Systematic profiling of ATP response to acquired drug-resistant EGFR family kinase mutations

Dingwa Zhang, Deyong He, Xiaoliang Pan, Lijun Liu

Abstract


Kinase-targeted cancer therapy (KTCT) with ATP-competitive inhi­bitors has been widely applied in the clinics. However, a number of kinase missense mutations are observed to confer acquired drug resistance during the therapy, largely limiting the clinical application of kinase inhibitors in KTCT. Instead of directly influencing inhibitor binding, kinase mutations can also cause generic resistance to ATP-competitive inhibitors by increasing ATP affinity. Here, we systematically characterize the intermolecular interaction of ATP molecule with clinically observed drug-resistant EGFR family kinase mutations involved in human cancer. Rigorous quantum mechanics/molecular mechanics (QM/MM) calculation and empirical Poisson-Boltzmann/surface area (PB/SA) analysis as well as in vitro kinase assay and surface plasmon resonance analysis are integrated to explore the binding capability of ATP to mutant residues in the structural context of kinase domain, which result in a comprehensive profile of ATP response to the acquired drug-resistant mutations of four EGFR family kinases (EGFR/ErbB1, ErbB2, ErbB3 and ErbB4). From the profile we are able to identify those potent mutations that may influence ATP binding significantly; these mutations are potential candidates to cause generic resistance for ATP-competitive inhibitors. Consequently, the well documented generic drug-resistant mutation EGFR T790M and its counterpart ErbB2 T798M are found to increase ATP affinity by establishing an additional S-π interaction between the side-chain thioether group of mutant Met residue and the aromatic adenine moiety of ATP molecule, while the EGFR D761Y is identified as a new generic drug-resistant mutation that can increase ATP affinity by eliminating unfavorable electrostatic repulsion. In contrast, the ErbB2 K753E and T768I are considered as two generic drug-sensitive mutations that can decrease ATP affinity by unfavorable charge reversal and by impairing favorable polar interaction, respectively. In addition, the EGFR L858R mutation is located at kinase activation loop and nearby kinase active site, thus largely complicating the multiply dependent relationship of kinase, ATP and inhibitor, which therefore exhibits divergent effects on different tested inhibitors.


Keywords


Molecular modeling; inhibitor; biomolecular interaction; missense mutation; human cancer

Full Text:

PDF (1,291 kB)

References


R. Roskoski, Pharmacol. Res. 79 (2014) 34-74 (https://doi.org/10.1016/j.phrs.2013.11.002)

C. L. Arteaga, Breast Cancer Res. 13 (2011) 304 (https://doi.org/10.1186/bcr2829)

H. Zhang, A. Berezov, Q. Wang, G. Zhang, J. Drebin, R. Murali, M. I. Greene, J. Clin. Invest. 117 (2007) 2051-2058 (https://doi.org/10.1172/JCI32278)

K. Ohashi, Y. E. Maruvka, F. Michor, W. Pao, J. Clin. Oncol. 31 (2013) 1070-80 2013 (https://doi.org/10.1200/JCO.2012.43.3912)

C. Ma, S. Wei, Y. Song, J. Thorac. Dis. 3 (2011) 10-18 (https://doi.org/10.3978/j.issn.2072-1439.2010.12.02)

D. J. Riese, R. M. Gallo, J. Settleman, Bioessays 29 (2007) 558-565 (https://doi.org/10.1002/bies.20582)

D. L. Gibbons, S. Pricl, J. Cortes, A. Quintás-Cardama, Cancer 118 (2012) 293-299 (https://doi.org/10.1002/cncr.26225)

C. H. Yun, K. E. Mengwasser, A. V. Toms, M. S. Woo, H. Greulich, K. K. Wong, M. Meyerson, M. J. Eck, Proc. Natl. Acad. Sci. USA 105 (2008) 2070-2075 (https://doi.org/10.1073/pnas.0709662105)

K. Hauser, C. Negron, S. K. Albanese, S. Ray, T. Steinbrecher, R. Abel, J. D. Chodera, L. Wang, Commun. Biol. 1 (2018) 70 (https://doi.org/10.1038/s42003-018-0075-x)

H. Wang, Z. Yang, Y. Liu, J. Chin. Chem. Soc. 67 (2020) 1-10 (https://doi.org/10.1002/jccs.201900435)

P. Zhou, C. Yang, Y. Ren, C. Wang, F. Tian, Food Chem. 141 (2013) 2967-2973 (https://doi.org/ 10.1016/j.foodchem.2013.05.140)

F. Tian, Y. Lv, P. Zhou, L. Yang, J. Comput. Aided Mol. Des. 25 (2011) 947 (https://doi.org/10.1007/s10822-011-9474-5)

P. Zhou, S. Zhang, Y. Wang, C. Yang, J. Huang, J. Biomol. Struct. Dyn. 34 (2016) 1806-1817.

X. Guo, D. He, L. Liu, R. Kuang, L. Liu, Comput. Theor. Chem. 991 (2012) 134 (https://doi.org/10.1016/j.comptc.2012.04.010)

X. Guo, D. He, L. Huang, L. Liu, L. Liu, H. Yang, Comput. Theor. Chem. 995 (2012) 17 (https://doi.org/10.1016/j.comptc.2012.06.017)

D. Zhang, D. He, X. Pan, Y. Xu, L. Liu, Chem. Pap. 73 (2019) 2469 (https://doi.org/10.1007/s11696-019-00797-8)

D. Zhang, D. He, X. Pan, Y. Xu, L. Liu, J. Serb. Chem. Soc. 84 (2019) (https://doi.org/10.2298/JSC181221029Z)

M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler, Nat. Genetics 25 (2000) 25–29 (https://doi.org/10.1038/75556)

C. Yang, S. Zhang, P. He, C. Wang, J. Huang, P. Zhou, J. Chem. Inf. Model. 55 (2015) 329-342 (https://doi.org/10.1021/ci500522v)

C. Yang, S. Zhang, Z. Bai, S. Hou, D. Wu, J. Huang, P. Zhou, Mol. Biosyst. 12 (2016) 1201-1213 (https://doi.org/10.1039/c5mb00800j)

Z. Bai, S. Hou, S. Zhang, Z. Li, P. Zhou, J. Chem. Inf. Model. 57 (2017) 835-845 (https://doi.org/10.1021/acs.jcim.6b00673)

H. Yu, P. Zhou, M. Deng, Z. Shang, J. Chem. Inf. Model. 54 (2014) 2022-2032 (https://doi.org/10.1021/ci5000246)

C. Yang, C. Wang, S. Zhang, J. Huang, P. Zhou P, Mol. Simul. 41 (2015) 741-751 (https://doi.org/10.1080/08927022.2014.929127)

C. Yang, S. Zhang, P. He, C. Wang, J. Huang, P. Zhou, J. Chem. Inf. Model. 55 (2015) 329-342 (https://doi.org/10.1021/ci500522v)

P. Zhou, C. Wang, F. Tian, Y. Ren, C. Yang, J. Huang, J. Comput. Aided Mol. Des. 27 (2013) 67-78 (https://doi.org/10.1007/s10822-012-9625-3)

P. Zhou, S. Hou, Z. Bai, Z. Li, H. Wang, Z. Chen, Y. Meng, Artif. Cells Nanomed. Biotechnol. 46 (2018) 1122-1131 (https://doi.org/10.1080/21691401.2017.1360327)

Z. Li, Q. Miao, F. Yan, Y. Meng, P. Zhou, Curr. Drug Metab. 20 (2019) 170-176 (https://doi.org/10.2174/1389200219666181012151944)

F. Tian, R. Tan, T. Guo, P. Zhou, L. Yang, Biosystems 11 (2013) 40-49 (https://doi.org/10.2174/10.1016/j.biosystems.2013.04.004)

Z. Li, F. Yan, Q. Miao, Y. Meng, L. Wen, Q. Jiang, P. Zhou P, J. Theor. Biol. 469 (2019) 25-34 (https://doi.org/10.1016/j.jtbi.2019.02.014)

H. Luo, T. Du, P. Zhou, L. Yang, H. Mei, H. Ng, W. Zhang, M. Shu, W. Tong, L. Shi, D. L. Mendrick, H. Hong, Comb. Chem. High Throughput. Screen. 18 (2015) 296-304 (https://doi.org/10.2174/1386207318666150305144015)

M. Thomas, Epidermal growth factor receptor tyrosine kinase inhibitors: application in non-small cell lung cancer. Cancer Nurs. 26 (2003) S21-S25 (https://doi.org/10.1097/00002820-200312001-00006)

W. Pao, V. A. Miller, K. A. Politi, G. J. Riely, R. Somwar, M. F. Zakowski, M. G. Kris, H. Varmus, PLoS Med. 2 (2005) e73 (https://doi.org/10.1371/journal.pmed.0020073)

M. J. Eck, C. H. Yun, Biochim. Biophys. Acta 1804 (2010) 559-566 (https://doi.org/10.1016/j.bbapap.2009.12.010)

F. Tian, C. Yang, C. Wang, T. Guo, P. Zhou, J. Mol. Model. 20 (2014) 2257 (https://doi.org/10.1007/s00894-014-2257-x)

P. Zhou, Q. Miao, F. Yan, Z. Li, Q. Jiang, L. Wen, Y. Meng, Mol. Omics 15 (2019) 280-295 (https://doi.org/10.1039/c9mo00041k)




DOI: https://doi.org/10.2298/JSC191124028Z

Copyright (c) 2020 Journal of the Serbian Chemical Society

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

IMPACT FACTOR 1.097
5 Year Impact Factor 1.023
(
138 of 177 journals)