1Karpov, PA, 2Brytsun, VM, 1Demchuk, OM, 1Pydiura, NO, 1Ozheredov, SP, 1Samofalova, DA, 1Spivak, SI, 1Yemets, AI, 2Kalchenko, VI, 1Blume, Ya.B, 1Rayevsky, AV
1Institute of Food Biotechnology and Genomics, the NAS of Ukraine, Kyiv
2Institute of Organic Chemistry, the NAS of Ukraine, Kyiv
Sci. innov. 2015, 11(1):85-93
https://doi.org/10.15407/scine11.01.085
Section: On the 10th Anniversary of the Journal
Language: English
Abstract: 

Within the framework of CSLabGrid virtual organization, the repository of 3-D models of cytoskeletal proteins (tubulins and FtsZ-proteins) has been created using Grid calculations. The repository of structures of canonical anti-microtubule compounds (inhibitors of tubulin polymerization) as well as library of ligands suitable for high-throughput screening (HTS) in Grid has been developed. Having screened the library, 1,164 compounds that demonstrated an elevated affinity with tubulin molecules: 205 to α-tubulin and 959 to β-tubulin are selected. Among 2,886 compounds synthesized at the Institute of Organic Chemistry of the NAS of Ukraine, 6 ones have been established to be promising inhibitors of α- and β-tubulin polymerization in such human pathogens as Pneumocystis carinii, Giardia intestinalis Ajellomyces capsulatus, Ajellomyces capsulatus, Neosartorya fumigata and Candida albicans. These compounds have been recommended for subsequent experimental evaluation of their biological activity as new pharmacological agents.

Keywords: antimitotic activity, benzimidazole compounds, cytoskeleton, drugs, Grid, high-throughput screening, molecular docking, structural bioinformatics, tubulin, tubulin depolymerization, virtual organization
References: 

1. Vignaud, T., Blanchoin, L., and Théry, M.: Directed Cytoskeleton Self-Organization. Trends Cell Biol., 22, 12, 671–682 (2012).
https://doi.org/10.1016/j.tcb.2012.08.012
2. Eren, E.C., Gautam, N., and Dixit, R.: Computer Simulation and Mathematical Models of the Noncentrosomal Plant Cortical Microtubule Cytoskeleton. Cytoskeleton, 69, 3, 144–154 (2012).
https://doi.org/10.1002/cm.21009
3. Massarotti, A., Theeramunkong, S., Mesenzani, O. et al. Identification of Novel Antitubulin Agents by Using a Virtual Screening Approach Based on a 7-Point Pharmacophore Model of the Tubulin Colchi-Site. Chem. Biol. Drug Des., 78, 6, 913–922 (2011).
https://doi.org/10.1111/j.1747-0285.2011.01245.x
4. Sui, M., Zhang, H., Di, X. et al.: G2 Checkpoint Abrogator Abates the Antagonistic Interaction between Antimic rotubule Drugs and Radiation Therapy. Radiother. Oncol., 104, 2, 243–248 (2012).
https://doi.org/10.1016/j.radonc.2012.04.021
5. Henriquez, F.L., Ingram, P.R., Muench, S.P. et al.: Molecular Basis for Resistance of Acanthamoeba Tubulins to All Major Classes of Antitubulin Compounds. Antimicrob Agents Chemother, 52, 3, 1133–1135 (2008).
https://doi.org/10.1128/AAC.00355-07
6. Zhao, Y., Wu, F., Wang, Y. et al.: Inhibitory Action of Chamaejasmin A against Human HEP-2 Epithelial Cells: Effect on Tubulin Protein. Mol Biol Rep., 39, 12, 11105–11112 (2012).
https://doi.org/10.1007/s11033-012-2016-y
7. Pilhofer, M. and Jensen, G.J.: The Bacterial Cytoskeleton: More than Twisted Filaments. Curr Opin Cell Biol., 25, 125–133 (2013).
https://doi.org/10.1016/j.ceb.2012.10.019
8. Haglund, C.M. and Welch, M.D.: Pathogens and Polymers: Microbe-Host Interactions Illuminate the Cytoskeleton. J. Cell Biol., 195, 1, 7–17 (2011).
https://doi.org/10.1083/jcb.201103148
9. Tuszynski, J.A., Craddock, T.J., Mane, J.Y. et al.: Modeling the Yew Tree Tubulin and a Comparison of Its Interaction with Paclitaxel to Human Tubulin. Pharm Res., 29, 11, 3007–3021 (2012).
https://doi.org/10.1007/s11095-012-0829-y
10. Calvo, E., Barasoain, I., Matesanz, R. et al.: Cyclostreptin Derivatives Specifically Target Cellular Tubulin and Further Map the Paclitaxel Site. Biochemistry, 51, 1, 329–341 (2012).
https://doi.org/10.1021/bi201380p
11. Sörensen, P.M., Iacob, R.E., Fritzsche, M. et al.: The Natural Product Cucurbitacin E Inhibits Depolymerization of Actin Filaments. ACS Chem Biol., 7, 9, 1502–1508 (2012).
https://doi.org/10.1021/cb300254s
12. Desouza, M., Gunning, P.W., and Stehn, J.R.: The Actin Cytoskeleton as a Sensor and Mediator of Apoptosis. Bioarchitecture, 2, 3, 75–87 (2012).
https://doi.org/10.4161/bioa.20975
13. Anderson-White, B., Beck, J.R., Chen, C.T. et al.: Cytoskeleton Assembly in Toxoplasma Gondii Cell Division. Int Rev Cell Mol Biol., 298, 1–31 (2012).
https://doi.org/10.1016/B978-0-12-394309-5.00001-8
14. Pei, W., Du, F., Zhang, Y., He, T., and Ren, H.: Control of the Actin Cytoskeleton in Root Hair Development. Plant Sci., 187, 10–18 (2012).
https://doi.org/10.1016/j.plantsci.2012.01.008
15. Demchuk, O., Karpov, P., and Blume, Ya.: Docking Small Ligands to Molecule of the Plant FtsZ Protein: Application of the CUDA Technology for Faster Computations. Cytol. Genetics, 46, 3, 172–179 (2012).
https://doi.org/10.3103/S0095452712030048
16. Pydiura, N., Karpov, P., and Blume, Ya.: Hybrid CPU-GPU Calculations – a Promising Future for Computational Biology. Third Int. Conference «High Performance Computing» HPC-UA 2013, 330–335 (2013).
17. Pydiura, N., Karpov, P., and Blume, Ya.: Hardware Environment for CSLabGrid: Reaching Maximum Efficacy of Computations in Structural Biology and Bioinformatics. Second Int. Conference «Cluster Computing» CC 2013, 191–194 (2013).
18. Pydiura, N., Karpov, P., and Blume, Ya.: On the Efficiency of CPU and Hybrid CPU-GPU Systems in Computational Biology Tasks. Comput. Sci. Applicat., 1, 1, 48–59 (2014).
19. Pydiura, N., Karpov, P., and Blume, Ya.: Design of Specific Cytoskeleton Related Biological Database and Data Management Environment for Bioinformatical Cytoskeleton Investigation and Collaboration within Virtual Grid-Organisation. Proc. of the Int. Moscow Conference on Comput. Mol. Biol., 297–298 (2011).
20. Roy, A., Kucukural, A., and Zhang, Y.: I-TASSER: a Unified Platform for Automated Protein Structure and Function Prediction. Nature Protocols, 5, 725–738 (2010).
https://doi.org/10.1038/nprot.2010.5
21. Webb, B. and Sali, A.: Comparative Protein Structure Mo deling Using MODELLER. Curr Protoc Bioinformatics, 47, 5.6.1 – 5.6.32 (2014).
https://doi.org/10.1002/0471250953.bi0506s47
22. Tsai, K.C., Wang, S.H., Hsiao, N.W. et al.: The Effect of Different Electrostatic Potentials on Docking Accuracy: a Case Study Using DOCK5.4. Bioorg Med Chem Lett., 18, 12, 3509–3512 (2008).
https://doi.org/10.1016/j.bmcl.2008.05.026
23. Ouyang, X., Chen, X., Piatnitski, E.L. et al.: Synthesis and Structure-Activity Relationships of 1,2,4-Triazoles as a Novel Class of Potent Tubulin Polymerization Inhibitors. Bioorg. Med. Chem. Lett., 15, 5154–5159 (2005).
https://doi.org/10.1016/j.bmcl.2005.08.056