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Emergence of a Promising Lead Compound in the Treatment of Triple Negative Breast Cancer: An Insight into Conformational Features and Ligand Binding Landscape of c-Src Protein with UM-164

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Abstract

UM-164, a potent Src/p38 inhibitor, is a promising lead compound for developing the first targeted therapeutic strategy against triple-negative breast cancer (TNBC). However, lack of understanding of conformational features of UM-164 in complex with Src serves a challenge in the rational design of novel Src dual inhibitors. Herein, we provide an in-depth insight into conformational features of Src-UM-164 using different computational approaches. This involved molecular dynamics (MD) simulation, principal component analysis (PCA), thermodynamics calculations, dynamic cross-correlation (DCCM) analysis, and hydrogen bond formation. Findings from this study revealed that (1) the binding of UM-164 to Src induces a more stable and compact conformation; (2) the binding of UM-164 results in increased correlation among the active site residue; (3) the presence of multiple phenyl rings and fluorinated phenyl group in UM-164 contributes to the steric effect; (4) a relatively high-binding free energy estimated for the Src-UM-164 system is affirmative of its experimental potency; (5) hydrophobic packing contributes significantly to the drug binding in Src-UM-164; and (6) observed increase in H-bond distance of interacting residue atoms and Dasatinib compared to UM-164. Findings from this study can serve as a baseline in the design of novel Src inhibitors with dual inhibitory properties.

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References

  1. Sharma, G. N., Dave, R., Sanadya, J., Sharma, P., & Sharma, K. K. (2010). Various types and management of breast cancer: an overview. Journal of Advanced Pharmaceutical Technology & Research, 2, 109–126.

    Google Scholar 

  2. Siegel, R. L., Miller, K. D., & Jemal, A. (2016). Cancer statistics, 66, 7–30.

    Google Scholar 

  3. Gilani, R. A., Phadke, S., Bao, L. W., Lachacz, E. J., Dziubinski, M. L., Brandvold, K. R., Steffey, M. E., Kwarcinski, F. E., Graveel, C. R., Kidwell, K. M., Merajver, S. D., & Soellner, M. B. (2016). UM-164: a potent c-Src/p38 kinase inhibitor with in vivo activity against triple-negative breast cancer. Clinical Cancer Research, 22, 20.

    Article  CAS  Google Scholar 

  4. Anders, C. K., & Carey, L. A. (2009). Biology, metastatic patterns, and treatment of patients with triple-negative breast cancer. Clinical Breast Cancer, 9, 73–81.

    Article  CAS  Google Scholar 

  5. Jiao, Q., Wu, A., Shao, G., Peng, H., Wang, M., Ji, S., Liu, P., & Zhang, J. (2014). The latest progress in research on triple negative breast cancer (TNBC): risk factors, possible therapeutic targets and prognostic markers. Journal Thoracic Disease, 6(9), 1329–1335. https://doi.org/10.3978/j.issn.2072-1439.2014.08.13.

    Google Scholar 

  6. Gluz, O., Liedtke, C., Gottschalk, N., Pusztai, L., Nitz, U., & Harbeck, N. (2009). Triple-negative breast cancer—current status and future directions’. Ann Oncologia, 20, 1913–1927.

    Article  CAS  Google Scholar 

  7. Peddi, P. F., Ellis, M. J., & Ma, C. (2012). Molecular basis of triple negative breast cancer and implications for therapy. International Journal of Breast Cancer, 2012, 217185.

    Article  PubMed  Google Scholar 

  8. Jafarzadeh, N., Ashraf, H., Khoshroo, F., Sepehri Shamloo, A., Bidouei, F., & Ghaffarzadehgan, K. (2015). Triple negative breast cancer: molecular classification, prognostic markers and targeted therapies. Razavi International Journal of Medicine, 3, 2.

    Article  Google Scholar 

  9. Bayraktar, S., & S. Glück. (2013). Molecularly targeted therapies for metastatic triple-negative breast cancer. Breast Cancer Research and Treatment, 138(1), 21–35. https://doi.org/10.1007/s10549-013-2421-5.

    Article  CAS  PubMed  Google Scholar 

  10. Finn, R. S., Dering, J., Ginther, C., Wilson, C. A., Glaspy, P., Tchekmedyian, N., & Slamon, D. J. (2007). Dasatinib, an orally active small molecule inhibitor of both the src and abl kinases, selectively inhibits growth of basal-type/“triple-negative” breast cancer cell lines growing in vitro. Breast Cancer Research and Treatment, 105(3), 319–326. https://doi.org/10.1007/s10549-006-9463-x.

    Article  CAS  PubMed  Google Scholar 

  11. Kwarcinski, F. E., Brandvold, K. R., Phadke, S., Beleh, O. M., Johnson, T. K., Meagher, J. L., Seeliger, M. A., Stuckey, J. A., & Soellner, M. B. (2016). Conformation-selective analogues of Dasatinib reveal insight into kinase inhibitor binding and selectivity. ACS Chemical Biology, 11(5), 1296–1304. https://doi.org/10.1021/acschembio.5b01018.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Vajpai, N., Strauss, A., Fendrich, G., Cowan-Jacob, S. W., Manley, P. W., Grzesiek, S., & Jahnke, W. (2008). Solution conformations and dynamics of ABL kinase-inhibitor complexes determined by NMR substantiate the different binding modes of imatinib/nilotinib and dasatinib. The Journal of Biological Chemistry, 283(26), 18292–18302. https://doi.org/10.1074/jbc.M801337200.

    Article  CAS  PubMed  Google Scholar 

  13. Finn, R. S., Bengala, C., Ibrahim, N., Roche, H., Sparano, J., Strauss, L. C., Fairchild, J., Sy, O., & Goldstein, L. J. (2011). Dasatinib as a single agent in triple-negative breast cancer: results of an open-label phase 2 study. Clinical Cancer Research, 17, 6905–6913.

    Article  CAS  PubMed  Google Scholar 

  14. Chen, R., & Chen, B. (2015). The role of dasatinib in the management of chronic myeloid leukemia. Drug Design, Development and Therapy, 9, 773–779.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Soverini, S., Martinelli, G., Colarossi, S., Gnani, A., Castagnetti, F., Rosti, G., Bosi, C., Paolini, S., Rondoni, M., Piccaluga, P. P., Palandri, F., Giannoulia, P., Marzocchi, G., Luatti, S., Testoni, N., Iacobucci, I., Cilloni, D., Saglio, G., & Baccarani, M. (2006). Presence or the emergence of a F317L BCR-ABL mutation may be associated with resistance to Dasatinib in Philadelphia chromosome–positive leukemia. Journal of Clinical Oncology, 24, 51–52.

    Article  Google Scholar 

  16. Boyle, P. (2012). Triple-negative breast cancer: epidemiological considerations and recommendations’. Ann Oncologia, 23, 7–12.

    Article  Google Scholar 

  17. Yadav, S., Sehrawat, A., Eroglu, Z., Somlo, G., Hickey, R., Yadav, S., Liu, X., Awasthi, Y. C., Awasthi, S., Ossovskaya, V., Wang, Y., Budoff, A., Xu, Q., Lituev, A., Metzger-Filho, O., Tutt, A., de Azambuja, E., Saini, K., Viale, G., Thompson, A., Newman, T., Stebbing, J., Ellis, P., Chu, Q., King, T., Hurd, T., Brouckaert, O., Wildiers, H., Floris, G., Neven, P., Liu, H., Scholz, C., Zang, C., Schefe, J., Habbel, P., Carey, L., Rugo, H., Marcom, P., Mayer, E., Esteva, F., O’Shaughnessy, J., Miles, D., Gray, R., Dieras, V., Perez, E., Somlo, G., Sparano, J., Cigler, T., Fleming, G., Luu, T., Silver, D., Richardson, A., Eklund, A., Wang, Z., Szallasi, Z., Tentori, L., Graziani, G., Tutt, A., Robson, M., Garber, J., Domchek, S., Audeh, M., Isakoff, S., Overmoyer, B., Tung, N., Gelman, R., Giranda, V., Yazdi, P., Wang, Y., Zhao, S., Patel, N., Lee, E., Michaelis, C., Ciosk, R., Nasmyth, K., Wetzer, S., Lehane, C., Uhlmann, F., Arumugam, P., Gruber, S., Tanaka, K., Haering, C., Mechtler, K., Hirano, T., Nasmyth, K., Haering, C., Hopfner, K.-P., Liu, Z., Scannell, D., Eisen, M., Tjian, R., Rhodes, J., McEwan, M., Horsfield, J., Rocquain, J., Gelsi-Boyer, V., Adélaïde, J., Murati, A., Carbuccia, N., Xu, H., Tomaszewski, J., McKay, M., Hagemann, C., Weigelin, B., Schommer, S., Schulze, M., Al-Jomah, N., Unal, E., Heidinger-Pauli, J., Kim, W., Guacci, V., Onn, I., Yamamoto, G., Irie, T., Aida, T., Nagoshi, Y., Tsuchiya, R., Jeong, H., Ryu, Y., An, J., Lee, Y., Kim, A., Atienza, J., Roth, R., Rosette, C., Smylie, K., Kammerer, S., Xu, H., Yan, M., Patra, J., Natrajan, R., Yan, Y., Ghiselli, G., Iozzo, R., Ghiselli, G., Coffee, N., Munnery, C., Koratkar, R., Siracusa, L., Yadav, S., Singhal, S., Singhal, J., Wickramarachchi, D., Knutson, E., Awasthi, S., Cheng, J., Singhal, S., Saini, M., Pandya, U., Singhal, S., Yadav, S., Drake, K., Singhal, J., Awasthi, S., Singhal, S., Wickramarachchi, D., Yadav, S., Singhal, J., Leake, K., Minamide, L., Bamburg, J., Sehrawat, A., Yadav, S., Awasthi, Y., Basu, A., Warden, C., Yadav, S., Singhal, J., Singhal, S., Awasthi, S., Singhal, S., Yadav, S., Singhal, J., Sahu, M., Awasthi, Y., Walsh, S., Xu, J., Xu, H., Kurundkar, A., Maheshwari, A., Boreddy, S., Sahu, R., Srivastava, S., Coene, E., Gadelha, C., White, N., Malhas, A., Thomas, B., Couchman, J., Kapoor, R., Sthanam, M., Wu, R., Wu, R., Couchman, J., Bard, M., Hegmans, J., Hemmes, A., Luider, T., Willemsen, R., Telli, M., Ford, J., McLellan, J., O’Neil, N., Barrett, I., Ferree, E., van Pel, D., Lehmann, B., Bauer, J., Chen, X., Sanders, M., & Chakravarthy, A. (2013). Role of SMC1 in overcoming drug resistance in triple negative breast cancer. PLoS One, 8, 64338.

    Article  Google Scholar 

  18. Klepeis, J. L., Lindorff-Larsen, K., Dror, R. O., & Shaw, D. E. (2009). Long-timescale molecular dynamics simulations of protein structure and function. Current Opinion in Structural Biology, 19(2), 120–127. https://doi.org/10.1016/j.sbi.2009.03.004.

    Article  CAS  PubMed  Google Scholar 

  19. David, C. C., & Jacobs, D. J. (2014). Principal component analysis: a method for determining the essential dynamics of proteins. Methods in Molecular Biology, 1084, 193–226. https://doi.org/10.1007/978-1-62703-658-0_11.

    Article  CAS  PubMed  Google Scholar 

  20. Maisuradze, G. G., Liwo, A., & Scheraga, H. A. (2009). Principal component analysis for protein folding dynamics. Journal of Molecular Biology, 385(1), 312–329. https://doi.org/10.1016/j.jmb.2008.10.018.

    Article  CAS  PubMed  Google Scholar 

  21. Durrant, J. D., & McCammon, J. A. (2011). Molecular dynamics simulations and drug discovery. BMC Biology, 9(1), 71. https://doi.org/10.1186/1741-7007-9-71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ndagi, U., Mhlongo, N. N., & Soliman, M. E. (2017). Re-emergence of an orphan therapeutic target for the treatment of resistant prostate cancer—a thorough conformational and binding analysis for ROR-γ protein. Journal of Biomolecular Structure & Dynamics, 1, 1–16.

    Google Scholar 

  23. Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF chimera. A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/10.1002/jcc.20084.

    Article  CAS  PubMed  Google Scholar 

  24. Hanwell, M. D., Curtis, D. E., Lonie, D. C., Vandermeersch, T., Zurek, E., & Hutchison, G. R. (2012). Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics, 4(1), 17. https://doi.org/10.1186/1758-2946-4-17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Bikadi, Z., & Hazai, E. (2009). Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock. Journal of Cheminformatics, 1(1), 15. https://doi.org/10.1186/1758-2946-1-15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Huey, R., & Morris, G. M. (2005). Using AutoDock with AutoDockTools: a tutorial (1st ed.). La Jolla: The script research institute molecular graphics laboratory.

    Google Scholar 

  28. Morris, G. M., Goodsell, D. S., Halliday, R. S., Huey, R., Hart, W. E., Belew, R. K., & Olson, A. J. (1998). Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry, 19(14), 1639–1662. https://doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639::AID-JCC10>3.0.CO;2-B.

    Article  CAS  Google Scholar 

  29. Salomon-Ferrer, R., Götz, A. W., Poole, D., Le Grand, S., & Walker, R. C. (2013). Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. Journal of Chemical Theory and Computation, 9(9), 3878–3888. https://doi.org/10.1021/ct400314y.

    Article  CAS  PubMed  Google Scholar 

  30. Web-based computational prediction of protonation states and biophysics (2016). Available from http://pubs.acs.org/doi/10.1021/jz501780a. Accessed October 24, 2016.

  31. Gaussian 2009. Available from http://gaussian.com/glossary/g09/. Accessed October 24, 2016.

  32. RESP ESP charge derived server home page 2010. Available from http://upjv.q4md-forcefieldtools.org/REDServer/. Accessed October 26, 2016.

  33. Wang, J. M., Wolf, R. M., Caldwell, J. W., Kollman, P. A., & Case, D. A. (2004). Development and testing of a general Amber force field. Journal of Computational Chemistry, 25(9), 1157–1174. https://doi.org/10.1002/jcc.20035.

    Article  CAS  PubMed  Google Scholar 

  34. Perez, A., MacCallum, J. L., Brini, E., Simmerling, C., & Dill, K. A. (2015). Grid-based backbone correction to the ff12SB protein force field for implicit-solvent simulations. Journal of Chemical Theory and Computation, 11(10), 4770–4779. https://doi.org/10.1021/acs.jctc.5b00662.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tan, Z., Spasic, A., & Mathews, D. H. (2015). 96 benchmark of Amber ff12SB force field by comparison of estimated hairpin loop folding stabilities to experiments. Journal of Biomolecular Structure & Dynamics, 33(sup1), 61–62. https://doi.org/10.1080/07391102.2015.1032713.

    Article  Google Scholar 

  36. Case, D. A., Darden, T., Iii, T. E. C., Simmerling, C., Brook, S., Roitberg, A., Wang, J., Southwestern, U. T., Duke, R. E., Hill, U., Luo, R., Irvine, U. C., Roe, D. R., Walker, R. C., Legrand, S., Swails, J. Cerutti, D., Kaus, J., Betz, R., Wolf, R. M., Merz, K. M., State, M., Seabra, G., Janowski, P., Paesani, F., Liu, J., Wu, X., Steinbrecher, T., Gohlke, H., Homeyer, N., Cai, Q., Smith, W., Mathews, D., Salomon-ferrer, R., Sagui, C.,State, N. C., Babin, V., Luchko, T., Gusarov, S.,Kovalenko, A., Berryman, J., & Kollman, P. A. (2015). Amber Reference Manual 2015, 1–883.

  37. Johnson, A., Johnson, T., & Khan, A. (2012). Thermostats in molecular dynamics simulations, 1st edn, 1–23.

  38. Berendsen, H. J. C., Postma, J. P. M., Van Gunsteren, W. F., Dinola, A., & Haak, J. R. (1984). Molecular dynamics with coupling to an external bath. The Journal of Chemical Physics, 81, 3684.

    Article  CAS  Google Scholar 

  39. Gonnet, P. (2007). P-SHAKE: A quadratically convergent SHAKE in O (n2). Journal of Computational Physics, 220(2), 740–750. https://doi.org/10.1016/j.jcp.2006.05.032.

    Article  Google Scholar 

  40. Roe, D. R., & Cheatham III, T. E. (2013). PTRAJ and CPPTRAJ: software for processing and analysis of molecular synamics trajectory data. Journal of Chemical Theory and Computation, 9(7), 3084–3095. https://doi.org/10.1021/ct400341p.

    Article  CAS  PubMed  Google Scholar 

  41. Seifert, E. (2014). OriginPro 9.1: scientific data analysis and graphing software—software review. Journal of Chemical Information and Modeling, 54, 1552–1552.

    Article  CAS  PubMed  Google Scholar 

  42. Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10, 449–461.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 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. Journal of Chemical Information and Modeling, 51, 69–82.

    Article  CAS  PubMed  Google Scholar 

  44. Arnold, G. E., & Ornstein, R. L. (1997). Molecular dynamics study of time-correlated protein domain motions and molecular flexibility: cytochrome P450BM-3. Biophysical Journal, 73(3), 1147–1159. https://doi.org/10.1016/S0006-3495(97)78147-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Carugo, O., & Pongor, S. (2001). A normalized root-mean-square distance for comparing protein three-dimensional structures. Protein Science, 10(7), 1470–1473. https://doi.org/10.1110/ps.690101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Loeffler, H. H., & Winn, M. D. (2013). Ligand binding and dynamics of the monomeric epidermal growth factor receptor ectodomain. Proteins, 81(11), 1931–1943. https://doi.org/10.1002/prot.24339.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ahmad, E., Rabbani, G., Zaidi, N., Khan, M. A., Qadeer, A., Ishtikhar, M., Singh, S., & Khan, R. H. (2013). Revisiting ligand-induced conformational changes in proteins: essence, advancements, implications and future challenges. Journal of Biomolecular Structure & Dynamics, 31(6), 630–648. https://doi.org/10.1080/07391102.2012.706081.

    Article  CAS  Google Scholar 

  48. Vendome, J., Posy, S., Jin, X., Bahna, F., Ahlsen, G., Shapiro, L., & Honig, B. (2011). Molecular design principles underlying β-strand swapping in the adhesive dimerization of cadherins. Nature Structural & Molecular Biology, 18(6), 693–700. https://doi.org/10.1038/nsmb.2051.

    Article  CAS  Google Scholar 

  49. Pucheta-Martínez, E., Saladino, G., Morando, M. A., Martinez-Torrecuadrada, J., Lelli, M., Sutto, L., D’Amelio, N., & Gervasio, F. L. (2016). An allosteric cross-talk between the activation loop and the ATP binding site regulates the activation of Src kinase. Scientific Reports, 6(1), 24235. https://doi.org/10.1038/srep24235.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Kruewel, T., Schenone, S., Radi, M., Maga, G., Rohrbeck, A., Botta, M., Borlak, J., Sirvent, A., Benistant, C., Roche, S., Yeatman, T., Horita, M., Andreu, E., Benito, A., Arbona, C., Sanz, C., Oetzel, C., Jonuleit, T., Gotz, A., Michels, H., Olivieri, A., Manzione, L., Donato, N., Wu, J., Stapley, J., Gallick, G., Lin, H., Hu, Y., Swerdlow, S., Duffy, T., Weinmann, R., Lee, F., Martinelli, G., Soverini, S., Rosti, G., Baccarani, M., Nautiyal, J., Majumder, P., Patel, B., Lee, F., Majumdar, A., Alvarez, R., Kantarjian, H., Cortes, J., Johnson, F., Agrawal, S., Burris, H., Rosen, L., Dhillon, N., Saad, F., Haura, E., Tanvetyanon, T., Chiappori, A., Williams, C., Simon, G., Saad, F., Lipton, A., Yardley, D., III, H. B., Markus, T., Spigel, D., Greco, F., Ischenko, I., Camaj, P., Seeliger, H., Kleespies, A., Guba, M., Huynh, H., Zhu, A., Duda, D., Sahani, D., Jain, R., Zhu, A., Yau, T., Chan, P., Epstein, R., Poon, R., Carraro, F., Pucci, A., Naldini, A., Schenone, S., Bruno, O., Carraro, F., Naldini, A., Pucci, A., Locatelli, G., Maga, G., Schenone, S., Brullo, C., Bruno, O., Bondavalli, F., Mosti, L., Manetti, F., Santucci, A., Locatelli, G., Maga, G., Spreafico, A., Reamon-Buettner, S., Borlak, J., Reamon-Buettner, S., Borlak, J., Johnson, F., Saigal, B., Talpaz, M., Donato, N., Johnson, D., Walker, C., Payton, M., Chung, G., Yakowec, P., Wong, A., Powers, D., Roche, S., Fumagalli, S., Courtneidge, S., Adolph, D., Flach, N., Mueller, K., Ostareck, D., Ostareck-Lederer, A., Ostareck-Lederer, A., Ostareck, D., Cans, C., NEubauer, G., Bomsztyk, K., Yeatman, T., Silva, C., Park, A., Shen, T., Chien, S., Guan, J., Park, J., Han, H., Watson, C., Kreuzaler, P., Mendrysa, S., Perry, M., Coluccia, A., Cirulli, T., Neri, P., Mangieri, D., Colanardi, M., Meng, X., Jin, Y., Yu, Y., Bai, J., Liu, G., Aggarwal, B., Gehlot, P., Burger, J., Stewart, D., Oh, J., Olman, M., Benveniste, E., Walenkamp, A., Boer, I., Bestebroer, J., Rozeveld, D., Timmer-Bosscha, H., Choi, D., Lee, H., Hur, K., Kim, J., Park, G., Lindstrom, A., Tot, T., Stendahl, U., Syrjanen, S., Syrjanen, K., Goetz, J., Lajoie, P., Wiseman, S., Nabi, I., Park, J., Han, H., Lappi-Blanco, E., Kaarteenaho-Wiik, R., Maasilta, P., Anttila, S., Paakko, P., Thom, I., Schult-Kronefeld, O., Burkholder, I., Schuch, G., Andritzky, B., Fuchs, B., Fujii, T., Dorfman, J., Goodwin, J., Zhu, A., Choma, D., Milano, V., Pumiglia, K., DiPersio, C., Skorski, T., Niida, H., Nakanishi, M., Song, L., Morris, M., Bagui, T., Lee, F., Jove, R., Fabarius, A., Giehl, M., Rebacz, B., Kraemer, A., Frank, O., Jia, H., Wu, J., Zhu, X., Chen, J., Yang, S., Alvarez, R., Kantarjian, H., Cortes, J., Fujimoto, N., Wislez, M., Zhang, J., Iwanaga, K., Dackor, J., Zhang, X., Chang, A., Ishizawar, R., Parsons, S., Alvarez, R., Kantarjian, H., Cortes, J., Sanchez-Prieto, R., Sanchez-Arevalo, V., Servitja, J., Gutkind, J., Capdeville, R., Buchdunger, E., Zimmermann, J., Matter, A., Redaelli, S., Piazza, R., Rostagno, R., Magistroni, V., Perini, P., Capdeville, R., Buchdunger, E., Zimmermann, J., Matter, A., Giles, F., O’Dwyer, M., Swords, R., Krystal, G., Fabbro, D., Ruetz, S., Buchdunger, E., Cowan-Jacob, S., Fendrich, G., Horita, M., Andreu, E., Benito, A., Arbona, C., Sanz, C., Oetzel, C., Jonuleit, T., Gotz, A., Michels, H., Giles, F., O’Dwyer, M., Swords, R., Bixby, D., Talpaz, M., Alvarez, R., Kantarjian, H., Cortes, J., Johnson, F., Saigal, B., Talpaz, M., Donato, N., Johnson, F., Saigal, B., Talpaz, M., Donato, N., Nautiyal, J., Majumder, P., Patel, B., Lee, F., Majumdar, A., Olivieri, A., Manzione, L., Nautiyal, J., Majumder, P., Patel, B., Lee, F., Majumdar, A., Olivieri, A., Manzione, L., Alvarez, R., Kantarjian, H., Cortes, J., Johnson, F., Agrawal, S., Burris, H., Rosen, L., Dhillon, N., Haura, E., Tanvetyanon, T., Chiappori, A., Williams, C., Simon, G., Carraro, F., Pucci, A., Naldini, A., Schenone, S., Bruno, O., Carraro, F., Naldini, A., Pucci, A., Locatelli, G., Maga, G., Manetti, F., Santucci, A., Locatelli, G., Maga, G., Spreafico, A., Schenone, S., Brullo, C., Bruno, O., Bondavalli, F., Mosti, L., Schenone, S., Bruno, O., Bondavalli, F., Ranise, A., Mosti, L., Bruno, O., Brullo, C., Bondavalli, F., Schenone, S., Ranise, A., Reamon-Buettner, S., Borlak, J., Niehof, M., Borlak, J., Rohrbeck, A., & Borlak, J. (2010). Molecular characterization of c-Abl/c-Src kinase inhibitors targeted against murine tumour progenitor cells that express stem cell markers. PLoS One, 5, 14143.

    Article  CAS  Google Scholar 

  51. Chen, D., Oezguen, N., Urvil, P., Ferguson, C., Dann, S. M., & Savidge, T. C. (2016). Regulation of protein-ligand binding affinity by hydrogen bond pairing. Science Advances, 2, 1501240–1501240.

    Article  CAS  Google Scholar 

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The authors acknowledge the School of Health Science, University of KwaZulu-Natal, Westville Campus for financial assistance and the Centre for High Performance Computing (CHPC, www.chpc.ac.za) Cape Town, South Africa for computational resources.

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Correspondence to Mahmoud E. Soliman.

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Ndagi, U., Mhlongo, N.N. & Soliman, M.E. Emergence of a Promising Lead Compound in the Treatment of Triple Negative Breast Cancer: An Insight into Conformational Features and Ligand Binding Landscape of c-Src Protein with UM-164. Appl Biochem Biotechnol 185, 655–675 (2018). https://doi.org/10.1007/s12010-017-2677-z

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  • DOI: https://doi.org/10.1007/s12010-017-2677-z

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