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Research Article

Evaluation of the efficacy of marine natural products against PARP-1/2 proteins in high-grade serous ovarian cancer: insights into MD and SMD simulations

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Received 22 Jan 2024, Accepted 20 Mar 2024, Published online: 17 Jun 2024

References

  • Arend, R. C., Scalise, C. B., Gordon, E. R., Davis, A. M., Foxall, M. E., Johnston, B. E., Crossman, D. K., & Cooper, S. J. (2022). Metabolic alterations and WNT signaling impact immune response in HGSOC. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 28(7), 1433–1445. https://doi.org/10.1158/1078-0432.CCR-21-2984
  • Asangba, A. E., Chen, J., Goergen, K. M., Larson, M. C., Oberg, A. L., Casarin, J., Multinu, F., Kaufmann, S. H., Mariani, A., Chia, N., & Walther-Antonio, M. R. S. (2023). Diagnostic and prognostic potential of the microbiome in ovarian cancer treatment response. Scientific Reports, 13(1), 730. https://doi.org/10.1038/s41598-023-27555-x
  • Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018). ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1), W257–W263. https://doi.org/10.1093/nar/gky318
  • Barkauskaite, E., Jankevicius, G., & Ahel, I. (2015). Structures and mechanisms of enzymes employed in the synthesis and degradation of PARP-dependent protein ADP-ribosylation. Molecular Cell, 58(6), 935–946. https://doi.org/10.1016/j.molcel.2015.05.007
  • Bayly, C. I., Cieplak, P., Cornell, W. D., & Kollman, P. A. (1993). A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: The RESP model. The Journal of Physical Chemistry, 97(40), 10269–10280. https://doi.org/10.1021/j100142a004
  • Berendsen, H. J. C., van der Spoel, D., & van Drunen, R. (1995). GROMACS: A message-passing parallel molecular dynamics implementation. Computer Physics Communications, 91(1–3), 43–56. https://doi.org/10.1016/0010-4655(95)00042-E
  • Bürkle, A., Brabeck, C., Diefenbach, J., & Beneke, S. (2005). The emerging role of poly(ADP-ribose) polymerase-1 in longevity. The International Journal of Biochemistry & Cell Biology, 37(5), 1043–1053. https://doi.org/10.1016/j.biocel.2004.10.006
  • Carroll, A. R., Copp, B. R., Davis, R. A., Keyzers, R. A., & Prinsep, M. R. (2023). Marine natural products. Natural Product Reports, 40(2), 275–325. https://doi.org/10.1039/d2np00083k
  • 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
  • DeLano, W. L. (2002). Pymol: An open-source molecular graphics tool. CCP4 Newsletter on Protein Crystallography, 40, 82–92. http://www.ccp4.ac.uk/newsletters/newsletter40/11_pymol.pdf
  • Desoubzdanne, D., Marcourt, L., Raux, R., Chevalley, S., Dorin, D., Doerig, C., Valentin, A., Ausseil, F., & Debitus, C. (2008). Alisiaquinones and Alisiaquinol, dual inhibitors of Plasmodium falciparum enzyme targets from a New Caledonian deep water sponge. Journal of Natural Products, 71(7), 1189–1192. https://doi.org/10.1021/np8000909
  • Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995). A smooth particle mesh Ewald method. The Journal of Chemical Physics, 103(19), 8577–8593. https://doi.org/10.1063/1.470117
  • Ferreira, L. L. G., & Andricopulo, A. D. (2019). ADMET modeling approaches in drug discovery. Drug Discovery Today, 24(5), 1157–1165. https://doi.org/10.1016/j.drudis.2019.03.015
  • Field, J. J., Pera, B., Gallego, J. E., Calvo, E., Rodríguez-Salarichs, J., Sáez-Calvo, G., Zuwerra, D., Jordi, M., Andreu, J. M., Prota, A. E., Ménchon, G., Miller, J. H., Altmann, K. H., & Díaz, J. F. (2018). Zampanolide binding to tubulin indicates cross-talk of taxane site with colchicine and nucleotide sites. Journal of Natural Products, 81(3), 494–505. https://doi.org/10.1021/acs.jnatprod.7b00704
  • Field, J. J., Singh, A. J., Kanakkanthara, A., Halafihi, T., Northcote, P. T., & Miller, J. H. (2009). Microtubule-stabilizing activity of zampanolide, a potent macrolide isolated from the Tongan marine sponge Cacospongia mycofijiensis. Journal of Medicinal Chemistry, 52(22), 7328–7332. https://doi.org/10.1021/jm901249g
  • Garcia-Carbonero, R., Supko, J. G., Maki, R. G., Manola, J., Ryan, D. P., Harmon, D., Puchalski, T. A., Goss, G., Seiden, M. V., Waxman, A., Quigley, M. T., Lopez, T., Sancho, M. A., Jimeno, J., Guzman, C., & Demetri, G. D. (2005). Ecteinascidin-743 (ET-743) for chemotherapy-naive patients with advanced soft tissue sarcomas: Multicenter phase II and pharmacokinetic study. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 23(24), 5484–5492. https://doi.org/10.1200/JCO.2005.05.028
  • Gudd, C. L. C., & Possamai, L. A. (2022). The role of myeloid cells in hepatotoxicity related to cancer immunotherapy. Cancers, 14(8), 1913. https://doi.org/10.3390/cancers14081913
  • Gul, W., & Hamann, M. T. (2005). Indole alkaloid marine natural products: An established source of cancer drug leads with considerable promise for the control of parasitic, neurological and other diseases. Life Sciences, 78(5), 442–453. https://doi.org/10.1016/j.lfs.2005.09.007
  • Hess, B., Bekker, H., Berendsen, H. J. C., & Fraaije, J. G. E. M. (1997). LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry, 18(12), 1463–1472. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H
  • Hohenstein, E. G., Chill, S. T., & Sherrill, C. D. (2008). Assessment of the performance of the M05-2X and M06-2X exchange-correlation functionals for noncovalent interactions in biomolecules. Journal of Chemical Theory and Computation, 4(12), 1996–2000. https://doi.org/10.1021/ct800308k
  • Hu, Y.-X., Fei, J.-W., Bie, L.-H., & Gao, J. (2023). Simulation of the ligand-leaving process of the human heat shock protein. Physical Chemistry Chemical Physics, 25(41), 28465–28472. https://doi.org/10.1039/d3cp03372d
  • Kaku, T., Ogawa, S., Kawano, Y., Ohishi, Y., Kobayashi, H., Hirakawa, T., & Nakano, H. (2003). Histological classification of ovarian cancer. Medical Electron Microscopy: Official Journal of the Clinical Electron Microscopy Society of Japan, 36(1), 9–17. https://doi.org/10.1007/s007950300002
  • Kim, M. Y., Zhang, T., & Kraus, W. L. (2005). Poly(ADP-Ribosyl)Ation by PARP-1: ‘PAR-Laying’ NAD+ into a Nuclear Signal. Genes and Development, 19, 1951–1967. https://doi.org/10.1101/gad.1331805
  • Kumari, R., Kumar, R., & Lynn, A, (2014). G-MMPBSA: A GROMACS tool for high-throughput MM-PBSA calculations. Journal of Chemical Information and Modeling, 54(7), 1951–1962. https://doi.org/10.1021/ci500020m
  • Kurman, R. J. (2013). Origin and molecular pathogenesis of ovarian high-grade serous carcinoma. Annals of Oncology: Official Journal of the European Society for Medical Oncology, 24 Suppl 10(10), x16–x21. https://doi.org/10.1093/annonc/mdt463
  • Lee, J. Y., Kim, S., Kim, Y. T., Lim, M. C., Lee, B., Jung, K. W., Kim, J. W., Park, S. Y., & Won, Y. J. (2018). Changes in ovarian cancer survival during the 20 years before the era of targeted therapy. BMC Cancer, 18(1), 601. https://doi.org/10.1186/s12885-018-4498-z
  • Leung, A. K. L. (2014). Poly(ADP-ribose): An organizer of cellular architecture. The Journal of Cell Biology, 205(5), 613–619. https://doi.org/10.1083/jcb.201402114
  • 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: Structure, Function, and Bioinformatics, 78(8), 1950–1958. https://doi.org/10.1002/prot.22711
  • Liu, Z., Qiu, P., Li, J., Chen, G., Chen, Y., Liu, H., & She, Z. (2018). Anti-inflammatory polyketides from the mangrove-derived fungus Ascomycota sp. SK2YWS-L. Tetrahedron, 74(7), 746–751. https://doi.org/10.1016/j.tet.2017.12.057
  • Madannejad, R., Shoaie, N., Jahanpeyma, F., Darvishi, M. H., Azimzadeh, M., & Javadi, H. (2019). Toxicity of carbon-based nanomaterials: Reviewing recent reports in medical and biological systems. Chemico-Biological Interactions, 307, 206–222. https://doi.org/10.1016/j.cbi.2019.04.036
  • Maier, J. A., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K., Simmerling, C., & Hauser, K. E. (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
  • Manasaryan, G., Suplatov, D., Pushkarev, S., Drobot, V., Kuimov, A., Švedas, V., & Nilov, D. (2021). Bioinformatic analysis of the nicotinamide binding site in poly(ADP‐ribose) polymerase family proteins. Cancers, 13(6), 1201. https://doi.org/10.3390/cancers13061201
  • Mark, P., & Nilsson, L. (2001). Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. The Journal of Physical Chemistry A, 105(43), 9954–9960. https://doi.org/10.1021/jp003020w
  • Matulonis, U. A., Sood, A. K., Fallowfield, L., Howitt, B. E., Sehouli, J., & Karlan, B. Y. (2016). Ovarian cancer. Nature Reviews. Disease Primers, 2(1), 16061. https://doi.org/10.1038/nrdp.2016.61
  • Ngo, S. T., Vu, K. B., Bui, L. M., & Vu, V. V. (2019). Effective estimation of ligand-binding affinity using biased sampling method. ACS Omega, 4(2), 3887–3893. https://doi.org/10.1021/acsomega.8b03258
  • Peyraud, F., & Italiano, A. (2020). Combined PARP inhibition and immune checkpoint therapy in solid tumors. Cancers, 12(6), 1502. https://doi.org/10.3390/cancers12061502
  • Piskunova, T. S., Yurova, M. N., Ovsyannikov, A. I., Semenchenko, A. V., Zabezhinski, M. A., Popovich, I. G., Wang, Z.-Q., & Anisimov, V. N. (2008). Deficiency in poly(ADP-ribose) polymerase-1 (PARP-1) accelerates aging and spontaneous carcinogenesis in mice. Current Gerontology and Geriatrics Research, 2008, 754190–754111. https://doi.org/10.1155/2008/754190
  • Pomponi, S. A. (1999). The bioprocess-technological potential of the sea. Progress in Industrial Microbiology, 35(C), 5–13. https://doi.org/10.1016/S0079-6352(99)80092-7
  • Priyankha, S., Rajapandian, V., Palanisamy, K., Esther Rubavathy, S. M., Thilagavathi, R., Selvam, C., & Prakash, M. (2023). Identification of indole-based natural compounds as inhibitors of PARP-1 against triple-negative breast cancer: A computational study. Journal of Biomolecular Structure & Dynamics, 42(5), 2667–2680. https://doi.org/10.1080/07391102.2023.2208215
  • Ruiz-Schutz, V. C., Gomes, L. M., Mariano, R. C., de Almeida, D. V. P., Pimenta, J. M., Dal Molin, G. Z., Kater, F. R., Yamamura, R., Correa Neto, N. F., Maluf, F. C., & Schutz, F. A. (2019). Risk of fatigue and anemia in patients with advanced cancer treated with olaparib: A meta-analysis of randomized controlled trials. Critical Reviews in Oncology/Hematology, 141, 163–173. https://doi.org/10.1016/j.critrevonc.2019.06.012
  • Sarfaraj, H. M., Sheeba, F., Saba, A., & Khan, M. S. (2012). Marine natural products: A lead for anti-cancer. Indian Journal of Geo-Marine Sciences, 41(1), 27–39.
  • Schwede, T., Kopp, J., Guex, N., & Peitsch, M. C. (2003). SWISS-MODEL: An automated protein homology-modeling server. Nucleic Acids Research, 31(13), 3381–3385. https://doi.org/10.1093/nar/gkg520
  • Shirts, M. R., Klein, C., Swails, J. M., Yin, J., Gilson, M. K., Mobley, D. L., Case, D. A., & Zhong, E. D. (2017). Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset. Journal of Computer-Aided Molecular Design, 31(1), 147–161. https://doi.org/10.1007/s10822-016-9977-1
  • Tao, J., Sun, D., Zhou, H., Zhu, J., Zhang, X., & Hou, H. (2022). Next-generation sequencing identifies potential novel therapeutic targets in Chinese HGSOC patients. Pathology Research and Practice, 238, 154074. https://doi.org/10.1016/j.prp.2022.154074
  • Tavernelli, I., Cotesta, S., & Di Iorio, E. E. (2003). Protein dynamics, thermal stability, and free-energy landscapes: A molecular dynamics investigation. Biophysical Journal, 85(4), 2641–2649. https://doi.org/10.1016/S0006-3495(03)74687-6
  • Torre, L. A., Trabert, B., DeSantis, C. E., Miller, K. D., Samimi, G., Runowicz, C. D., Gaudet, M. M., Jemal, A., & Siegel, R. L. (2018). Ovarian cancer statistics, 2018. A Cancer Journal for Clinicians, 68(4), 284–296. https://doi.org/10.3322/caac.21456
  • Trott, O., & Olson, A. J. (2009). 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
  • Van Gunsteren, W. F., & Berendsen, H. J. C. (1988). A leap-frog algorithm for stochastic dynamics. Molecular Simulation, 1(3), 173–185. https://doi.org/10.1080/08927028808080941
  • Wang, H. Q., Li, H. L., Han, J. L., Feng, Z. P., Deng, H. X., & Han, X. (2023). MMDAE-HGSOC: A novel method for high-grade serous ovarian cancer molecular subtypes classification based on multi-modal deep autoencoder. Computational Biology and Chemistry, 105, 107906. https://doi.org/10.1016/j.compbiolchem.2023.107906
  • William, H., & Dalke Andrew, S. K. (1996). Visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33–38. https://doi.org/10.1016/j.carbon.2017.07.012
  • Xi, Y., Zhang, Y., Zheng, K., Zou, J., Gui, L., Zou, X., Chen, L., Hao, J., & Zhang, Y. (2023). A chemotherapy response prediction model derived from tumour-promoting B and Tregs and proinflammatory macrophages in HGSOC. Frontiers in Oncology, 13, 1171582. https://doi.org/10.3389/fonc.2023.1171582
  • Xu, Y., Zhang, Q., Miao, C., Dongol, S., Li, Y., Jin, C., Dong, R., Li, Y., Yang, X., & Kong, B. (2019). CCNG1 (Cyclin G1) regulation by mutant-P53 via induction of Notch3 expression promotes high-grade serous ovarian cancer (HGSOC) tumorigenesis and progression. Cancer Medicine, 8(1), 351–362. https://doi.org/10.1002/cam4.1812
  • Yu, S.-W., Andrabi, S. A., Wang, H., Kim, N. S., Poirier, G. G., Dawson, T. M., & Dawson, V. L. (2006). Apoptosis-inducing factor mediates poly(ADP-ribose) (PAR) polymer-induced cell death. Proceedings of the National Academy of Sciences of the United States of America, 103(48), 18314–18319. https://doi.org/10.1073/pnas.0606528103
  • Zuo, X., Zhao, H., & Li, D. (2021). Systematic inhibitor selectivity between PARP1 and PARP2 enzymes: Molecular implications for ovarian cancer personalized therapy. Journal of Molecular Recognition, 34(7), e2891. https://doi.org/10.1002/jmr.2891

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