89
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Combining QSAR techniques, molecular docking, and molecular dynamics simulations to explore anti-tumor inhibitors targeting Focal Adhesion Kinase

, , , , &
Received 13 Sep 2023, Accepted 15 Dec 2023, Published online: 03 Jan 2024

References

  • Adasme-Carreño, F., Muñoz-Gutierrez, C., Caballero, J., & Alzate-Morales, J. H. (2014). Performance of the MM/GBSA scoring using a binding site hydrogen bond network-based frame selection: The protein kinase case. Physical Chemistry Chemical Physics: PCCP, 16(27), 14047–14058. https://doi.org/10.1039/c4cp01378f
  • Adeniji, S. E., Uba, S., & Uzairu, A. (2018). In silico study for investigating and predicting the activities of 1,2,4-triazole derivaties as potent anti-tubercular agents. The Journal of Engineering and Exact Sciences, 4(2), 0246–0254. https://doi.org/10.18540/jcecvl4iss2pp0246-0254
  • Ambure, P., Aher, R. B., Gajewicz, A., Puzyn, T., & Roy, K. (2015). “NanoBRIDGES” software: Open access tools to perform QSAR and nano-QSAR modeling. Chemometrics and Intelligent Laboratory Systems, 147, 1–13. https://doi.org/10.1016/j.chemolab.2015.07.007
  • Aoumeur, N., Belaidi, S., Tchouar, N., Ouassaf, M., Lanez, T., & Chtita, S. (2021). Molecular docking studies for the identifications of novel antimicrobial compounds targeting of Staphylococcus aureus. Moroccan Journal of Chemistry, 9(2), 9–2.
  • Başoğlu, F., Ulusoy-Güzeldemirci, N., Akalın-Çiftçi, G., Çetinkaya, S., & Ece, A. (2021). Novel imidazo[2,1-b]thiazole-based anticancer agents as potential focal adhesion kinase inhibitors: Synthesis, in silico and in vitro evaluation. Chemical Biology & Drug Design, 98(2), 270–282. https://doi.org/10.1111/cbdd.13896
  • Ben Mahdi, M. H., Andrieu, V., & Pasquier, C. (2000). Focal adhesion kinase regulation by oxidative stress in different cell types. IUBMB Life, 50(4), 291–299. https://doi.org/10.1080/15216540051081038
  • Brown, S. P., & Muchmore, S. W. (2009). Large-scale application of high-throughput molecular mechanics with Poisson-Boltzmann surface area for routine physics-based scoring of protein-ligand complexes. Journal of Medicinal Chemistry, 52(10), 3159–3165. https://doi.org/10.1021/jm801444x
  • Chavda, J., & Bhatt, H. (2019). 3D-QSAR (CoMFA, CoMSIA, HQSAR and topomer CoMFA), MD simulations and molecular docking studies on purinylpyridine derivatives as B-Raf inhibitors for the treatment of melanoma cancer. Structural Chemistry, 30(6), 2093–2107. https://doi.org/10.1007/s11224-019-01334-9
  • Cheng, G., Mei, X. B., Yan, Y. Y., Chen, J., Zhang, B., Li, J., Dong, X. W., Lin, N. M., & Zhou, Y. B. (2019). Identification of new NIK inhibitors by discriminatory analysis-based molecular docking and biological evaluation. Archiv Der Pharmazie, 352(7), e1800374. https://doi.org/10.1002/ardp.201800374
  • Chhatbar, D. M., Chaube, U. J., Vyas, V. K., & Bhatt, H. G. (2019). CoMFA, CoMSIA, Topomer CoMFA, HQSAR, molecular docking and molecular dynamics simulations study of triazine morpholino derivatives as mTOR inhibitors for the treatment of breast cancer. Computational Biology and Chemistry, 80, 351–363. https://doi.org/10.1016/j.compbiolchem.2019.04.017
  • Clark, M., Cramer, R. D., & Van Opdenbosch, N. (1989). Validation of the general purpose tripos 5.2 force field. Journal of Computational Chemistry, 10(8), 982–1012. https://doi.org/10.1002/jcc.540100804
  • Consonni, V., Ballabio, D., & Todeschini, R. (2009). Comments on the definition of the Q2 parameter for QSAR validation. Journal of Chemical Information and Modeling, 49(7), 1669–1678. https://doi.org/10.1021/ci900115y
  • Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1), 42717. https://doi.org/10.1038/srep42717
  • 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
  • Ferreira, L. G., & Andricopulo, A. D. (2013). Inhibitors of Trypanosoma brucei trypanothione reductase: Comparative molecular field analysis modeling and structural basis for selective inhibition. Future Medicinal Chemistry, 5(15), 1753–1762. https://doi.org/10.4155/fmc.13.140
  • Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10(5), 449–461. https://doi.org/10.1517/17460441.2015.1032936
  • Golbraikh, A., & Tropsha, A. (2002). Beware of q2! Journal of Molecular Graphics & Modelling, 20(4), 269–276. https://doi.org/10.1016/s1093-3263(01)00123-1
  • Golbraikh, A., & Tropsha, A. (2002). Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Molecular Diversity, 5(4), 231–243. https://doi.org/10.1023/a:1021372108686
  • Gomes, R. A., Genesi, G. L., Maltarollo, V. G., & Trossini, G. H. G. (2017). Quantitative structure-activity relationships (HQSAR, CoMFA, and CoMSIA) studies for COX-2 selective inhibitors. Journal of Biomolecular Structure & Dynamics, 35(7), 1436–1445. https://doi.org/10.1080/07391102.2016.1185379
  • Groendyke, B. J., Nabet, B., Mohardt, M. L., Zhang, H., Peng, K., Koide, E., Coffey, C. R., Che, J., Scott, D. A., Bass, A. J., & Gray, N. S. (2021). Discovery of a pyrimidothiazolodiazepinone as a potent and selective focal adhesion kinase (FAK) inhibitor. ACS Medicinal Chemistry Letters, 12(1), 30–38. https://doi.org/10.1021/acsmedchemlett.0c00338
  • Halperin, I., Ma, B., Wolfson, H., & Nussinov, R. (2002). Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins, 47(4), 409–443. https://doi.org/10.1002/prot.10115
  • Hashemzadeh, H., & Raissi, H. (2018). Covalent organic framework as smart and high efficient carrier for anticancer drug delivery: A DFT calculations and molecular dynamics simulation study. Journal of Physics D: Applied Physics, 51(34), 345401. https://doi.org/10.1088/1361-6463/aad3e8
  • Hospital, A., Goñi, J. R., Orozco, M., & Gelpí, J. L. (2015). Molecular dynamics simulations: Advances and applications. Advances and Applications in Bioinformatics and Chemistry: AABC, 8, 37–47. https://doi.org/10.2147/AABC.S70333
  • Huang, S., Song, C., Wang, X., Zhang, G., Wang, Y., Jiang, X., Sun, Q., Huang, L., Xiang, R., Hu, Y., Li, L., & Yang, S. (2017). Discovery of new SIRT2 inhibitors by utilizing a consensus docking/scoring strategy and structure-activity relationship analysis. Journal of Chemical Information and Modeling, 57(4), 669–679. https://doi.org/10.1021/acs.jcim.6b00714
  • Janati-Fard, F., Housaindokht, M. R., & Monhemi, H. (2016). Investigation of structural stability and enzymatic activity of glucose oxidase and its subunits. Journal of Molecular Catalysis B: Enzymatic, 134, 16–24. https://doi.org/10.1016/j.molcatb.2016.09.008
  • Jian-Bo, T., Yi, F., Tian-Hao, W., & Xing, Z. (2021). QSAR Study of Thieno [2, 3-d] Pyrimidine as a promising scaffold using HQSAR, CoMFA and CoMSIA. Chinese Journal of Structural Chemistry, 40(5), 565–575.
  • Kang, Y., Hu, W., Ivan, C., Dalton, H. J., Miyake, T., Pecot, C. V., Zand, B., Liu, T., Huang, J., Jennings, N. B., Rupaimoole, R., Taylor, M., Pradeep, S., Wu, S. Y., Lu, C., Wen, Y., Huang, J., Liu, J., & Sood, A. K. (2013). Role of focal adhesion kinase in regulating YB-1-mediated paclitaxel resistance in ovarian cancer. Journal of the National Cancer Institute, 105(19), 1485–1495. https://doi.org/10.1093/jnci/djt210
  • Khanna, I. (2012). Drug discovery in pharmaceutical industry: Productivity challenges and trends. Drug Discovery Today, 17(19–20), 1088–1102. https://doi.org/10.1016/j.drudis.2012.05.007
  • Kothandan, G., Gadhe, C. G., Madhavan, T., Choi, C. H., & Cho, S. J. (2011). Docking and 3D-QSAR (quantitative structure activity relationship) studies of flavones, the potent inhibitors of p-glycoprotein targeting the nucleotide binding domain. European Journal of Medicinal Chemistry, 46(9), 4078–4088. https://doi.org/10.1016/j.ejmech.2011.06.008
  • Kumari, M., Dohare, N., Maurya, N., Dohare, R., & Patel, R. (2017). Effect of 1-methyl-3-octyleimmidazolium chloride on the stability and activity of lysozyme: A spectroscopic and molecular dynamics studies. Journal of Biomolecular Structure & Dynamics, 35(9), 2016–2030. https://doi.org/10.1080/07391102.2016.1204946
  • Lechertier, T., & Hodivala-Dilke, K. (2012). Focal adhesion kinase and tumour angiogenesis. The Journal of Pathology, 226(2), 404–412. https://doi.org/10.1002/path.3018
  • Lietha, D., & Eck, M. J. (2008). Crystal structures of the FAK kinase in complex with TAE226 and related bis-anilino pyrimidine inhibitors reveal a helical DFG conformation. PloS One, 3(11), e3800. https://doi.org/10.1371/journal.pone.0003800
  • Liu, X. H., Xu, X. Y., Tan, C. X., Weng, J. Q., Xin, J. H., & Chen, J. (2015). Synthesis, crystal structure, herbicidal activities and 3D-QSAR study of some novel 1,2,4-triazolo[4,3-a]pyridine derivatives. Pest Management Science, 71(2), 292–301. https://doi.org/10.1002/ps.3804
  • Ma, W. W. (2011). Development of focal adhesion kinase inhibitors in cancer therapy. Anti-Cancer Agents in Medicinal Chemistry, 11(7), 638–642. https://doi.org/10.2174/187152011796817628
  • Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
  • Mustafa, M., Abuo-Rahma, G. E. A., Abd El-Hafeez, A. A., Ahmed, E. R., Abdelhamid, D., Ghosh, P., & Hayallah, A. M. (2021). Discovery of antiproliferative and anti-FAK inhibitory activity of 1,2,4-triazole derivatives containing acetamido carboxylic acid skeleton. Bioorganic & Medicinal Chemistry Letters, 40, 127965. https://doi.org/10.1016/j.bmcl.2021.127965
  • Pérez-Areales, F. J., Betari, N., Viayna, A., Pont, C., Espargaró, A., Bartolini, M., De Simone, A., Rinaldi Alvarenga, J. F., Pérez, B., Sabate, R., Lamuela-Raventós, R. M., Andrisano, V., Luque, F. J., & Muñoz-Torrero, D. (2017). Design, synthesis and multitarget biological profiling of second-generation anti-Alzheimer rhein-huprine hybrids. Future Medicinal Chemistry, 9(10), 965–981. https://doi.org/10.4155/fmc-2017-0049
  • Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D., Hess, B., & Lindahl, E. (2013). GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics (Oxford, England), 29(7), 845–854. https://doi.org/10.1093/bioinformatics/btt055
  • Pu, C., Yan, G., Shi, J., & Li, R. (2017). Assessing the performance of docking scoring function, FEP, MM-GBSA, and QM/MM-GBSA approaches on a series of PLK1 inhibitors. MedChemComm, 8(7), 1452–1458. https://doi.org/10.1039/c7md00184c
  • Quispe, P. A., Lavecchia, M. J., & León, I. E. (2022). Focal adhesion kinase inhibitors in the treatment of solid tumors: Preclinical and clinical evidence. Drug Discovery Today, 27(2), 664–674. https://doi.org/10.1016/j.drudis.2021.11.025
  • Rácz, A., Bajusz, D., & Héberger, K. (2015). Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters. SAR and QSAR in Environmental Research, 26(7-9), 683–700. https://doi.org/10.1080/1062936X.2015.1084647
  • Rastelli, G., Del Rio, A., Degliesposti, G., & Sgobba, M. (2010). Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA. Journal of Computational Chemistry, 31(4), 797–810. https://doi.org/10.1002/jcc.21372
  • Roberts, W. G., Ung, E., Whalen, P., Cooper, B., Hulford, C., Autry, C., Richter, D., Emerson, E., Lin, J., Kath, J., Coleman, K., Yao, L., Martinez-Alsina, L., Lorenzen, M., Berliner, M., Luzzio, M., Patel, N., Schmitt, E., LaGreca, S., … Vajdos, F. (2008). Antitumor activity and pharmacology of a selective focal adhesion kinase inhibitor, PF-562,271. Cancer Research, 68(6), 1935–1944. https://doi.org/10.1158/0008-5472.CAN-07-5155
  • Rocha, J. A., Rego, N. C. S., Carvalho, B. T. S., Silva, F. I., Sousa, J. A., Ramos, R. M., Passos, I. N. G., de Moraes, J., Leite, J., & Lima, F. C. A. (2018). Computational quantum chemistry, molecular docking, and ADMET predictions of imidazole alkaloids of Pilocarpus microphyllus with schistosomicidal properties. PloS One, 13(6), e0198476. https://doi.org/10.1371/journal.pone.0198476
  • Roy, K., Das, R. N., Ambure, P., & Aher, R. B. (2016). Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems, 152, 18–33. https://doi.org/10.1016/j.chemolab.2016.01.008
  • Schultze, A., & Fiedler, W. (2011). Clinical importance and potential use of small molecule inhibitors of focal adhesion kinase. Anti-Cancer Agents in Medicinal Chemistry, 11(7), 593–599. https://doi.org/10.2174/187152011796817727
  • Shimizu, T., Fukuoka, K., Takeda, M., Iwasa, T., Yoshida, T., Horobin, J., Keegan, M., Vaickus, L., Chavan, A., Padval, M., & Nakagawa, K. (2016). A first-in-Asian phase 1 study to evaluate safety, pharmacokinetics and clinical activity of VS-6063, a focal adhesion kinase (FAK) inhibitor in Japanese patients with advanced solid tumors. Cancer Chemotherapy and Pharmacology, 77(5), 997–1003. https://doi.org/10.1007/s00280-016-3010-1
  • Sonoda, Y., Matsumoto, Y., Funakoshi, M., Yamamoto, D., Hanks, S. K., & Kasahara, T. (2000). Anti-apoptotic role of focal adhesion kinase (FAK). Induction of inhibitor-of-apoptosis proteins and apoptosis suppression by the overexpression of FAK in a human leukemic cell line, HL-60. The Journal of Biological Chemistry, 275(21), 16309–16315. https://doi.org/10.1074/jbc.275.21.16309
  • Sterling, T., & Irwin, J. J. (2015). ZINC 15–ligand discovery for everyone. Journal of Chemical Information and Modeling, 55(11), 2324–2337. https://doi.org/10.1021/acs.jcim.5b00559
  • Sulzmaier, F. J., Jean, C., & Schlaepfer, D. D. (2014). FAK in cancer: Mechanistic findings and clinical applications. Nature Reviews. Cancer, 14(9), 598–610. https://doi.org/10.1038/nrc3792
  • Surti, M., Patel, M., Adnan, M., Moin, A., Ashraf, S. A., Siddiqui, A. J., Snoussi, M., Deshpande, S., & Reddy, M. N. (2020). Ilimaquinone (marine sponge metabolite) as a novel inhibitor of SARS-CoV-2 key target proteins in comparison with suggested COVID-19 drugs: Designing, docking and molecular dynamics simulation study. RSC Advances, 10(62), 37707–37720. https://doi.org/10.1039/d0ra06379g
  • Tai, Y. L., Chen, L. C., & Shen, T. L. (2015). Emerging roles of focal adhesion kinase in cancer. BioMed Research International, 2015, 690690–690613. https://doi.org/10.1155/2015/690690
  • Tavora, B., Batista, S., Reynolds, L. E., Jadeja, S., Robinson, S., Kostourou, V., Hart, I., Fruttiger, M., Parsons, M., & Hodivala‐Dilke, K. M. (2010). Endothelial FAK is required for tumour angiogenesis. EMBO Molecular Medicine, 2(12), 516–528. https://doi.org/10.1002/emmm.201000106
  • Ton, A. T., Gentile, F., Hsing, M., Ban, F., & Cherkasov, A. (2020). Rapid identification of potential inhibitors of SARS-CoV-2 main protease by deep docking of 1.3 billion compounds. Molecular Informatics, 39(8), e2000028. https://doi.org/10.1002/minf.202000028
  • Tong, W., Lowis, D. R., Perkins, R., Chen, Y., Welsh, W. J., Goddette, D. W., Heritage, T. W., & Sheehan, D. M. (1998). Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor. Journal of Chemical Information and Computer Sciences, 38(4), 669–677. https://doi.org/10.1021/ci980008g
  • Tropsha, A., Gramatica, P., & Gombar, V. K. (2003). The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Combinatorial Science, 22(1), 69–77. https://doi.org/10.1002/qsar.200390007
  • 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
  • Vanommeslaeghe, K., & MacKerell, A. D., Jr. (2012). Automation of the CHARMM General Force Field (CGenFF) I: Bond perception and atom typing. Journal of Chemical Information and Modeling, 52(12), 3144–3154. https://doi.org/10.1021/ci300363c
  • Verma, J., Khedkar, V. M., & Coutinho, E. C. (2010). 3D-QSAR in drug design–a review. Current Topics in Medicinal Chemistry, 10(1), 95–115. https://doi.org/10.2174/156802610790232260
  • Wang, R., Zhao, X., Yu, S., Chen, Y., Cui, H., Wu, T., Hao, C., Zhao, D., & Cheng, M. (2020). Discovery of 7H-pyrrolo[2,3-d]pyridine derivatives as potent FAK inhibitors: Design, synthesis, biological evaluation and molecular docking study. Bioorganic Chemistry, 102, 104092. https://doi.org/10.1016/j.bioorg.2020.104092
  • Wei, W., Feng, Z., Liu, Z., Li, X., He, H., Ran, K., Shi, Y., Zhu, Y., Ye, T., Gao, C., Wang, N., & Yu, L. (2022). Design, synthesis and biological evaluation of 7-((7H-pyrrolo[2,3-d]pyrimidin-4-yl)oxy)-2,3-dihydro-1H-inden-1-one derivatives as potent FAK inhibitors for the treatment of ovarian cancer. European Journal of Medicinal Chemistry, 228, 113978. https://doi.org/10.1016/j.ejmech.2021.113978
  • Wold, S. (1978). Cross-validatory estimation of the number of components in factor and principal components models. Technometrics, 20(4), 397–405. https://doi.org/10.1080/00401706.1978.10489693
  • Wold, S., Eriksson, L., & Clementi, S. (1995). Statistical validation of QSAR results. Chemometric methods in molecular design, 309–338. https://doi.org/10.1002/9783527615452.ch5
  • Wu, X., Wang, J., Liang, Q., Tong, R., Huang, J., Yang, X., Xu, Y., Wang, W., Sun, M., & Shi, J. (2022). Recent progress on FAK inhibitors with dual targeting capabilities for cancer treatment. Biomedicine & Pharmacotherapy = Biomedecine & Pharmacotherapie, 151, 113116. https://doi.org/10.1016/j.biopha.2022.113116
  • Xiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C., Yin, M., Zeng, X., Wu, C., Lu, A., Chen, X., Hou, T., & Cao, D. (2021). ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research, 49(W1), W5–W14. https://doi.org/10.1093/nar/gkab255
  • Yadav, D. K., Khan, F., & Negi, A. S. (2012). Pharmacophore modeling, molecular docking, QSAR, and in silico ADMET studies of gallic acid derivatives for immunomodulatory activity. Journal of Molecular Modeling, 18(6), 2513–2525. https://doi.org/10.1007/s00894-011-1265-3
  • Yan, W., Lin, G., Zhang, R., Liang, Z., & Wu, W. (2020). Studies on the bioactivities and molecular mechanism of antioxidant peptides by 3D-QSAR, in vitro evaluation and molecular dynamic simulations. Food & Function, 11(4), 3043–3052. https://doi.org/10.1039/c9fo03018b
  • Yang, H., Lou, C., Sun, L., Li, J., Cai, Y., Wang, Z., Li, W., Liu, G., & Tang, Y. (2019). admetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties. Bioinformatics (Oxford, England), 35(6), 1067–1069. https://doi.org/10.1093/bioinformatics/bty707
  • Yang, T., Wu, J. C., Yan, C., Wang, Y., Luo, R., Gonzales, M. B., Dalby, K. N., & Ren, P. (2011). Virtual screening using molecular simulations. Proteins, 79(6), 1940–1951. https://doi.org/10.1002/prot.23018
  • Zaboli, M., & Raissi, H. (2017). The influence of nicotine on pioglitazone encapsulation into carbon nanotube: The investigation of molecular dynamic and density functional theory. Journal of Biomolecular Structure & Dynamics, 35(3), 520–534. https://doi.org/10.1080/07391102.2016.1152565
  • Zhang, J., Shan, Y., Pan, X., Wang, C., Xu, W., & He, L. (2011). Molecular docking, 3D-QSAR studies, and in silico ADME prediction of p-aminosalicylic acid derivatives as neuraminidase inhibitors. Chemical Biology & Drug Design, 78(4), 709–717. https://doi.org/10.1111/j.1747-0285.2011.01179.x

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.