299
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

In-silico discovery of inhibitors against human papillomavirus E1 protein

, , , ORCID Icon & ORCID Icon
Pages 5583-5596 | Received 19 Apr 2022, Accepted 12 Jun 2022, Published online: 24 Jun 2022

References

  • CfsSubsetEval. (2022). Cse.iitb.ac.in. Retrieved 2022, March 14, from https://www.cse.iitb.ac.in/infolab/cep/day7/lab/tools/weka-3-5-3/doc/weka/attributeSelection/CfsSubsetEval.html.
  • Alviz-Amador, A., Galindo-Murillo, R., Pérez-González, H., Rodríguez-Cavallo, E., Vivas-Reyes, R., & Méndez-Cuadro, D. (2020). AMBER parameters and topology data of 2-pentylpyrrole adduct of arginine with 4-hydroxy-2-nonenal. Data in Brief, 29, 105294. https://doi.org/10.1016/j.dib.2020.105294
  • Balupuri, A., Gadhe, C., Balasubramanian, P., Kothandan, G., & Cho, S. (2014). In silico study on indole derivatives as anti HIV-1 agents: A combined docking, molecular dynamics and 3D-QSAR study. Archives of Pharmacal Research, 37(8), 1001–1015. https://doi.org/10.1007/s12272-013-0313-1
  • Bennion, B., Be, N., McNerney, M., Lao, V., Carlson, E., Valdez, C., Malfatti, M., Enright, H., Nguyen, T., Lightstone, F., & Carpenter, T. (2017). Predicting a drug’s membrane permeability: A computational model validated with in vitro permeability Assay Data. The Journal of Physical Chemistry. B, 121(20), 5228–5237. https://doi.org/10.1021/acs.jpcb.7b02914
  • Bergvall, M., Melendy, T., & Archambault, J. (2013). The E1 proteins. Virology, 445(1–2), 35–56. https://doi.org/10.1016/j.virol.2013.07.020
  • Bernier, A., Cleret-Buhot, A., Zhang, Y., Goulet, J., Monteiro, P., Gosselin, A., DaFonseca, S., Wacleche, V., Jenabian, M., Routy, J., Tremblay, C., & Ancuta, P. (2013). Transcriptional profiling reveals molecular signatures associated with HIV permissiveness in Th1Th17 cells and identifies peroxisome proliferator-activated receptor gammaas an intrinsic negative regulator of viral replication. Retrovirology, 10, 160. https://doi.org/10.1186/1742-4690-10-160
  • bin Othman, M., & Yau, T. (2007). (2006). Comparison of different classification techniques using WEKA for breast cancer [Paper presentation]. 3rd Kuala Lumpur International Conference on Biomedical Engineering (pp. 520–523). https://doi.org/10.1007/978-3-540-68017-8_131
  • Blower, P., & Cross, K. (2006). Decision tree methods in pharmaceutical research. Current Topics in Medicinal Chemistry, 6(1), 31–39. https://doi.org/10.2174/156802606775193301
  • Castro-Muñoz, L., Manzo-Merino, J., Muñoz-Bello, J., Olmedo-Nieva, L., Cedro-Tanda, A., Alfaro-Ruiz, L., Hidalgo-Miranda, A., Madrid-Marina, V., & Lizano, M. (2019). The Human Papillomavirus (HPV) E1 protein regulates the expression of cellular genes involved in immune response. Scientific Reports, 9, 13620. https://doi.org/10.1038/s41598-019-49886-4
  • Chen, L., Gui, C., Luo, X., Yang, Q., Günther, S., Scandella, E., Drosten, C., Bai, D., He, X., Ludewig, B., Chen, J., Luo, H., Yang, Y., Yang, Y., Zou, J., Thiel, V., Chen, K., Shen, J., Shen, X., & Jiang, H. (2005). Cinanserin is an inhibitor of the 3C-like proteinase of severe acute respiratory syndrome coronavirus and strongly reduces virus replication in vitro. Journal of Virology, 79(11), 7095–7103. https://doi.org/10.1128/JVI.79.11.7095-7103.2005
  • Cheng, L., Wang, Y., & Du, J. (2020). Human papillomavirus vaccines: An updated review. Vaccines, 8(3), 391. https://doi.org/10.3390/vaccines8030391
  • Chen, Y., Tian, Y., Gao, Y., Wu, F., Luo, X., Ju, X., & Liu, G. (2020). In silico design of novel HIV-1 NNRTIs based on combined modeling studies of dihydrofuro[3,4-d]pyrimidines. Frontiers in Chemistry, 8. https://doi.org/10.3389/fchem.2020.00164
  • Chen, X., Xie, W., Yang, Y., Hua, Y., Xing, G., Liang, L., Deng, C., Wang, Y., Fan, Y., Liu, H., Lu, T., Chen, Y., & Zhang, Y. (2020). Discovery of dual FGFR4 and EGFR inhibitors by machine learning and biological evaluation. Journal of Chemical Information and Modeling, 60(10), 4640–4652. https://doi.org/10.1021/acs.jcim.0c00652
  • Chu, W., Zhang, J., Zheng, Q., Chen, L., Xue, Q., & Zhang, H. (2013). Insights into the drug resistance induced by the BaDHPS mutations: molecular dynamic simulations and MM/GBSA studies. Journal of Biomolecular Structure & Dynamics, 31(10), 1127–1136. https://doi.org/10.1080/07391102.2012.726529
  • Crooks, G., Hon, G., Chandonia, J., & Brenner, S. (2004). WebLogo: A sequence logo generator. Genome Research, 14(6), 1188–1190. https://doi.org/10.1101/gr.849004
  • D’Abramo, C. M., & Archambault, J. (2011). Small molecule inhibitors of human papillomavirus protein–protein interactions. The Open Virology Journal, 5, 80–95. https://doi.org/10.2174/1874357901105010080
  • Daina, A., Michielin, O., & Zoete, V. (2014). iLOGP: A simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. Journal of Chemical Information and Modeling, 54(12), 3284–3301. https://doi.org/10.1021/ci500467k
  • 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. https://doi.org/10.1038/srep42717
  • Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine learning – ICML’06. https://doi.org/10.1145/1143844.1143874
  • Doorbar, J., Egawa, N., Griffin, H., Kranjec, C., & Murakami, I. (2015). Human papillomavirus molecular biology and disease association. Reviews in Medical Virology, 25(S1), 2–23. https://doi.org/10.1002/rmv.1822
  • Faucher, A., White, P., Brochu, C., Grand-Maître, C., Rancourt, J., & Fazal, G. (2004). Discovery of small-molecule inhibitors of the ATPase activity of human papillomavirus E1 helicase. Journal of Medicinal Chemistry, 47(1), 18–21. https://doi.org/10.1021/jm034206x
  • Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10(5), 449–461. https://doi.org/10.1517/17460441.2015.1032936
  • González-Medina, M., & Medina-Franco, J. (2017). Platform for unified molecular analysis: PUMA. Journal of Chemical Information and Modeling, 57(8), 1735–1740. https://doi.org/10.1021/acs.jcim.7b00253
  • Guan, L., Yang, H., Cai, Y., Sun, L., Di, P., Li, W., Liu, G., & Tang, Y. (2019). ADMET-score – A comprehensive scoring function for evaluation of chemical drug-likeness. MedChemComm, 10(1), 148–157. https://doi.org/10.1039/c8md00472b
  • Haghshenas, M., Golini-moghaddam, T., Rafiei, A., Emadeian, O., Shykhpour, A., & Ashrafi, G. (2013). Prevalence and type distribution of high-risk human papillomavirus in patients with cervical cancer: a population-based study. Infectious Agents and Cancer, 8(1), 20. https://doi.org/10.1186/1750-9378-8-20
  • Hirose, Y., Yamaguchi-Naka, M., Onuki, M., Tenjimbayashi, Y., Tasaka, N., Satoh, T., Tanaka, K., Iwata, T., Sekizawa, A., Matsumoto, K., & Kukimoto, I. (2020). High levels of within-host variations of human papillomavirus 16 E1/E2 genes in invasive cervical cancer. Frontiers in Microbiology, 11. https://doi.org/10.3389/fmicb.2020.596334
  • Husaiyin, S., Han, L., Wang, L., Ma, C., Ainiwaer, Z., Rouzi, N., Akemujiang, M., Simayil, H., Aniwa, Z., Nurimanguli, R., & Niyazi, M. (2018). Factors associated with high-risk HPV infection and cervical cancer screening methods among rural Uyghur women aged > 30 years in Xinjiang. BMC Cancer, 18, 1162. https://doi.org/10.1186/s12885-018-5083-1
  • Iserhienrhien, O., & Okolie, P. (2020). Acute and sub-acute toxicity profile of methanol leaf extract of Geophila obvallata on renal and hepatic indices in Wistar rats. Cogent Food & Agriculture, 6(1), 1794240. https://doi.org/10.1080/23311932.2020.1794240
  • Iserhienrhien, O., & Okolie, P. (2022). Epa.gov. Retrieved 2022, March 14, from https://www.epa.gov/sites/default/files/2015-09/documents/ghscriteria-summary.pdf.
  • Ivanov, J., Polshakov, D., Kato-Weinstein, J., Zhou, Q., Li, Y., Granet, R., Garner, L., Deng, Y., Liu, C., Albaiu, D., Wilson, J., & Aultman, C. (2020). Quantitative structure–activity relationship machine learning models and their applications for identifying viral 3CLpro- and RdRp-targeting compounds as potential therapeutics for COVID-19 and related viral infections. ACS Omega, 5(42), 27344–27358. https://doi.org/10.1021/acsomega.0c03682
  • Jones, G., Willett, P., Glen, R., Leach, A., & Taylor, R. (1997). Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology, 267(3), 727–748. https://doi.org/10.1006/jmbi.1996.0897
  • Jorgensen, W., Chandrasekhar, J., Madura, J., Impey, R., & Klein, M. (1983). Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics, 79(2), 926–935. https://doi.org/10.1063/1.445869
  • Keyvanpour, M., & Shirzad, M. (2021). An Analysis of QSAR research based on machine learning concepts. Current Drug Discovery Technologies, 18(1), 17–30. https://doi.org/10.2174/1570163817666200316104404
  • Knapp, B., Frantal, S., Cibena, M., Schreiner, W., & Bauer, P. (2011). Is an intuitive convergence definition of molecular dynamics simulations solely based on the root mean square deviation possible? Journal of Computational Biology, 18(8), 997–1005. https://doi.org/10.1089/cmb.2010.0237
  • Kombe Kombe, A., Li, B., Zahid, A., Mengist, H., Bounda, G., Zhou, Y., & Jin, T. (2020). Epidemiology and burden of human papillomavirus and related diseases, molecular pathogenesis, and vaccine evaluation. Frontiers in Public Health, 8, 552028. https://doi.org/10.3389/fpubh.2020.552028
  • Korb, O., Stützle, T., & Exner, T. E. (2009). Empirical scoring functions for advanced protein − ligand docking with PLANTS. Journal of Chemical Information and Modeling, 49(1), 84–96. https://doi.org/10.1021/ci800298z
  • Lešnik, S., Bertalan, É., Bren, U., & Bondar, A. (2021). Opioid receptors and protonation-coupled binding of opioid drugs. International Journal of Molecular Sciences, 22(24), 13353. https://doi.org/10.3390/ijms222413353
  • Lind, A., & Anderson, P. (2019). Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties. PLoS One, 14(7), e0219774. https://doi.org/10.1371/journal.pone.0219774
  • Lobanov, M., Bogatyreva, N., & Galzitskaya, O. (2008). Radius of gyration as an indicator of protein structure compactness. Molecular Biology, 42(4), 623–628. https://doi.org/10.1134/S0026893308040195
  • Lu, X., Zhang, Y., Chen, S., Li, Y., Jia, D., Wang, W., Gao, B., & Liu, H. (2013). Molecular dynamics simulation study on the mechanism of the inhibition of ATP hydrolysis with inhibitors in human papillomavirus type 18 E1 Helicase. Proceedings of the International Conference on Computer, Networks and Communication Engineering (ICCNCE 2013). https://doi.org/10.2991/iccnce.2013.12
  • Mendez, D., Gaulton, A., Bento, A., Chambers, J., De Veij, M., Félix, E., Magariños, M., Mosquera, J., Mutowo, P., Nowotka, M., Gordillo-Marañón, M., Hunter, F., Junco, L., Mugumbate, G., Rodriguez-Lopez, M., Atkinson, F., Bosc, N., Radoux, C., Segura-Cabrera, A., Hersey, A., & Leach, A. (2019). ChEMBL: Towards direct deposition of bioassay data. Nucleic Acids Research, 47(D1), D930–D940. https://doi.org/10.1093/nar/gky1075
  • Milanetti, E., Raimondo, D., & Tramontano, A. (2016). Prediction of the permeability of neutral drugs inferred from their solvation properties. Bioinformatics (Oxford, England), 32(8), 1163–1169. https://doi.org/10.1093/bioinformatics/btv725
  • Miranda, P., Silva, N., Pitol, B., Silva, I., Lima-Filho, J., Carvalho, R., Stocco, R., Beçak, W., & Lima, A. (2013). Persistence or clearance of human papillomavirus infections in women in Ouro Preto, Brazil. BioMed Research International, 2013, 1–6. https://doi.org/10.1155/2013/578276
  • Monteiro, J., Fonseca, R., Ferreira, T., Rodrigues, L., da Silva, A., Gomes, S., Silvestre, R., Silva, A., Pamplona, I., Vallinoto, A., Ishak, R., & Machado, L. (2021). Prevalence of high risk HPV in HIV-infected women from Belém. A Cross-Sectional Study. Frontiers in Public Health. https://doi.org/10.3389/fpubh.2021.649152
  • Neves, B., Braga, R., Melo-Filho, C., Moreira-Filho, J., Muratov, E., & Andrade, C. (2018). QSAR-based virtual screening: Advances and applications in drug discovery. Frontiers in Pharmacology, 9. https://doi.org/10.3389/fphar.2018.01275
  • Omeragic, A., Kara-Yacoubian, N., Kelschenbach, J., Sahin, C., Cummins, C., Volsky, D., & Bendayan, R. (2022a). Efatutazone: Uses, interactions, mechanism of action | DrugBank Online. Go.drugbank.com. Retrieved 2022, March 14, from https://go.drugbank.com/drugs/DB11894.
  • Omeragic, A., Kara-Yacoubian, N., Kelschenbach, J., Sahin, C., Cummins, C., Volsky, D., & Bendayan, R. (2019). Peroxisome proliferator-activated receptor-gamma agonists exhibit anti-inflammatory and antiviral effects in an EcoHIV mouse model. Scientific Reports, 9, 9428. https://doi.org/10.1038/s41598-019-45878-6
  • Omeragic, A., Kara-Yacoubian, N., Kelschenbach, J., Sahin, C., Cummins, C., Volsky, D., & Bendayan, R. (2022b). Lobeglitazone: Uses, interactions, mechanism of action | DrugBank Online. Go.drugbank.com. Retrieved 2022, March 14, from https://go.drugbank.com/drugs/DB09198.
  • Patel, L., Shukla, T., Huang, X., Ussery, D., & Wang, S. (2020). Machine Learning methods in drug discovery. Molecules, 25(22), 5277. https://doi.org/10.3390/molecules25225277
  • Pettersen, E., Goddard, T., Huang, C., Couch, G., Greenblatt, D., Meng, E., & Ferrin, T. (2004). UCSF Chimera? A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/10.1002/jcc.20084
  • Pundir, S., Martin, M., & O’Donovan, C. (2016). UniProt Tools. Current Protocols in Bioinformatics, 53(1), 1.29.1-1.29.15. https://doi.org/10.1002/0471250953.bi0129s53
  • Richardson, P., Griffin, I., Tucker, C., Smith, D., Oechsle, O., Phelan, A., Rawling, M., Savory, E., & Stebbing, J. (2020). Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. The Lancet, 395(10223), e30–e31. doi: https://doi.org/10.1016/S0140-6736(20)30304-4
  • Rifai, E., van Dijk, M., Vermeulen, N., Yanuar, A., & Geerke, D. (2019). A comparative linear interaction energy and MM/PBSA study on SIRT1–ligand binding free energy calculation. Journal of Chemical Information and Modeling, 59(9), 4018–4033. https://doi.org/10.1021/acs.jcim.9b00609
  • Rogers, D., & Hahn, M. (2010). Extended-connectivity fingerprints. Journal of Chemical Information and Modeling, 50(5), 742–754. https://doi.org/10.1021/ci100050t
  • Sakai, M., Nagayasu, K., Shibui, N., Andoh, C., Takayama, K., Shirakawa, H., & Kaneko, S. (2021). Prediction of pharmacological activities from chemical structures with graph convolutional neural networks. Scientific Reports, 11, 525. https://doi.org/10.1038/s41598-020-80113-7
  • Shen, M., Xiao, Y., Golbraikh, A., Gombar, V., & Tropsha, A. (2003). Development and validation of k -nearest-neighbor QSPR models of metabolic stability of drug candidates. Journal of Medicinal Chemistry, 46(14), 3013–3020. https://doi.org/10.1021/jm020491t
  • Sievers, F., & Higgins, D. (2018). Clustal Omega for making accurate alignments of many protein sequences. Protein Science, 27(1), 135–145. https://doi.org/10.1002/pro.3290
  • Soliman, K., Grimm, F., Wurm, C., & Egner, A. (2021). Predicting the membrane permeability of organic fluorescent probes by the deep neural network based lipophilicity descriptor DeepFl-LogP. Scientific Reports, 11, 6991. https://doi.org/10.1038/s41598-021-86460-3
  • Steele, J., Mann, C., Rookes, S., Rollason, T., Murphy, D., Freeth, M., Gallimore, P., & Roberts, S. (2005). T-cell responses to human papillomavirus type 16 among women with different grades of cervical neoplasia. British Journal of Cancer, 93(2), 248–259. https://doi.org/10.1038/sj.bjc.6602679
  • Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews. Drug Discovery, 18(6), 463–477. https://doi.org/10.1038/s41573-019-0024-5
  • Vignaux, P., Minerali, E., Foil, D., Puhl, A., & Ekins, S. (2020). Machine learning for discovery of GSK3β inhibitors. ACS Omega, 5(41), 26551–26561. https://doi.org/10.1021/acsomega.0c03302
  • White, P., Faucher, A., & Goudreau, N. (2010). Small molecule inhibitors of the human papillomavirus E1-E2 interaction. Current Topics in Microbiology and Immunology, 348, 61–88. https://doi.org/10.1007/82_2010_92
  • Wishart, D., Feunang, Y., Guo, A., Lo, E., Marcu, A., Grant, J., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., Assempour, N., Iynkkaran, I., Liu, Y., Maciejewski, A., Gale, N., Wilson, A., Chin, L., Cummings, R., Le, D., … Wilson, M. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082. https://doi.org/10.1093/nar/gkx1037
  • Wishart, D., Feunang, Y., Guo, A., Lo, E., Marcu, A., Grant, J., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., Assempour, N., Iynkkaran, I., Liu, Y., Maciejewski, A., Gale, N., Wilson, A., Chin, L., Cummings, R., Le, D., … Wilson, M. (2022). Cinalukast: Uses, interactions, mechanism of action | DrugBank Online. In: Go.drugbank.com. Retrieved 2022, March 14, from https://go.drugbank.com/drugs/DB00587.
  • Yang, H., Lou, C., Sun, L., Li, J., Cai, Y., Wang, Z., Li, W., Liu, G., & Tang, Y. (2019). admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics (Oxford, England), 35(6), 1067–1069. https://doi.org/10.1093/bioinformatics/bty707
  • Yap, C. (2011). PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry, 32(7), 1466–1474. https://doi.org/10.1002/jcc.21707
  • Yuan, S., Chan, H., & Hu, Z. (2017). Using PyMOL as a platform for computational drug design. WIREs Computational Molecular Science, 7(2), e1298. https://doi.org/10.1002/wcms.1298
  • Zhang, Q., Hughes-Oliver, J., & Ng, R. (2009). A model-based ensembling approach for developing QSARs. Journal of Chemical Information and Modeling, 49(8), 1857–1865. https://doi.org/10.1021/ci900080f
  • Zhang, H., Liu, W., Liu, Z., Ju, Y., Xu, M., Zhang, Y., Wu, X., Gu, Q., Wang, Z., & Xu, J. (2018). Discovery of indoleamine 2,3-dioxygenase inhibitors using machine learning based virtual screening. MedChemComm, 9(6), 937–945. https://doi.org/10.1039/c7md00642j
  • Zhang, S., Xu, H., Zhang, L., & Qiao, Y. (2020). Cervical cancer: Epidemiology, risk factors and screening. Chinese Journal of Cancer Research = Chung-Kuo Yen Cheng Yen Chiu, 32(6), 720–728. https://doi.org/10.21147/j.issn.1000-9604.2020.06.05

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.