Structural characterization of EGFR exon 19 deletion mutation using molecular dynamics simulation
Autoři:
Mahlet Z. Tamirat aff001; Marika Koivu aff002; Klaus Elenius aff002; Mark S. Johnson aff001
Působiště autorů:
Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland
aff001; Medicity Research Laboratories and Institute of Biomedicine, University of Turku, Turku, Finland
aff002; Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
aff003; Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
aff004; Department of Oncology and Radiotherapy, University of Turku and Turku University Hospital, Turku, Finland
aff005
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222814
Souhrn
Epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor important in diverse biological processes including cell proliferation and survival. Upregulation of EGFR activity due to over-expression or mutation is widely implicated in cancer. Activating somatic mutations of the EGFR kinase are postulated to affect the conformation and/or stability of the protein, shifting the EGFR inactive-active state equilibrium towards the activated state. Here, we examined a common EGFR deletion mutation, Δ746ELREA750, which is frequently observed in non-small cell lung cancer patients. By using molecular dynamics simulation, we investigated the structural effects of the mutation that lead to the experimentally reported increases in kinase activity. Simulations of the active form wild-type and ΔELREA EGFRs revealed the deletion stabilizes the αC helix of the kinase domain, which is located adjacent to the deletion site, by rigidifying the flexible β3-αC loop that accommodates the ELREA sequence. Consequently, the αC helix is stabilized in the “αC-in” active conformation that would prolong the time of the activated state. Moreover, in the mutant kinase, a salt bridge between E762 and K745, which is key for EGFR activity, was also stabilized during the simulation. Additionally, the interaction between EGFR and ATP was favored by ΔELREA EGFR over wild-type EGFR, as reflected by the number of hydrogen bonds formed and the free energy of binding. Simulation of inactive EGFR suggested the deletion would promote a shift from the inactive conformation towards active EGFR, which is supported by the inward movement of the αC helix. The MDS results also align with the effects of tyrosine kinase inhibitors on ΔELREA and wild-type EGFR lung cancer cell lines, where more pronounced inhibition was observed against ΔELREA than for wild-type EGFR by inhibitors recognizing the active kinase conformation.
Klíčová slova:
Biology and life sciences – Biochemistry – Enzymology – Enzymes – Protein kinases – Tyrosine kinases – Proteins – Salt bridges – Genetics – Mutation – Deletion mutation – Somatic mutation – Computational biology – Physical sciences – Chemistry – Electrochemistry – Physical chemistry – Chemical bonding – Hydrogen bonding – Computational chemistry – Molecular dynamics – Physics – Thermodynamics – Free energy – Biophysics – Biophysical simulations
Zdroje
1. Wieduwilt MJ, Moasser MM. The epidermal growth factor receptor family: biology driving targeted therapeutics. Cell Mol Life Sci. 2008;65:1566–84. doi: 10.1007/s00018-008-7440-8 18259690
2. Lemmon MA, Schlessinger J, Ferguson KM. The EGFR family: not so prototypical receptor tyrosine kinases. Cold Spring Harb Perspect Biol. 2014;6:a020768. doi: 10.1101/cshperspect.a020768 24691965
3. Seshacharyulu P, Ponnusamy MP, Haridas D, Jain M, Ganti AK, Batra SK. Targeting the EGFR signaling pathway in cancer therapy. Expert Opin Ther Targets. 2012;16(1):15–31. doi: 10.1517/14728222.2011.648617 22239438
4. Downward J, Yarden Y, Mayes E, Scrace G, Totty N, Stockwell P, et al. Close similarity of epidermal growth factor receptor and v-erb-B oncogene protein sequences. Nature 1984;307(5951):521–7. doi: 10.1038/307521a0 6320011
5. Vogt PK. Retroviral oncogenes: a historical primer. Nat Rev Cancer. 2012;12(9):639–48. doi: 10.1038/nrc3320 22898541
6. Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating Mutations in the Epidermal Growth Factor Receptor Underlying Responsiveness of Non–Small-Cell Lung Cancer to Gefitinib. N Engl J Med. 2004;350(21):2129–39. doi: 10.1056/NEJMoa040938 15118073
7. Paez JG, Jänne PA, Lee JC, Tracy S, Greulich H, Gabriel S, et al. EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy. Science. 2004;304(5676):1497–500. doi: 10.1126/science.1099314 15118125
8. Vogel CL, Cobleigh MA, Tripathy D, Gutheil JC, Harris LN, Fehrenbacher L, et al. Efficacy and Safety of Trastuzumab as a Single Agent in First-Line Treatment of HER2 -Overexpressing Metastatic Breast Cancer. J Clin Oncol. 2002;20(3):719–26. doi: 10.1200/JCO.2002.20.3.719 11821453
9. Tvorogov D, Sundvall M, Kurppa K, Hollmén M, Repo S, Johnson MS, et al. Somatic mutations of ErbB4: selective loss-of-function phenotype affecting signal transduction pathways in cancer. J Biol Chem. 2009;284(9):5582–91. doi: 10.1074/jbc.M805438200 19098003
10. Kurppa KJ, Denessiouk K, Johnson MS, Elenius K. Activating ERBB4 mutations in non-small cell lung cancer. Oncogene. 2016;35(10):1283–91. doi: 10.1038/onc.2015.185 26050618
11. Fuller SJ, Sivarajah K, Sugden PH. ErbB receptors, their ligands, and the consequences of their activation and inhibition in the myocardium. J Mol Cell Cardiol. 200844(5):831–54.
12. Leahy DJ. Structure and Function of the Epidermal Growth Factor (EGF/ErbB) Family of Receptors. Adv Protein Chem. 2004;68:1–27. doi: 10.1016/S0065-3233(04)68001-6 15500857
13. Roskoski R. ErbB/HER protein-tyrosine kinases: Structures and small molecule inhibitors. Pharmacol Res. 2014;87:42–59. doi: 10.1016/j.phrs.2014.06.001 24928736
14. Hubbard SR, Till JH. Protein Tyrosine Kinase Structure and Function. Annu Rev Biochem. 2000;69(1):373–98.
15. Huse M, Kuriyan J. The Conformational Plasticity of Protein Kinases. Cell. 2002;109(3):275–82. doi: 10.1016/s0092-8674(02)00741-9 12015977
16. Jura N, Zhang X, Endres NF, Seeliger MA, Schindler T, Kuriyan J. Catalytic Control in the EGF Receptor and Its Connection to General Kinase Regulatory Mechanisms. Mol Cell. 2011;42:9–22. doi: 10.1016/j.molcel.2011.03.004 21474065
17. Kumar A, Petri ET, Halmos B, Boggon TJ. Structure and clinical relevance of the epidermal growth factor receptor in human cancer. J Clin Oncol. 2008;26(10):1742–51. doi: 10.1200/JCO.2007.12.1178 18375904
18. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Res. 2000;28(1):235–42. doi: 10.1093/nar/28.1.235 10592235
19. Zhang X, Gureasko J, Shen K, Cole PA, Kuriyan J. An Allosteric Mechanism for Activation of the Kinase Domain of Epidermal Growth Factor Receptor. Cell. 2006;125(6):1137–49. doi: 10.1016/j.cell.2006.05.013 16777603
20. Hasenahuer MA, Barletta GP, Fernandez-Alberti S, Parisi G, Fornasari MS. Pockets as structural descriptors of EGFR kinase conformations. PLoS One. 2017;12(12):e0189147. doi: 10.1371/journal.pone.0189147 29228029
21. Zhang X, Chang A. Somatic mutations of the epidermal growth factor receptor and non-small-cell lung cancer. J Med Genet. 2007;44(3):166–72. doi: 10.1136/jmg.2006.046102 17158592
22. Gajiwala KS, Feng J, Ferre R, Ryan K, Brodsky O, Weinrich S, et al. Insights into the Aberrant Activity of Mutant EGFR Kinase Domain and Drug Recognition. Structure. 2013;21(2):209–19. doi: 10.1016/j.str.2012.11.014 23273428
23. Irmer D, Funk JO, Blaukat A. EGFR kinase domain mutations–functional impact and relevance for lung cancer therapy. Oncogene. 2007;26(39):5693–701. doi: 10.1038/sj.onc.1210383 17353898
24. Oliveira S, van Bergen en Henegouwen PM, Storm G, Schiffelers RM. Molecular biology of epidermal growth factor receptor inhibition for cancer therapy. Expert Opin Biol Ther. 2006;6(6):605–17. doi: 10.1517/14712598.6.6.605 16706607
25. Siegelin MD, Borczuk AC. Epidermal growth factor receptor mutations in lung adenocarcinoma. Lab Investig. 2014;94(2):129–37. doi: 10.1038/labinvest.2013.147 24378644
26. Gazdar AF. Activating and resistance mutations of EGFR in non-small-cell lung cancer: role in clinical response to EGFR tyrosine kinase inhibitors. Oncogene. 2009;28 Suppl 1(Suppl 1):S24–31.
27. Sgambato A, Casaluce F, Maione P, Rossi A, Rossi E, Napolitano A, et al. The Role of EGFR Tyrosine Kinase Inhibitors in the First-Line Treatment of Advanced Non Small Cell Lung Cancer Patients Harboring EGFR Mutation. Curr Med Chem. 2012;19(20):3337–52. doi: 10.2174/092986712801215973 22664249
28. Sullivan I, Planchard D. Next-Generation EGFR Tyrosine Kinase Inhibitors for Treating EGFR-Mutant Lung Cancer beyond First Line. Front Med. 2016;3:76.
29. Yun C-H, Boggon TJ, Li Y, Woo MS, Greulich H, Meyerson M, et al. Structures of lung cancer-derived EGFR mutants and inhibitor complexes: mechanism of activation and insights into differential inhibitor sensitivity. Cancer Cell. 2007;11(3):217–27. doi: 10.1016/j.ccr.2006.12.017 17349580
30. Yun C-H, Mengwasser KE, Toms A V, Woo MS, Greulich H, Wong K-K, et al. The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP. Proc Natl Acad Sci. 2008;105(6):2070–5. doi: 10.1073/pnas.0709662105 18227510
31. Sogabe S, Kawakita Y, Igaki S, Iwata H, Miki H, Cary DR, et al. Structure-Based Approach for the Discovery of Pyrrolo[3,2- d ]pyrimidine-Based EGFR T790M/L858R Mutant Inhibitors. ACS Med Chem Lett. 2013;4(2):201–5. doi: 10.1021/ml300327z 24900643
32. Sordella R, Bell DW, Haber DA, Settleman J. Gefitinib-Sensitizing EGFR Mutations in Lung Cancer Activate Anti-Apoptotic Pathways. Science. 2004;305(5687):1163–7. doi: 10.1126/science.1101637 15284455
33. de Gunst MM, Gallegos-Ruiz MI, Giaccone G, Rodriguez JA. Functional analysis of cancer-associated EGFR mutants using a cellular assay with YFP-tagged EGFR intracellular domain. Mol Cancer. 2007;6:56. doi: 10.1186/1476-4598-6-56 17877814
34. Su J, Zhong W, Zhang X, Huang Y, Yan H, Yang J, et al. Molecular characteristics and clinical outcomes of EGFR exon 19 indel subtypes to EGFR TKIs in NSCLC patients. Oncotarget. 2017;8(67):111246–57. doi: 10.18632/oncotarget.22768 29340050
35. Šali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol. 1993;234(3):779–815. doi: 10.1006/jmbi.1993.1626 8254673
36. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem. 2004;25(13):1605–12. doi: 10.1002/jcc.20084 15264254
37. Schrödinger Release 2018–3: Maestro, version Schrödinger LLC, New York, NY, 2018.
38. Olsson M. H. M.; Søndergard C. R.; Rostkowski M.; Jensen J. H. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa predictions. J. Chem Theor Comput. 2011;7(2): 525–537.
39. Case DA, Be-Shalom IY, Brozell SR, Cerutti DS, Cheatham TE, Cruzeiro VWD, et al. AMBER 2018, University of California, San Francisco.
40. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J Chem Theory Comput. 2015;11(8):3696–713. doi: 10.1021/acs.jctc.5b00255 26574453
41. Meagher KL, Redman LT, Carlson HA. Development of polyphosphate parameters for use with the AMBER force field. J Comput Chem. 2003;24(9):1016–25. doi: 10.1002/jcc.10262 12759902
42. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML. Comparison of simple potential functions for simulating liquid water. J Chem Phys. 1983;79(2):926–35.
43. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG. A smooth particle mesh Ewald method. J Chem Phys. 1995;103(19):8577–93.
44. Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR. Molecular dynamics with coupling to an external bath. J Chem Phys. 1984;81(8):3684–90.
45. Roe DR, Cheatham TE. PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput. 2013;9(7):3084–95. doi: 10.1021/ct400341p 26583988
46. Humphrey W, Dalke A, Schulten K. VMD: Visual molecular dynamics. J Mol Graph. 1996;14(1):33–8. 8744570
47. Bakan A, Meireles LM, Bahar I. ProDy: Protein Dynamics Inferred from Theory and Experiments. Bioinformatics. 2011;27(11):1575–7. doi: 10.1093/bioinformatics/btr168 21471012
48. Miller BR, McGee TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE. MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. J Chem Theory Comput. 2012;8(9):3314–21. doi: 10.1021/ct300418h 26605738
49. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–607. doi: 10.1038/nature11003 22460905
50. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41:955–61.
51. Seashore-Ludlow B, Rees MG, Cheah JH, Cokol M, Price E V., Coletti ME, et al. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer Discov. 2015;5:1210–23. doi: 10.1158/2159-8290.CD-15-0235 26482930
52. RStudio Team (2018). RStudio; Integrated Development for R. RStudio, Inc., Boston, MA.
53. Guha U, Chaerkady R, Marimuthu A, Patterson AS, Kashyap MK, Harsha HC, et al. Comparisons of tyrosine phosphorylated proteins in cells expressing lung cancer-specific alleles of EGFR and KRAS. Proc Natl Acad Sci. 2008;105(37):14112–7. doi: 10.1073/pnas.0806158105 18776048
54. Furuyama K, Harada T, Iwama E, Shiraishi Y, Okamura K, Ijichi K, et al. Sensitivity and kinase activity of epidermal growth factor receptor (EGFR) exon 19 and others to EGFR-tyrosine kinase inhibitors. Cancer Sci. 2013;104(5):584–9. doi: 10.1111/cas.12125 23387505
55. Foster SA, Whalen DM, Özen A, Wongchenko MJ, Yin J, Yen I, et al. Activation Mechanism of Oncogenic Deletion Mutations in BRAF, EGFR, and HER2. Cancer Cell. 2016;29(4):477–93. doi: 10.1016/j.ccell.2016.02.010 26996308
56. Wang Z, Cole PA. Catalytic mechanisms and regulation of protein kinases. Methods Enzymol 2014;548:1–21. doi: 10.1016/B978-0-12-397918-6.00001-X 25399640
57. Melvin RL, Salsbury FR. Visualizing ensembles in structural biology. J Mol Graph Model. 2016;67:44–53. doi: 10.1016/j.jmgm.2016.05.001 27179343
58. Shan Y, Arkhipov A, Kim ET, Pan AC, Shaw DE. Transitions to catalytically inactive conformations in EGFR kinase. Proc Natl Acad Sci. 2013;110(18):7270–5. doi: 10.1073/pnas.1220843110 23576739
59. Ou S-HI, Schrock AB, Bocharov E V., Klempner SJ, Haddad CK, Steinecker G, et al. HER2 Transmembrane Domain (TMD) Mutations (V659/G660) That Stabilize Homo- and Heterodimerization Are Rare Oncogenic Drivers in Lung Adenocarcinoma That Respond to Afatinib. J Thorac Oncol 2017;12(3):446–57. doi: 10.1016/j.jtho.2016.11.2224 27903463
60. Wood ER, Truesdale AT, McDonald OB, Yuan D, Hassell A, Dickerson SH, et al. A unique structure for epidermal growth factor receptor bound to GW572016 (Lapatinib): relationships among protein conformation, inhibitor off-rate, and receptor activity in tumor cells. Cancer Res. 2004;64(18):6652–9. doi: 10.1158/0008-5472.CAN-04-1168 15374980
61. Kannan S, Pradhan MR, Tiwari G, Tan W-C, Chowbay B, Tan EH, et al. Hydration effects on the efficacy of the Epidermal growth factor receptor kinase inhibitor afatinib. Sci Rep. 2017;7(1):1540. doi: 10.1038/s41598-017-01491-z 28484248
Článek vyšel v časopise
PLOS One
2019 Číslo 9
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy