SpringerLink
Log in

Battling BTK mutants with noncovalent inhibitors that overcome Cys481 and Thr474 mutations in Waldenström macroglobulinemia therapy: structural mechanistic insights on the role of fenebrutinib

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

Recently, the non-covalent Bruton tyrosine kinase (BTK) inhibitor fenebrutinib was presented as a therapeutic option with strong inhibitory efficacy against a single (C481S) and double (T474S/C481S) BTK variant in the treatment of Waldenström macroglobulinemia (WM). However, the molecular events surrounding its inhibition mechanism towards this variant remain unresolved. Herein, we employed in silico methods such as molecular dynamic simulation coupled with binding free energy estimations to explore the mechanistic activity of the fenebrutinib on (C481S) and (T474S/C481S) BTK variant, at a molecular level. Our investigations reveal that amino acid arginine contributed immensely to the total binding energy, this establishing the cruciality of amino acid residues, Arg132 and Arg156 in (C481S) and Arg99, Arg137, and Arg132 in (T474S/C481S) in the binding of fenebrutinib towards both BTK variants. The structural orientations of fenebrutinib within the respective hydrophobic pockets allowed favorable interactions with binding site residues, accounting for its superior binding affinity by 24.5% and relative high hydrogen bond formation towards (T474S/C481S) when compared with (C481S) BTK variants. Structurally, fenebrutinib impacted the stability, flexibility, and solvent accessible surface area of both BTK variants, characterized by various alterations observed in the bound and unbound structures, which proved enough to disrupt their biological function. Findings from this study, therefore, provide insights into the inhibitory mechanism of fenebrutinib at the atomistic level and reveal its high selectivity towards BTK variants. These insights could be key in designing and develo** BTK mutants’ inhibitors to treat Waldenström macroglobulinemia (WM).

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Gertz MA (2019) Waldenström macroglobulinemia: 2019 update on diagnosis, risk stratification, and management. Am J Hematol 94(2):266–276

    Article  PubMed  Google Scholar 

  2. Pal Singh S, Dammeijer F, Hendriks RW (2018) Correction: role of Bruton’s tyrosine kinase in B cells and malignancies. Mol Cancer 17(1):57

  3. Treon SP, Xu L, Yang G et al (2012) MYD88 L265P somatic mutation in Waldenström’s macroglobulinemia. N Engl J Med 367(9):826–833

    Article  CAS  PubMed  Google Scholar 

  4. Cao Y, Hunter ZR, Liu X et al (2015) The WHIM-like CXCR4S338X somatic mutation activates AKT and ERK, and promotes resistance to ibrutinib and other agents used in the treatment of Waldenstrom’s Macroglobulinemia. Leukemia 29(1):169–176

    Article  PubMed  Google Scholar 

  5. Roccaro AM, Sacco A, Jimenez C et al (2014) C1013G/CXCR4 acts as a driver mutation of tumor progression and modulator of drug resistance in lymphoplasmacytic lymphoma. Blood 123(26):4120–4131

    Article  CAS  PubMed  Google Scholar 

  6. Hunter ZR, Xu L, Yang G et al (2014) The genomic landscape of Waldenström macroglobulinemia is characterized by highly recurring MYD88 and WHIM-like CXCR4 mutations, and small somatic deletions associated with B-cell lymphomagenesis. Blood 123(11):1637–1646

    Article  CAS  PubMed  Google Scholar 

  7. Varettoni M, Arcaini L, Zibellini S et al (2013) Prevalence and clinical significance of the MYD88 (L265P) somatic mutation in Waldenström’s macroglobulinemia and related lymphoid neoplasms. Blood 121(13):2522–2528

    Article  CAS  PubMed  Google Scholar 

  8. Abeykoon JP, Paludo J, King RL et al (2018) MYD88 mutation status does not impact overall survival in Waldenström macroglobulinemia. Am J Hematol 93(2):187–194

    Article  CAS  PubMed  Google Scholar 

  9. Bagratuni T, Ntanasis-Stathopoulos I, Gavriatopoulou M et al (2018) Detection of MYD88 and CXCR4 mutations in cell-free DNA of patients with IgM monoclonal gammopathies. Leukemia 32(12):2617–2625

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Smith CIE (2017) Enigmas in tumor resistance to kinase inhibitors and calculation of the drug resistance index for cancer (DRIC). Semin Cancer Biol 45:36–49

    Article  CAS  PubMed  Google Scholar 

  11. Burger JA, Wiestner A (2018) Targeting B cell receptor signalling in cancer: preclinical and clinical advances. Nat Rev Cancer 18(3):148–167

    Article  CAS  PubMed  Google Scholar 

  12. Herman SEM, Gordon AL, Hertlein E et al (2011) Bruton tyrosine kinase represents a promising therapeutic target for treatment of chronic lymphocytic leukemia and is effectively targeted by PCI-32765. Blood 117(23):6287–6296

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Smith CIE (2017) From identification of the BTK kinase to effective management of leukemia. Oncogene 36(15):2045–2053

    Article  CAS  PubMed  Google Scholar 

  14. Lucas F, Woyach JA (2019) Inhibiting Bruton’s tyrosine kinase in CLL and other B-cell malignancies. Target Oncol 14(2):125–138

    Article  PubMed  Google Scholar 

  15. Treon SP, Gustine J, Meid K et al (2018) Ibrutinib monotherapy in symptomatic, treatment-naïve patients with Waldenström macroglobulinemia. J Clin Oncol 36(27):2755–2761

    Article  CAS  PubMed  Google Scholar 

  16. Charalambous A, Schwarzbich M-A, Witzens-Harig M (2018) Ibrutinib. Recent Results Cancer Res 133–168

  17. Furman RR, Cheng S, Lu P et al (2014) Ibrutinib resistance in chronic lymphocytic leukemia. N Engl J Med 370(24):2352–2354

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Woyach JA, Ruppert AS, Guinn D et al (2017) BTK C481S -Mediated resistance to ibrutinib in chronic lymphocytic leukemia. J Clin Oncol 35(13):1437–1443

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Johnson AR, Kohli PB, Katewa A et al (2016) Battling Btk mutants with noncovalent inhibitors that overcome Cys481 and Thr474 mutations. ACS Chem Biol 11(10):2897–2907

    Article  CAS  PubMed  Google Scholar 

  20. Reiff SD, Muhowski EM, Guinn D et al (2018) Noncovalent inhibition of C481S Bruton tyrosine kinase by GDC-0853: a new treatment strategy for ibrutinib-resistant CLL. Blood 132(10):1039–1049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Crawford JJ, Johnson AR, Misner DL et al (2018) Discovery of GDC-0853: a potent, selective, and noncovalent Bruton’s tyrosine kinase inhibitor in early clinical development. J Med Chem 61(6):2227–2245

    Article  CAS  PubMed  Google Scholar 

  22. Kohrt HE, Sagiv-Barfi I, Rafiq S et al (2014) Ibrutinib antagonizes rituximab-dependent NK cell–mediated cytotoxicity. Blood 123(12):1957–1960

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Herman AE, Chinn LW, Kotwal SG et al (2018) Safety, pharmacokinetics, and pharmacodynamics in healthy volunteers treated with GDC-0853, a selective reversible Bruton’s tyrosine kinase inhibitor. Clin Pharmacol Ther 103(6):1020–1028

    Article  CAS  PubMed  Google Scholar 

  24. Estupiñán HY, Wang Q, Berglöf A et al (2021) BTK gatekeeper residue variation combined with cysteine 481 substitution causes super-resistance to irreversible inhibitors acalabrutinib, ibrutinib and zanubrutinib. Leukemia 35(5):1317–1329

    Article  PubMed  PubMed Central  Google Scholar 

  25. Usha T, Shanmugarajan D, Goyal AK, Kumar CS, Middha SK (2018) Recent updates on computer-aided drug discovery: time for a paradigm shift. Curr Top Med Chem 17(30):3296–3307

    Article  Google Scholar 

  26. Berman HM, Battistuz T, Bhat TN et al (2002) The protein data bank. Acta Crystallogr D Biol Crystallogr 58(6):899–907

  27. Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera - a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612

    Article  CAS  PubMed  Google Scholar 

  28. Kusumaningrum S, Budianto E, Kosela S, Sumaryono W, Juniarti F (2014) The molecular docking of 1,4-naphthoquinone derivatives as inhibitors of Polo-like kinase 1 using Molegro Virtual Docker. J Appl Pharm Sci 4(11):47–53

    Google Scholar 

  29. Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49(11):3315–3321

    Article  CAS  PubMed  Google Scholar 

  30. Dunbrack RL (2002) Rotamer libraries in the 21st century. Curr Opin Struct Biol 12(4):431–440

    Article  CAS  PubMed  Google Scholar 

  31. Allouche A (2012) Software news and updates Gabedit — a graphical user interface for computational chemistry softwares. J Comput Chem 32:174–182

    Article  Google Scholar 

  32. Trott O, Olson A (2010) Autodock vina: improving the speed and accuracy of docking. J Comput Chem 31(2):455–461

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234

    Article  PubMed  Google Scholar 

  34. Mhlongo NN, Ebrahim M, Skelton AA, Kruger HG, Williams IH, Soliman MES (2015) Dynamics of the thumb-finger regions in a GH11 xylanase Bacillus circulans: comparison between the Michaelis and covalent intermediate. RSC Adv 5(100):82381–82394

    Article  CAS  Google Scholar 

  35. Ramharack P, Oguntade S, Soliman MES (2017) Delving into Zika virus structural dynamics-a closer look at NS3 helicase loop flexibility and its role in drug discovery. RSC Adv 7(36):22133–22144

    Article  CAS  Google Scholar 

  36. Case DA, Walker RC, Cheatham TE et al (2018) Amber 2018. University of California, San Francisco 2018:1–923

    Google Scholar 

  37. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174

    Article  CAS  PubMed  Google Scholar 

  38. Grest GS, Kremer K (1986) Molecular dynamics simulation for polymers in the presence of a heat bath. Phys Rev A (Coll Park) 33(5):3628–3631

    Article  CAS  Google Scholar 

  39. Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81(8):3684–3690

    Article  CAS  Google Scholar 

  40. Ryckaert J-P, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23(3):327–341

    Article  CAS  Google Scholar 

  41. Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095

    Article  CAS  PubMed  Google Scholar 

  42. Seifert E (2014) OriginPro 9.1: scientific data analysis and graphing software - software review. J Chem Inf Model 54(5):1552

    Article  CAS  PubMed  Google Scholar 

  43. Abdullahi M, Olotu FA, Soliman ME (2017) Dynamics of allosteric modulation of lymphocyte function associated antigen-1 closure-open switch: unveiling the structural mechanisms associated with outside-in signaling activation. Biotechnol Lett 39(12):1843–1851

    Article  CAS  PubMed  Google Scholar 

  44. Olotu FA, Soliman MES (2018) From mutational inactivation to aberrant gain-of-function: unraveling the structural basis of mutant p53 oncogenic transition. J Cell Biochem 119(3):2646–2652

    Article  CAS  PubMed  Google Scholar 

  45. Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods 1 The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51(1):69–82

    Article  CAS  PubMed  Google Scholar 

  46. Homeyer N, Gohlke H (2012) Free energy calculations by the molecular mechanics Poisson−Boltzmann surface area method. Mol Inform 31(2):114–122

    Article  CAS  PubMed  Google Scholar 

  47. Mukherjee J, Gupta MN (2015) Increasing importance of protein flexibility in designing biocatalytic processes. Biotechnology Reports 6:119–123

    Article  PubMed  PubMed Central  Google Scholar 

  48. **e Y, An J, Yang G et al (2014) enhanced enzyme kinetic stability by increasing rigidity within the active site. J Biol Chem 289(11):7994–8006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Celej MS, Montich GG, Fidelio GD (2003) Protein stability induced by ligand binding correlates with changes in protein flexibility. Protein Sci 12(7):1496–1506

    Article  PubMed  PubMed Central  Google Scholar 

  50. Liu K, Kokubo H (2017) Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations: a cross-docking study. J Chem Inf Model 57(10):2514–2522

    Article  CAS  PubMed  Google Scholar 

  51. Agoni C, Salifu EY, Munsamy G, Olotu FA, Soliman M (2019) CF 3 -Pyridinyl substitution on antimalarial therapeutics: probing differential ligand binding and dynamical inhibitory effects of a novel triazolopyrimidine-based inhibitor on plasmodium falciparum dihydroorotate dehydrogenase. Chem Biodivers 16(12):e1900365

  52. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10(5):449–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Zhang C, Feng L-J, Huang Y et al (2017) Discovery of novel phosphodiesterase-2a inhibitors by structure-based virtual screening, structural optimization, and bioassay. J Chem Inf Model 57(2):355–364

    Article  PubMed  Google Scholar 

  54. Kumalo HM, Soliman ME (2016) Per-residue energy footprints-based pharmacophore modeling as an enhanced in silico approach in drug discovery: a case study on the identification of novel β-secretase1 (BACE1) inhibitors as anti-Alzheimer agents. Cell Mol Bioeng 9(1):175–189

    Article  CAS  Google Scholar 

  55. Patil R, Das S, Stanley A, Yadav L, Sudhakar A, Varma AK (2010) Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing. PLoS ONE 5(8):e12029

    Article  PubMed  PubMed Central  Google Scholar 

  56. Azeyedo WD Jr (2010) MolDock applied to structure-based virtual screening. Curr Drug Targets 11(3):327–334

    Article  Google Scholar 

  57. Wang S, Mondal S, Zhao C et al (2019) Noncovalent inhibitors reveal BTK gatekeeper and auto-inhibitory residues that control its transforming activity. JCI Insight 4(12):e127566

  58. Salifu EY, Agoni C, Olotu FA, Dokurugu YM, Soliman MES (2019) Halting ionic shuttle to disrupt the synthetic machinery—structural and molecular insights into the inhibitory roles of bedaquiline towards Mycobacterium tuberculosis ATP synthase in the treatment of tuberculosis. J Cell Biochem 120(9):16108–16119

    Article  CAS  PubMed  Google Scholar 

  59. Karshikoff A, Nilsson L, Ladenstein R (2015) Rigidity versus flexibility: the dilemma of understanding protein thermal stability. FEBS J 282(20):3899–3917

    Article  CAS  PubMed  Google Scholar 

  60. Pitera JW (2014) Expected distributions of root-mean-square positional deviations in proteins. J Phys Chem B 118(24):6526–6530

    Article  CAS  PubMed  Google Scholar 

  61. Agoni C, Ramharack P, Munsamy G, Soliman MES (2020) Human rhinovirus inhibition through capsid “canyon” perturbation: structural insights into the role of a novel benzothiophene derivative. Cell Biochem Biophys 78(1):3–13

    Article  CAS  PubMed  Google Scholar 

  62. Agoni C, Ramharack P, Salifu EY, Soliman MES (2020) The dual-targeting activity of the metabolite substrate of para-amino salicyclic acid in the mycobacterial folate pathway: atomistic and structural perspectives. Protein J 39(2):106–117

    Article  CAS  PubMed  Google Scholar 

  63. Lobanov MYu, Bogatyreva NS, Galzitskaya O, v. (2008) Radius of gyration as an indicator of protein structure compactness. Mol Biol 42(4):623–628

    Article  CAS  Google Scholar 

  64. Salleh AB, Rahim ASMA, Rahman RNZRA, Rahman TC, Leow MB (2012) The role of Arg157Ser in improving the compactness and stability of ARM lipase. J Comput Sci Syst Biol 5(2):38–46

    Article  Google Scholar 

  65. Agoni C, Ramharack P, Soliman ME (2018) Co-inhibition as a strategic therapeutic approach to overcome rifampin resistance in tuberculosis therapy: atomistic insights. Future Med Chem 10(14):1665–1675

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the School of Health Sciences, the University of KwaZulu-Natal, Westville Campus for their financial support. We also acknowledge the Center for High-Performance Computing (CHPC, www.chpc.ac.za), Cape Town, for computational resources.

Author information

Authors and Affiliations

  1. Molecular Bio-Computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban, 4001, South Africa

    Ghazi Elamin, Aimen Aljoundi & Mahmoud E. S. Soliman

  2. Faculty of Science, Department of Chemistry, University of Tabuk, Tabuk, 7149, Saudi Arabia

    Mohamed Issa Alahmdi

  3. Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Tabuk University, Tabuk, 71491, Saudi Arabia

    Nader E. Abo-Dya

  4. Faculty of Pharmacy, Department of Pharmaceutical Organic Chemistry, Zagazig University, Zagazig, Egypt

    Nader E. Abo-Dya

Authors
  1. Ghazi Elamin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aimen Aljoundi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Issa Alahmdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nader E. Abo-Dya
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mahmoud E. S. Soliman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Ghazi Elamin conceptualized, implemented, analyzed, interpreted, and wrote the manuscript, Aimen Aljoundi, Mohamed Issa Alahmdi, and Nader E. Abo-Dya performed molecular dynamics simulation, while Mahmoud E.S Soliman revised and approved the manuscript for submission.

Corresponding author

Correspondence to Mahmoud E. S. Soliman.

Ethics declarations

Ethics approval and consent to participate

This research does not involve the use of animal samples.

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's note

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

Rights and permissions

Springer Nature or its licensor 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

Elamin, G., Aljoundi, A., Alahmdi, M.I. et al. Battling BTK mutants with noncovalent inhibitors that overcome Cys481 and Thr474 mutations in Waldenström macroglobulinemia therapy: structural mechanistic insights on the role of fenebrutinib. J Mol Model 28, 355 (2022). https://doi.org/10.1007/s00894-022-05345-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-022-05345-y

Keywords

Search

Navigation