Skip to main content
Log in

Computational Screening of Potential Inhibitors of β-N-Acetyl-D-Hesosaminidases Using Combined Core-Fragment Growth and Pharmacophore Restraints

  • Published:
Applied Biochemistry and Biotechnology Aims and scope Submit manuscript

Abstract

As a type of β-N-acetyl-D-hexosaminidase enzyme purified from the Ostriniafurnacalis (Asian corn borer), OfHex1 has been previously reported to participate in chitin degradation, indicating that it may be an ideal target for designing new environmentally friendly pesticides. Besides, a natural product, TMG-chitotriomycin, has been found to be an effective inhibitor of OfHex1, and some studies have shown that the interactions between TMG unit and residues in − 1 subsite of OfHex1 are very conservative and important, inspiring us to design new inhibitors of β-N-acetyl-D-hexosaminidase with a new strategy. In the present study, the virtual screening of TMG unit as the core fragment was conducted using the combined computational methods, such as docking, molecular dynamics, pharmacophore model, and pesticide-likeness rule. Nine compounds with the binding free energy lower than TMG-β-(GlcNAc)2 were obtained. According to the decomposition energy and the interactions analysis, compounds 2, 3, 6 and 8, forming the hydrogen bonds with Val327 and Trp490, were considered as the possible lead structures for the further study. Our findings indicated that fragment-based lead discovery strategy might provide valuable insights into designing novel potential OfHex1 inhibitors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ort, D. R., & Long, S. P. (2014). Limits on ields in the corn belt. Science, 344, 483–484.

    Article  Google Scholar 

  2. Li, J., Coates, B. S., Kim, K. S., Bourguet, D., Ponsard, S., He, K. L., & Wang, Z. Y. (2014). The genetic structure of Asian corn borer, Ostrinia furnacalis, populations in China: haplotype variance in Northern populations and potential impact on management of resistance to transgenic maize. The Journal of Heredity, 105(5), 642–655.

    Article  CAS  Google Scholar 

  3. Wang, Z., He, K., & Yan, S. (2005). Large-scale augmentative biological control of Asian corn borer using Trichogramma in China: a success story. Second International Symposium on Biological Control of Arthropods, Davos, Switzerland, 12-16 September, 2005 (pp. 487–494).

  4. Intra, J., Pavesi, G., & Horner, D. S. (2008). Phylogenetic analyses suggest multiple changes of substrate specificity within the Glycosyl hydrolase 20 family. BMC Evolutionary Biology, 8, 17.

    Article  Google Scholar 

  5. Liu, T. A., Zhang, H. T., Liu, F. Y., Wu, Q. Y., Shen, X., & Yang, Q. (2011). Structural determinants of an insect beta-N-Acetyl-D-hexosaminidase specialized as a chitinolytic enzyme. The Journal of Biological Chemistry, 286(6), 4049–4058.

    Article  CAS  Google Scholar 

  6. Knapp, S., Vocadlo, D., Gao, Z. N., Kirk, B., Lou, J. P., & Withers, S. G. (1996). NAG-thiazoline, an N-acetyl-beta-hexosaminidase inhibitor that implicates acetamido participation. Journal of the American Chemical Society, 118(28), 6804–6805.

    Article  CAS  Google Scholar 

  7. Horsch, M., Hoesch, L., Vasella, A., & Rast, D. M. (1991). N-acetylglucosaminono-1,5-lactone oxime and the corresponding (phenylcarbamoyl) oxime - novle and potent inhibitors of beta-N-acetylglucosaminidase. European Journal of Biochemistry, 197(3), 815–818.

    Article  CAS  Google Scholar 

  8. Liu, T., Guo, P., Zhou, Y., Wang, J., Chen, L., Yang, H. B., Qian, X. H., & Yang, Q. (2014). A crystal structure-guided rational design switching non-carbohydrate inhibitors' specificity between two beta-GlcNAcase homologs. Scientific Reports, 4, 6.

    Google Scholar 

  9. Usuki, H., Nitoda, T., Ichikawa, M., Yamaji, N., Iwashita, T., Komura, H., & Kanzaki, H. (2008). TMG-chitotriomycin, an enzyme inhibitor specific for insect and fungal beta-N-acetylglucosaminidases, produced by actinomycete Streptomyces anulatus NBRC 13369. Journal of the American Chemical Society, 130(12), 4146–4152.

    Article  CAS  Google Scholar 

  10. Yang, Y., Liu, T. A., Yang, Y. L., Wu, Q. Y., Yang, Q., & Yu, B. A. (2011). Synthesis, evaluation, and mechanism of N,N,N-Trimethyl-D-glucosamine-(1 - > 4)-chitooligosaccharides as Selective Inhibitors of Glycosyl Hydrolase Family 20 beta-N-Acetyl-D-hexosaminidases. ChemBioChem, 12(3), 457–467.

    Article  CAS  Google Scholar 

  11. Murray, C. W., & Rees, D. C. (2009). The rise of fragment-based drug discovery. Nature Chemistry, 1(3), 187–192.

    Article  CAS  Google Scholar 

  12. Hao, G. F., Wang, F., Li, H., Zhu, X. L., Yang, W. C., Huang, L. S., Wu, J. W., Berry, E. A., & Yang, G. F. (2012). Computational Discovery of Picomolar Q(o) Site Inhibitors of Cytochrome bc(1) Complex. Journal of the American Chemical Society, 134(27), 11168–11176.

    Article  CAS  Google Scholar 

  13. Hao, G. F., Dong, Q. J., & Yang, G. F. (2011). A comparative study on the constitutive properties of marketed pesticides. Molecular Informatics, 30(6-7), 614–622.

    Article  CAS  Google Scholar 

  14. Liu, C. L. (2002). Shi Jie Nong Yao Da Quan : Chu Cao Ji Juan (in Chinese). Beijing: Chemical Industry Press.

    Google Scholar 

  15. Kolb, P., & Caflisch, A. (2006). Automatic and efficient decomposition of two-dimensional structures of small molecules for fragment-based high-throughput docking. Journal of Medicinal Chemistry, 49(25), 7384–7392.

    Article  CAS  Google Scholar 

  16. Jhoti, H., Williams, G., Rees, D. C., & Murray, C. W. (2013). The ‘rule of three’ for fragment-based drug discovery: where are we now? Nature Reviews. Drug Discovery, 12(8), 644–64+.

    Article  CAS  Google Scholar 

  17. Durrant, J. D., Amaro, R. E., & McCammon, J. A. (2009). AutoGrow: A Novel algorithm for protein inhibitor design. Chemical Biology & Drug Design, 73(2), 168–178.

    Article  CAS  Google Scholar 

  18. CASE D A, E. A. (2012) AMBER 12. University of California, San Francisco.

  19. Hu, S., Zhao, X., & Zhang, L. (2019). Computational study for the unbinding routes of β-N-Acetyl-D-hexosaminidase inhibitor: insight from steered molecular dynamics simulations. International Journal of Molecular Sciences, 20, 1–14.

    Google Scholar 

  20. Hu, S., Dong, Y. W., Zhao, X., & Zhang, L. (2019). Insights into the structure-affinity relationships and solvation effects between OfHex1 and inhibitors using molecular dynamics simulations. Journal of Molecular Graphics & Modelling, 90, 1–8.

    Article  CAS  Google Scholar 

  21. MOE2014. Chemical Computing Group Inc 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7.

  22. Hopkins, A. L., Groom, C. R., & Alex, A. (2004). Ligand efficiency: a useful metric for lead selection. Drug Discovery Today, 9, 430–431.

    Article  Google Scholar 

  23. Case, D. A., Darden, T. A., Cheatham, T. E. III, Simmerling, C. L., Wang, J., Duke, R. E., ... Roberts, B. (2012). AMBER 12. San Francisco: University of California.

  24. Wang, J. M., Wolf, R. M., Caldwell, J. W., Kollman, P. A., & Case, D. A. (2004). Development and testing of a general amber force field. Journal of Computational Chemistry, 25(9), 1157–1174.

    Article  CAS  Google Scholar 

  25. Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J. L., Dror, R. O., & Shaw, D. E. (2010). Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins, 78, 1950–1958.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Jakalian, A., Jack, D. B., & Bayly, C. I. (2002). Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. Journal of Computational Chemistry, 23(16), 1623–1641.

    Article  CAS  Google Scholar 

  27. Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005). The Amber biomolecular simulation programs. Journal of Computational Chemistry, 26, 3314–3321.

    Article  Google Scholar 

  28. Miller, B. R., McGee, T. D., Swails, J. M., Homeyer, N., Gohlke, H., & Roitberg, A. E. (2012). MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. Journal of Chemical Theory and Computation, 8(9), 3314–3321.

    Article  CAS  Google Scholar 

  29. Pan, Y. M., Gao, D. Q., & Zhan, C. G. (2008). Modeling the catalysis of anti-cocaine catalytic antibody: competing reaction pathways and free energy barriers. Journal of the American Chemical Society, 130(15), 5140–5149.

    Article  CAS  Google Scholar 

  30. Verma, S., & Prabhakar, Y. S. (2015). Target based drug design - a reality in virtual sphere. Current Medicinal Chemistry, 22(13), 1603–1630.

    Article  CAS  Google Scholar 

  31. Abad-Zapatero, C., & Metz, J. T. (2005). Ligand efficiency indices as guideposts for drug discovery. Drug Discovery Today, 10(7), 464–469.

    Article  Google Scholar 

Download references

Acknowledgments

We thank Prof. Guangfu Yang and Dr. Gefei Hao who provide PFVS program.

Funding

This work was supported by the National Natural Science Foundation of China [No. 21272265] and the National Key R&D Program of China [No. 2017YFD0200504].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Li Zhang.

Ethics declarations

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

The following are available online at www.mdpi.com/link, Table S1: The ΔΔGcal values of compounds 10-20,Fig. S1: The structures of compounds 10-20, Fig. S2: The decomposition energies of the important residues on OfHex1 binding compounds 1 and 3-9. Fig. S3-S10: The interactions of compounds 1, 3, 4, 5, 6, 7, 8, 9 and OfHex1 in + 1 subsite, respectively. (DOCX 1367 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, S., Zhao, X. & Zhang, L. Computational Screening of Potential Inhibitors of β-N-Acetyl-D-Hesosaminidases Using Combined Core-Fragment Growth and Pharmacophore Restraints. Appl Biochem Biotechnol 189, 1262–1273 (2019). https://doi.org/10.1007/s12010-019-03064-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12010-019-03064-4

Keywords

Navigation