Experimental and computational evaluation of dipeptidyl peptidase III inhibitors based on quinazolinone-Schiff’s bases

References

• Abramić, M., Zubanović, M., & Vitale, L. (1988). Dipeptidyl peptidase III from human erythrocytes. Biological Chemistry Hoppe-Seyler, 369(1), 29–38. https://doi.org/10.1515/bchm3.1988.369.1.29
   PubMed Web of Science ®Google Scholar
• Agić, D., Brkić, H., Tomić, S., Karačić, Z., Špoljarević, M., Lisjak, M., Bešlo, D., & Abramić, M. (2017). Validation of flavonoids as potential dipeptidyl peptidase III inhibitors: Experimental and computational approach. Chemical Biology & Drug Design, 89(4), 619–627. https://doi.org/10.1111/cbdd.12887
   PubMed Web of Science ®Google Scholar
• Agić, D., Karnaš, M., Šubarić, D., Lončarić, M., Tomić, S., Karačić, Z., Bešlo, D., Rastija, V., Molnar, M., Popović, B. M., & Lisjak, M. (2021). Coumarin derivatives act as novel inhibitors of human dipeptidyl peptidase III: Combined in vitro and in silico study. Pharmaceuticals, 14(6), 540. https://doi.org/10.3390/ph14060540
   Web of Science ®Google Scholar
• Baršun, M., Jajčanin, N., Vukelić, B., Špoljarić, J., & Abramić, M. (2007). Human dipeptidyl peptidase III acts as a post-proline-cleaving enzyme on endomorphins. Biological Chemistry, 388(3), 343–348. https://doi.org/10.1515/BC.2007.039
   PubMed Web of Science ®Google Scholar
• Bezerra, G. A., Dobrovetsky, E., Viertlmayr, R., Dong, A., Binter, A., Abramić, M., Macheroux, P., Dhe-Paganon, S., & Gruber, K. (2012). Entropy-driven binding of opioid peptides induces a large domain motion in human dipeptidyl peptidase III. Proceedings of the National Academy of Sciences of the United States of America, 109(17), 6525–6530. https://doi.org/10.1073/pnas.1118005109
   PubMed Web of Science ®Google Scholar
• Blagojević, B., Agić, D., Serra, A. T., Matić, S., Matovina, M., Bijelić, S., & Popović, B. M. (2021). An in vitro and in silico evaluation of bioactive potential of cornelian cherry (Cornus mas L.) extracts rich in polyphenols and iridoids. Food Chemistry, 335, 127619. https://doi.org/10.1016/j.foodchem.2020.127619
   PubMed Web of Science ®Google Scholar
• Case, D., Betz, R., Cerutti, D., Cheatham, T. E. III, Darden, T., Duke, R. E., Giese, T., Gohlke, H., Goetz, A. W., & Homeyer, N. (2016). AMBER 2016. University of California.
   Google Scholar
• Ćehić, M., Suć Sajko, J., Karačić, Z., Piotrowski, P., Šmidlehner, T., Jerić, I., Schmuck, C., Piantanida, I., & Tomić, S. (2020). The guanidiniocarbonylpyrrole-fluorophore conjugates as theragnostic tools for dipeptidyl peptidase III monitoring and inhibition. Journal of Biomolecular Structure & Dynamics, 38(13), 3790–3800. https://doi.org/10.1080/07391102.2019.1664936
   PubMed Web of Science ®Google Scholar
• Chen, J. M., & Barrett, A. J. (2004). Dipeptidyl-peptidase III. In A. J. Barrett, N. D. Rawlings, & J. F. Woessner (Eds.), Handbook of proteolytic enzymes. (2nd ed., Vol 1, pp. 809–812). Elsevier Academic Press.
   Google Scholar
• Chen, J., Liu, X., Zhang, S., Chen, J., Sun, H., Zhang, L., & Zhang, Q. (2020). Molecular mechanism with regard to the binding selectivity of inhibitors toward FABP5 and FABP7 explored by multiple short molecular dynamics simulations and free energy analyses. Physical Chemistry Chemical Physics : PCCP, 22(4), 2262–2275. https://doi.org/10.1039/c9cp05704h
   PubMed Web of Science ®Google Scholar
• Consonni, V., & Todeschini, R. (2011). Structure-Activity relationships by autocorrelation descriptors and genetic algorithms. In H. Lodhi & Y. Yamanishi (Eds.), Chemoinformatics and advanced machine learning perspectives: Complex computational methods and collaborative techniques (pp. 60–94). IGI Global. https://doi.org/10.4018/978-1-61520-911-8.ch005
   Google Scholar
• Dassault Systèmes BIOVIA. (2019). Discovery studio visualizer, release 2019. Dassault Systèmes.
   Google Scholar
• Eriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T. D., McDowell, R. M., & Gramatica, P. (2003). Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environmental Health Perspectives, 111(10), 1361–1375. https://doi.org/10.1289/ehp.5758
   PubMed Web of Science ®Google Scholar
• Estrada, E., & Ramirez, A. (1996). Edge adjacency relationships and molecular topographic descriptors. Definition and QSAR applications. Journal of Chemical Information and Computer Sciences, 36(4), 837–843. https://doi.org/10.1021/ci950186z
   Google Scholar
• Gramatica, P. (2007). Principles of QSAR models validation: Internal and external. QSAR & Combinatorial Science, 26(5), 694–701. https://doi.org/10.1002/qsar.200610151
   Google Scholar
• Gramatica, P., Chirico, N., Papa, E., Cassani, S., & Kovarich, S. (2013). QSARINS: A new software for the development,analysis, and validation of QSAR MLR models. Journal of Computational Chemistry, 34(24), 2121–2132. https://doi.org/10.1002/jcc.23361
   Web of Science ®Google Scholar
• GraphPad Prism version 5.0.0 for Windows. GraphPad Prism version 5.0.0 for Windows. GraphPad Software, San Diego, California USA, www.graphpad.com
   Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Harney, A. S., Sole, L. B., & Meade, T. J. (2012). Kinetics and thermodynamics of irreversible inhibition of matrix metalloproteinase 2 by a Co(III) Schiff base complex. Journal of Biological Inorganic Chemistry : JBIC : A Publication of the Society of Biological Inorganic Chemistry, 17(6), 853–860. https://doi.org/10.1007/s00775-012-0902-3
   PubMed Web of Science ®Google Scholar
• He, M., Mangiameli, D. P., Kachala, S., Hunter, K., Gillespie, J., Bian, X., Shen, H.-C. J., & Libutti, S. K. (2010). Expression signature developed from a complex series of mouse models accurately predicts human breast cancer survival. Clinical Cancer Research, 16(1), 249–259. https://doi.org/10.1158/1078-0432.CCR-09-1602
   PubMed Web of Science ®Google Scholar
• Hocquet, A., & Langgård, M. (1998). An evaluation of the MM + force field. Journal of Molecular Modeling. 4(3), 94–112. https://doi.org/10.1007/s008940050128
   Web of Science ®Google Scholar
• Hricovíniová, Z., Hricovíni, M., & Kozics, K. (2018). New series of quinazolinone derived Schiff’s bases: synthesis, spectroscopic properties and evaluation of their antioxidant and cytotoxic activity. Chemical Papers, 72(4), 1041–1053. https://doi.org/10.1007/s11696-017-0345-y
   Web of Science ®Google Scholar
• Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD-visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33–38. https://doi.org/10.1016/0263-7855(96)00018-5
   PubMedGoogle Scholar
• Jha, S., Taschler, U., Domenig, O., Poglitsch, M., Bourgeois, B., Pollheimer, M., Pusch, L. M., Malovan, G., Frank, S., Madl, T., Gruber, K., Zimmermann, R., & Macheroux, P. (2020). Dipeptidyl peptidase 3 modulates the renin-angiotensin system in mice. The Journal of Biological Chemistry, 295(40), 13711–13723. https://doi.org/10.1074/jbc.RA120.014183
   PubMed Web of Science ®Google Scholar
• Kajal, A., Bala, S., Kamboj, S., Sharma, N., & Saini, V. (2013). Schiff bases: A versatile pharmacophore. Journal of Catalysts, 2013, 893512. https://doi.org/10.1155/2013/893512
   Google Scholar
• Karačić, Z., Špoljarić, J., Rožman, M., & Abramić, M. (2012). Molecular determinants of human dipeptidyl peptidase sensitivity to thiol modifying reagents. Biological Chemistry, 393(12), 1523–1532. https://doi.org/10.1515/hsz-2012-0181
   PubMed Web of Science ®Google Scholar
• Kiralj, R., & Ferreira, M. M. C. (2009). Basic validation procedures for regression models in QSAR and QSPR studies: Theory and application. Journal of the Brazilian Chemical Society, 20(4), 770–787. https://doi.org/10.1590/S0103-50532009000400021
   Web of Science ®Google Scholar
• Komar, M., Prašnikar, F., Gazivoda Kraljević, T., Aladić, K., & Molnar, M. (2021). 3-Amino-2-methylquinazolin-4-(3H)-one Schiff bases synthesis - a green chemistry approach - a comparison of microwave and ultrasound promoted synthesis with mechanosynthesis. Current Green Chemistry, 8(1), 62–69. https://doi.org/10.2174/2213346107999201231125434
   Web of Science ®Google Scholar
• Kumar, P., Reithofer, V., Reisinger, M., Wallner, S., Pavkov-Keller, T., Macheroux, P., & Gruber, K. (2016). Substrate complexes of human dipeptidyl peptidase III reveal the mechanism of enzyme inhibition. Scientific Reports, 6, 23787. https://doi.org/10.1038/srep23787
   PubMed Web of Science ®Google Scholar
• Li, J. J., Nahra, J., Johnson, A. R., Bunker, A., O'Brien, P., Yue, W.-S., Ortwine, D. F., Man, C.-F., Baragi, V., Kilgore, K., Dyer, R. D., & Han, H.-K. (2008). Quinazolinones and pyrido[3,4-d]pyrimidin-4-ones as orally active and specific matrix metalloproteinase-13 inhibitors for the treatment of osteoarthritis. Journal of Medicinal Chemistry, 51(4), 835–841. https://doi.org/10.1021/jm701274v
   PubMed Web of Science ®Google Scholar
• Liu, Y., Kern, J. T., Walker, J. R., Johnson, J. A., Schultz, P. G., & Luesch, H. (2007). A genomic screen for activators of the antioxidant response element. Proceedings of the National Academy of Sciences of the United States of America, 104(12), 5205–5210. https://doi.org/10.1073/pnas.0700898104
   PubMed Web of Science ®Google Scholar
• Lu, K., Alcivar, A. L., Ma, J., Foo, T. K., Zywea, S., Mahdi, A., Huo, Y., Kensler, T. W., Gatza, M. L., & Xia, B. (2017). NRF2 induction supporting breast cancer cell survival is enabled by oxidative stress–induced DPP3–KEAP1 interaction. Cancer Research, 77(11), 2881–2892. https://doi.org/10.1158/0008-5472.CAN-16-2204
   PubMed Web of Science ®Google Scholar
• Maier, J. A., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K. E., & Simmerling, C. (2015). ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. Journal of Chemical Theory and Computation, 11(8), 3696–3713. https://doi.org/10.1021/acs.jctc.5b00255
   PubMed Web of Science ®Google Scholar
• Matić, J., Šupljika, F., Tir, N., Piotrowski, P., Schmuck, C., Abramić, M., Piantanida, I., & Tomić, S. (2016). Guanidiniocarbonyl-pyrrole-aryl conjugates as inhibitors of human dipeptidyl peptidase III: combined experimental and computational study. RSC Advances, 6(86), 83044–83052. https://doi.org/10.1039/C6RA16966J
   Web of Science ®Google Scholar
• Matić, S., Kekez, I., Tomin, M., Bogár, F., Šupljika, F., Kazazić, S., Hanić, M., Jha, S., Brkić, H., & Bourgeois, B. (2020). Binding of dipeptidyl peptidase III to the oxidative stress cell sensor Kelch-like ECH-associated protein 1 is a two-step process. Journal of Biomolecular Structure and Dynamics, 39(18), 1–12. https://doi.org/10.1080/07391102.2020.1804455
   Web of Science ®Google Scholar
• Pang, X., Shimizu, A., Kurita, S., Zankov, D. P., Takeuchi, K., Yasuda‐Yamahara, M., Kume, S., Ishida, T., & Ogita, H. (2016). Novel therapeutic role for dipeptidyl peptidase III in the treatment of hypertension. Hypertension (Dallas, Tex. : 1979), 68(3), 630–641. https://doi.org/10.1161/HYPERTENSIONAHA.116.07357
   PubMed Web of Science ®Google Scholar
• Prajapati, S. C., & Chauhan, S. S. (2016). Human dipeptidyl peptidase III mRNA variant I and II are expressed concurrently in multiple tumor derived cell lines and translated at comparable efficiency in vitro. Molecular Biology Reports, 43(6), 457–462. https://doi.org/10.1007/s11033-016-3996-9
   PubMed Web of Science ®Google Scholar
• Radwan, A. A., & Alanazi, F. K. (2020). Biological activity of quinazolinones. In A.G. Al-Kaf (Eds.), Quinazolinone and quinazoline derivatives. IntechOpen. https://doi.org/10.5772/intechopen.90621
   Google Scholar
• Rakesh, K. P., Manukumar, H. M., & Channe Gowda, D. (2015). Schiff’s bases of quinazolinone derivatives: Synthesis and SAR studies of a novel series of potential anti-inflammatory and antioxidants. Bioorganic & Medicinal Chemistry Letters, 25(5), 1072–1077. https://doi.org/10.1016/j.bmcl.2015.01.010
   PubMed Web of Science ®Google Scholar
• Ryckaert, J. P., Ciccotti, G., & Berendsen, H. J. (1977). Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. Journal of Computational Physics. 23(3), 327–341. https://doi.org/10.1016/0021-9991(77)90098-5
   Web of Science ®Google Scholar
• Sato, H., Kimura, K., Yamamoto, Y., & Hazato, T. (2003). Activity of DPP III in human cerebrospinal fluid derived from patients with pain. Masui, 52, 257–263.
   PubMedGoogle Scholar
• Shi, N., Zheng, Q., & Zhang, H. (2020). Molecular dynamics investigations of binding mechanism for triazoles inhibitors to CYP51. Frontiers in Molecular Biosciences, 7, 586540. https://doi.org/10.3389/fmolb.2020.586540.
   PubMed Web of Science ®Google Scholar
• Šimaga, Š., Babić, D., Osmak, M., Ilić-Forko, J., Vitale, L., Miličić, D., & Abramić, M. (1998). Dipeptidyl peptidase III in malignant and non-malignant gynaecological tissue. Eur. J. Cancer,.34(3), 399–405. https://doi.org/10.1016/S0959-8049(97)00401-2
   PubMed Web of Science ®Google Scholar
• Šimaga, Š., Babić, D., Osmak, M., Šprem, M., & Abramić, M. (2003). Tumor cytosol dipeptidyl peptidase III activity is increased with histological aggressiveness of ovarian primary carcinomas. Gynecologic Oncology, 91(1), 194–200. https://doi.org/10.1016/S0090-8258(03)00462-1
   PubMed Web of Science ®Google Scholar
• Spartan ’08. (2009). Wavefunction, Inc.; Irvine, CA, USA.
   Google Scholar
• Špoljarić, J., Salopek-Sondi, B., Makarević, J., Vukelić, B., Agić, D., Šimaga, Š., Jajcanin-Jozić, N., & Abramić, M. (2009). Absolutely conserved tryptophan in M49 family of peptidases contributes to catalysis and binding of competitive inhibitors. Bioorganic Chemistry, 37(3), 70–76. https://doi.org/10.1016/j.bioorg.2009.03.002
   PubMed Web of Science ®Google Scholar
• Stewart, J. J. P. (1989). Optimization of parameters for semiempirical methods I. Method. Journal of Computational Chemistry, 10(2), 209–220. https://doi.org/10.1002/jcc.540100208
   Web of Science ®Google Scholar
• Tetko, I. V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D., Ertl, P., Palyulin, V. A., Radchenko, E. V., Zefirov, N. S., Makarenko, A. S., Tanchuk, V. Y., & Prokopenko, V. V. (2005). Virtual computational chemistry laboratory-Design and description. Journal of Computer-Aided Molecular Design, 19(6), 453–463. https://doi.org/10.1007/s10822-005-8694-y
   PubMed Web of Science ®Google Scholar
• Todeschini, R., & Consonni, V. (2009). Molecular descriptors for chemoinformatics. 2nd ed. Wiley‐VCH.
   Google Scholar
• Todeschini, R., Consonni, V., & Maiocchi, A. (1999). The K correlation index: Theory development and its application in chemometrics. Chemometrics and Intelligent Laborary Systems. 46(1), 13–29. https://doi.org/10.1016/S0169-7439(98)00124-5
   Web of Science ®Google Scholar
• Tomić, A., & Tomić, S. (2014). Hunting the human DPP III active conformation: combined thermodynamic and QM/MM calculations. Dalton Transactions (Cambridge, England : 2003), 43(41), 15503–15514. https://doi.org/10.1039/c4dt02003k
   PubMed Web of Science ®Google Scholar
• Tomić, A., & Tomić, S. (2022). demystifying dpp iii catalyzed peptide hydrolysis-computational study of the complete catalytic cycle of human DPP III catalyzed tynorphin hydrolysis. International Journal of Molecular Sciences, 23(3), 1858. https://doi.org/10.3390/ijms23031858
   PubMed Web of Science ®Google Scholar
• Tomić, A., Berynskyy, M., Wade, R. C., & Tomić, S. (2015). Molecular simulations reveal that the long range fluctuations of human DPP III change upon ligand binding. Molecular BioSystems, 11(11), 3068–3080. https://doi.org/10.1039/C5MB00465A
   PubMedGoogle Scholar
• Tomić, A., Gonzalez, M., & Tomić, S. (2012). The large scale conformational change of the human DPP III - substrate prefers the "closed" form. Journal of Chemical Information and Modeling, 52(6), 1583–1594. https://doi.org/10.1021/ci300141k
   PubMed Web of Science ®Google Scholar
• Tomić, A., Horvat, G., Ramek, M., Agić, D., Brkić, H., & Tomić, S. (2019). New zinc Ion parameters suitable for classical MD simulations of zinc metallopeptidases. Journal of Chemical Information and Modeling, 59(8), 3437–3453. https://doi.org/10.1021/acs.jcim.9b00235
   PubMed Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Wang, J., 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
   PubMed Web of Science ®Google Scholar
• Zhang, H., Yamamoto, Y., Shumiya, S., Kunimatsu, M., Nishi, K., Ohkubo, I., & Kani, K. (2001). Peptidases play an important role in cataractogenesis: An immunohistochemical study on lenses derived from shumiya cataract rats. The Histochemical Journal, 33(9/10), 511–521.[ Mismatch] https://doi.org/10.1023/A:1014943522613
   PubMedGoogle Scholar
• Zhang, S., Lv, S., Fu, X., Han, L., Han, W., & Li, W. (2021). Molecular dynamics simulations study of the interactions between human dipeptidyl-peptidase III and two substrates. Molecules, 26(21), 6492. https://doi.org/10.3390/molecules26216492
   PubMed Web of Science ®Google Scholar