMed  Google Scholar 

  25. Kolb P., Huang D., Dey F., Caflisch A. 2008. Discovery of kinase inhibitors by high-throughput docking and scoring based on a transferable linear interaction energy model. J. Med. Chem. 51, 1179–1188. https://doi.org/10.1021/jm070654j

    Article  CAS  PubMed  Google Scholar 

  26. Wang Z., Sun H., Yao X., Li D., Xu L., Li Y., Tian Sh., Hou T. 2016. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys. Chem. Chem. Phys.18, 12964–12975. https://doi.org/10.1039/c6cp01555g

    Article  CAS  PubMed  Google Scholar 

  27. Pagadala N.S., Syed K., Tuszynski J. 2017. Software for molecular docking: A review. Biophys. Rev.9, 91–102. https://doi.org/10.1007/s12551-016-0247-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jacob R.B., Andersen T., McDougal O.M. 2012. Accessible high-throughput virtual screening molecular docking software for students and educators. PLoS Comput. Biol. 8, e1002499. https://doi.org/10.1371/journal.pcbi.1002499

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S., Olson A.J. 2009. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem.30, 2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jaghoori M.M., Bleijlevens B., Olabarriaga S.D. 2016. 1001 Ways to run AutoDock Vina for virtual screening. J. Comput. Aided Mol. Des. 30, 237–249. https://doi.org/10.1007/s10822-016-9900-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Daina A., Michielin O., Zoete V. 2017. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 7, 42717. https://doi.org/10.1038/srep42717

    Article  PubMed  PubMed Central  Google Scholar 

  32. Cheng F., Li W., Zhou Y., Shen J., Wu Z., Liu G., Lee P.W., Tang Y. 2012. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 52, 3099–3105. https://doi.org/10.1021/ci300367a

    Article  CAS  PubMed  Google Scholar 

  33. Filimonov D.A., Lagunin A.A., Gloriozova T.A., Rudik A.V., Druzhilovskii D.S., Pogodin P.V., Poroikov V.V. 2014. Prediction of the biological activity spectra of organic compounds using the pass online web resource. Chem. Heterocyclic Compounds.50, 444–457. https://doi.org/10.1007/s10593-014-1496-1

    Article  CAS  Google Scholar 

  34. Müh U., Hare B.J., Duerkop B.A., Schuster M., Hanzelka B.L., Heim R., Olson E.R., Greenberg E.P. 2006. A structurally unrelated mimic of a Pseudomonas aeruginosa acyl-homoserine lactone quorum-sensing signal. Proc. Nat. Acad. Sci. U. S. A.103, 16948–16952. https://doi.org/10.1073/pnas.0608348103

    Article  CAS  Google Scholar 

  35. O’Loughlin C.T., Miller L.C., Siryaporn A., Drescher K., Semmelhack M.F., Bassler B.L. 2013. A quorum-sensing inhibitor blocks Pseudomonas aeruginosa virulence and biofilm formation. Proc. Nat. Acad. Sci. U. S. A.110, 17981–17986. https://doi.org/10.1073/pnas.1316981110

    Article  Google Scholar 

  36. Smith K.M., Bu Y., Suga H. 2003. Induction and inhibition of Pseudomonas aeruginosa quorum sensing by synthetic autoinducer analogs. Chem. Biol. 10, 81–89. https://doi.org/10.1016/S1074-5521(03)00002-4

    Article  CAS  PubMed  Google Scholar 

  37. Starkey M., Lepine F., Maura D., Bandyopadhaya A., Lesic B., He J., Kitao T., Righi V., Milot S., Tzika A., Rahme L. 2014. Identification of anti-virulence compounds that disrupt quorum-sensing regulated acute and persistent pathogenicity. PLoS Pathog. 10, e1004321. https://doi.org/10.1371/journal.ppat.1004321

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yang L., Rybtke M.T., Jakobsen T.H., Hentzer M., Bjarnsholt T., Givskov M., Tolker-Nielsen T. 2009. Computer-aided identification of recognized drugs as Pseudomonas aeruginosa quorum-sensing inhibitors. Antimicrob. Agents Chemother.53, 2432–2443. https://doi.org/10.1128/AAC.01283-08

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Geske G.D., Wezeman R.J., Siegel A.P., Blackwell H.E. 2005. Small molecule inhibitors of bacterial quorum sensing and biofilm formation. J. Am. Chem. Soc.127, 12762–12763. https://doi.org/10.1021/ja0530321

    Article  CAS  PubMed  Google Scholar 

  40. D’Almeida R.E., Molina R.D.I., Viola C.M., Luciardi M.C., Nieto Peñalver C., Bardón A., Arena M.E. 2017. Comparison of seven structurally related coumarins on the inhibition of quorum sensing of Pseudomonas aeruginosa and Chromobacterium violaceum.Bioorg. Chem.73, 37–42. https://doi.org/10.1016/j.bioorg.2017.05.011

    Article  CAS  PubMed  Google Scholar 

  41. Csizmadia F. 2000. JChem: Java applets and modules supporting chemical database handling from web browsers. J. Chem. Inf. Comput. Sci.40, 323–324. https://doi.org/10.1021/ci9902696

    Article  CAS  PubMed  Google Scholar 

  42. Yuan S., Chan H.C.S., Hu Z. 2017. Using PyMOL as a platform for computational drug design. Wiley Interdisc. Rev.: Comput. Mol. Sci.7, e1298. https://doi.org/10.1002/wcms.1298

    Article  CAS  Google Scholar 

  43. Humphrey W., Dalke A., Schulten K. 1996. VMD: Visual molecular dynamics. J. Mol. Graph.14, 33–8, 27. https://doi.org/10.1016/0263-7855(96)00018-5

  44. Laskowski R.A., Swindells M.B. 2011. LigPlot+: Multiple ligand–protein interaction diagrams for drug discovery. J. Chem. Inf. Mod.51, 2778–2786. https://doi.org/10.1021/ci200227u

    Article  CAS  Google Scholar 

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FUNDING

We are grateful for the financial support to the Ministry of Education and Science of the Republic of Armenia (grant no. 10-2/I-1).

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Authors and Affiliations

  1. Institute of Biomedicine and Pharmacy, Russian-Armenian University, 0051, Yerevan, Armenia

    N. Abelyan, H. Grabski & S. Tiratsuyan

  2. Faculty of Biology, Yerevan State University, 0025, Yerevan, Armenia

    S. Tiratsuyan

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  1. N. Abelyan
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  2. H. Grabski
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Correspondence to N. Abelyan.

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Abbreviations: QS, quorum-sensing; LBD, ligand binding domain; DBD, DNA binding domain; PubChem, database of chemical molecules; OdDHL, N-(3-oxododecanoyl)-L-homoserine lactone; BHL, N-butanoyl-L-homoserine lactone; PQS, Pseudomonas quinolone system; IQS, Integrated Quorum Sensing system; HAQ, 4-Hydroxy-2-alkylquinoline; HSL, N-Acyl homoserine lactone; HIA, human intestinal absorption; BBB, blood-brain barrier.

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Abelyan, N., Grabski, H. & Tiratsuyan, S. In silico Screening of Flavones and its Derivatives as Potential Inhibitors of Quorum-Sensing Regulator LasR of Pseudomonas aeruginosa. Mol Biol 54, 134–143 (2020). https://doi.org/10.1134/S0026893320010021

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