Identification of potential human chymase inhibitors using molecular docking and molecular dynamics simulation
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Abstract
Chymase is a hydrolase class of enzymes that involves hydrolysis of peptide bonds. It is abundant in secretory granules of mast cells. Mast cell chymase is involved in the synthesis of angiotensin-II from its precursor protein. In addition, chymase is involved in converting TGF-β and matrix metalloproteinase to their active form. Chymase involved in heart failure has been proven, and its inhibition may reduce the progression. Hence, to identify the potential inhibitors against the chymase, the present study employed structure-based virtual screening, molecular docking, and molecular dynamics simulation to identify potential chymase inhibitors. Initially, compounds were selected based on their physicochemical and pharmacokinetic properties. Further, the binding affinities using molecular docking and interaction analyses were performed to find potential chymase inhibitors. The study identified chymase inhibitor ZINC000008382327, bearing significant binding affinity, specificity, and efficacy towards the chymase. Next, the stability and binding mode of chymase with ZINC000008382327 were assessed using molecular dynamics simulations. The simulation analysis using root mean square deviations and fluctuations revealed that inhibitor ZINC000008382327 affected the structure and dynamics of the chymase protein. It was also shown that the chymase forms a stable complex with ZINC000008382327 during the simulation. Thus, the present computational study put forward that the identified compound can be further exploited as a potential chemical scaffold to design and develop new human chymase inhibitors.
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Keywords
ADMET, Docking, Mast cell chymase, Molecular dynamics simulation
References
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Biovia, D.S.(2015).Discovery Studio Modeling Environment. San Diego, Dassault Syst. Release, San Diego, 4.
Case, D.A., Cheatham, T., Darden,T., Gohlke, H., Luo,R., Merz, K.M., Onufriev, C., Simmerling, B. & Woods, R. (2005). The amber biomolecular simulation programs. J. Computat.Chem., 26, 1668-1688.
Caughey, G.H. (2016). Mast cell proteases as pharmacological targets. Eur J Pharmacol., 778, 44-55.
Chou, K.C. & Mao, B. (2004). Collective motion in DNA and its role in drug intercalation. Biopolymers., 27: 1795–1815.
Chou, K.C., Zhang, C.T. & Maggiora, G.M. (1994). Solitary wave dynamics as a mechanism for explaining the internal motion during microtubule growth. Biopolymers., 34: 143–153.
Daina, A., Michielin, O. & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep., 7, 42717.
DeLano, W.L. (2002). The PyMOL Molecular Graphics System. Delano Scientific, San Carlos.
Doggrell, S.A. & Wanstall, J.C. (2004). Vascular chymase: Pathophysiological role and therapeutic potential of inhibition. Cardiovasc. Res., 61(4), 653-62.
Humphrey, W., Dalke, A. & Schulten, K.(1996).VMD: Visual molecular dynamics. J Mol Graph., 14(1), 33-38.
Kervinen, J., Crysler, C., Bayoumy, S., Abad, C.M., Spurlino, J., Deckman, I., Greco, N.M., Maryanoff, E.B. & Garavilla, D.W. (2010). Potency variation of small-molecule chymase inhibitors across species. Biochem Pharmacol., 80(7), 1033-41.
Khan, F. R., Kazmi, S. M. R., Iqbal, N. T., Iqbal, J., Ali, S. T. & Abbas, S. A. (2020). A Quadruple Blind, Randomised Controlled Trial of Gargling Agents in Reducing Intraoral Viral Load Among Hospitalised COVID-19 Patients: A Structured Summary of a Study Protocol for a Randomised Controlled Trial. Trials 21, 1–4.
Kumbhar, B.V. & Bhandare, V.V. (2021). Exploring the interaction of Peloruside-A with drdrug-resistant βII and αβIII tubulin isotypes in human ovarian carcinoma using a molecular modeling approach. J Biomol Struct Dyn., 39(6), 1990-2002.
Kumbhar, B.V., Bhandare, V.V., Panda, D. & Kunwar, A. (2020). Delineating the interaction of combretastatin A-4 with αβ tubulin isotypes present in drug resistant human lung carcinoma using a molecular modeling approach. J Biomol Struct Dyn., 38(2), 426-438.
Kumbhar, B.V., Borogaon, A., Panda, D. & Kunwar, A. (2016). Exploring the origin of differential binding affinities of human tubulin isotypes αβII, αβIII and αβIV for DAMA-colchicine using homology modelling, molecular docking and molecular dynamics simulations. PLoS One., 11(5), e0156048.
Kushwaha, P.P., Singh, A.K., Bansal, T., et al. (2021). Identification of Natural Inhibitors Against SARS-CoV-2 Drugable Targets Using Molecular Docking, Molecular Dynamics Simulation, and MM-PBSA Approach. Front Cell Infect Microbiol., 11:730288.
Lagunin, A., Stepanchikova, A., Filimonov, D. & Poroikov, V. (2000). PASS: Prediction of activity spectra for biologically active substances. Bioinformatics., 16(8), 747-748.
Leskinen, M.J., Lindstedt, K.A., Wang, Y. & Kovanen, P.T. (2003). Mast cell chymase induces smooth muscle cell apoptosis by a mechanism involving fibronectin degradation and disruption of focal adhesions. Arterioscler Thromb Vasc Biol., 23(2), 238-43.
Lundequist, A., Abrink, M. & Pejler, G. (2006). Mast cell-dependent activation of pro matrix metalloprotease 2: A role for serglycin proteoglycan-dependent mast cell proteases. Biological Chemistry., 387 (10-11), 1513-1519.
Miyazaki, M., Takai, S. (2001). Local angiotensin II-generating system in vascular tissues: The roles of chymase. Hypertens. Res., 24(3), 189-93.
Morris, G.M., Ruth, H., Lindstrom, W., Sanner, F.M., Belew, K.R., Goodsell, S.D. & Olson, J.A. (2009). Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem., 30, 2785-2791.
Mpiana, P. T., Tshibangu, D. S., Kilembe, J. T., Gbolo, B. Z., Mwanangombo, D. T., Inkoto, C. L., et al. (2020). Aloe Vera (L.) Burm. F. As a Potential Anti-COVID-19 Plant: A Mini-Review of Its Antiviral Activity. Eur. J. Med. Plants., 31, 86–93.
Nesari, T. M., Bhardwaj, A., ShriKrishna, R., Ruknuddin, G., Ghildiyal, S., Das, A., et al. (2021). Neem (Azadirachta Indica A. Juss) Capsules for Prophylaxis of COVID-19 Infection: A Pilot, Double-Blind, Randomized Controlled Trial. Altern. Ther. Health Med., 23, 1–8.
Pejler, G. (2020). Novel Insight into the in vivo Function of Mast Cell Chymase: Lessons from Knockouts and Inhibitors. J. Innate Immun., 12(5), 357-372.
Roszkowska-Chojecka, M.M., Walkowska, A., Gawrys, O., Baranowska, I., Kalisz, M., Litwiniuk, A., Martynska, L. & Jezierska, K.E. (2015). Effects of chymostatin, a chymase inhibitor, on blood pressure, plasma and tissue angiotensin II, renal haemodynamics and renal excretion in two models of hypertension in the rat. Exp Physiol., 100 (9),1093-105.
Sterling, T. & Irwin, J.J. (2015). ZINC15 Ligand Discovery for Everyone. J Chem Inf Model., 55(11), 2324-37.
Takai, S. & Jin, D., Sakaguchi, M., Katayama, S., Muramatsu, M., Sakaguchi, M., Matsumura, E., Kim, S., Miyazaki, M. (2003). A novel chymase inhibitor, 4-[1-{[bis-(4-methyl-phenyl)-methyl]-carbamoyl}-3- (2-ethoxy-benzyl)-4-oxo-azetidine-2-yloxy]-benzoic acid (BCEAB), suppressed cardiac fibrosis in cardiomyopathic hamsters. J Pharmacol Exp Ther., 305(1), 17-23.
Tani, M., Harimaya, K., Gyobu, Y., Sasaki, T., Takenouchi, O., Kawamura, T., Kamimura, T. & Harada, T.(2004). SF2809 compounds, novel chymase inhibitors from Dactylosporangium sp. 1. Taxonomy, fermentatation, isolation and biological properties. J Antibiot (Tokyo)., 57(2), 83-8.
Tchougounova, E., Lundequist, A., Fajardo, I., Winberg, J., Abrink, M. & Pejler, G. (2005). A key role for mast cell chymase in the activation of pro-matrix metalloprotease-9 and pro-matrix metalloprotease-2. J Biol Chem., 280(10), 9291-9296.
Tomimori, Y., Manno, A., Tanaka, T., Takahashi, F.J., Muto, T., Nagahira, K. (2019). ASB17061, a novel chymase inhibitor, prevented the development of angiotensin II-induced abdominal aortic aneurysm in apolipoprotein E-deficient mice. Eur J Pharmacol., 5, 856, 172403.
Trott, O. & Olson, A.J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem., 31(2), 455-461.
Turner, P.J. (2005). XMGRACE, Version 5.1.19. Center for Coastal and Land-Margin Research, Oergon Graduate Institute of Science and Technology, Beaverton.
VanDer, S.D., Lindahl, E., Hess, B., Groenhof, G., Mark, E.A. & Berendsen, J.C.H. (2005). GROMACS: Fast, flexible, and free. J. Comput. Chem., 26, 1701-1718.
Wang, J.F. & Chou, K.C. (2009). Insight into the molecular switch mechanism of human Rab5a from molecular dynamics simulations. Biochemical and Biophysical Research Communications, 390, 608–612.
Warnecke, A., Sandalova, T., Achour, A. & Harris, R.A. (2014). PyTMs: a useful PyMOL plugin for modeling common post-translational modifications. BMC Bioinformatics., 15, 370.
Zhang, M., Huang, W., Bai, J., Nie, X. & Wang, W. (2016). Chymase inhibition protects diabetic rats from renal lesions. Mol Med Rep., 14(1), 121-8.
Arooj, M., Kim, S., Sakkiah, S., Cao, P.G., Lee, Y. & Lee W.K. (2013). Molecular Modeling Study for Inhibition Mechanism of Human Chymase and Its Application in Inhibitor Design. PLoS One., 8(4), e62740.
Balkrishna, A., Pokhrel, S., Singh, H., Joshi, M., Mulay, V. P., Haldar, S., et al. (2021). Withanone from Withania Somnifera Attenuates SARS-CoV-2 RBD and Host ACE2 Interactions to Rescue Spike Protein Induced Pathologies in Humanized Zebrafish Model. Drug Des. Devel. Ther., 15, 1111–1133.
Barret, R.N.(2018). Lipinski’s rule of five. Therapeutical Chemistry, 1(1), 7-100.
Biovia, D.S.(2015).Discovery Studio Modeling Environment. San Diego, Dassault Syst. Release, San Diego, 4.
Case, D.A., Cheatham, T., Darden,T., Gohlke, H., Luo,R., Merz, K.M., Onufriev, C., Simmerling, B. & Woods, R. (2005). The amber biomolecular simulation programs. J. Computat.Chem., 26, 1668-1688.
Caughey, G.H. (2016). Mast cell proteases as pharmacological targets. Eur J Pharmacol., 778, 44-55.
Chou, K.C. & Mao, B. (2004). Collective motion in DNA and its role in drug intercalation. Biopolymers., 27: 1795–1815.
Chou, K.C., Zhang, C.T. & Maggiora, G.M. (1994). Solitary wave dynamics as a mechanism for explaining the internal motion during microtubule growth. Biopolymers., 34: 143–153.
Daina, A., Michielin, O. & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep., 7, 42717.
DeLano, W.L. (2002). The PyMOL Molecular Graphics System. Delano Scientific, San Carlos.
Doggrell, S.A. & Wanstall, J.C. (2004). Vascular chymase: Pathophysiological role and therapeutic potential of inhibition. Cardiovasc. Res., 61(4), 653-62.
Humphrey, W., Dalke, A. & Schulten, K.(1996).VMD: Visual molecular dynamics. J Mol Graph., 14(1), 33-38.
Kervinen, J., Crysler, C., Bayoumy, S., Abad, C.M., Spurlino, J., Deckman, I., Greco, N.M., Maryanoff, E.B. & Garavilla, D.W. (2010). Potency variation of small-molecule chymase inhibitors across species. Biochem Pharmacol., 80(7), 1033-41.
Khan, F. R., Kazmi, S. M. R., Iqbal, N. T., Iqbal, J., Ali, S. T. & Abbas, S. A. (2020). A Quadruple Blind, Randomised Controlled Trial of Gargling Agents in Reducing Intraoral Viral Load Among Hospitalised COVID-19 Patients: A Structured Summary of a Study Protocol for a Randomised Controlled Trial. Trials 21, 1–4.
Kumbhar, B.V. & Bhandare, V.V. (2021). Exploring the interaction of Peloruside-A with drdrug-resistant βII and αβIII tubulin isotypes in human ovarian carcinoma using a molecular modeling approach. J Biomol Struct Dyn., 39(6), 1990-2002.
Kumbhar, B.V., Bhandare, V.V., Panda, D. & Kunwar, A. (2020). Delineating the interaction of combretastatin A-4 with αβ tubulin isotypes present in drug resistant human lung carcinoma using a molecular modeling approach. J Biomol Struct Dyn., 38(2), 426-438.
Kumbhar, B.V., Borogaon, A., Panda, D. & Kunwar, A. (2016). Exploring the origin of differential binding affinities of human tubulin isotypes αβII, αβIII and αβIV for DAMA-colchicine using homology modelling, molecular docking and molecular dynamics simulations. PLoS One., 11(5), e0156048.
Kushwaha, P.P., Singh, A.K., Bansal, T., et al. (2021). Identification of Natural Inhibitors Against SARS-CoV-2 Drugable Targets Using Molecular Docking, Molecular Dynamics Simulation, and MM-PBSA Approach. Front Cell Infect Microbiol., 11:730288.
Lagunin, A., Stepanchikova, A., Filimonov, D. & Poroikov, V. (2000). PASS: Prediction of activity spectra for biologically active substances. Bioinformatics., 16(8), 747-748.
Leskinen, M.J., Lindstedt, K.A., Wang, Y. & Kovanen, P.T. (2003). Mast cell chymase induces smooth muscle cell apoptosis by a mechanism involving fibronectin degradation and disruption of focal adhesions. Arterioscler Thromb Vasc Biol., 23(2), 238-43.
Lundequist, A., Abrink, M. & Pejler, G. (2006). Mast cell-dependent activation of pro matrix metalloprotease 2: A role for serglycin proteoglycan-dependent mast cell proteases. Biological Chemistry., 387 (10-11), 1513-1519.
Miyazaki, M., Takai, S. (2001). Local angiotensin II-generating system in vascular tissues: The roles of chymase. Hypertens. Res., 24(3), 189-93.
Morris, G.M., Ruth, H., Lindstrom, W., Sanner, F.M., Belew, K.R., Goodsell, S.D. & Olson, J.A. (2009). Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem., 30, 2785-2791.
Mpiana, P. T., Tshibangu, D. S., Kilembe, J. T., Gbolo, B. Z., Mwanangombo, D. T., Inkoto, C. L., et al. (2020). Aloe Vera (L.) Burm. F. As a Potential Anti-COVID-19 Plant: A Mini-Review of Its Antiviral Activity. Eur. J. Med. Plants., 31, 86–93.
Nesari, T. M., Bhardwaj, A., ShriKrishna, R., Ruknuddin, G., Ghildiyal, S., Das, A., et al. (2021). Neem (Azadirachta Indica A. Juss) Capsules for Prophylaxis of COVID-19 Infection: A Pilot, Double-Blind, Randomized Controlled Trial. Altern. Ther. Health Med., 23, 1–8.
Pejler, G. (2020). Novel Insight into the in vivo Function of Mast Cell Chymase: Lessons from Knockouts and Inhibitors. J. Innate Immun., 12(5), 357-372.
Roszkowska-Chojecka, M.M., Walkowska, A., Gawrys, O., Baranowska, I., Kalisz, M., Litwiniuk, A., Martynska, L. & Jezierska, K.E. (2015). Effects of chymostatin, a chymase inhibitor, on blood pressure, plasma and tissue angiotensin II, renal haemodynamics and renal excretion in two models of hypertension in the rat. Exp Physiol., 100 (9),1093-105.
Sterling, T. & Irwin, J.J. (2015). ZINC15 Ligand Discovery for Everyone. J Chem Inf Model., 55(11), 2324-37.
Takai, S. & Jin, D., Sakaguchi, M., Katayama, S., Muramatsu, M., Sakaguchi, M., Matsumura, E., Kim, S., Miyazaki, M. (2003). A novel chymase inhibitor, 4-[1-{[bis-(4-methyl-phenyl)-methyl]-carbamoyl}-3- (2-ethoxy-benzyl)-4-oxo-azetidine-2-yloxy]-benzoic acid (BCEAB), suppressed cardiac fibrosis in cardiomyopathic hamsters. J Pharmacol Exp Ther., 305(1), 17-23.
Tani, M., Harimaya, K., Gyobu, Y., Sasaki, T., Takenouchi, O., Kawamura, T., Kamimura, T. & Harada, T.(2004). SF2809 compounds, novel chymase inhibitors from Dactylosporangium sp. 1. Taxonomy, fermentatation, isolation and biological properties. J Antibiot (Tokyo)., 57(2), 83-8.
Tchougounova, E., Lundequist, A., Fajardo, I., Winberg, J., Abrink, M. & Pejler, G. (2005). A key role for mast cell chymase in the activation of pro-matrix metalloprotease-9 and pro-matrix metalloprotease-2. J Biol Chem., 280(10), 9291-9296.
Tomimori, Y., Manno, A., Tanaka, T., Takahashi, F.J., Muto, T., Nagahira, K. (2019). ASB17061, a novel chymase inhibitor, prevented the development of angiotensin II-induced abdominal aortic aneurysm in apolipoprotein E-deficient mice. Eur J Pharmacol., 5, 856, 172403.
Trott, O. & Olson, A.J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem., 31(2), 455-461.
Turner, P.J. (2005). XMGRACE, Version 5.1.19. Center for Coastal and Land-Margin Research, Oergon Graduate Institute of Science and Technology, Beaverton.
VanDer, S.D., Lindahl, E., Hess, B., Groenhof, G., Mark, E.A. & Berendsen, J.C.H. (2005). GROMACS: Fast, flexible, and free. J. Comput. Chem., 26, 1701-1718.
Wang, J.F. & Chou, K.C. (2009). Insight into the molecular switch mechanism of human Rab5a from molecular dynamics simulations. Biochemical and Biophysical Research Communications, 390, 608–612.
Warnecke, A., Sandalova, T., Achour, A. & Harris, R.A. (2014). PyTMs: a useful PyMOL plugin for modeling common post-translational modifications. BMC Bioinformatics., 15, 370.
Zhang, M., Huang, W., Bai, J., Nie, X. & Wang, W. (2016). Chymase inhibition protects diabetic rats from renal lesions. Mol Med Rep., 14(1), 121-8.
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Identification of potential human chymase inhibitors using molecular docking and molecular dynamics simulation. (2023). Journal of Applied and Natural Science, 15(1), 1-8. https://doi.org/10.31018/jans.v15i1.3324
