Evaluation of a sesquiterpene as possible drug lead against gelatinases via molecular dynamics simulations

References

• Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015). Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1, 19–25. doi:10.1016/j.softx.2015.06.001
   Google Scholar
• Aimes, R. T., & Quigley, J. P. (1995). Matrix metalloproteinase-2 is an interstitial collagenase inhibitor-free enzyme catalyzes the cleavage of collagen fibrils and soluble native type i collagen generating the specific 3/4-and 1/4-length fragments. Journal of Biological Chemistry, 270(11), 5872–5876. doi:10.1074/jbc.270.11.5872
   Google Scholar
• Amadei, A., Linssen, A. B. M., & Berendsen, H. J. C. (1993). Essential dynamics of proteins. Proteins: Structure, Function, and Genetics, 17(4), 412–425. doi:10.1002/prot.340170408
   PubMed Web of Science ®Google Scholar
• Basith, S., Manavalan, B., Gosu, V., & Choi, S. (2013). Evolutionary, structural and functional interplay of the iκ b family members. PloS One, 8(1), e54178. doi:0.1371/journal.pone.0054178 doi:10.1371/journal.pone.0054178
   Google Scholar
• Bayly, C. I., Cieplak, P., Cornell, W., & Kollman, P. A. (1993). A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: The RESP model. Journal of Physical Chemistry, 97(40), 10269–10280. doi:10.1021/j100142a004
   Web of Science ®Google Scholar
• Bennett, C. H. (1976). Efficient estimation of free energy differences from Monte Carlo data. Journal of Computational Physics, 22(2), 245–268. doi:10.1016/0021-9991(76)90078-4
   Web of Science ®Google Scholar
• Bhati, A. P., Wan, S., Wright, D. W., & Coveney, P. V. (2017). Rapid, accurate, precise, and reliable relative free energy prediction using ensemble based thermodynamic integration. Journal of Chemical Theory and Computation, 13(1), 210–222. doi:10.1021/acs.jctc.6b00979
   PubMed Web of Science ®Google Scholar
• Bhuyan, R., & Seal, A. (2019). Exploration and validation of diphosphate-based Plasmodium LytB inhibitors using computational approaches. Journal of Molecular Recognition, 32(2), e2762. doi:10.1002/jmr.2762
   PubMed Web of Science ®Google Scholar
• Brandstetter, H., Grams, F., Glitz, D., Lang, A., Huber, R., Bode, W., Krell, H., & Engh, R. A. (2001). The 1.8-å crystal structure of a matrix metalloproteinase 8-barbiturate inhibitor complex reveals a previously unobserved mechanism for collagenase substrate recognition. Journal of Biological Chemistry, 276(20), 17405–17412. doi:10.1074/jbc.M007475200
   PubMed Web of Science ®Google Scholar
• Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005). The amber biomolecular simulation programs. Journal of Computational Chemistry, 26(16), 1668–1688. doi:10.1002/jcc.20290
   PubMed Web of Science ®Google Scholar
• 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, 42717. doi:10.1038/srep42717
   PubMed Web of Science ®Google Scholar
• David, C. C., & Jacobs, D. J. (2014). Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins. In D. Livesay (Eds.), Protein Dynamics. Methods in Molecular Biology (Methods and Protocols) (Vol. 1084). Totowa, NJ: Humana Press.
   Google Scholar
• Day, J. A., & Cohen, S. M. (2013). Investigating the selectivity of metalloenzyme inhibitors. Journal of Medicinal Chemistry, 56(20), 7997–8007. doi:10.1021/jm401053m
   PubMed Web of Science ®Google Scholar
• Dhanaraj, V., Ye, Q.-Z., Johnson, L. L., Hupe, D. J., Ortwine, D. F., Dunbar, Jr., J. B., Rubin, J. R., Pavlovsky, A., Humblet, C., & Blundell, T. L. (1996). X-ray structure of a hydroxamate inhibitor complex of stromelysin catalytic domain and its comparison with members of the zinc metalloproteinase superfamily. Structure, 4(4), 375–386. doi:10.1016/S0969-2126(96)00043-3
   PubMed Web of Science ®Google Scholar
• Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995). A smooth particle mesh Ewald method. Journal of Chemical Physics, 103(19), 8577–8593. doi:10.1063/1.470117
   Web of Science ®Google Scholar
• Fabre, B., Ramos, A., & de Pascual-Teresa, B. (2014). Targeting matrix metalloproteinases: Exploring the dynamics of the S12032 pocket in the design of selective, small molecule inhibitors: Miniperspective. Journal of Medicinal Chemistry, 57(24), 10205–10219. doi:10.1021/jm500505f
   PubMed Web of Science ®Google Scholar
• Farooq, U., Naz, S., Zehra, B., Khan, A., Ali, S. A., Ahmed, A., Sarwar, R., Bukhari, S. M., Rauf, A., Ahmad, I., & Mabkhot, Y. N. (2017). Isolation and characterization of three new anti-proliferative sesquiterpenes from Polygonum barbatum and their mechanism via apoptotic pathway. BMC Cancer, 17(1), 694. doi:10.1186/s12885-017-3667-9
   PubMed Web of Science ®Google Scholar
• Feng, Y., Likos, J. J., Zhu, L., Woodward, H., Munie, G., McDonald, J. J., Stevens, A. M., Howard, C. P., Crescenzo, G. A. D., & Welsch, D. (2002). Solution structure and backbone dynamics of the catalytic domain of matrix metalloproteinase-2 complexed with a hydroxamic acid inhibitor. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 1598(1–2), 10–23. doi:10.1016/s0167-4838(02)00307-2
   PubMed Web of Science ®Google Scholar
• Fridman, R., Fuerst, T. R., Bird, R. E., Hoyhtya, M., Oelkuct, M., Kraus, S., Komarek, D., Liotta, L. A., Berman, M. L., & Stetler-Stevenson, W. G. (1992). Domain structure of human 72-kDa gelatinase/type IV collagenase. Characterization of proteolytic activity and identification of the tissue inhibitor of metalloproteinase-2 (TIMP-2) binding regions. Journal of Biological Chemistry, 267(22), 15398–15405.
   PubMed Web of Science ®Google Scholar
• Frisch, M. J. E. A., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., Scalmani, G., Barone, V., Mennucci, B., & Petersson, G. (2009). Gaussian 09, revision a. 02. Gaussian Inc.
   Google Scholar
• Gimeno, A., Beltrán-Debón, R., Mulero, M., Pujadas, G., & Garcia-Vallvé, S. (2020). Understanding the variability of the S12032 pocket to improve matrix metalloproteinase inhibitor selectivity profiles. Drug Discovery Today, 25(1), 38–57. doi:10.1016/j.drudis.2019.07.013
   PubMed Web of Science ®Google Scholar
• Götz, A. W., Williamson, M. J., Xu, D., Poole, D., Grand, S. L., & Walker, R. C. (2012). Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. Journal of Chemical Theory and Computation, 8(5), 1542–1555. doi:10.1021/ct200909j
   PubMed Web of Science ®Google Scholar
• Grand, S. L., Götz, A. W., & Walker, R. C. (2013). SPFP: Speed without compromise—A mixed precision model for GPU accelerated molecular dynamics simulations. Computer Physics Communications, 184(2), 374–380. doi:10.1016/j.cpc.2012.09.022
   Web of Science ®Google Scholar
• Gross, J., & Lapiere, C. M. (1962). Collagenolytic activity in amphibian tissues: A tissue culture assay. Proceedings of the National Academy of Sciences, 48(6), 1014–1022. doi:10.1073/pnas.48.6.1014
   PubMed Web of Science ®Google Scholar
• Jablonska, A., Matejczyk, M., & Rosochacki, S. (2016). Matrix metalloproteinases (MMPS) as a target for anticancer drugs. Journal of Enzyme Inhibition and Medicinal Chemistry, 31, 177–183. doi:10.3109/14756366.2016.1161620
   PubMed Web of Science ®Google Scholar
• Jana, S., & Singh, S. K. (2019). Identification of selective MMP-9 inhibitors through multiple e-pharmacophore, ligand-based pharmacophore, molecular docking, and density functional theory approaches. Journal of Biomolecular Structure and Dynamics, 37(4), 944–965. doi:10.1080/07391102.2018.1444510
   PubMed Web of Science ®Google Scholar
• Johnson, A. R., Pavlovsky, A. G., Ortwine, D. F., Prior, F., Man, C.-F., Bornemeier, D. A., Banotai, C. A., Mueller, W. T., McConnell, P., Yan, C., Baragi, V., Lesch, C., Roark, W. H., Wilson, M., Datta, K., Guzman, R., Han, H.-K., & Dyer, R. D. (2007). Discovery and characterization of a novel inhibitor of matrix metalloprotease-13 that reduces cartilage damage in vivo without joint fibroplasia side effects. Journal of Biological Chemistry, 282(38), 27781–27791. doi:10.1074/jbc.M703286200
   PubMed Web of Science ®Google Scholar
• Joshi, T., Joshi, T., Sharma, P., Chandra, S., & Pande, V. (2020). Molecular docking and molecular dynamics simulation approach to screen natural compounds for inhibition of Xanthomonas oryzae pv. Oryzae by targeting peptide deformylase. Journal of Biomolecular Structure and Dynamics. doi:10.1080/07391102.2020.1719200
   PubMed Web of Science ®Google Scholar
• Kalva, S., Singam, E. R. A., Rajapandian, V., Saleena, L. M., & Subramanian, V. (2014). Discovery of potent inhibitor for matrix metalloproteinase-9 by pharmacophore based modeling and dynamics simulation studies. Journal of Molecular Graphics and Modelling, 49, 25–37. doi:10.1016/j.jmgm.2013.12.008
   PubMed Web of Science ®Google Scholar
• Kumar, G., & Patnaik, R. (2018). Inhibition of gelatinases (MMP-2 and MMP-9) by Withania somnifera phytochemicals confers neuroprotection in stroke: An in silico analysis. Interdisciplinary Sciences: Computational Life Sciences, 10(4), 722–733. doi:10.1007/s12539-017-0231-x
   PubMed Web of Science ®Google Scholar
• Lee, A., Fan, C., Chen, Y., Cheng, C., Sung, Y., Hsu, C., & Kao, T. (2015). Curcumin inhibits invasiveness and epithelial-mesenchymal transition in oral squamous cell carcinoma through reducing matrix metalloproteinase 2, 9 and modulating p53-E-cadherin pathway. Integrative Cancer Therapies, 14(5), 484–490. doi:10.1177/1534735415588930
   PubMed Web of Science ®Google Scholar
• Lee, H., Hsu, W., Liu, A., Hsu, C., & Sun, Y. (2014). Using thermodynamic integration MD simulation to compute relative protein–ligand binding free energy of a GSK3β kinase inhibitor and its analogs. Journal of Molecular Graphics and Modelling, 51, 37–49. doi:10.1016/j.jmgm.2014.04.010
   PubMed Web of Science ®Google Scholar
• Li, P., & Merz, K. M. (2016). MCPB.py: A python based metal center parameter builder. Journal of Chemical Information and Modeling, 56(4), 599–604. doi:10.1021/acs.jcim.5b00674
   PubMed Web of Science ®Google Scholar
• Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J. L., Dror, R. O., & Shaw, D. E. (2010). Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins, 78(8), 1950–1958. doi:10.1002/prot.22711
   PubMed Web of Science ®Google Scholar
• Marcial, B. L., Sousa, S. F., Santos, H. F. D., & Ramos, M. J. (2014). Structural and dynamics analysis of matrix metalloproteinases MMP-2 complexed with chemically modified tetracyclines (CMTs). Journal of Biomolecular Structure and Dynamics, 32(12), 1907–1918. doi:10.1080/07391102.2013.842186
   PubMed Web of Science ®Google Scholar
• Mills, J. H., Khare, S. D., Bolduc, J. M., Forouhar, F., Mulligan, V. K., Lew, S., Seetharaman, J., Tong, L., Stoddard, B. L., & Baker, D. (2013). Computational design of an unnatural amino acid dependent metalloprotein with atomic level accuracy. Journal of the American Chemical Society, 135(36), 13393–13399. doi:10.1021/ja403503m
   PubMed Web of Science ®Google Scholar
• Morgunova, E., Tuuttila, A., Bergmann, U., Isupov, M., Lindqvist, Y., Schneider, G., & Tryggvason, K. (1999). Structure of human pro-matrix metalloproteinase-2: Activation mechanism revealed. Science, 284(5420), 1667–1670. doi:10.1126/science.284.5420.1667
   PubMed Web of Science ®Google Scholar
• Nagase, H., & Woessner, J. F. (1999). Matrix metalloproteinases. Journal of Biological Chemistry, 274, 21491–21494. doi:10.1074/jbc.274.31.21491
   PubMed Web of Science ®Google Scholar
• Nuti, E., Cantelmo, A. R., Gallo, C., Bruno, A., Bassani, B., Camodeca, C., Tuccinardi, T., Vera, L., Orlandini, E., Nencetti, S., Stura, E. A., Martinelli, A., Dive, V., Albini, A., & Rossello, A. (2015). N-o-isopropyl sulfonamido-based hydroxamates as matrix metalloproteinase inhibitors: Hit selection and in vivo antiangiogenic activity. Journal of Medicinal Chemistry, 58(18), 7224–7240. doi:10.1021/acs.jmedchem.5b00367
   PubMed Web of Science ®Google Scholar
• Peters, M. B., Yang, Y., Wang, B., Füsti-Molnár, L., Weaver, M. N., & Merz, K. M. (2010). Structural survey of zinc-containing proteins and development of the zinc AMBER force field (ZAFF). Journal of Chemical Theory and Computation, 6(9), 2935–2947. doi:10.1021/ct1002626
   PubMed Web of Science ®Google Scholar
• 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. doi:10.1002/jcc.20084
   PubMed Web of Science ®Google Scholar
• Rempe, R. G., Hartz, A. M. S., & Bauer, B. (2016). Matrix metalloproteinases in the brain and blood–brain barrier: Versatile breakers and makers. Journal of Cerebral Blood Flow and Metabolism, 36(9), 1481–1507. doi:10.1177/0271678X16655551
   PubMed Web of Science ®Google Scholar
• Ryckaert, J., Ciccotti, G., & Berendsen, H. J. C. (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. doi:10.1016/0021-9991(77)90098-5
   Web of Science ®Google Scholar
• Salomon-Ferrer, R., Case, D. A., & Walker, R. C. (2013). An overview of the amber biomolecular simulation package. Wiley Interdisciplinary Reviews: Computational Molecular Science, 3(2), 198–210. doi:10.1002/wcms.1121
   Web of Science ®Google Scholar
• 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. doi:10.1021/ct400314y
   PubMed Web of Science ®Google Scholar
• Shamsara, J. (2018). Identification of non-zinc binding inhibitors of MMP-2 through virtual screening and subsequent rescoring. Drug Research, 68(09), 529–535. doi:10.1055/a-0586-8308
   Google Scholar
• Shi, Z., Li, J., Shi, L., & Li, X. (2012). An updated patent therapeutic agents targeting mmps. Recent Patents on Anti-Cancer Drug Discovery, 7(1), 74–101. doi:0.2174/157489212798357976 doi:10.2174/157489212798357976
   PubMed Web of Science ®Google Scholar
• Singh, A., Somvanshi, P., & Grover, A. (2019). Pyrazinamide drug resistance in RpsA mutant (Δ438A) of Mycobacterium tuberculosis: Dynamics of essential motions and free-energy landscape analysis. Journal of Cellular Biochemistry, 120(5), 7386–7402. doi:10.1002/jcb.28013
   Web of Science ®Google Scholar
• Singh, T., Adekoya, O. A., & Jayaram, B. (2015). Understanding the binding of inhibitors of matrix metalloproteinases by molecular docking, quantum mechanical calculations, molecular dynamics simulations, and a MMGBSA/MMBappl study. Molecular BioSystems, 1(4), 1041–1051. doi:10.1039/C5MB00003C
   Google Scholar
• Sixto-López, Y., Bello, M., & Correa-Basurto, J. (2019). Insights into structural features of HDAC1 and its selectivity inhibition elucidated by molecular dynamic simulation and molecular docking. Journal of Biomolecular Structure and Dynamics, 37(3), 584–610. doi:10.1080/07391102.2018.1441072
   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. doi:10.1002/jcc.21334
   PubMed Web of Science ®Google Scholar
• Verma, A., Rizvi, S. M. D., Shaikh, S., Ansari, M. A., Shakil, S., Ghazal, F., Siddiqui, M. H., Haneef, M., & Rehman, A. (2014). Compounds isolated from Ageratum houstonianum inhibit the activity of matrix metalloproteinases (MMP-2 and MMP-9): An oncoinformatics study. Pharmacognosy Magazine, 10(37), 18. doi:10.4103/0973-1296.126653
   PubMed Web of Science ®Google Scholar
• Wang, J., Cieplak, P., & Kollman, P. A. (2000). How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? Journal of Computational Chemistry, 21(12), 1049–1074. doi:10.1002/1096-987X(200009)21:12¡1049::AID-JCC3¿3.0.CO;2-F
   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. doi:10.1002/jcc.20035
   PubMed Web of Science ®Google Scholar
• Wart, H. E. V., & Birkedal-Hansen, H. (1990). The cysteine switch: A principle of regulation of metalloproteinase activity with potential applicability to the entire matrix metalloproteinase gene family. Proceedings of the National Academy of Sciences, 87(14), 5578–5582. doi:10.1073/pnas.87.14.5578
   PubMed Web of Science ®Google Scholar
• Whittaker, M., Floyd, C. D., Brown, P., & Gearing, A. J. H. (1999). Design and therapeutic application of matrix metalloproteinase inhibitors. Chemical Reviews, 99(9), 2735–2776. doi:10.1021/cr9804543
   PubMed Web of Science ®Google Scholar
• Yao, C., Chen, B., Kang, Z., Liu, Y., Qi, X., & Wang, Q. (2020). Binding, selectivity and sequence recognition of matrix metalloproteinase-2 to oligopeptides. Journal of Biomolecular Structure and Dynamics., 38(1), 275–282. doi:10.1080/07391102.2019.1578694
   PubMed Web of Science ®Google Scholar
• Zehra, B., Ahmed, A., Sarwar, R., Khan, A., Farooq, U., Abid Ali, S., & Al-Harrasi, A. (2019). Apoptotic and antimetastatic activities of betulin isolated from Quercus incana against non-small cell lung cancer cells. Cancer Management and Research, Volume 11, 1667–1683. doi:10.2147/CMAR.S186956
   Web of Science ®Google Scholar