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Electrostatic explanation of D1228V/H/N-induced c-Met resistance and sensitivity to type I and type II kinase inhibitors in targeted gastric cancer therapy

  • Zhen Xu
  • Pingping Hu
  • Dong FangEmail author
  • Lingna Ni
  • Jianzhong Xu
Original Paper
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Abstract

The c-Met D1228V/H/N mutation clinically causes acquired resistance to type I tyrosine kinase inhibitors (TKIs), while maintaining sensitivity to type II TKIs in targeted gastric cancer therapy. The mutation is located in the activation loop (A-loop) region of the c-Met kinase domain, which substitutes the negatively charged residue Asp1228 with electroneutral amino acid Val, His, or Asn, thus electrostatically destabilizing the DFG-in conformation of A-loop and inducing its transition to DFG-out state. The transition is spontaneous in a dynamics point of view and the A-loop exhibits a large intrinsic disorder during the transitional dynamics course. In DFG-in conformation, the wild-type Asp1228 is surrounded by a number of positively charged residues within its first and second shells, which can also form a hydrogen-bonding network with its vicinal residues Phe1089, Lys1110, Asp1222, and Met1229 in the first shell. Type I and type II TKIs respond oppositely to the mutation; the former shows a generic resistance to the mutation, whereas the latter is generally sensitized by the mutation. Both types of TKIs do not directly interact with the mutation. Instead, the mutation-induced conformational change in A-loop reshapes kinase active site and then influences the site interactions with inhibitor ligands, thus conferring different selectivity to the type I and type II TKIs.

Graphical abstract

The molecular mechanism of D1228V/H/N mutation-induced inhibitor resistance and sensitivity in c-Met kinase is investigated. The mutation electrostatically destabilizes the DFG-in conformation of kinase A-loop and induces its spontaneous transition to DFG-out state, which reshapes kinase active site and influences the site interactions with inhibitor ligands

Keywords

c-Met kinase D1228V/H/N mutation Tyrosine kinase inhibitor Acquired resistance Targeted gastric cancer therapy Electrostatic stability Molecular dynamics 

Notes

Acknowledgements

This work was supported by the Social Development Fund of Zhenjiang (No. SH2015069) and the Clinical Medicine Development Project of Jiangsu University (No. JLY20160104).

Compliance with ethical standards

Conflict of interest

The authors report no declarations of interest.

References

  1. 1.
    Robinson DR, Wu YM, Lin SF (2000) The protein tyrosine kinase family of the human genome. Oncogene 19:5548–5557CrossRefGoogle Scholar
  2. 2.
    Hubbard SR, Miller WT (2007) Receptor tyrosine kinases: mechanisms of activation and signaling. Curr Opin Cell Biol 19:117–123CrossRefGoogle Scholar
  3. 3.
    Krause DS, Van Etten RA (2005) Tyrosine kinases as targets for cancer therapy. N Engl J Med 353:172–187CrossRefGoogle Scholar
  4. 4.
    Teng L, Lu J (2013) cMET as a potential therapeutic target in gastric cancer. Int J Mol Med 32:1247–1254CrossRefGoogle Scholar
  5. 5.
    Inokuchi M, Otsuki S, Fujimori Y, Sato Y, Nakagawa M, Kojima K (2015) Clinical significance of MET in gastric cancer. World J Gastrointest Oncol 7:317–327CrossRefGoogle Scholar
  6. 6.
    Wang H, Lu J, Tang J, Chen S, He K, Jiang X, Jiang W, Teng L (2017) Establishment of patient-derived gastric cancer xenografts: a useful tool for preclinical evaluation of targeted therapies involving alterations in HER-2, MET and FGFR2 signaling pathways. BMC Cancer 17:191CrossRefGoogle Scholar
  7. 7.
    Marano L, Chiari R, Fabozzi A, De Vita F, Boccardi V, Roviello G, Petrioli R, Marrelli D, Roviello F, Patriti A (2015) C-met targeting in advanced gastric cancer: an open challenge. Cancer Lett 365:30–36CrossRefGoogle Scholar
  8. 8.
    Tovar EA, Graveel CR (2017) MET in human cancer: germline and somatic mutations. Ann Transl Med 5:205CrossRefGoogle Scholar
  9. 9.
    Maritano D, Accornero P, Bonifaci N, Ponzetto C (2000) Two mutations affecting conserved residues in the met receptor operate via different mechanisms. Oncogene 19:1354–1361CrossRefGoogle Scholar
  10. 10.
    Bahcall M, Sim T, Paweletz CP, Patel JD, Alden RS, Kuang Y, Sacher AG, Kim ND, Lydon CA, Awad MM, Jaklitsch MT, Sholl LM, Jänne PA, Oxnard GR (2016) Acquired METD1228V mutation and resistance to MET inhibition in lung cancer. Cancer Discov 6:1334–1341CrossRefGoogle Scholar
  11. 11.
    Heist RS, Sequist LV, Borger D, Gainor JF, Arellano RS, Le LP, Dias-Santagata D, Clark JW, Engelman JA, Shaw AT, Iafrate AJ (2016) Acquired resistance to crizotinib in NSCLC with MET exon 14 skipping. J Thorac Oncol 11:1242–1245CrossRefGoogle Scholar
  12. 12.
    Engstrom LD, Aranda R, Lee M, Tovar EA, Essenburg CJ, Madaj Z, Chiang H, Briere D, Hallin J, Lopez-Casas PP, Baños N, Menendez C, Hidalgo M, Tassell V, Chao R, Chudova DI, Lanman RB, Olson P, Bazhenova L, Patel SP, Graveel C, Nishino M, Shapiro GI, Peled N, Awad MM, Jänne PA, Christensen JG (2017) Glesatinib exhibits antitumor activity in lung cancer models and patients harboring MET exon 14 mutations and overcomes mutation-mediated resistance to type I MET inhibitors in nonclinical models. Clin Cancer Res 23:6661–6672CrossRefGoogle Scholar
  13. 13.
    Organ SL, Tsao MS (2011) An overview of the c-MET signaling pathway. Ther Adv Med Oncol 3:S7–S19CrossRefGoogle Scholar
  14. 14.
    Ou SI, Young L, Schrock AB, Johnson A, Klempner SJ, Zhu VW, Miller VA, Ali SM (2017) Emergence of preexisting MET Y1230C mutation as a resistance mechanism to crizotinib in NSCLC with MET exon 14 skipping. J Thorac Oncol 12:137–140CrossRefGoogle Scholar
  15. 15.
    Tiedt R, Degenkolbe E, Furet P, Appleton BA, Wagner S, Schoepfer J, Buck E, Ruddy DA, Monahan JE, Jones MD, Blank J, Haasen D, Drueckes P, Wartmann M, McCarthy C, Sellers WR, Hofmann F (2011) A drug resistance screen using a selective MET inhibitor reveals a spectrum of mutations that partially overlap with activating mutations found in cancer patients. Cancer Res 71:5255–5264CrossRefGoogle Scholar
  16. 16.
    Jiang H, Shao W, Wang Y, Xu R, Zhou L, Mu X (2018) Molecular mechanism of D816X mutation-induced c-Kit activation and -mediated inhibitor resistance in gastrointestinal stromal tumor. J Mol Graph Model 84:189–196CrossRefGoogle Scholar
  17. 17.
    Liu T, Wang Z, Guo P, Ding N (2018) Electrostatic mechanism of V600E mutation-induced B-Raf constitutive activation in colorectal cancer: molecular implications for the selectivity difference between inhibitors. Eur Biophys J 47:1–10CrossRefGoogle Scholar
  18. 18.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242CrossRefGoogle Scholar
  19. 19.
    López-Blanco JR, Canosa-Valls AJ, Li Y, Chacón P (2016) RCD+: fast loop modeling server. Nucleic Acids Res 44:W395–W400CrossRefGoogle Scholar
  20. 20.
    Tian F, Lv Y, Zhou P, Yang L (2011) Characterization of PDZ domain-peptide interactions using an integrated protocol of QM/MM, PB/SA, and CFEA analyses. J Comput Aided Mol Des 25:947–958CrossRefGoogle Scholar
  21. 21.
    Tian F, Tan R, Guo T, Zhou P, Yang L (2013) Fast and reliable prediction of domain-peptide binding affinity using coarse-grained structure models. Biosystems 113:40–49CrossRefGoogle Scholar
  22. 22.
    Tian F, Yang C, Wang C, Guo T, Zhou P (2014) Mutatomics analysis of the systematic thermostability profile of Bacillus subtilis lipase a. J Mol Model 20:2257CrossRefGoogle Scholar
  23. 23.
    Zhou P, Hou S, Bai Z, Li Z, Wang H, Chen Z, Meng Y (2018) Disrupting the intramolecular interaction between proto-oncogene c-Src SH3 domain and its self-binding peptide PPII with rationally designed peptide ligands. Artif Cells Nanomed Biotechnol 46:1122–1131CrossRefGoogle Scholar
  24. 24.
    Krivov GG, Shapovalov MV, Dunbrack RL (2009) Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77:778–795CrossRefGoogle Scholar
  25. 25.
    Luo H, Du T, Zhou P, Yang L, Mei H, Ng H, Zhang W, Shu M, Tong W, Shi L, Mendrick DL, Hong H (2015) Molecular docking to identify associations between drugs and class I human leukocyte antigens for predicting idiosyncratic drug reactions. Comb Chem High Throughput Screen 18:296–304CrossRefGoogle Scholar
  26. 26.
    Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17:490–519CrossRefGoogle Scholar
  27. 27.
    Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791CrossRefGoogle Scholar
  28. 28.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461PubMedPubMedCentralGoogle Scholar
  29. 29.
    Zhou P, Zhang S, Wang Y, Yang C, Huang J (2016) Structural modeling of HLA-B1502 peptide carbamazepine T-cell receptor complex architecture: implication for the molecular mechanism of carbamazepine-induced Stevens–Johnson syndrome toxic epidermal necrolysis. J Biomol Struct Dyn 34:1806–1817CrossRefGoogle Scholar
  30. 30.
    Hendsch ZS, Tidor B (1994) Do salt bridges stabilize proteins? A continuum electrostatic analysis. Protein Sci 3:211–226CrossRefGoogle Scholar
  31. 31.
    Kumar S, Nussinov R (1999) Salt bridge stability in monomeric proteins. J Mol Biol 293:1241–1255CrossRefGoogle Scholar
  32. 32.
    Rocchia W, Alexov E, Honig B (2001) Extending the applicability of the nonlinear. Poisson–Boltzmann equation: multiple dielectric constants and multivalent ions. J Phys Chem B 105:6507–6514CrossRefGoogle Scholar
  33. 33.
    Sitkoff D, Sharp KA, Honig B (1994) Accurate calculation of hydration free energies using macroscopic solvent models. J Phys Chem 98:1978–1988CrossRefGoogle Scholar
  34. 34.
    Gordon JC, Myers JB, Folta T, Shoja V, Heath LS, Onufriev A (2005) H++: a server for estimating pKas and adding missing hydrogens to macromolecules. Nucleic Acids Res 33:W368–W371CrossRefGoogle Scholar
  35. 35.
    Duan Y, Wu C, Chowdhury SS, Lee MC, Xiong GM, Zhang W, Yang R, Cieplak P, Luo R, Lee TS, Caldwell J, Wang JM, Kollman P (2003) A point-charge force field for molecular mechanics simulations of proteins. J Comput Chem 24:1999–2012CrossRefGoogle Scholar
  36. 36.
    Case DA, Cheatham TE, Darden T, Gohlke H, Luo R, Merz KM, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688CrossRefGoogle Scholar
  37. 37.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey R, Klein M (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935CrossRefGoogle Scholar
  38. 38.
    Yang C, Wang C, Zhang S, Huang J, Zhou P (2015) Structural and energetic insights into the intermolecular interaction among human leukocyte antigens, clinical hypersensitive drugs and antigenic peptides. Mol Simul 41:741–751CrossRefGoogle Scholar
  39. 39.
    Yang C, Zhang S, He P, Wang C, Huang J, Zhou P (2015) Self-binding peptides: folding or binding. J Chem Inf Model 55:329–342CrossRefGoogle Scholar
  40. 40.
    Yang C, Zhang S, Bai Z, Hou S, Wu D, Huang J, Zhou P (2016) A two-step binding mechanism for the self-binding peptide recognition of target domains. Mol BioSyst 12:1201–1213CrossRefGoogle Scholar
  41. 41.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald and n.log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092CrossRefGoogle Scholar
  42. 42.
    Ryckaert JP, 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 23:327–341CrossRefGoogle Scholar
  43. 43.
    Homeyer N, Gohlke H (2012) Free energy calculations by the molecular mechanics Poisson–Boltzmann surface area method. Mol Inf 31:114–122CrossRefGoogle Scholar
  44. 44.
    Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general Amber force field. J Comput Chem 25:1157–1174CrossRefGoogle Scholar
  45. 45.
    Yu H, Zhou P, Deng M, Shang Z (2014) Indirect readout in protein-peptide recognition: a different story from classical biomolecular recognition. J Chem Inf Model 54:2022–2032CrossRefGoogle Scholar
  46. 46.
    Bai Z, Hou S, Zhang S, Li Z, Zhou P (2017) Targeting self-binding peptides as a novel strategy to regulate protein activity and function: a case study on the proto-oncogene tyrosine protein kinase c-Src. J Chem Inf Model 57:835–845CrossRefGoogle Scholar
  47. 47.
    Roskoski R (2016) Classification of small molecule protein kinase inhibitors based upon the structures of their drug–enzyme complexes. Pharmacol Res 103:26–48CrossRefGoogle Scholar
  48. 48.
    Ren Y, Chen X, Feng M, Wang Q, Zhou P (2011) Gaussian process: a promising approach for the modeling and prediction of peptide binding affinity to MHC proteins. Protein Pept Lett 18:670–678CrossRefGoogle Scholar
  49. 49.
    Zhou P, Yang C, Ren Y, Wang C, Tian F (2013) What are the ideal properties for functional food peptides with antihypertensive effect? A computational peptidology approach. Food Chem 141:2967–2973CrossRefGoogle Scholar
  50. 50.
    Zhou P, Wang C, Tian F, Ren Y, Yang C, Huang J (2013) Biomacromolecular quantitative structure–activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein–protein binding affinity. J Comput Aided Mol Des 27:67–78CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhen Xu
    • 1
  • Pingping Hu
    • 2
  • Dong Fang
    • 3
    Email author
  • Lingna Ni
    • 1
  • Jianzhong Xu
    • 1
  1. 1.Department of OncologyChangzhou Tumor Hospital Affiliated to Soochow UniversityChangzhouChina
  2. 2.Department of PathologyZhenjiang Hospital of Chinese Traditional and Western MedicineZhenjiangChina
  3. 3.Department of OncologyZhenjiang Hospital of Chinese Traditional and Western MedicineZhenjiangChina

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