Phytochemicals as potential inhibitors of interleukin-8 for anticancer therapy: in silico evaluation and molecular dynamics analysis

References

• Beg, A., Khan, F. I., Lobb, K. A., Islam, A., Ahmad, F., & Hassan, M. I. (2019). High throughput screening, docking, and molecular dynamics studies to identify potential inhibitors of human calcium/calmodulin-dependent protein kinase IV. Journal of Biomolecular Structure & Dynamics, 37(8), 2179–2192. https://doi.org/10.1080/07391102.2018.1479310
   PubMed Web of Science ®Google Scholar
• Berendsen, H. J. C., Grigera, J. R., & Straatsma, T. P. (1987). The missing term in effective pair potentials. Journal of Physical Chemistry, 91(24), 6269–6271. https://doi.org/10.1021/j100308a038
   Web of Science ®Google Scholar
• Bizzarri, C., Allegretti, M., Bitondo, R., Cervellera, M., Colotta, F., & Bertini, R. (2003). Pharmacological inhibition of interleukin-8 (CXCL8) as a new approach for the prevention and treatment of several human diseases. Current Medicinal Chemistry - Anti-Inflammatory & anti-Allergy Agents, 2(1), 67–79. https://doi.org/10.2174/1568014033355844
   Google Scholar
• Darden, T., York, D., & Pedersen, L. (1993). Particle mesh Ewald: An N log (N) method for Ewald sums in large systems. The Journal of Chemical Physics, 98(12), 10089–10092. https://doi.org/10.1063/1.464397
   Web of Science ®Google Scholar
• DeLano, W. L. (2002). Pymol: An open-source molecular graphics tool. CCP4 Newsl. Protein Crystallogr, 40(1), 82–92.
   Google Scholar
• Ha, H., Debnath, B., & Neamati, N. (2017). Role of the CXCL8-CXCR1/2 axis in cancer and inflammatory diseases. Theranostics, 7(6), 1543–1588. https://doi.org/10.7150/thno.15625
   PubMed Web of Science ®Google Scholar
• Hess, B., Bekker, H., Berendsen, H. J., & Fraaije, J. G. (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
   Web of Science ®Google Scholar
• Hollingsworth, S. A., & Dror, R. O. (2018). Molecular dynamics simulation for all. Neuron, 99(6), 1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011
   PubMed Web of Science ®Google Scholar
• Huey, R., Morris, G. M., & Forli, S. (2012). Using AutoDock 4 and AutoDock vina with AutoDockTools: A tutorial. The Scripps Research Institute Molecular Graphics Laboratory, 10550(92037), 1000.
   Google Scholar
• Kabsch, W., & Sander, C. (1983). Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22(12), 2577–2637. https://doi.org/10.1002/bip.360221211
   PubMed Web of Science ®Google Scholar
• Kitchen, D. B., Decornez, H., Furr, J. R., & Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Reviews. Drug Discovery, 3(11), 935–949. https://doi.org/10.1038/nrd1549
   PubMed Web of Science ®Google Scholar
• Lagunin, A., Stepanchikova, A., Filimonov, D., & Poroikov, V. (2000). PASS: Prediction of activity spectra for biologically active substances. Bioinformatics (Oxford, England), 16(8), 747–748. https://doi.org/10.1093/bioinformatics/16.8.747
   PubMed Web of Science ®Google Scholar
• Lipinski, C. A. (2004). Lead-and drug-like compounds: The rule-of-five revolution. Drug Discovery Today. Technologies, 1(4), 337–341. https://doi.org/10.1016/j.ddtec.2004.11.007
   PubMedGoogle Scholar
• Lippitz, B. E. (2013). Cytokine patterns in patients with cancer: A systematic review. The Lancet. Oncology, 14(6), e218-228–e228. https://doi.org/10.1016/S1470-2045(12)70582-X
   Web of Science ®Google Scholar
• Maisuradze, G. G., Liwo, A., & Scheraga, H. A. (2009). Principal component analysis for protein folding dynamics. Journal of Molecular Biology, 385(1), 312–329. https://doi.org/10.1016/j.jmb.2008.10.018
   PubMed Web of Science ®Google Scholar
• Mallamace, F., Corsaro, C., Mallamace, D., Vasi, S., Vasi, C., Baglioni, P., Buldyrev, S. V., Chen, S.-H., & Stanley, H. E. (2016). Energy landscape in protein folding and unfolding. Proceedings of the National Academy of Sciences of the United States of America, 113(12), 3159–3163. https://doi.org/10.1073/pnas.1524864113
   PubMed Web of Science ®Google Scholar
• Matsushima, K., Baldwin, E. T., & Mukaida, N. (1992). Interleukin-8 and MCAF: Novel leukocyte recruitment and activating cytokines. Chemical Immunology, 51, 236–265.
   PubMedGoogle Scholar
• McGibbon, R. T., Beauchamp, K. A., Harrigan, M. P., Klein, C., Swails, J. M., Hernández, C. X., Schwantes, C. R., Wang, L.-P., Lane, T. J., & Pande, V. S. (2015). MDTraj: A modern open library for the analysis of molecular dynamics trajectories. Biophysical Journal, 109(8), 1528–1532. https://doi.org/10.1016/j.bpj.2015.08.015
   PubMed Web of Science ®Google Scholar
• Nguyen, T. T., Viet, M. H., & Li, M. S. (2014). Effects of water models on binding affinity: evidence from all-atom simulation of binding of tamiflu to A/H5N1 neuraminidase. TheScientificWorldJournal, 2014, 536084. https://doi.org/10.1155/2014/536084
   PubMedGoogle Scholar
• Papaleo, E., Mereghetti, P., Fantucci, P., Grandori, R., & De Gioia, L. (2009). Free-energy landscape, principal component analysis, and structural clustering to identify representative conformations from molecular dynamics simulations: The myoglobin case. Journal of Molecular Graphics & Modelling, 27(8), 889–899. https://doi.org/10.1016/j.jmgm.2009.01.006
   PubMed Web of Science ®Google Scholar
• Parrinello, M., & Rahman, A. (1980). Crystal structure and pair potentials: a molecular-dynamics study. Physical Review Letters, 45(14), 1196–1199. https://doi.org/10.1103/PhysRevLett.45.1196
   Web of Science ®Google Scholar
• Santegoets, S., Welters, M., & Van Der Burg, S. (2016). Monitoring of the Immune Dysfunction in Cancer Patients. Vaccines, 4(3), 29. https://doi.org/10.3390/vaccines4030029
   Web of Science ®Google Scholar
• Shityakov, S., & Förster, C. (2014). In silico structure-based screening of versatile P-glycoprotein inhibitors using polynomial empirical scoring functions. Advances and Applications in Bioinformatics and Chemistry: AABC, 7, 1–9. Published 2014 Mar 24. https://doi.org/10.2147/AABC.S56046
   PubMedGoogle Scholar
• Studio, D. (2008). Discovery studio. Accelrys, [2.1]
   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
• Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. C. (2005). GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 26(16), 1701–1718. https://doi.org/10.1002/jcc.20291
   PubMed Web of Science ®Google Scholar
• Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., Darian, E., Guvench, O., Lopes, P., Vorobyov, I., & Mackerell, A. D. (2009). CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. Journal of Computational Chemistry, 31(4), 671–690. NA-NA. https://doi.org/10.1002/jcc.21367
   Web of Science ®Google Scholar
• Vivek-Ananth, R., Mohanraj, K., Sahoo, A. K., & Samal, A. (2023). IMPPAT 2.0: An enhanced and expanded phytochemical atlas of Indian medicinal plants. ACS Omega, 8(9), 8827–8845. https://doi.org/10.1021/acsomega.3c00156
   PubMedGoogle Scholar
• Yoshimura, T., Matsushima, K., Tanaka, S., Robinson, E. A., Appella, E., Oppenheim, J. J., & Leonard, E. J. (1987). Purification of a human monocyte-derived neutrophil chemotactic factor that has peptide sequence similarity to other host defense cytokines. Proceedings of the National Academy of Sciences of the United States of America, 84(24), 9233–9237. https://doi.org/10.1073/pnas.84.24.9233
   PubMed Web of Science ®Google Scholar