Skip to main content
SpringerLink
Log in

Integrated machine learning-based virtual screening and biological evaluation for identification of potential inhibitors against cathepsin K

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Cathepsin K is a type of cysteine proteinase that is primarily expressed in osteoclasts and has a key role in the breakdown of bone matrix protein during bone resorption. Many studies suggest that the deficiency of cathepsin K is concomitant with a suppression of osteoclast functioning, therefore rendering the resorptive properties of cathepsin K the most prominent target for osteoporosis. This innovative work has identified a novel anti-osteoporotic agent against Cathepsin K by using a comparison of machine learning and deep learning-based virtual screening followed by their biological evaluation. Out of ten shortlisted compounds, five of the compounds (JFD02945, JFD02944, RJC01981, KM08968 and SB01934) exhibit more than 50% inhibition of the Cathepsin K activity at 0.1 μM concentration and are considered to have a promising inhibitory effect against Cathepsin K. The comprehensive docking, MD simulation, and MM/PBSA investigations affirm the stable and effective interaction of these compounds with Cathepsin K to inhibit its function. Furthermore, the compounds RJC01981, KM08968 and SB01934 are represented to have promising anti-osteoporotic properties for the management of osteoporosis owing to their significantly well predicted ADMET properties.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Aghamohammadzadeh N, Najafipour F, Bahrami AMIR, Niafar MITRA, Baglar L, Hajiegrary H, Aliasgharzadeh A, Mobasseri M (2009) Evaluation of effective factors in decreased bone density in patients with osteoporosis and osteopenia. J Gorgan Univ Med Sci 11(2)

  2. Zamani B, Ebadi SAR, Ahmadvand AFSHIN, Mousavi GH (2010) The frequency of osteoporosis in hip fracture following minor trauma and the resulting mortality rate and direct treatment costs in patients over 45 years old in Kashan Naghavi Hospital during 2005–2007. J Kerman Univ Med Sci 17(2):137–144

    Google Scholar 

  3. Langdahl BL (2021) Overview of treatment approaches to osteoporosis. Br J Pharmacol 178(9):1891–1906

    Article  CAS  PubMed  Google Scholar 

  4. Skjødt MK, Frost M, Abrahamsen B (2019) Side effects of drugs for osteoporosis and metastatic bone disease. Br J Clin Pharmacol 85(6):1063-1071

  5. Panwar P, Law S, Jamroz A, Azizi P, Zhang D, Ciufolini M, Brömme D (2018) Tanshinones that selectively block the collagenase activity of cathepsin K provide a novel class of ectosteric antiresorptive agents for bone. Br J Pharmacol 175(6):902–923

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Turk V, Turk B, Turk D (2001) Lysosomal cysteine proteases: facts and opportunities. EMBO J 20(17):4629–4633

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Vasiljeva O, Reinheckel T, Peters C, Turk D, Turk V, Turk B (2007) Emerging roles of cysteine cathepsins in disease and their potential as drug targets. Curr Pharm Des 13(4):387–403

    Article  CAS  PubMed  Google Scholar 

  8. Dai R, Wu Z, Chu HY, Lu J, Lyu A, Liu J, Zhang G (2020) Cathepsin K: the action in and beyond bone. Front Cell Develop Biol 8:433

    Article  Google Scholar 

  9. Balkan W, Martinez AF, Fernandez I, Rodriguez MA, Pang M, Troen BR (2009) Identification of NFAT binding sites that mediate stimulation of cathepsin K promoter activity by RANK ligand. Gene 446(2):90–98

    Article  CAS  PubMed  Google Scholar 

  10. Hou WS, Brömme D, Zhao Y, Mehler E, Dushey C, Weinstein H, Miranda CS, Fraga C, Greig F, Carey J, Rimoin DL (1999) Characterization of novel cathepsin K mutations in the pro and mature polypeptide regions causing pycnodysostosis. J Clin Investig 103(5):731–738

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Duong LT, Leung AT, Langdahl B (2016) Cathepsin K inhibition: a new mechanism for the treatment of osteoporosis. Calcif Tissue Int 98(4):381–397

    Article  CAS  PubMed  Google Scholar 

  12. Lu J, Wang M, Wang Z, Fu Z, Lu A, Zhang G (2018) Advances in the discovery of cathepsin K inhibitors on bone resorption. J Enzyme Inhib Med Chem 33(1):890–904

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Rocho FR, Bonatto V, Lameiro RF, Lameira J, Leitão A, Montanari CA (2022) A patent review on cathepsin K inhibitors to treat osteoporosis (2011–2021). Expert Opin Ther Pat 32(5):561–573

    Article  CAS  PubMed  Google Scholar 

  14. Peng W, Shen H, Lin B, Han P, Li C, Zhang Q, Ye B, Rahman K, Xin H, Qin L, Han T (2018) Docking study and antiosteoporosis effects of a dibenzylbutane lignan isolated from Litsea cubeba targeting Cathepsin K and MEK1. Med Chem Res 27(9):2062–2070

  15. Yang H, Heyer J, Zhao H, Liang S, Guo R, Zhong L (2020) The potential role of cathepsin K in non-small cell lung cancer. Molecules (Basel, Switzerland) 25(18):4136. https://doi.org/10.3390/molecules25184136

    Article  CAS  PubMed  Google Scholar 

  16. Lecaille F, Chazeirat T, Bojarski KK, Renault J, Saidi A, Prasad VGNV, Samsonov S, Lalmanach G (2020) Rat cathepsin K: enzymatic specificity and regulation of its collagenolytic activity. Biochimica et biophysica acta. Proteins Proteomics 1868(2):140318. https://doi.org/10.1016/j.bbapap.2019.140318

  17. Senjor E, Kos J, Nanut MP (2023) Cysteine cathepsins as therapeutic targets in immune regulation and immune disorders. Biomedicines 11(2):476. https://doi.org/10.3390/biomedicines11020476

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Choudhury D, Biswas S (2021) Structure-guided protein engineering of human cathepsin L for efficient collagenolytic activity. Prot Eng Design Select: PEDS 34:gzab005. https://doi.org/10.1093/protein/gzab005

  19. Fernández-Torras A, Comajuncosa-Creus A, Duran-Frigola M, Aloy P (2022) Connecting chemistry and biology through molecular descriptors. Curr Opin Chem Biol 66:102090

    Article  PubMed  Google Scholar 

  20. Grisoni F, Consonni V, Todeschini R (2018) Impact of molecular descriptors on computational models. In Computational chemogenomics. Humana Press, New York, pp 171–209

  21. Moriwaki H, Tian YS, Kawashita N, Takagi T (2018) Mordred: a molecular descriptor calculator. J Cheminformatics 10(1):1–14

    Article  Google Scholar 

  22. Sheridan RP (2013) Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inf Model 53(4):783–790

    Article  CAS  PubMed  Google Scholar 

  23. Clevert DA, Unterthiner T, Hochreiter S (2015) Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289

  24. Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580

  25. Vapnik V, Golowich S, Smola A (1996) Support vector method for function approximation, regression estimation and signal processing. Adv Neural Inform Proc Syst 9

  26. Schölkopf B, Simard P, Smola A, Vapnik V (1997) Prior knowledge in support vector kernels. Adv Neural Inform Proc Syst 10

  27. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(2)

  28. Zhao B, Janson CA, Amegadzie BY, D’Alessio K, Griffin C, Hanning CR, Jones C, Kurdyla J, McQueney M, Qiu X, Smith WW, Abdel-Meguid SS (1997) Crystal structure of human osteoclast cathepsin K complex with E-64. Nat Struct Biol 4(2):109–111. https://doi.org/10.1038/nsb0297-109

    Article  CAS  PubMed  Google Scholar 

  29. Schrödinger Release 2020-1 (2021) Protein Preparation Wizard; Epik, Schrödinger, LLC, New York, 2021; Impact, Schrödinger, LLC, New York; Prime, Schrödinger, LLC, New York, 2021

  30. Schrödinger Release 2020-1 (2021) LigPrep, Schrödinger, LLC, New York

  31. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49(21):6177–6196. https://doi.org/10.1021/jm051256o

    Article  CAS  PubMed  Google Scholar 

  32. Zoete V, Cuendet MA, Grosdidier A, Michielin O (2011) SwissParam: a fast force field generation tool for small organic molecules. J Comput Chem 32(11):2359–2368

    Article  CAS  PubMed  Google Scholar 

  33. Hess B (2008) P-LINCS: a parallel linear constraint solver for molecular simulation. J Chem Theory Comput 4(1):116–122

    Article  CAS  PubMed  Google Scholar 

  34. Gonzalez-Medina M, Medina-Franco JL (2017) Platform for unified molecular analysis: PUMA. J Chem Inf Model 57(8):1735–1740

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

SP and PPM acknowledges CSIR, Govt. of India, for Senior Research Fellowship. AC acknowledges CSIR, Govt. of India, for junior Research Fellowship. DBT-Grants GAP0384 (BT/PR40131/BTIS/137/26/2021) is with "DBT-Grants GAP0384 (BT/PR40131/BTIS/137/26/2021 and GAP0449 (BT/PR40197/BTIS/137/68/2023) are is also gratefully acknowledged for funding the research reported in this manuscript. Authors are grateful to CSIR-CDRI chemical repository for providing compounds for biological evaluation from Maybridge library. This manuscript bears a CSIR-CDRI communication number 10763.

Author information

Authors and Affiliations

  1. Biochemistry and Structural Biology Division, CSIR-Central Drug Research Institute, Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India

    Shahid Parwez, Animesh Chaurasia, Pinaki Parsad Mahapatra, Shakil Ahmed & Mohammad Imran Siddiqi

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    Shahid Parwez, Animesh Chaurasia, Pinaki Parsad Mahapatra, Shakil Ahmed & Mohammad Imran Siddiqi

Authors
  1. Shahid Parwez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Animesh Chaurasia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pinaki Parsad Mahapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shakil Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Imran Siddiqi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SP,AC : Literature review, Experiments, data collection, analysis and compilation, drafting/preparation of manuscript PPM, SA: work related to biological evaluation. MIS/SA: Project design, overall supervision of the project, analysis of results, preparation of manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Mohammad Imran Siddiqi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 10114 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parwez, S., Chaurasia, A., Mahapatra, P.P. et al. Integrated machine learning-based virtual screening and biological evaluation for identification of potential inhibitors against cathepsin K. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10845-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11030-024-10845-5

Keywords

Search

Navigation