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
|