1Karpov, PA, 1Demchuk, OM, 2Brytsun, VM, 1Lytvyn, DI, 1Pydiura, NO, 1Rayevsky, AV, 1Samofalova, DA, 1Spivak, SI, 3Volochnyuk, DM, 1Yemets, AI, 1Blume, Ya.B
1Institute of Food Biotechnology and Genomics, the NAS of Ukraine, Kyiv
2Institute of Organic Chemistry, the NAS of Ukraine, Kyiv
3Institute of Organic Chemistry, NAS of Ukraine, Kyiv
Sci. innov. 2016, 12(3):43-55
https://doi.org/10.15407/scine12.03.043
Section: Scientific Framework of the Innovation Activity
Language: English
Abstract: 
Within the framework of virtual organization CSLabGrid, high-throughput molecular screening has been performed for new antituberculosis compounds. Using the FlexX program installed on the Institute of Food Biotechnology and Genomics (IFBG) Cluster and models of four promising ligand binding sites on the surface of FtsZ protein from Mycobacterium tuberculosis, virtual screening has been done for the database containing 2886 compounds synthesized in the Institute of Organic Chemistry of the NAS of Ukraine. Based on the LE and ΔG scores, the docking scores of CCDC Gold, and the results of molecular dynamics, a group of Mycobacterial FtsZ inhibitors has been selected. In vitro validation have revealed 6 compounds with the highest inhibition of GTPase activity of FtsZ. Also, based on in vitro experiment, three of selected compounds demonstrste strong inhibition of FtsZ polymerization together with inhibition of its GTPase activity.
Keywords: bioinformatics, high-throughput screening (HTS), in vitro, structural biology, tuberculosis
References: 
1. World Health Organization. Global tuberculosis report. Kyiv, 2012. http://apps.who.int/iris/bitstream/10665/75938/1/9789241564502_eng.pdf
2. Kumar K., Awasthi D., Berger W.T., Tonge P.J., Slayden R.A., Ojima I. Discovery of anti-TB agents that target the cell-division protein FtsZ. Future Med Chem. 2010, 2(8): 1305-1323.
https://doi.org/10.4155/fmc.10.220
3. Osnovni zasady organizacii' medychnoi' dopomogy hvorym na tuberkul'oz (posibnyk z organizacijno-meto dych noi' roboty). Ju. I. Feshhenko, V. M. Mel'nyk, V. G. Matusevych, I.O. Novozhylova, V.O. Juhymec', M.I. Lyn nyk. Za red. Ju. I. Feshhenka, V. M. Mel'nyka. Elektron. dani. Kyiv, 2012. http://www.ifp.kiev.ua/ftp1/original/2012/feschenko2012-1.pdf [in Ukrainian].
4. Levyc'ka N.A., Bazhora Ju.I., Nikolajevs'kyj V.V., Asmolov O.K. Medykamentozna rezystentnist' miko bakterij tuberkul'ozu, shho buly vydileni vid hvoryh v mykolai'vs'kij oblasti Ukrai'ny protjagom 2000-2002. Ukrai'ns'kyj pul'monologichnyj zhurnal. 2003, 4: 17-20 [in Ukrainian].
5. Cheren'ko C. Tuberkul'oz — hvoroba social'na. UNIAN. Zdorov'ja. 2008, 77: 9-18 [in Ukrainian].
6. de Colombani P., Veen J. (Ed.) Review of the National Tuberculosis Programme in Ukraine. 10-22 October 2010, WHO Regional Office for Europe. World Health Organization: 2011.
7. Musser J.M., Amin A., Ramaswamy S. Negligible genetic diversity of mycobacterium tuberculosis host immune system protein targets: evidence of limited selective pressure. Genetics. 2000, 155(1): 7-16.
8. Hudson A., Imamura T., Gutteridge W., Kanyok T., Nunn P. The current anti-TB drugre search and development pipeline. TDR/PRD/TB/03.1W. 2003: http://www.who.int/tdr/publications/documents/anti-tbdrug.pdf
9. Tripathi R.P., Tewari N., Dwivedi N., Tiwari V.K. Fighting tuberculosis: an old disease with new challenges. Med Res Rev. 2005, 25(1): 93-131.
https://doi.org/10.1002/med.20017
10. Pavan F.R., Sato D.N., Higuchi C.T., Santos A.C.B., Vilegas W., Leite, C.Q.F. In vitro anti-Mycobacterium tu berculosis activity of some Brazilian "Cerrado" plants. Revista Brasileira de Farmacognosia. 2009, 19: 204-206.
11. Mani N., Gross C.H., Parsons J.D., Hanzelka B., Müh U., Mullin S., Liao Y., Grillot A.L., Stamos D., Charifson P.S., Grossman T.H. In vitro characterization of the anti bacterial spectrum of novel bacterial type II topoisomerase inhibitors of the aminobenzimidazole class. Antimicrob Agents Chemother. 2006, 50(4): 1228-1237.
https://doi.org/10.1128/AAC.50.4.1228-1237.2006
12. Grossman T.H., Bartels D.J., Mullin S., Gross C.H., Parsons J.D., Liao Y., Grillot A.L., Stamos D., Olson E.R., Charifson P.S., Mani N. Dual targeting of GyrB and ParE by a novel aminobenzimidazole class of antibacterial compounds. Antimicrob Agents Chemother. 2007, 51(2): 657-666.
https://doi.org/10.1128/AAC.00596-06
13. Brycun V.N., Karpov P.A., Emec A.I., Lozinskij M.O., Bljum Ja.B. Protivotuberkuleznye svojstva proizvodnyh imidazola i benzimidazola. Zhurnal org. ta farm. himii. 2011, 9(3, 35): 3-14 [in Russian].
14. Koch A., Mizrahi V., Warner D.F. The impact of drug resistance on Mycobacterium tuberculosis physiology: what can we learn from rifampicin? Emerging Microbes & Infections. 2014, 3(e17): doi:10.1038/emi.2014.17.
https://doi.org/10.1038/emi.2014.17
15. Loose M., Mitchison T.J. The bacterial cell division proteins FtsA and FtsZ self-organize into dynamic cytoskeletal patterns. Nature Cell Biology. 2014, 16: 38-46.
16. Chen Y., Anderson D.E., Rajagopalan M., Erickson H.P. Assembly dynamics of Mycobacterium tuberculosis FtsZ. J Biol Chem. 2007, 282(38): 27736-27743.
https://doi.org/10.1074/jbc.M703788200
17. White E.L., Ross L.J., Reynolds R.C., Seitz L.E., Moore G.D., Borhani D.W. Slow polymerization of Mycobacterium tuberculosis FtsZ. J Bac te riol. 2000, 182(14): 4028-4034.
https://doi.org/10.1128/JB.182.14.4028-4034.2000
18. Kapoor S., Panda D. Targeting FtsZ for antibacterial therapy: a promising avenue. Expert Opin Ther Targets. 2009, 13(9): 1037-1051.
https://doi.org/10.1517/14728220903173257
19. MacDonald L.M., Armson A., Thompson A.R., Reynoldson J.A. Characterisation of benzimidazole binding withrecombinant tubulin from Giardia duodenalis, Encephalitozoon intestinalis, and Cryptosporidium parvum. Mol Biochem Parasitol. 2004, 138(1): 89-96.
https://doi.org/10.1016/j.molbiopara.2004.08.001
20. Robinson M.W., McFerran N., Trudgett A., Hoey L., Fairweather I. A possible model of benzimidazole binding to beta-tubulin disclosed by invoking an inter-domain movement. J. Mol. Graph. Model. 2004, 23(3): 275-284.
21. Sambanthamoorthy K., Gokhale A.A., Lao W., Parashar V., Neiditch M.B., Semmelhack M.F., Lee I., Waters C.M. Identification of a novel benzimidazole that inhibits bacterial biofilm formation in a broad-spectrum manner. Antimicrob Agents Chemother. 2011, 55(9): 4369-4378.
https://doi.org/10.1128/AAC.00583-11
22. Küçükbay H., Durmaz R., Okuyucu N., Günal S., Kazaz C. Synthesis and antibacterial activities of new bis-benzimidazoles. Arzneimittelforschung. 2004, 54(1): 64-68.
23. Karpov P.A., Demchuyk O.M., Blume Ya.B., Britsun V.M., Volochnyk D.M. Discovery of new anti-TB compounds that target Mycobacterial FtsZ: highthroughput screening and molecular docking. Moscow Conference on Computational Molecular Biology (MCCMB'13). Moscow, Russia July 25-28. 2013: 223-224.
24. Adams D.W., Errington J. Bacterial cell division: assembly, maintenance and disassembly of the Z ring. Nat. Rev. Microbiol. 2009, 7: 642-653.
25. Blaauwen T.D.E.N., Buddelmeijer N., Hameete C.O.R.M., Nanninga N. Timing of FtsZ Assembly in Escherichia coli. Journal of bacreriology. 1999, 181: 5167-5175.
26. Goehring N.W., Beckwith J. Diverse Paths to Midcell: Assembly of the Bacterial Cell Division Machinery. Current Biology. 2005, 15(13): R514-R526.
27. Hong W., Deng W., Xie J. The Structure, Function, and Regulation of Mycobacterium FtsZ. Cell Biochem Biophys. 2013, 65(2): 97-105.
https://doi.org/10.1007/s12013-012-9415-5
28. Król E., Scheffers D. FtsZ Polymerization Assays : Simple Protocols and Considerations. Journal of Visualized Experiments. 2013, 81: 1-13.
29. Pydiura N., Karpov P., Blume Ya. Design of specific cytoskeleton related biological database and data management environment for bioinformatical cytoskeleton investigation and collaboration within virtual Grid-organisation. Proceedings of the International Moscow Conference on Computational Molecular Biology (MCCMB'11). July 21-24, 2011, Moscow, Rossia: 297-298.
30. Pydiura N., Karpov P., Blume Ya. Hardware environment for CSLabGrid: Reaching maximum efficacy of computations in structural biology and bioinformatics. Second International Conference "Cluster Computing" CC 2013 (Ukraine, Lviv, June 3-5, 2013), Ukraine, Lviv; 06/2013: 191-194.
31. Pydiura N., Karpov P., Blume Ya. On the Efficiency of CPU and Hybrid CPU-GPU Systems in Computational Biology Tasks. Computer Science and Applications. 2014, 1(1): 48-59.
32. Karpov P.A., Bryrsun V.M., Rayevsky A.V., Demchuk O.M., Pydiura N.O., Ozheredov S.P., Samofalova D.A., Spivak S.I., Yemets A.I., Kalchenko V.I., Blume Ya.B. Highthroughput screening of new antimitotic compounds based on CSLabGrid Virtual Organization. Sci. Innov. 2015, 11(1): 85-93.
https://doi.org/10.15407/scine11.01.085
33. Cole S.T., Brosch R., Parkhill J., Garnier T., Churcher C.M., Harris D.E., Gordon S.V., Eiglmeier K., Gas S., Barry C.E. III, Tekaia F., Badcock K., Basham D., Brown D., Chil lingworth T., Connor R., Davies R.M., Devlin K., Barrell B.G. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature. 1998, 393: 537-544.
34. Fleischmann R.D., Alland D., Eisen J.A., Carpenter L., White O., Peterson J.D., DeBoy R.T., Dodson R.J., Gwinn M.L., Haft D.H., Hickey E.K., Kolonay J.F., Nelson W.C., Umayam L.A., Ermolaeva M.D., Salzberg S.L., Delcher A., Utterback T.R., Fraser C.M. Whole-genome comparison of Mycobacterium tuberculosis clinical and laboratory strains J. Bacteriol. 2002, 184: 5479-5490.
35. The UniProt Consortium. The Universal Protein Resource (UniProt). Nucl. Acids Res. 2008, 36: 190-195.
36. Guex N., Peitsch M.C. SWISS-MODEL and the SwissPdbViewer: An environment for comparative protein modeling. Electrophoresis. 1997, 18(15): 2714-2723.
https://doi.org/10.1002/elps.1150181505
37. Eswar N., Marti-Renom M.A., Webb B., Madhusudhan M.S., Eramian D., Shen M.Y., Pieper U., Sali A. Comparative Protein Structure Modeling With MODELLER. Cur Prot in Bioinform, John Wiley & Sons, Inc. 2006. Sup.15: 5.6.1-5.6.30.
38. Roy A., Kucukural A., Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols. 2010, 5: 725-738.
39. Roy A., Yang J., Zhang Y. COFACTOR: An accurate com parative algorithm for structure-based protein function annotation. Nucl. Acids Res. 2012, 40: W471-W477.
40. Kuntal B.K., Aparoy P., Reddanna P. EasyModeller: A graphical interface to MODELLER. BMC Res Notes. 2010, 3(226).
https://doi.org/10.1186/1756-0500-3-226
41. Melo F., Feytmans E. Assessing protein structures with a non-local atomic interaction energy. J. Mol. Biol. 1998, 277: 1141-1152.
42. Laskowski R.A., MacArthur M.W., Moss D.S., Thornton J.M. PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Cryst. 1993, 26: 283-291.
43. Chen V.B., Arendall W.B., Headd J.J., Keedy D.A., Immormino R.M., Kapral G.J., Murray L.W., Richardson J.S., Richardson D.C. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D. Biol. Crystallogr. 2010, 66(Pt.1): 12-21.
44. Eisenberg D., Lüthy R., Bowie J.U. VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol. 1997, 277: 396-404.
45. Zoete V., Cuendet M.A., Grosdidier A., Michielin O. SwissParam: a fast force field generation tool for small organic molecules. J. Comput. Chem. 2011, 32(11): 2359-2368.
46. Verdonk M.L., Cole J.C., Hartshorn M.J., Murray C.W., Taylor R.D. Improved protein-ligand docking using GOLD. Proteins. 2003, 52(4): 609-623.
https://doi.org/10.1002/prot.10465
47. Hartshorn M.J., Verdonk M.L., Chessari G., Brewerton S.C., Mooij W.T., Mortenson P.N., Murray C.W. Diverse, HighQuality Test Set for the Validation of Protein-Ligand Docking Performance. J. Med. Chem. 2007, 50: 726-741.
48. Huang S.-Y., Zou X. Advances and Challenges in ProteinLigand Docking. Int. J. Mol. Sci. 2010, 11: 3016-3034.
49. Schneider N., Lange G., Hindle S., Klein R., Rarey M. A consistent description of HYdrogen bond and DEhydration energies in protein-ligand complexes: methods behind the HYDE scoring function. J. Comput. Aided. Mol. Des. 2013, 27(1): 15-29.
50. Schneider N., Hindle S., Lange G., Klein R., Albrecht J., Briem H., Beyer K., Claußen H., Gastreich M., Lemmen C., Rarey R. Substantial improvements in large-scale redock ing and screening using the novel HYDE scoring function. J. Comput. Aided. Mol. Des. 2012b. 26: 701-723.
51. Hess B., Kutzner C., van der Spoel D., Lindahl E.GROMACS 4: algorithms for highly efficient, loadbalanced, and scalable molecular simulation. J. Chem. Theory Comput. 2008, 4(3): 435-447.
https://doi.org/10.1021/ct700301q
52. Pronk S., Páll S., Schulz R., Larsson P., Bjelkmar P., Apostolov R., Shirts M.R., Smith J.C., Kasson P.M., van der Spoel D., Hess B., Lindahl E. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics. 2013, 29(7):845-854.
https://doi.org/10.1093/bioinformatics/btt055
53. Vanommeslaeghe K., Hatcher E., Acharya C., Kundu S., Zhong S., Shim J., Darian E., Guvench O., Lopes P., Vorobyov I., Mackerell A.D.Jr. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 2009, 31(4): 671-690.
54. Essmann U., Perera L., Berkowitz M.L., Darden T., Lee H, Pedersen L.G. A smooth particle-mesh Ewald potential. J. Chem. Phys. 1995, 103(19): 8577-8592.
55. Hess B., Bekker H., Berendsen H.J.C., Fraaije J.G.E.M. LINCS: a linear constraint solver for molecular simulations. J. Comput. Phys. 1997, 18: 1463-1472.
56. Almlöf M., Brandsdal B.O., Aqvist J. Binding affinity prediction with different force fields: examination of the linear interaction energy method. J. Comput. Chem. 2004, 25(10): 1242-1254.
57. Stacklies W., Seifert C., Graeter F. Implementation of force distribution analysis for molecular dynamics simulations. BMC Bioinformatics. 2011, 12(101): 1-5.
https://doi.org/10.1186/1471-2105-12-101