Applied Biochemistry and Biotechnology

, Volume 186, Issue 1, pp 85–108 | Cite as

Ligand-Based Pharmacophore Screening Strategy: a Pragmatic Approach for Targeting HER Proteins

  • Nivya James
  • K. RamanathanEmail author


Targeting ErbB family of receptors is an important therapeutic option, because of its essential role in the broad spectrum of human cancers, including non-small cell lung cancer (NSCLC). Therefore, in the present work, considerable effort has been made to develop an inhibitor against HER family proteins, by combining the use of pharmacophore modelling, docking scoring functions, and ADME property analysis. Initially, a five-point pharmacophore model was developed using known HER family inhibitors. The generated model was then used as a query to screen a total of 468,880 compounds of three databases namely ZINC, ASINEX, and DrugBank. Subsequently, docking analysis was carried out to obtain hit molecules that could inhibit the HER receptors. Further, analysis of GLIDE scores and ADME properties resulted in one hit namely BAS01025917 with higher glide scores, increased CNS involvement, and good pharmaceutically relevant properties than reference ligand, afatinib. Furthermore, the inhibitory activity of the lead compounds was validated by performing molecular dynamic simulations. Of note, BAS01025917 was found to possess scaffolds with a broad spectrum of antitumor activity. We believe that this novel hit molecule can be further exploited for the development of a pan-HER inhibitor with low toxicity and greater potential.





The authors gratefully acknowledge VIT University, Vellore for the support through Seed Grant for Research.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

12010_2018_2724_MOESM1_ESM.docx (34 kb)
ESM 1 (DOCX 33 kb)


  1. 1.
    Roskoski, R. (2014). The ErbB/HER family of protein-tyrosine kinases and cancer. Pharmacological Research, 79, 34–74.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Wieduwilt, M. J., & Moasser, M. M. (2008). The epidermal growth factor receptor family: biology driving targeted therapeutics. Cellular and Molecular Life Sciences, 65(10), 1566–1584.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Hsieh, A. A., & Moasser, M. M. (2007). Targeting HER proteins in cancer therapy and the role of the non-target HER3. British Journal of Cancer, 97(4), 453–457.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Cappuzzo, F. (2014). The human epidermal growth factor receptor (HER) family: structure and function. In Guide to targeted therapies: EGFR mutations in NSCLC (pp. 7–17).Google Scholar
  5. 5.
    Chan, B. A., & Hughes, B. G. (2015). Targeted therapy for non-small cell lung cancer: current standards and the promise of the future. Translational lung cancer research, 4(1), 36–54.PubMedPubMedCentralGoogle Scholar
  6. 6.
    Schneider, P. M., Hung, M. C., Chiocca, S. M., Manning, J., Zhao, X., Fang, K., & Roth, J. A. (1989). Differential expression of the c-erbB-2 gene in human small cell and non-small cell lung cancer. Cancer Research, 49(18), 4968–4971.PubMedPubMedCentralGoogle Scholar
  7. 7.
    Hirsch, F. R., Varella-Garcia, M., Franklin, W. A., Veve, R., Chen, L., Helfrich, B., Zeng, C., Baron, A., & Bunn, P. A. (2002). Evaluation of HER-2/neu gene amplification and protein expression in non-small cell lung carcinomas. British Journal of Cancer, 86(9), 1449–1456.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Pellegrini, C., Falleni, M., Marchetti, A., Cassani, B., Miozzo, M., Buttitta, F., Roncalli, M., Coggi, G., & Bosari, S. (2003). HER-2/neu alterations in non-small cell lung cancer. Clinical Cancer Research, 9(10), 3645–3652.PubMedPubMedCentralGoogle Scholar
  9. 9.
    Mar, N., Vredenburgh, J. J., & Wasser, J. S. (2015). Targeting HER2 in the treatment of non-small cell lung cancer. Lung Cancer, 87(3), 220–225.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Citri, A., Skaria, K. B., & Yarden, Y. (2003). The deaf and the dumb: the biology of ErbB-2 and ErbB-3. Experimental Cell Research, 284(1), 54–65.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Graus-Porta, D., Beerli, R. R., Daly, J. M., & Hynes, N. E. (1997). ErbB-2, the preferred heterodimerization partner of all ErbB receptors, is a mediator of lateral signaling. The EMBO Journal, 16(7), 1647–1655.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Yi, E. S., Harclerode, D., Gondo, M., Stephenson, M., Brown, R. W., Younes, M., & Cagle, P. T. (1997). High c-erbB-3 protein expression is associated with shorter survival in advanced non-small cell lung carcinomas. Modern pathology: an official journal of the United States and Canadian Academy of Pathology, Inc, 10(2), 142–148.Google Scholar
  13. 13.
    Koutsopoulos, A. V., Mavroudis, D., Dambaki, K. I., Souglakos, J., Tzortzaki, E. G., Drositis, J., Delides, G. S., Georgoulias, V., & Stathopoulos, E. N. (2007). Simultaneous expression of c-erbB-1, c-erbB-2, c-erbB-3 and c-erbB-4 receptors in non-small-cell lung carcinomas: correlation with clinical outcome. Lung Cancer, 57(2), 193–200.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Boudeau, J., Miranda-Saavedra, D., Barton, G. J., & Alessi, D. R. (2006). Emerging roles of pseudokinases. Trends in Cell Biology, 16(9), 443–452.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Shi, F., Telesco, S. E., Liu, Y., Radhakrishnan, R., & Lemmon, M. A. (2010). ErbB3/HER3 intracellular domain is competent to bind ATP and catalyze autophosphorylation. Proceedings of the National Academy of Sciences, 107(17), 7692–7697.CrossRefGoogle Scholar
  16. 16.
    Ma, J., Lyu, H., Huang, J., & Liu, B. (2014). Targeting of erbB3 receptor to overcome resistance in cancer treatment. Molecular Cancer, 13(1), 105.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Schulze, W. X., Deng, L., & Mann, M. (2005). Phosphotyrosine interactome of the ErbB-receptor kinase family. Molecular Systems Biology, 1(1), E1–E13.CrossRefGoogle Scholar
  18. 18.
    Srinivasan, R., Poulsom, R., Hurst, H. C., & Gullick, W. J. (1998). Expression of the c-erbB-4/HER4 protein and mRNA in normal human fetal and adult tissues and in a survey of nine solid tumour types. The Journal of Pathology, 185(3), 236–245.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Al Moustafa, A. E., Alaoui-Jamali, M., Paterson, J., & O'Connor-McCourt, M. (1999). Expression of P185erbB-2, P160erbB-3, P180erbB-4, and heregulin alpha in human normal bronchial epithelial and lung cancer cell lines. Anticancer Research, 19(1A), 481–486.PubMedPubMedCentralGoogle Scholar
  20. 20.
    Starr, A., Greif, J., Vexler, A., Ashkenazy-Voghera, M., Gladesh, V., Rubin, C., Kerber, G., Marmor, S., Lev-Ari, S., Inbar, M., & Yarden, Y. (2006). ErbB4 increases the proliferation potential of human lung cancer cells and its blockage can be used as a target for anti-cancer therapy. International Journal of Cancer, 119(2), 269–274.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Lejeune, S., & Machiels, J. P. (2015). Pan-HER inhibitors. Belgian Journal of Medical Oncology, 9(3), 99–103.Google Scholar
  22. 22.
    Wang, X., Batty, K. M., Crowe, P. J., Goldstein, D., & Yang, J. L. (2015). The potential of panHER inhibition in cancer. Frontiers in Oncology, 5, 2.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Preethi, B., Shanthi, V., & Ramanathan, K. (2015). Investigation of nalidixic acid resistance mechanism in Salmonella enterica using molecular simulation techniques. Applied Biochemistry and Biotechnology, 177(2), 528–540.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Karthick, V., Shanthi, V., Rajasekaran, R., & Ramanathan, K. (2012). Exploring the cause of oseltamivir resistance against mutant H274Y neuraminidase by molecular simulation approach. Applied Biochemistry and Biotechnology, 167(2), 237–249.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, E. W. (2014). Computational methods in drug discovery. Pharmacological Reviews, 66(1), 334–395.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    James, N., & Ramanathan, K. (2017). Discovery of potent ALK inhibitors using pharmacophore-informatics strategy. Cell Biochemistry and Biophysics, 1–14.Google Scholar
  27. 27.
    Rohini, K., & Shanthi, V. (2017) Discovery of potent neuraminidase inhibitors using a combination of pharmacophore-based virtual screening and molecular simulation approach. Applied Biochemistry and Biotechnology, 1–20.Google Scholar
  28. 28.
    Dhanachandra Singh, K. H., Karthikeyan, M., Kirubakaran, P., & Nagamani, S. (2011). Pharmacophore filtering and 3D-QSAR in the discovery of new JAK2 inhibitors. Journal of Molecular Graphics & Modelling, 30, 186–197.CrossRefGoogle Scholar
  29. 29.
    Dash, R. C., Bhosale, S. H., Shelke, S. M., Suryawanshi, M. R., Kanhed, A. M., & Mahadik, K. R. (2012). Scaffold hopping for identification of novel D 2 antagonist based on 3D pharmacophore modelling of illoperidone analogs. Molecular Diversity, 16(2), 367–375.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Pinheiro, A. S., Duarte, J. B. C., Alves, C. N., & de Molfetta, F. A. (2015). Virtual screening and molecular dynamics simulations from a bank of molecules of the amazon region against functional NS3-4A protease-helicase enzyme of hepatitis C virus. Applied Biochemistry and Biotechnology, 176(6), 1709–1721.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Joung, J. Y., Lee, H. Y., Park, J., Lee, J. Y., Chang, B. H., No, K. T., Nam, K. Y., & Hwang, J. S. (2014). Identification of novel rab27a/melanophilin blockers by pharmacophore-based virtual screening. Applied Biochemistry and Biotechnology, 172(4), 1882–1897.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Morya, V. K., Dewaker, V., & Kim, E. K. (2012). In silico study and validation of phosphotransacetylase (PTA) as a putative drug target for Staphylococcus aureus by homology-based modelling and virtual screening. Applied Biochemistry and Biotechnology, 168(7), 1792–1805.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Ramatenki, V., Dumpati, R., Vadija, R., Vellanki, S., Potlapally, S.R., Rondla, R., & Vuruputuri, U. (2017). Identification of new lead molecules against UBE2NL enzyme for cancer therapy. Applied Biochemistry and Biotechnology, 1–21.Google Scholar
  34. 34.
    Sudha, A., Srinivasan, P., & Rameshthangam, P. (2015). Exploration of potential EGFR inhibitors: a combination of pharmacophore-based virtual screening, atom-based 3D-QSAR and molecular docking analysis. Journal of Receptors and Signal Transduction, 35(2), 137–148.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Gogoi, D., Baruah, V. J., Chaliha, A. K., Kakoti, B. B., Sarma, D., & Buragohain, A. K. (2016). 3D pharmacophore-based virtual screening, docking and density functional theory approach towards the discovery of novel human epidermal growth factor receptor-2 (HER2) inhibitors. Journal of Theoretical Biology, 411, 68–80.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235–242.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Wlodawer, A., Minor, W., Dauter, Z., & Jaskolski, M. (2008). Protein crystallography for non-crystallographers, or how to get the best (but not more) from published macromolecular structures. The FEBS Journal, 275(1), 1–21.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Kleywegt, G. J. (2000). Validation of protein crystal structures. Acta Crystallographica Section D: Biological Crystallography, 56(3), 249–265.CrossRefGoogle Scholar
  39. 39.
    Pilotto, S., Rossi, A., Vavalà, T., Follador, A., Tiseo, M., Galetta, D., Morabito, A., Di Maio, M., Martelli, O., Caffo, O., & Piovano, P. L. (2017). Outcomes of first-generation EGFR-TKIs against non-small-cell lung cancer harboring uncommon EGFR mutations: a post-hoc analysis of the BE-POSITIVE study. Clinical Lung Cancer.Google Scholar
  40. 40.
    Helena, A. Y., & Riely, G. J. (2013). Second-generation epidermal growth factor receptor tyrosine kinase inhibitors in lung cancers. Journal of the National Comprehensive Cancer Network, 11(2), 161–169.CrossRefGoogle Scholar
  41. 41.
    Wu, J., Chen, W., Xia, G., Zhang, J., Shao, J., Tan, B., Zhang, C., Yu, W., Weng, Q., Liu, H., & Hu, M. (2013). Design, synthesis, and biological evaluation of novel conformationally constrained inhibitors targeting EGFR. ACS Medicinal Chemistry Letters, 4(10), 974–978.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Law, V., Knox, C., Djoumbou, Y., Jewison, T., Guo, A. C., Liu, Y., Maciejewski, A., Arndt, D., Wilson, M., Neveu, V., & Tang, A. (2013). DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Research, 42(D1), D1091–D1097.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Voigt, J. H., Bienfait, B., Wang, S., & Nicklaus, M. C. (2001). Comparison of the NCI open database with seven large chemical structural databases. Journal of Chemical Information and Computer Sciences, 41(3), 702–712.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Irwin, J. J., & Shoichet, B. K. (2005). ZINC—a free database of commercially available compounds for virtual screening. Journal of Chemical Information and Modeling, 45(1), 177–182.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Glaab, E. (2015). Building a virtual ligand screening pipeline using free software: a survey. Briefings in Bioinformatics, 17(2), 352–366.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Shelley, J. C., Cholleti, A., Frye, L. L., Greenwood, J. R., Timlin, M. R., & Uchimaya, M. (2007). Epik: a software program for pKa prediction and protonation state generation for drug-like molecules. Journal of Computer-Aided Molecular Design, 21(12), 681–691.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Kalliokoski, T., Salo, H. S., Lahtela-Kakkonen, M., & Poso, A. (2009). The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening. Journal of Chemical Information and Modeling, 49(12), 2742–2748.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Yilmaz, O. G., Olmez, E. O., & Ulgen, K. O. (2014). Targeting the Akt1 allosteric site to identify novel scaffolds through virtual screening. Computational Biology and Chemistry, 48, 1–13.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Milletti, F., & Vulpetti, A. (2010). Tautomer preference in PDB complexes and its impact on structure-based drug discovery. Journal of Chemical Information and Modeling, 50(6), 1062–1074.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Khan, M. F., Verma, G., Akhtar, W., Shaquiquzzaman, M., Akhter, M., Rizvi, M. A., & Alam, M. M. (2016). Pharmacophore modeling, 3D-QSAR, docking study and ADME prediction of acyl 1,3,4-thiadiazole amides and sulfonamides as antitubulin agents. Arabian Journal of Chemistry.
  51. 51.
    Muralidharan, A. R., Selvaraj, C., Singh, S., Nelson Jesudasan, C. A., Geraldine, P., & Thomas, P. (2014). Virtual screening based on pharmacophoric features of known calpain inhibitors to identify potent inhibitors of calpain. Medicinal Chemistry Research: An International Journal for Rapid Communications on Design And Mechanisms of Action of Biologically Active Agents, 23(5), 2445–2455.CrossRefGoogle Scholar
  52. 52.
    Dixon, S. L., Smondyrev, A. M., Knoll, E. H., Rao, S. N., Shaw, D. E., & Friesner, R. A. (2006). PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. Journal of Computer-Aided Molecular Design., 20(10-11), 647–671.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Ash, J., & Fourches, D. (2017). Characterizing the chemical space of ERK2 kinase inhibitors using descriptors computed from molecular dynamics trajectories. Journal of chemical information and modelling, 57(6), 1286–1299.CrossRefGoogle Scholar
  54. 54.
    Rajput, V. S., Mehra, R., Kumar, S., Nargotra, A., Singh, P. P., & Khan, I. A. (2016). Screening of antitubercular compound library identifies novel shikimate kinase inhibitors of Mycobacterium tuberculosis. Applied Microbiology and Biotechnology, 100(12), 5415–5426.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Watts, K. S., Dalal, P., Murphy, R. B., Sherman, W., Friesner, R. A., & Shelley, J. C. (2010). ConfGen: a conformational search method for efficient generation of bioactive conformers. Journal of Chemical Information and Modeling, 50(4), 534–546.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    De Falco, F., Di Giovanni, C., Cerchia, C., De Stefano, D., Capuozzo, A., Irace, C., Iuvone, T., Santamaria, R., Carnuccio, R., & Lavecchia, A. (2016). Novel non-peptide small molecules preventing IKKß/NEMO association inhibit NF-κB activation in LPS-stimulated J774 macrophages. Biochemical Pharmacology, 104, 83–94.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Lionta, E., Spyrou, G., Vassilatis, D. K., & Cournia, Z. (2014). Structure-based virtual screening for drug discovery: principles, applications and recent advances. Current Topics in Medicinal Chemistry, 14(16), 1923–1938.CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Vass, M., Tarcsay, Á., & Keserű, G. M. (2012). Multiple ligand docking by Glide: implications for virtual second-site screening. Journal of computer-aided molecular design., 26(7), 821–834.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013). Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. Journal of Computer-Aided Molecular Design, 27(3), 221–234.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L. (2004). Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of Medicinal Chemistry, 47(7), 1750–1759.CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Elancheran, R., Saravanan, K., Choudhury, B., Divakar, S., Kabilan, S., Ramanathan, M., Das, B., Devi, R., & Kotoky, J. (2016). Design and development of oxobenzimidazoles as novel androgen receptor antagonists. Medicinal Chemistry Research., 25(4), 539–552.CrossRefGoogle Scholar
  62. 62.
    Di Capua, A., Sticozzi, C., Brogi, S., Brindisi, M., Cappelli, A., Sautebin, L., Rossi, A., Pace, S., Ghelardini, C., Mannelli, L. D. C., & Valacchi, G. (2016). Synthesis and biological evaluation of fluorinated 1,5-diarylpyrrole-3-alkoxyethyl ether derivatives as selective COX-2 inhibitors endowed with anti-inflammatory activity. European Journal of Medicinal Chemistry, 109, 99–106.CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    McKim, J., & James, M. (2010). Building a tiered approach to in vitro predictive toxicity screening: a focus on assays with in vivo relevance. Combinatorial Chemistry & High Throughput Screening, 13(2), 188–206.CrossRefGoogle Scholar
  64. 64.
    Wang, Y., Xing, J., Xu, Y., Zhou, N., Peng, J., Xiong, Z., Liu, X., Luo, X., Luo, C., Chen, K., & Zheng, M. (2015). In silico ADME/T modelling for rational drug design. Quarterly Reviews of Biophysics, 48(4), 488–515.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Jorgensen, W. L., & Duffy, E. M. (2002). Prediction of drug solubility from structure. Advanced Drug Delivery Reviews, 54(3), 355–366.CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Vilar, S., Chakrabarti, M., & Costanzi, S. (2010). Prediction of passive blood–brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors. Journal of Molecular Graphics and Modelling, 28(8), 899–903.CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Ntie-Kang, F. (2013). An in silico evaluation of the ADMET profile of the StreptomeDB database. SpringerPlus, 2(1), 353.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Sun, H. (2004). A universal molecular descriptor system for prediction of logP, logS, logBB, and absorption. Journal of Chemical Information and Computer Sciences, 44(2), 748–757.CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Malik, R., Bunkar, D., Choudhary, B. S., Srivastava, S., Mehta, P., & Sharma, M. (2016). High throughput virtual screening and in silico ADMET analysis for rapid and efficient identification of potential PAP 248-286 aggregation inhibitors as anti-HIV agents. Journal of Molecular Structure, 1122, 239–246.CrossRefGoogle Scholar
  70. 70.
    Chauhan, N., Vidyarthi, A. S., & Poddar, R. (2012). Comparative analysis of different DNA-binding drugs for Leishmaniasis cure: a pharmacoinformatics approach. Chemical Biology & Drug Design, 80(1), 54–63.CrossRefGoogle Scholar
  71. 71.
    Chung, T. D., Terry, D. B., & Smith, L. H. (2015). In vitro and in vivo assessment of ADME and PK properties during lead selection and lead optimization—guidelines, benchmarks and rules of thumb.Google Scholar
  72. 72.
    Goyal, S., Grover, S., Dhanjal, J. K., Goyal, M., Tyagi, C., Chacko, S., & Grover, A. (2014). Mechanistic insights into mode of actions of novel oligopeptidase B inhibitors for combating leishmaniasis. Journal of Molecular Modeling, 20(3), 2099.CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Ramezani, F., Amanlou, M., & Rafii-Tabar, H. (2014). Gold nanoparticle shape effects on human serum albumin corona interface: a molecular dynamic study. Journal of Nanoparticle Research, 16(7), 2512.CrossRefGoogle Scholar
  74. 74.
    Schuttelkopf, A. W., & Van Aalten, D. M. F. (2004). PRODRG—a tool for highthroughput crystallography of protein–ligand complexes. Acta Crystallographica, 60(Pt 8), 1355–1363.PubMedPubMedCentralGoogle Scholar
  75. 75.
    Karthick, V., Shanthi, V., Rajasekaran, R., & Ramanathan, K. (2013). In silico analysis of drug-resistant mutant of neuraminidase (N294S) against oseltamivir. Protoplasma, 250(1), 197–207.CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Teli, M. K., & Rajanikant, G. K. (2012). Pharmacophore generation and atom-based 3D-QSAR of novel quinoline-3-carbonitrile derivatives as Tpl2 kinase inhibitors. Journal of Enzyme Inhibition and Medicinal Chemistry, 27(4), 558–570.CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Yonesaka, K., Kudo, K., Nishida, S., Takahama, T., Iwasa, T., Yoshida, T., Tanaka, K., Takeda, M., Kaneda, H., Okamoto, I., & Nishio, K. (2015). The pan-HER family tyrosine kinase inhibitor afatinib overcomes HER3 ligand heregulin-mediated resistance to EGFR inhibitors in non-small cell lung cancer. Oncotarget, 6(32), 33602–33611.CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Zhou, W., Wang, Y., Lu, A., & Zhang, G. (2016). Systems pharmacology in small molecular drug discovery. International Journal of Molecular Sciences, 17(12), 246.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Sun, M., Behrens, C., Feng, L., Ozburn, N., Tang, X., Yin, G., Komaki, R., Varella-Garcia, M., Hong, W. K., Aldape, K. D., & Wistuba, I. I. (2009). HER family receptor abnormalities in lung cancer brain metastases and corresponding primary tumors. Clinical Cancer Research, 15(15), 4829–4837.CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Ramar, V., & Pappu, S. (2016). Exploring the inhibitory potential of bioactive compound from Luffa acutangula against NF-κB—a molecular docking and dynamics approach. Computational Biology and Chemistry, 62, 29–35.CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Yun, C. H., Boggon, T. J., Li, Y., Woo, M. S., Greulich, H., Meyerson, M., & Eck, M. J. (2007). Structures of lung cancer-derived EGFR mutants and inhibitor complexes: mechanism of activation and insights into differential inhibitor sensitivity. Cancer Cell, 11(3), 217–227.CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Urich, R., Wishart, G., Kiczun, M., Richters, A., Tidten-Luksch, N., Rauh, D., Sherborne, B., Wyatt, P. G., & Brenk, R. (2013). De novo design of protein kinase inhibitors by in silico identification of hinge region-binding fragments. ACS Chemical Biology, 8(5), 1044–1052.CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Aertgeerts, K., Skene, R., Yano, J., Sang, B. C., Zou, H., Snell, G., Jennings, A., Iwamoto, K., Habuka, N., Hirokawa, A., & Ishikawa, T. (2011). Structural analysis of the mechanism of inhibition and allosteric activation of the kinase domain of HER2 protein. Journal of Biological Chemistry, 286(21), 18756–18765.CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Kamath, S., & Buolamwini, J. K. (2006). Targeting EGFR and HER-2 receptor tyrosine kinases for cancer drug discovery and development. Medicinal Research Reviews, 26(5), 569–594.CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Bridges, A. J., Zhou, H., Cody, D. R., Rewcastle, G. W., McMichael, A., Showalter, H. H., Fry, D. W., Kraker, A. J., & Denny, W. A. (1996). Tyrosine kinase inhibitors. 8. An unusually steep structure–activity relationship for analogues of 4-(3-bromoanilino)-6,7-dimethoxyquinazoline (PD 153035), a potent inhibitor of the epidermal growth factor receptor. Journal of Medicinal Chemistry, 39(1), 267–276.CrossRefPubMedPubMedCentralGoogle Scholar
  86. 86.
    Hammarén, H. M., Virtanen, A. T., & Silvennoinen, O. (2016). Nucleotide-binding mechanisms in pseudokinases. Bioscience Reports, 36(1), e00282.CrossRefGoogle Scholar
  87. 87.
    Jura, N., Shan, Y., Cao, X., Shaw, D. E., & Kuriyan, J. (2009). Structural analysis of the catalytically inactive kinase domain of the human EGF receptor 3. Proceedings of the National Academy of Sciences, 106(51), 21608–21613.CrossRefGoogle Scholar
  88. 88.
    Dong, C. L., Guo, F. C., & Xue, J. (2017). Computational insights into HER3 gatekeeper T768I resistance mutation to bosutinib in HER3-related breast cancer. Medicinal Chemistry Research, 26(9), 1926–1934.CrossRefGoogle Scholar
  89. 89.
    Sahu, A., Patra, P. K., Yadav, M. K., & Varma, M. (2017). Identification and characterization of ErbB4 kinase inhibitors for effective breast cancer therapy. Journal of Receptors and Signal Transduction, 37(5), 470–480.CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Meharena, H. S., Chang, P., Keshwani, M. M., Oruganty, K., Nene, A. K., Kannan, N., Taylor, S. S., & Kornev, A. P. (2013). Deciphering the structural basis of eukaryotic protein kinase regulation. PLoS Biology, 11(10), e1001680.CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Ghorab, M. M., & Alsaid, M. S. (2016). Anticancer activity of some novel thieno [2, 3-d] pyrimidine derivatives. Biomedical Research, 27(1).Google Scholar
  92. 92.
    Elrazaz, E. Z., Serya, R. A., Ismail, N. S., El Ella, D. A. A., & Abouzid, K. A. (2015). Thieno [2, 3-d] pyrimidine based derivatives as kinase inhibitors and anticancer agents. Future Journal of Pharmaceutical Sciences, 1(2), 33–41.CrossRefGoogle Scholar
  93. 93.
    Wu, C. H., Coumar, M. S., Chu, C. Y., Lin, W. H., Chen, Y. R., Chen, C. T., Shiao, H. Y., Rafi, S., Wang, S. Y., Hsu, H., & Chen, C. H. (2010). Design and synthesis of tetrahydropyridothieno [2, 3-d] pyrimidine scaffold based epidermal growth factor receptor (EGFR) kinase inhibitors: the role of side chain chirality and Michael acceptor group for maximal potency. Journal of Medicinal Chemistry, 53(20), 7316–7326.CrossRefPubMedPubMedCentralGoogle Scholar
  94. 94.
    Rheault, T. R., Caferro, T. R., Dickerson, S. H., Donaldson, K. H., Gaul, M. D., Goetz, A. S., Mullin, R. J., McDonald, O. B., Petrov, K. G., Rusnak, D. W., & Shewchuk, L. M. (2009). Thienopyrimidine-based dual EGFR/ErbB-2 inhibitors. Bioorganic & Medicinal Chemistry Letters, 19(3), 817–820.CrossRefGoogle Scholar
  95. 95.
    Agrawal, S., Singh, N. K., Aggarwal, R. C., Sodhi, A., & Tandon, P. (1986). Synthesis, structure, and antitumor activity of N-salicyloyl-N'-(2-furylthiocarbonyl) hydrazine and its copper (II) complex. Journal of Medicinal Chemistry, 29(2), 199–202.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biotechnology, School of Bio Sciences and TechnologyVellore Institue of TechnologyVelloreIndia

Personalised recommendations