Display options
Cite
Halder AK, Cordeiro MNDS. Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases. Biomolecules. 2021;11(11)doi: 10.3390/biom11111670.
Halder, A. K., & Cordeiro, M. N. D. S. (2021). Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases. Biomolecules, 11(11), . https://doi.org/10.3390/biom11111670
Halder, Amit Kumar, and Cordeiro, M Natália D S. "Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases." Biomolecules vol. 11,11 (2021). doi: https://doi.org/10.3390/biom11111670
Halder AK, Cordeiro MNDS. Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases. Biomolecules. 2021 Nov 10;11(11). doi: 10.3390/biom11111670. PMID: 34827668; PMCID: PMC8615736.
Copy Download .nbib Format: NLM
Share it on

Biomolecules. 2021 Nov 10;11(11). doi: 10.3390/biom11111670.

Multi-Target In Silico Prediction of Inhibitors for Mitogen-Activated Protein Kinase-Interacting Kinases.

Biomolecules

Amit Kumar Halder, M Natália D S Cordeiro

Affiliations

  1. LAQV-REQUIMTE/Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
  2. Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713212, India.

PMID: 34827668 PMCID: PMC8615736 DOI: 10.3390/biom11111670

Abstract

The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer. This work reports, for the first time, a multi-target (or multi-tasking) in silico modeling approach (mt-QSAR) for probing the inhibitory potential of these isoforms against MNKs. Linear and non-linear mt-QSAR classification models were set up from a large dataset of 1892 chemicals tested under a variety of assay conditions, based on the Box-Jenkins moving average approach, along with a range of feature selection algorithms and machine learning tools, out of which the most predictive one (>90% overall accuracy) was used for mechanistic interpretation of the likely inhibition of MNK-1 and MNK-2. Considering that the latter model is suitable for virtual screening of chemical libraries-i.e., commercial, non-commercial and in-house sets, it was made publicly accessible as a ready-to-use FLASK-based application. Additionally, this work employed a focused kinase library for virtual screening using an mt-QSAR model. The virtual hits identified in this process were further filtered by using a similarity search, in silico prediction of drug-likeness, and ADME profiles as well as synthetic accessibility tools. Finally, molecular dynamic simulations were carried out to identify and select the most promising virtual hits. The information gathered from this work can supply important guidelines for the discovery of novel MNK-1/2 inhibitors as potential therapeutic agents.

Keywords: MNK-1 and MNK-2 inhibitors; mt-QSAR modeling; virtual screening

References

  1. Chem Biol. 2014 Apr 24;21(4):441-452 - PubMed
  2. Drug Discov Today Technol. 2004 Dec;1(4):337-41 - PubMed
  3. J Cheminform. 2020 Jun 12;12(1):43 - PubMed
  4. Future Med Chem. 2016;8(3):271-85 - PubMed
  5. J Chem Theory Comput. 2009 Sep 8;5(9):2371-7 - PubMed
  6. IEEE Trans Neural Netw. 1998;9(1):224-9 - PubMed
  7. J Med Chem. 2020 Jan 23;63(2):621-637 - PubMed
  8. Methods Mol Biol. 2019;1888:255-272 - PubMed
  9. Front Chem. 2020 Jan 08;7:873 - PubMed
  10. J Chem Inf Model. 2019 Mar 25;59(3):1062-1072 - PubMed
  11. Eur J Med Chem. 2021 Jul 5;219:113420 - PubMed
  12. ACS Comb Sci. 2017 Aug 14;19(8):501-512 - PubMed
  13. Front Chem. 2021 Mar 10;9:634663 - PubMed
  14. Expert Opin Ther Targets. 2019 Mar;23(3):187-199 - PubMed
  15. J Comput Chem. 2005 Dec;26(16):1668-88 - PubMed
  16. Mol Inform. 2013 Feb;32(2):133-138 - PubMed
  17. J Chem Inf Model. 2019 Jun 24;59(6):2538-2544 - PubMed
  18. Mol Divers. 2015 May;19(2):305-19 - PubMed
  19. J Med Chem. 2002 Jun 6;45(12):2615-23 - PubMed
  20. Expert Opin Drug Discov. 2015 Mar;10(3):245-56 - PubMed
  21. Nucleic Acids Res. 2007 Jul;35(Web Server issue):W522-5 - PubMed
  22. J Comput Chem. 2009 Dec;30(16):2785-91 - PubMed
  23. J Cheminform. 2021 Apr 15;13(1):29 - PubMed
  24. Biochim Biophys Acta. 2015 Jul;1849(7):774-80 - PubMed
  25. Chem Soc Rev. 2020 Jun 7;49(11):3525-3564 - PubMed
  26. J Comput Aided Mol Des. 2011 Jun;25(6):533-54 - PubMed
  27. Int J Mol Sci. 2019 Aug 27;20(17): - PubMed
  28. J Cheminform. 2015 May 20;7:20 - PubMed
  29. Bioorg Med Chem. 2013 May 15;21(10):2727-32 - PubMed
  30. Sci Rep. 2017 Mar 03;7:42717 - PubMed
  31. Front Pharmacol. 2018 Nov 13;9:1275 - PubMed
  32. Molecules. 2019 Oct 30;24(21): - PubMed
  33. Front Biosci. 2008 May 01;13:5359-73 - PubMed
  34. J Cheminform. 2021 Jul 15;13(1):53 - PubMed
  35. J Med Chem. 2018 Apr 26;61(8):3516-3540 - PubMed
  36. Acta Crystallogr F Struct Biol Commun. 2018 Mar 1;74(Pt 3):156-160 - PubMed
  37. Pharm Pat Anal. 2021 Jan;10(1):25-35 - PubMed
  38. Arch Biochem Biophys. 1978 Jan 30;185(2):584-91 - PubMed

Publication Types