Identification of plant α-tubulin amino acids playing a key role in specific binding of nitroaniline compounds

  • S. P. Ozheredov
  • O. M. Demchuk
  • P. A. Karpov
  • S. I. Spivak
  • Ya. B. Blume


Aim. Computational prediction of amino acid residues critical for specific binding of nitro- and dinitroaniline compounds in plant α-tubulin. Methods. Protein structure modeling (I-Tasser, Grid-computing) and ligand library preparation, molecular docking (CCDC Gold), molecular dynamics (MD, Gromacs computing in Grid). Evaluation of the amino acid ensemble associated with ligand binding based on results of MD energy perturbations of protein-ligand complex. Results. The structural model of plant α-tubulin from Avena sativa was build. Also, the virtual library of 25 nitroaniline compounds was prepared. The docking of ligands into the interdimer contact of α-tubulin and MD simulations of the leading complexes reveal differences in ligands conformational energy during the exchange between free and binding states. The mean number of hydrogen bonds and dynamics of their formation in complex were compared. These computations allow us to select a.a. residues playing key role in specific interaction with nitro- and dinitroaniline compounds in plant α-tubulin. Conclusions. Computational prediction specify 28 a.a. residues playing the main role in binding of nitro- and dinitroaniline compounds with plant α-tubulin from Avena sativa: Arg2, Glu3, Ile4, Cys129, Thr130, Gly131, Leu132, Gln133, Gly134, Gly162, Lys163, Lys164, Ser165, Leu242, Arg243, Asp245, Gly246, Ala247, Ile248, Asn249, Val250, Asp251,Val252, Thr253, Glu254, Phe255, Thr257, Asn258.

Keywords: plant, α-tubulin, nitroaniline compounds, molecular docking, molecular dynamics, ligand binding.


Wade R.H. On and around microtubules: an overview. Mol. Biotechnol. 2009. Vol. 43 (2). P. 177–191. doi: 10.1007/s12033-009-9193-5.

Geitmann A., Emons A.M. The cytoskeleton in plant and fungal cell tip growth. J. Microsc. 2000. Vol. 198 (3). Р. 218–245.

Nogales E. Structural insight into microtubule function. Annu. Rev. Biophys. Biomol. Struct. 2001. Vol. 30. P. 397–420. doi: 10.1146/annurev.biophys.30.1.397.

Grant W.F., Owens E.T. Chromosome aberration assays in Pisum for the study of environmental mutagens. Mutat. Res. 2000. Vol. 488. P. 93–118. doi: 10.1016/S1383-5742(00)00064-8.

Sheval E.V., Kazhura Y.I., Poleshuk N.A., Lazareva E.M., Smirnova E.A., Maximova N.P., Polyakov V.Y. Trifluralin-induced disorganization of microtubular cytoskeleton alters the development of roots in Hordeum vulgare L. Acta Biol. Hung. 2008. Vol. 59 (4). P. 465–478. doi: 10.1556/ABiol.59.2008.4.7.

Britsun В.M., Yemets А.І., Lozinskii M.О., Blume Ya.B. 2,6-Dinitroanilines: synthesis, herbicidal and antiprotozoan properties. Ukr. Bioorg. Acta. 2009. Vol. 7 (1). P. 16–27.

Yemets A.I., Blume Ya.B. Mutant genes of plant tubulins as selective marker genes for genetic engineering. Cytol. Genet. 2007. Vol. 41 (3). P. 156–166. doi: 10.3103/S0095452707030048.

Nyporko A.Y., Yemets A.I., Brytsun V.N., Lozinsky M.O., Blume Y.B. Structural and biological characterization of the tubu-lin interaction with dinitroanilines. Cytol. Genet. 2009. Vol. 43. P. 267–282.

Endeshaw M.M., Li C., de Leon J., Yao N., Latibeaudiere K., Premalatha K., Morrissette N., Werbovetz K.A. Synthesis and evaluation of oryzalin analogs against Toxoplasma gondii. Bioorg. Med. Chem. Lett. 2010. Vol. 20 (17). P. 5179–5183. doi: 10.1016/j.bmcl.2010.07.003.

Krieger E., Nabuurs S.B., Vriend G. Homology modeling. Meth. Biochem. Anal. 2003. Vol. 44. P. 509–523. doi: 10.1002/0471721204.ch25.

Zoete V., Cuendet M.A., Grosdidier A., Michielin O. SwissParam, a fast force field generation tool for small organic molecules. J. Comput. Chem. 2011. Vol. 32 (11). P. 2359–2368. doi: 10.1002/jcc.21816.

Chu Z., Chen J., Nyporko A., Han H., Yu Q., Powles S. Novel α-tubulin mutations conferring resistance to dinitroaniline herbicides in Lolium rigidum. Front. Plant Sci. 2018. Vol. 9:97. doi: 10.3389/fpls.2018.00097.

Christ C.D., Fox Th. Accuracy assessment and automation of free energy calculations for drug design. J. Chem. Inf. Model. 2014. Vol. 54 (1). P. 108–120. doi: 10.1021/ci4004199.

Hansen N., van Gunsteren W.F. Practical aspects of free-energy calculations: a review. J. Chem. Theory Comput. 2014. Vol. 10 (7). P. 2632–2647. doi: 10.1021/ct500161f.