Skip to main content

Advertisement

SpringerLink
Log in

Elucidating the potential effects of point mutations on FGFR3 inhibitor resistance via combined molecular dynamics simulation and community network analysis

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

FGFR3 kinase mutations are associated with a variety of malignancies, but FGFR3 mutant inhibitors have rarely been studied. Furthermore, the mechanism of pan-FGFR inhibitors resistance caused by kinase domain mutations is still unclear. In this study, we try to explain the mechanism of drug resistance to FGFR3 mutation through global analysis and local analysis based on molecular dynamics simulation, binding free energy analysis, umbrella sampling and community network analysis. The results showed that FGFR3 mutations caused a decrease in the affinity between drugs and FGFR3 kinase, which was consistent with the reported experimental results. Possible mechanisms are that mutations affect drug-protein affinity by altering the environment of residues near the hinge region where the protein binds to the drug, or by affecting the A-loop and interfering with the allosteric communication networks. In conclusion, we systematically elucidated the underlying mechanism of pan-FGFR inhibitor resistance caused by FGFR3 mutation based on molecular dynamics simulation strategy, which provided theoretical guidance for the development of FGFR3 mutant kinase inhibitors.

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

Similar content being viewed by others

References

  1. Chen L, Yin ZYL et al (2021) Fibroblast growth factor receptor fusions in cancer: opportunities and challenges. J Exp Clin Cancer Res 40:345. https://doi.org/10.1186/s13046-021-02156-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Krook MA, Reeser JW, Ernst G et al (2021) Fibroblast growth factor receptors in cancer: genetic alterations, diagnostics, therapeutic targets and mechanisms of resistance. Br J Cancer 124:880–892. https://doi.org/10.1038/s41416-020-01157-0

    Article  CAS  PubMed  Google Scholar 

  3. Savarirayan R, De Bergua JM, Arundel P et al (2022) Infigratinib in children with achondroplasia: the PROPEL and PROPEL 2 studies. Ther Adv Musculoskelet Dis 14:1759720X221084848. https://doi.org/10.1177/1759720X221084848

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Babina IS, Turner NC (2017) Advances and challenges in targeting FGFR signalling in cancer. Nat Rev Cancer 17:318–332. https://doi.org/10.1038/nrc.2017.8

    Article  CAS  PubMed  Google Scholar 

  5. Markham A (2019) Erdafitinib: First Global approval. Drugs 79:1017–1021. https://doi.org/10.1007/s40265-019-01142-9

  6. Hoy SM (2020) Pemigatinib: First Approval Drugs 80:923–929

    PubMed  Google Scholar 

  7. Yu J, Mahipal A, Kim R (2021) Targeted therapy for Advanced or Metastatic Cholangiocarcinoma: Focus on the clinical potential of Infigratinib. OncoTargets Ther 14:5145–5160. https://doi.org/10.2147/OTT.S272208

    Article  CAS  Google Scholar 

  8. Subbiah V, Iannotti NO, Gutierrez M et al (2022) FIGHT-101, a first-in-human study of potent and selective FGFR 1–3 inhibitor pemigatinib in pan-cancer patients with FGF/FGFR alterations and advanced malignancies. Ann Oncol 33:522–533. https://doi.org/10.1016/j.annonc.2022.02.001

    Article  CAS  PubMed  Google Scholar 

  9. Mahipal A, Tella SH, Kommalapati A, Yu J, Kim R (2020) Prevention and treatment of FGFR inhibitor-associated toxicities. Crit Rev Oncol Hematol 155:103091. https://doi.org/10.1016/j.critrevonc.2020.103091

    Article  PubMed  Google Scholar 

  10. Mellor HR (2014) Targeted inhibition of the FGF19-FGFR4 pathway in hepatocellular carcinoma; translational safety considerations. Liver Int 34:e1–9. https://doi.org/10.1111/liv.12462

    Article  CAS  PubMed  Google Scholar 

  11. Gattineni J, Bates C, Twombley K et al (2009) FGF23 decreases renal NaPi-2a and NaPi-2c expression and induces hypophosphatemia in vivo predominantly via FGF receptor 1. Am J Physiol Renal Physiol 297:F282–291. https://doi.org/10.1152/ajprenal.90742.2008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Haigis KM, Cichowski K, Elledge SJ (2019) Tissue-specificity in cancer: the rule, not the exception. Science 363:1150–1151. https://doi.org/10.1126/science.aaw3472

    Article  CAS  PubMed  Google Scholar 

  13. ICGC/TCGA (2020) Pan-cancer analysis of whole genomes. Nature 578:82–93. https://doi.org/10.1038/s41586-020-1969-6

    Article  CAS  Google Scholar 

  14. Pao W, Miller V, Zakowski M et al (2004) EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci U S A 101:13306–13311. https://doi.org/10.1073/pnas.0405220101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Sordella R, Bell DW, Haber DA, Settleman J (2004) Gefitinib-sensitizing EGFR mutations in lung cancer activate anti-apoptotic pathways. Science 305:1163–1167. https://doi.org/10.1126/science.1101637

    Article  CAS  PubMed  Google Scholar 

  16. Wang F, Dong X, Yang F, Xing N (2022) Comparative analysis of differentially mutated genes in non-muscle and muscle-invasive bladder Cancer in the Chinese Population by whole exome sequencing. Front Genet 13:831146. https://doi.org/10.3389/fgene.2022.831146

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Roubal K, Myint ZW, Kolesar JM (2020) Erdafitinib: a novel therapy for FGFR-mutated urothelial cancer. Am J Health Syst Pharm 77:346–351. https://doi.org/10.1093/ajhp/zxz329

    Article  PubMed  Google Scholar 

  18. Chandrani P, Prabhash K, Prasad R et al (2017) Drug-sensitive FGFR3 mutations in lung adenocarcinoma. Ann Oncol 28:597–603. https://doi.org/10.1093/annonc/mdw636

    Article  CAS  PubMed  Google Scholar 

  19. Ota N, Yoshimoto Y, Darwis NDM et al (2022) High tumor mutational burden predicts worse prognosis for cervical cancer treated with radiotherapy. Jpn J Radiol 40:534–541. https://doi.org/10.1007/s11604-021-01230-5

    Article  CAS  PubMed  Google Scholar 

  20. Rosty C, Aubriot MH, Cappellen D et al (2005) Clinical and biological characteristics of cervical neoplasias with FGFR3 mutation. Mol Cancer 4:15. https://doi.org/10.1186/1476-4598-4-15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Cohen P, Cross D, Jänne PA (2021) Kinase drug discovery 20 years after imatinib: progress and future directions. Nat Rev Drug Discov 20:551–569. https://doi.org/10.1038/s41573-021-00195-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Rosenzweig SA (2018) Acquired resistance to drugs targeting tyrosine kinases. Adv Cancer Res 138:71–98. https://doi.org/10.1016/bs.acr.2018.02.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Goyal L, Saha SK, Liu LY et al (2017) Polyclonal secondary FGFR2 mutations drive acquired resistance to FGFR inhibition in patients with FGFR2 Fusion-Positive Cholangiocarcinoma. Cancer Discov 7:252–263. https://doi.org/10.1158/2159-8290.CD-16-1000

    Article  CAS  PubMed  Google Scholar 

  24. Chell V, Balmanno K, Little AS et al (2013) Tumour cell responses to new fibroblast growth factor receptor tyrosine kinase inhibitors and identification of a gatekeeper mutation in FGFR3 as a mechanism of acquired resistance. Oncogene 32:3059–3070. https://doi.org/10.1038/onc.2012.319

    Article  CAS  PubMed  Google Scholar 

  25. Patani H, Bunney TD, Thiyagarajan N et al (2016) Landscape of activating cancer mutations in FGFR kinases and their differential responses to inhibitors in clinical use. Oncotarget 7:24252–24268. https://doi.org/10.18632/oncotarget.8132

    Article  PubMed  PubMed Central  Google Scholar 

  26. Chen L, Marsiglia WM, Chen H, Katigbak J, Mohammadi M (2020) Molecular basis for receptor tyrosine kinase a-loop tyrosine transphosphorylation. Nat Chem Biol 16:1–11. https://doi.org/10.1038/s41589-019-0455-7

    Article  CAS  Google Scholar 

  27. A.Waterhouse M, Bertoni S, Bienert et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46:W296–W303. https://doi.org/10.1093/nar/gky427

    Article  CAS  Google Scholar 

  28. Schrodinger LLC (2015) The PyMOL Molecular Graphics System, Version 2.5.

  29. Abraham MJ, Murtola T, Schulz R et al (2015) Gromacs: high performance molecular simulations through multi-level parallelism from laptops to supercomputers - sciencedirect. SoftwareX, s1-2 19–25, https://doi.org/10.1016/j.softx.2015.06.001

  30. Huang J, Mackerell AD (2013) Charmm36 all-atom additive protein force field: validation based on comparison to nmr data. J Comput Chem 34:2135–2145. https://doi.org/10.1002/jcc.23354

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Tucker JA, Klein T, Breed J, Breeze AL, Norman RA (2014) Structural insights into FGFR kinase isoform selectivity: diverse binding modes of AZD4547 and ponatinib in complex with FGFR1 and FGFR4. Structure 22:1764–1774. https://doi.org/10.1016/j.str.2014.09.019

    Article  CAS  PubMed  Google Scholar 

  32. Bunney TD, Wan S, Thiyagarajan N et al (2015) The effect of mutations on drug sensitivity and kinase activity of fibroblast growth factor receptors: a combined experimental and theoretical study. EBioMedicine 2:194–204. https://doi.org/10.1016/j.ebiom.2015.02.009

    Article  PubMed  PubMed Central  Google Scholar 

  33. Vanommeslaeghe K, Hatcher E, Acharya C, CHARMM General Force Field et al (2010) A Force field for Drug-Like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Field. J Comput Chem 31:671–690. https://doi.org/10.1002/jcc.21367

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yu W, He X, Vanommeslaeghe K, MacKerell AD Jr (2012) Extension of the CHARMM General Force Field to Sulfonyl-Containing Compounds and its utility in Biomolecular Simulations. J Comput Chem 33:2451–2468. https://doi.org/10.1002/jcc.23067

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Vanommeslaeghe K, MacKerell AD Jr (2012) Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing. J Chem Inf Model 52:3144–3154. https://doi.org/10.1021/ci300363c

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Vanommeslaeghe K, Raman EP, MacKerell AD Jr (2012) Automation of the CHARMM General Force Field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model 52:3155–3168. https://doi.org/10.1021/ci3003649

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Miller BR 3rd, McGee TD Jr, Swails JM, Homeyer N, Gohlke H, Roitberg AE (2012) MMPBSA.py: an efficient program for end-state Free Energy Calculations. J Chem Theory Comput 8:3314–3321. https://doi.org/10.1021/ct300418h

    Article  CAS  PubMed  Google Scholar 

  38. Li J (2022) gmxtools Zenodo. https://doi.org/10.5281/zenodo.6408973

    Article  Google Scholar 

  39. Lang EJM, Heyes LC, Jameson GB, Parker EJ Calculated pKa variations expose dynamic allosteric communication networks. J Am Chem Soc 138 (2016) 2036–2045, https://doi.org/10.1021/jacs.5b13134

  40. Guo XY, Qi RP, Xu DG, Liu XH, Yang X (2015) Structural and energetic insight into the interactions between the benzolactam inhibitors and tumor marker HSP90α. Comput Biol Chem 58:182–191. https://doi.org/10.1016/j.compbiolchem.2015.07.013

    Article  CAS  PubMed  Google Scholar 

  41. Kästner J (2011) Umbrella sampling. Wiley Interdisciplinary Reviews: Computational Molecular Science 1:932–942

    Google Scholar 

  42. Souaille M, Roux Bt (2001) Extension to the weighted histogram analysis method: combining umbrella sampling with free energy calculations. Comput Phys Commun 135:40–57. https://doi.org/10.1016/s0010-4655(00)00215-0

    Article  CAS  Google Scholar 

  43. Kumar S, Rosenberg JM, Bouzida D, Swendsen RH, Kollman PA (1992) The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J Comput Chem 13:1011–1021. https://doi.org/10.1002/jcc.540130812

    Article  CAS  Google Scholar 

  44. Chodera JD, Swope WC, Pitera JW, Seok C, Dill KA (2007) Use of the weighted histogram analysis method for the analysis of simulated and parallel tempering simulations. J Chem Theory Comput 3:26–41. https://doi.org/10.1021/ct0502864

    Article  CAS  PubMed  Google Scholar 

  45. Goetz R, Mohammadi M (2013) Exploring mechanisms of FGF signaling through the lens of structural biology. Nat Rev Mol Cell Biol 14:166–180. https://doi.org/10.1038/nrm3528

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chen H, Marsiglia WM, Cho MK et al (2017) Elucidation of a four-site allosteric network in fibroblast growth factor receptor tyrosine kinases. eLife 6:21137. https://doi.org/10.7554/eLife.21137

    Article  Google Scholar 

  47. Lefebvre C, Rubez G, Khartabil H, Boisson JC, Contreras-García J, Hénon E (2017) Accurately extracting the signature of intermolecular interactions present in the NCI plot of the reduced density gradient versus electron density. Phys Chem Chem Phys 19:17928–17936. https://doi.org/10.1039/c7cp02110k

    Article  CAS  PubMed  Google Scholar 

  48. Lu T, Chen Q (2022) Independent gradient model based on Hirshfeld partition: a new method for visual study of interactions in chemical systems. J Comput Chem 43:539–555. https://doi.org/10.1002/jcc.26812

    Article  CAS  PubMed  Google Scholar 

  49. Lu T, Chen F (2012) Multiwfn: a multifunctional wavefunction analyzer. J Comput Chem 33:580–592. https://doi.org/10.1002/jcc.22885

    Article  CAS  PubMed  Google Scholar 

  50. Zhang J, Adrián FJ, Jahnke W et al (2010) Targeting bcr-abl by combining allosteric with ATP-binding-site inhibitors. Nature 463:501–506. https://doi.org/10.1038/nature08675

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wylie AA, Schoepfer J, Jahnke W et al (2017) The allosteric inhibitor ABL001 enables dual targeting of BCR-ABL1. Nature 543:733–737. https://doi.org/10.1038/nature21702

    Article  CAS  PubMed  Google Scholar 

  52. Qureshi R, Ghosh A, Yan H (2020) Correlated motions and dynamics in different domains of EGFR with L858R and T790M mutations. IEEE/ACM Trans Comput Biol Bioinform 19:383–394. https://doi.org/10.1109/TCBB.2020.2995569

    Article  Google Scholar 

  53. Girvan M, Newman ME (2002) Community structure in social and biological networks, Proc. Natl. Acad. Sci. U.S.A. 99 7821–7826, https://doi.org/10.1073/pnas.122653799

  54. Gavine PR, Mooney L, Kilgour E et al (2012) AZD4547: an orally bioavailable, potent, and selective inhibitor of the fibroblast growth factor receptor tyrosine kinase family. Cancer Res 72:20452056. https://doi.org/10.1158/0008-5472.CAN-11-3034

    Article  CAS  Google Scholar 

  55. Trudel S, Li ZH, Wei E et al (2005) CHIR–258, a novel, multitargeted tyrosine kinase inhibitor for the potential treatment of t(4;14) multiple myeloma. Blood 105:2941–2948. https://doi.org/10.1182/blood-2004-10-3913

    Article  CAS  PubMed  Google Scholar 

  56. Angibaud PR et al (2014) Discovery of JNJ–42756493, a potent fibroblast growth factor receptor (FGFR) inhibitor using a fragment-based approach. Cancer Res 74 Suppl(abstr 4748). https://doi.org/10.1158/1538-7445.AM2014-4748

  57. Adasme MF, Linnemann KL, Bolz SN et al (2021) PLIP 2021: expanding the scope of the protein-ligand interaction profiler to DNA and RNA. Nucleic Acids Res 49:W530–W534. https://doi.org/10.1093/nar/gkab294

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Liu X, Shi D, Zhou S, Liu H, Liu H, Yao X (2012) Molecular dynamics simulations and novel drug discovery. Expert Opin Drug Discov 13(1):23–37. https://doi.org/10.1080/17460441.2018.1403419

    Article  CAS  Google Scholar 

  59. Campos SR, Machuqueiro M, Baptista AM (2010) Constant-pH molecular dynamics simulations reveal a β-rich form of the human prion protein. J Phys Chem B 114(39):12692–12700. https://doi.org/10.1021/jp104753t

    Article  CAS  PubMed  Google Scholar 

  60. Chen W, van der Kamp MW, Daggett V (2014) Structural and dynamic properties of the human prion protein. Biophys J 106(5):1152–1163. https://doi.org/10.1016/j.bpj.2013.12.053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Santini S, Derreumaux P (2004) Helix H1 of the prion protein is rather stable against environmental perturbations: molecular dynamics of mutation and deletion variants of PrP (90–231). Cell Mol Life Sci 61:951–960. https://doi.org/10.1007/s00018-003-3455-3

    Article  CAS  PubMed  Google Scholar 

  62. Liu H, Yao X (2010) Molecular basis of the interaction for an essential subunit PA – PB1 in influenza virus RNA polymerase: insights from molecular dynamics simulation and free energy calculation. Mol Pharm 7:75–85. https://doi.org/10.1021/mp900131p

    Article  CAS  PubMed  Google Scholar 

  63. Sharon A, Balaraju T, Bal C (2011) A catalytic 3D model development of HIV-integrase and drug resistance understanding by molecular dynamics simulation. Antiviral Res 90:A43–A44. https://doi.org/10.1021/mp900131p

    Article  CAS  Google Scholar 

  64. Pan YL, Liu YL, Chen JZ (2018) Computational Simulation Studies on the binding selectivity of 1-(1H-Benzimidazol-5-yl)-5-aminopyrazoles in complexes with FGFR1 and FGFR4. Molecules 23(4):767. https://doi.org/10.3390/molecules23040767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Fu W, Chen L, Wang Z et al (2017) Theoretical studies on FGFR isoform selectivity of FGFR1/FGFR4 inhibitors by molecular dynamics simulations and free energy calculations. Phys Chem Chem Phys 19(5):3649–3659. https://doi.org/10.1039/c6cp07964d

    Article  CAS  PubMed  Google Scholar 

  66. Dehghanian F, Alavi S (2010) Molecular mechanisms of the anti-cancer drug, LY2874455, in overcoming the FGFR4 mutation-based resistance. Sci Rep 11(1):16593. https://doi.org/10.1038/s41598-021-96159-0

    Article  CAS  Google Scholar 

  67. Wu C, Chen X, Chen D et al (2019) Insight into ponatinib resistance mechanisms in rhabdomyosarcoma caused by the mutations in FGFR4 tyrosine kinase using molecular modeling strategies. Int J Biol Macromol 135:294–302. https://doi.org/10.1016/j.ijbiomac.2019.05.138

    Article  CAS  PubMed  Google Scholar 

  68. Changeux JP (2013) The concept of allosteric modulation: an overview. Drug Discov Today Technol 10:e223–e228. https://doi.org/10.1016/j.ddtec.2012.07.007

    Article  PubMed  Google Scholar 

  69. Ni D, Liu Y, Kong R, Yu Z, Lu S, Zhang J (2022) Computational elucidation of allosteric communication in proteins for allosteric drug design. Drug Discov Today 27(8):2226–2234. https://doi.org/10.1016/j.drudis.2022.03.012

    Article  CAS  PubMed  Google Scholar 

  70. Huang Z, Zhao J, Deng W et al (2018) Identification of a cellularly active SIRT6 allosteric activator. Nat Chem Biol 14:1118–1126. https://doi.org/10.1038/s41589-018-0150-0

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of Zhejiang Province, China (Grant No. LGF20B020001); National Natural Science Foundation of China (Grant No. 81402839, 81803580); MOE Key Laboratory of Tumor Molecular Biology (Grant No. 50411651-2020-4) and the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515011184).

Author information

Authors and Affiliations

  1. The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China

    Bo Liu, Juntao Ding, Yugang Liu & Wulan Li

  2. Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325000, China

    Bo Liu, Juntao Ding, Jianzhang Wu & Wulan Li

  3. School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China

    Bo Liu, Juntao Ding, Yugang Liu, Jianzhang Wu & Wulan Li

  4. The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China

    Jianzhang Wu

  5. Institute of Tissue Transplantation and Immunology, College of Life Science and Technology, Jinan University, Guangzhou, 510632, China

    Xiaoping Wu

  6. MOE Key Laboratory of Tumor Molecular Biology, Jinan University, Guangzhou, 510632, China

    Xiaoping Wu

  7. Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314102, China

    Qian Chen

Authors
  1. Bo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juntao Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yugang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianzhang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoping Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wulan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.L. is responsible for research performing, data analyzing and paper writing. J.D. and X.W. are responsible data analyzing. Y.L. is responsible for script writing. J.W., Q.C. and W.L. are responsible for research designing and revised paper writing.

Corresponding authors

Correspondence to Qian Chen or Wulan Li.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

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

Liu, B., Ding, J., Liu, Y. et al. Elucidating the potential effects of point mutations on FGFR3 inhibitor resistance via combined molecular dynamics simulation and community network analysis. J Comput Aided Mol Des 37, 325–338 (2023). https://doi.org/10.1007/s10822-023-00510-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-023-00510-8

Keywords

Search

Navigation