Skip to main content

Advertisement

SpringerLink
Log in

Computational insights into NIMA-related kinase 6: unraveling mutational effects on structure and function

  • Published:
Molecular and Cellular Biochemistry Aims and scope Submit manuscript

Abstract

The NEK6 (NIMA-related kinase 6) serine/threonine kinase is a pivotal player in a multitude of cellular processes, including the regulation of the cell cycle and the response to DNA damage. Its significance extends to disease pathogenesis, as changes in NEK6 activity have been linked to the development of cancer. Non-synonymous single nucleotide polymorphisms (nsSNPs) in NEK6 have been linked to cancer as they alter the protein’s native structure and function. The association between NEK6 activity and cancer development has prompted researchers to explore the effects of genetic variations within the NEK6 gene. Therefore, we utilized advanced computational tools to analyze 155 high-confidence nsSNPs in the NEK6 gene. From this analysis, 21 nsSNPs were identified as potentially harmful, raising concerns about their impact on NEK6 activity and cancer risk. These 21 mutations were then examined for structural alterations, and eight of nsSNPs (I51M, V76A, I134N, Y152D, R171Q, V186G, L237R, and C285S) were found to destabilize the protein. Among the destabilizing mutations screened, a specific mutation, R171Q, stood out due to its conserved nature. To understand its impact on the protein and conformation, all-atom molecular dynamics simulations (MDS) for 100 ns were performed for both Wildtype NEK6 (WT-NEK6) and R171Q. The simulations revealed that the R171Q variant was unstable and led to significant conformational changes in NEK6. This study provides valuable insights into NEK6 dysfunction caused by single amino acid alterations, offering a novel understanding of the molecular mechanisms underlying NEK6-related cancer progression.

Graphical abstract

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

Data will be shared upon reasonable request.

Consent to publish

Not applicable.

Abbreviations

EVs:

Eigenvectors

FATHMM:

Functional analysis through hidden Markov models

FEL:

Free energy landscape

HOPES:

High-throughput evaluation of protein stability

NEK6:

NIMA (Never In Mitosis Gene A)-related kinase 6

nsSNP:

Non-synonymous single nucleotide polymorphism

MDS:

Molecular dynamics simulations

PANTHER:

Protein ANalysis THrough Evolutionary Relationships

PCA:

Principal component analysis

PhD-SNP:

Predicting human deleterious single nucleotide polymorphisms

PMut:

Protein mutation analysis

PolyPhen-2:

Polymorphism Phenotyping v2

Provean:

Protein variation effect analyzer

Rg:

Radius of gyration

RMSD:

Root-mean-square deviation

RMSF:

Root-mean-square fluctuation

SASA:

Solvent accessible surface area

SIFT:

Sorting intolerant from tolerant

SNAP:

Screening for non-acceptable polymorphisms

WT:

Wild-type

References

  1. O’Regan L, Blot J, Fry AM (2007) Mitotic regulation by NIMA-related kinases. Cell Div 2:1–12. https://doi.org/10.1186/1747-1028-2-25

    Article  CAS  Google Scholar 

  2. Panchal NK, Evan Prince S (2022) The NEK family of serine/threonine kinases as a biomarker for cancer. Clin Exp Med. https://doi.org/10.1007/s10238-021-00782-0

    Article  PubMed  Google Scholar 

  3. Sdelci S, Bertran MT, Roig J (2011) Nek9, Nek6, Nek7 and the separation of centrosomes. Cell Cycle 10:3816–3817. https://doi.org/10.4161/cc.10.22.18226

    Article  CAS  PubMed  Google Scholar 

  4. Li MZ, Yu L, Liu Q et al (1999) Assignment of NEK6, a NIMA-related gene, to human chromosome 9q33. 3 → q34.11 by radiation hybrid mapping. Cytogenet Cell Genet 87:271–272. https://doi.org/10.1159/000015445

    Article  CAS  PubMed  Google Scholar 

  5. Moraes EC, Meirelles GV, Honorato RV et al (2015) Kinase inhibitor profile for human nek1, nek6, and nek7 and analysis of the structural basis for inhibitor specificity. Molecules 20:1176–1191. https://doi.org/10.3390/molecules20011176

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Johnson LN, Noble ME, Owen DJ (1996) Active and inactive protein kinases: structural basis for regulation. Cell 85:149–158. https://doi.org/10.1016/s0092-8674(00)81092-2

    Article  CAS  PubMed  Google Scholar 

  7. Belham C, Roig J, Caldwell JA et al (2003) A mitotic cascade of NIMA family kinases: Nercc1/Nek9 activates the Nek6 and Nek7 kinases. J Biol Chem 278:34897–34909. https://doi.org/10.1074/jbc.M303663200

    Article  CAS  PubMed  Google Scholar 

  8. Meirelles GV, Silva JC, Mendonça YDA et al (2011) Human Nek6 is a monomeric mostly globular kinase with an unfolded short N-terminal domain. BMC Struct Biol. https://doi.org/10.1186/1472-6807-11-12

    Article  PubMed  PubMed Central  Google Scholar 

  9. Atish DC, Anna CS, Maura BC et al (2017) Castration resistance in prostate cancer is mediated by the kinase NEK6. Cancer Res 77:753–765. https://doi.org/10.1158/0008-5472.CAN-16-0455

    Article  CAS  Google Scholar 

  10. Jee HJ, Kim AJ, Song N et al (2010) Nek6 overexpression antagonizes p53-induced senescence in human cancer cells. Cell Cycle 9:4703–4710. https://doi.org/10.4161/cc.9.23.14059

    Article  CAS  PubMed  Google Scholar 

  11. De Donato M, Fanelli M, Mariani M et al (2015) Nek6 and Hif-1α cooperate with the cytoskeletal gateway of drug resistance to drive outcome in serous ovarian cancer. Am J Cancer Res 5:1862–1877

    PubMed  PubMed Central  Google Scholar 

  12. Cao F, Wu X, Shan Y et al (2021) Circular RNA NEK6 contributes to the development of non-small-cell lung cancer by competitively binding with miR-382-5p to elevate BCAS2 expression at post-transcriptional level. BMC Pulm Med 21:325. https://doi.org/10.1186/s12890-021-01617-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jee HJ, Kim H-J, Kim AJ et al (2011) Nek6 suppresses the premature senescence of human cancer cells induced by camptothecin and doxorubicin treatment. Biochem Biophys Res Commun 408:669–673. https://doi.org/10.1016/j.bbrc.2011.04.083

    Article  CAS  PubMed  Google Scholar 

  14. Sampson J, O’Regan L, Dyer MJS et al (2017) Hsp72 and Nek6 cooperate to cluster amplified centrosomes in cancer cells. Cancer Res 77:4785–4796. https://doi.org/10.1158/0008-5472.CAN-16-3233

    Article  CAS  PubMed  Google Scholar 

  15. De DM, Fanelli M, Mariani M et al (2015) Nek6 and Hif-1α cooperate with the cytoskeletal gateway of drug resistance to drive outcome in serous ovarian cancer. Am J Cancer Res 5:1862–1877

    Google Scholar 

  16. Choudhury AD, Schinzel AC, Cotter MB et al (2017) Castration resistance in prostate cancer is mediated by the kinase NEK6. Cancer Res 77:753–765. https://doi.org/10.1158/0008-5472.CAN-16-0455

    Article  CAS  PubMed  Google Scholar 

  17. Desai M, Chauhan JB (2018) Computational analysis for the determination of deleterious nsSNPs in human MTHFR gene. Comput Biol Chem 74:20–30. https://doi.org/10.1016/j.compbiolchem.2018.02.022

    Article  CAS  PubMed  Google Scholar 

  18. Desai M, Chauhan JB (2017) Computational analysis for the determination of deleterious nsSNPs in human MTHFD1 gene. Comput Biol Chem 70:7–14. https://doi.org/10.1016/j.compbiolchem.2017.07.001

    Article  CAS  PubMed  Google Scholar 

  19. Dash R, Munni YA (2020) Computational SNP analysis and molecular simulation revealed the most computational SNP analysis and molecular simulation revealed the most deleterious missense variants in the NBD1 domain of human ABCA1 transporter. https://doi.org/10.3390/ijms21207606

  20. Solayman M, Saleh MA, Paul S et al (2017) In silico analysis of nonsynonymous single nucleotide polymorphisms of the human adiponectin receptor 2 (ADIPOR2) gene. Comput Biol Chem 68:175–185. https://doi.org/10.1016/j.compbiolchem.2017.03.005

    Article  CAS  PubMed  Google Scholar 

  21. Panchal NK, Bhale A, Verma VK, Beevi SS (2020) Computational and molecular dynamics simulation approach to analyze the impact of XPD gene mutation on protein stability and function. Mol Simul 46:1200–1219. https://doi.org/10.1080/08927022.2020.1810852

    Article  CAS  Google Scholar 

  22. Tanwar G, Mazumder AG, Bhardwaj V et al (2019) Target identification, screening and in vivo evaluation of pyrrolone-fused benzosuberene compounds against human epilepsy using Zebrafish model of pentylenetetrazol-induced seizures. Sci Rep. https://doi.org/10.1038/s41598-019-44264-6

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kumar A, Rajendran V, Sethumadhavan R, Purohit R (2012) In silico prediction of a disease-associated STIL mutant and its affect on the recruitment of centromere protein J (CENPJ). FEBS Open Bio 2:285–293. https://doi.org/10.1016/j.fob.2012.09.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bhardwaj V, Purohit R (2020) Computational investigation on effect of mutations in PCNA resulting in structural perturbations and inhibition of mismatch repair pathway. J Biomol Struct Dyn 38:1963–1974. https://doi.org/10.1080/07391102.2019.1621210

    Article  CAS  PubMed  Google Scholar 

  25. Kumar Bhardwaj V, Purohit R, Kumar S (2021) Himalayan bioactive molecules as potential entry inhibitors for the human immunodeficiency virus. Food Chem. https://doi.org/10.1016/j.foodchem.2020.128932

    Article  PubMed  Google Scholar 

  26. Jee HJ, Kim H-J, Kim AJ et al (2013) The inhibition of Nek6 function sensitizes human cancer cells to premature senescence upon serum reduction or anticancer drug treatment. Cancer Lett 335:175–182. https://doi.org/10.1016/j.canlet.2013.02.012

    Article  CAS  PubMed  Google Scholar 

  27. O’Regan L, Fry AM (2009) The Nek6 and Nek7 protein kinases are required for robust mitotic spindle formation and cytokinesis. Mol Cell Biol 29:3975–3990. https://doi.org/10.1128/mcb.01867-08

    Article  PubMed  PubMed Central  Google Scholar 

  28. Mottaz A, David FPA, Veuthey AL, Yip YL (2010) Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar. Bioinformatics 26:851–852. https://doi.org/10.1093/bioinformatics/btq028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. López-Ferrando V, Gazzo A, De La Cruz X et al (2017) PMut: a web-based tool for the annotation of pathological variants on proteins, 2017 update. Nucleic Acids Res 45:W222–W228. https://doi.org/10.1093/nar/gkx313

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Choi Y, Chan AP (2015) PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31:2745–2747. https://doi.org/10.1093/bioinformatics/btv195

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Savojardo C, Fariselli P, Martelli PL, Casadio R (2016) INPS-MD: a web server to predict stability of protein variants from sequence and structure. Bioinformatics 32:2542–2544. https://doi.org/10.1093/bioinformatics/btw192

    Article  CAS  PubMed  Google Scholar 

  32. Worth CL, Preissner R, Blundell TL (2011) SDM—a server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Res 39:215–222. https://doi.org/10.1093/nar/gkr363

    Article  CAS  Google Scholar 

  33. Pires DEV, Ascher DB, Blundell TL (2014) MCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30:335–342. https://doi.org/10.1093/bioinformatics/btt691

    Article  CAS  PubMed  Google Scholar 

  34. Pires DEV, Ascher DB, Blundell TL (2014) DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Res 42:314–319. https://doi.org/10.1093/nar/gku411

    Article  CAS  Google Scholar 

  35. Parthiban V, Gromiha MM, Schomburg D (2006) CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 34:239–242. https://doi.org/10.1093/nar/gkl190

    Article  CAS  Google Scholar 

  36. Rogers MF, Shihab HA, Mort M et al (2018) FATHMM-XF: accurate prediction of pathogenic point mutations via extended features. Bioinformatics 34:511–513. https://doi.org/10.1093/bioinformatics/btx536

    Article  CAS  PubMed  Google Scholar 

  37. Celniker G, Nimrod G, Ashkenazy H et al (2013) ConSurf: using evolutionary data to raise testable hypotheses about protein function. Isr J Chem 53:199–206. https://doi.org/10.1002/ijch.201200096

    Article  CAS  Google Scholar 

  38. Ashkenazy H, Abadi S, Martz E et al (2016) ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res 44:W344–W350. https://doi.org/10.1093/nar/gkw408

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5:725–738. https://doi.org/10.1038/nprot.2010.5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Parra RG, Schafer NP, Radusky LG et al (2016) Protein Frustratometer 2: a tool to localize energetic frustration in protein molecules, now with electrostatics. Nucleic Acids Res 44:W356–W360. https://doi.org/10.1093/NAR/GKW304

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Seeliger D, De Groot BL (2010) Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J Comput Aided Mol Des 24:417–422. https://doi.org/10.1007/s10822-010-9352-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Panchal NK, Mohanty S, Prince SE (2023) NIMA-related kinase-6 (NEK6) as an executable target in cancer. Clin Transl Oncol 25:66–77

    Article  CAS  PubMed  Google Scholar 

  43. Jessica Montgomery BM (2016) Nek6 controls mitotic progression through regulating Eml3 localisation to spindle microtubules. Thesis

  44. Yin M-J, Shao L, Voehringer D et al (2003) The serine/threonine kinase Nek6 is required for cell cycle progression through mitosis. J Biol Chem 278:52454–52460. https://doi.org/10.1074/jbc.M308080200

    Article  CAS  PubMed  Google Scholar 

  45. Lee M-Y, Kim H-J, Kim M-A et al (2008) Nek6 is involved in G2/M phase cell cycle arrest through DNA damage-induced phosphorylation. Cell Cycle 7:2705–2709. https://doi.org/10.4161/cc.7.17.6551

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

All the authors are grateful to the Vellore Institute of Technology, Vellore, India for providing the essential facilities to carry out this work.

Funding

No funding has been received for this work.

Author information

Authors and Affiliations

  1. Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, 632 014, India

    Nagesh Kishan Panchal & Sabina Evan Prince

  2. Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, 632014, India

    Shruti Mohanty

Authors
  1. Nagesh Kishan Panchal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shruti Mohanty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sabina Evan Prince
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

" N.K.P and S.E.P: Conceptualization, Literature survey; N.K.P and S.M : Performed experiment ; N.K.P: Wrote the Original draft ; S.E.P: Supervised the work. All authors reviewed the manuscript "

Corresponding author

Correspondence to Sabina Evan Prince.

Ethics declarations

Conflict of interest

We affirm that there are no conflicts of interest to disclose regarding the publication of this manuscript.

Ethical approval

Not applicable.

Informed consent

Not applicable: no human/animal subjects were involved in the study.

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 62 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

Panchal, N.K., Mohanty, S. & Prince, S.E. Computational insights into NIMA-related kinase 6: unraveling mutational effects on structure and function. Mol Cell Biochem (2023). https://doi.org/10.1007/s11010-023-04910-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11010-023-04910-0

Keywords

Search

Navigation