Biomics

To develop effective and environmentally friendly methods for protecting crop plants from pests, it is necessary to know the structure of pests hydrolases and the mechanisms of their interaction with plant inhibitors. The aim of this work was a modeling of the spatial structure of Colorado potato beetle alphaamylase, analysis of the effect of revealed structural features on interaction with protein inhibitors, as well as modeling of interaction with amylase inhibitors from various organisms.

The spatial structure of the Colorado potato beetle alpha-amylase was obtained by the method of computer modeling using the IntFOLD and NOMAD-Ref services. Colorado potato beetle amylase differs in the structure and physicochemical properties of the active site in comparison with flour beetle amylase, but has the same conformation of the main chain. The possible effect of these differences on the interaction of the enzyme with plant inhibitors – a lectin-like bean inhibitor and a RATI inhibitor – was shown. The CABSdock service was used to model the interaction of Colorado potato beetle amylase with polypeptide inhibitors of amylase from various plant species, bacteria, and actinomycetes. Among the structures obtained, the best values of complex free energy were possessed by the knottin-like inhibitor of amaranth and the purothionin-like inhibitor of oats; the least favorable is the binding with the inhibitors of the actinobacterium Thermopolyspora flexuosa and the aquatic plant Alternanthera sessilis. The model structure of a knottin-like inhibitor in a complex with Colorado potato beetle amylase, in comparison with the known structure of a complex with flour beetle amylase, has a similar chain folding, but significantly different conformation. The data obtained can be used to search for new effective pests’ amylase inhibitors and environmentally friendly methods of protecting potato plants from insect pests, including using the genetic transformation of plants.

alpha-amylase; Leptinotarsa decemlineata; amylase inhibitors; protein structure modeling; peptide-protein interactions

1. Tsvetkov V.O., Yarullina L.G. Structural and Functional Characteristics of Hydrolytic Enzymes of Phytophagon Insects and Plant Protein Inhibitors (Review). Applied Biochemistry and Microbiology. 2019. V. 55(5). P. 460–469. doi: 10.1134/S0003683819050156.
2. Seddigh S., Darabi M. Functional, structural, and phylogenetic analysis of mitochondrial cytochrome b (cytb) in insects. Mitochondrial DNA Part A. 2018. V. 29(2). P. 236-249. doi: 10.1080/24701394.2016.1275596.
3. Xiang Z. Advances in homology protein structure modeling. Curr Protein Pept Sci. 2006. V. 7(3). P. 217- 227. doi: 10.2174/138920306777452312.
4. Dalton J.A.R., Jackson R.M. An evaluation of automated homology modelling methods at low target– template sequence similarity. Bioinformatics. 2007. V. 23(15). P. 1901–1908. doi: 10.1093/bioinformatics/btm262.
5. Pearson W.R. An introduction to sequence similarity ("homology") searching. Curr Protoc Bioinformatics. 2013. Chapter 3. Unit 3.1. doi:10.1002/0471250953.bi0301s42.
6. Ubhayasekera W. Homology Modeling for Enzyme Design. Methods Mol Biol. 2018. V. 1796. P. 301-320. doi: 10.1007/978-1-4939-7877-9_21.
7. Jitonnom J. Computer-aided pesticide design: A short review. In book: Short Views on Insect Biochemistry and Molecular Biology. South India. 2014. P. 685-707.
8. Rogozhin E.A. Comparative structure-function analysis of defense proteins ans peptides from wild and cultivated plants of compositae family: revelation of determinants providing a high resistance level to biotic stress factors. The All-Russian Scientific Conference with International Participation and Schools of Young Scientists "Mechanisms of resistance of plants and microorganisms to unfavorable environmental". 2018. P. 670-673. doi: 10.31255/978-5-94797-319-8-670-673.
9. Ryazantsev D.Y., Rogozhin E.A., Tsvetkov V.O., Yarullina L.G., Smirnov A.N., Zavriev S.K. Diversity of Harpin-Like Defense Peptides from Barnyard Grass (Echinochloa crusgalli L.) Seeds. Dokl Biochem Biophys. 2019 V. 484(1). P. 6-8. doi: 10.1134/S1607672919010022.
10. Ciemny M., Kurcinski M., Kamel K., Kolinski A., Alam N., Schueler-Furman O., Kmiecik S. Proteinтpeptide docking: opportunities and challenges. Drug Discovery Today. 2018. V. 23(8). P. 1530-1537. doi: 10.1016/j.drudis.2018.05.006.
11. Agrawal P., Singh H., Srivastava H.K., Singh S., Kishore G., Raghava G.P.S. Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinformatics. 2019. V. 19. Article number 426. doi: 10.1186/s12859-018-2449-y.
12. Roche D.B., Buenavista M.T., Tetchner S.J., McGuffin L.J. The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction. Nucleic Acids Research. 2011. V. 39(suppl_2). P. W171–W176. doi: 10.1093/nar/gkr184.
13. Lindahl E., Azuara C., Koehl P., Delarue M. NOMAD-Ref: visualization, deformation and refinement of macromolecular structures based on all-atom normal mode analysis. Nucleic Acids Research. 2006. V. 34 (Web Server issue). P. W52-W56. doi: 10.1093/nar/gkl082.
14. Kurcinski M., Badaczewska-Dawid A., Kolinski M., Kolinski A., Kmiecik S. Flexible docking of peptides to proteins using CABS-dock. Protein Science. 2020. V. 29. P. 211-222. doi: 10.1002/pro.3771.
15. Greenblatt H.M., Ryan C.A., James M.N.G. Structure of the complex of Streptomyces griseus proteinase B and polypeptide chymotrypsin inhibitor-1 from Russet Burbank potato tubers at 2.1 Å resolution. Journal of Molecular Biology. 1989. V. 205(1). P. 201-228. doi: 10.1016/0022-2836(89)90376-8.