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Computational Studies of 3D-QSAR on a Highly Active Series of Naturally Occurring Nonnucleoside Inhibitors of HIV-1 RT (NNRTI)

    https://doi.org/10.1142/S2737416520500362Cited by:4 (Source: Crossref)

    HIV is one of the deadliest viruses in the history of mankind, it is the root cause of Acquired Immunodeficiency Syndrome (AIDS) around the world. Despite the fact that the antiviral therapy used against HIV-1 infection is effective, there is also rapidly growing cases of drug resistance in the infected patient along with different severe side effects. Therefore, it is of dire and immediate need to find novel inhibitors against HIV-1 Reverse Transcriptase (RT). In this study, the potential of naturally occurring compounds extracted from plants has been studied with the help of Three-Dimensional-Quantitative Structure–Activity Relationships (3D-QSAR) analysis. A total of 20 compounds, retrieved from a ZINC database, were analyzed with the help of 3D-QSAR to identify a potential inhibitor of HIV-1 RT. By evaluation of seven models generated with the help of MIF analysis and 3D-QSAR modeling, compound 3 (ZINC ID: ZINC20759448) was observed to outperform others by showing optimal results in QSAR studies. This compound has also been biologically validated by a recently reported previous study. Thus, this compound can be used as a potential drug against infection caused by HIV-1, specifically AIDS.

    References

    • 1. Freed, E. O. HIV-1 Assembly, Release and Maturation. Nat. Rev. Microbiol. 2015, 13 (8), 484–496. CrossrefGoogle Scholar
    • 2. Fauci, A. S. Human Immunodeficiency Virus Disease: AIDS and Related Disease. Harrison’s Int. Med. 2008, 1137–1204. Google Scholar
    • 3. Girum, T.; Wasie, A.; Worku, A. Trend of HIV/AIDS for the Last 26 Years and Predicting Achievement of the 90–90-90 HIV Prevention Targets by 2020 in Ethiopia: A Time Series Analysis. BMC Infect. Dis. 2018, 18 (1), 320. CrossrefGoogle Scholar
    • 4. Bhattacharya, J. HIV Prevention & Treatment Strategies-Current Challenges & Future Prospects. Ind. J. Med. Res. 2018, 148 (6), 671. CrossrefGoogle Scholar
    • 5. Rasool, N.; Hussain, W. Three Major Phosphoacceptor Sites in HIV-1 Capsid Protein Enhances its Structural Stability and Resistance Against Inhibitor: Explication Through Molecular Dynamics Simulation, Molecular Docking and DFT Analysis. Comb. Chem. High Throughput Screen, 2019, 23, 41–54. CrossrefGoogle Scholar
    • 6. Larsen, K.; Mathiharan, Y.; Kappel, K.; Coey, A.; Chen, D.-H..; Madigan, L.; Skiniotis, G.; Puglisi, J.; Puglisi, E. V. Structural Characterization of the HIV-1 Reverse Transcriptase Initiation Complex. Biophys. J. 2018, 114 (3), 193a. CrossrefGoogle Scholar
    • 7. Carcelli, M.; Rogolino, D.; Gatti, A.; Pala, N.; Corona, A.; Caredda, A.; Tramontano, E.; Pannecouque, C.; Naesens, L.; Esposito, F. Chelation Motifs Affecting Metal-Dependent Viral Enzymes: N′-acylhydrazone Ligands as Dual Target Inhibitors of HIV-1 Integrase and Reverse Transcriptase Ribonuclease h Domain. Front Microbiol. 2017, 8, 440. CrossrefGoogle Scholar
    • 8. Sanna, C.; Rigano, D.; Corona, A.; Piano, D.; Formisano, C.; Farci, D.; Franzini, G.; Ballero, M.; Chianese, G.; Tramontano, E. Dual HIV-1 Reverse Transcriptase and Integrase Inhibitors from Limonium Morisianum Arrigoni, an Endemic Species of Sardinia (Italy). Nat. Prod. Res. 2019, 33 (12), 1798–1803. CrossrefGoogle Scholar
    • 9. Anglemyer A. T, Rutherford G, Horvath H, Vitoria M, Doherty M. Antiretroviral Therapy for Asymptomatic Adults and Adolescents with HIV-1 Infection and CD4+ T-cell Counts 500 cells/μL: A Meta-analysis, ARC Journal of AIDS 2018, 2 (2), 38–53. Google Scholar
    • 10. Torres, M.; Moayedi, S. Management of Human Immunodeficiency Virus in the Emergency Department. Emerg. Med. Clin. 2018, 36 (4), 777–794. CrossrefGoogle Scholar
    • 11. Wong, J. H.; Ng, T. B.; Wang, H.; Cheung, R. C.; Ng, C. C. W.; Ye, X.; Yang, J.; Liu, F.; Ling, C.; Chan, K. Antifungal Proteins with Antiproliferative Activity on Cancer Cells and HIV-1 Enzyme Inhibitory Activity from Medicinal Plants and Medicinal Fungi. Curr. Protein. Pept. Sci. 2019, 20 (3), 265–276. CrossrefGoogle Scholar
    • 12. Akhtar, A.; Hussain, W.; Rasool, N. Probing the Pharmacological Binding Properties, and Reactivity of Selective Phytochemicals as Potential HIV-1 Protease Inhibitors. Univ. Sci. 2019, 24 (3), 441–464. CrossrefGoogle Scholar
    • 13. Arif, N.; Subhani, A.; Hussain, W.; Rasool, N. In Silico Inhibition of BACE-1 by Selective Phytochemicals as Novel Potential Inhibitors: Molecular Docking and DFT Studies. Curr. Drug. Discov. Technol, 2019, 17 (3), 397–411. CrossrefGoogle Scholar
    • 14. Hussain, W.; Ali, M.; Sohail Afzal, M.; Rasool, N. Penta-1,4-diene-3-One Oxime Derivatives Strongly Inhibit the Replicase Domain of Tobacco Mosaic Virus: Elucidation Through Molecular Docking and Density Functional Theory Mechanistic Computations. J. Antivirals. Antiretrovirals 2018, 10 (3), 28–34. CrossrefGoogle Scholar
    • 15. Hussain, W.; Qaddir, I.; Mahmood, S.; Rasool, N. In Silico Targeting of Non-structural 4B Protein From Dengue Virus 4 with Spiropyrazolopyridone: Study of Molecular Dynamics Simulation, ADMET and Virtual Screening. Virus Disease 2018, 29, 1–10. Google Scholar
    • 16. Qaddir, I.; Rasool, N.; Hussain, W.; Mahmood, S. Computer-Aided Analysis of Phytochemicals as Potential Dengue Virus Inhibitors Based on Molecular Docking, ADMET and DFT Studies. J. Vector Borne Dis. 2017, 54 (3), 255. CrossrefGoogle Scholar
    • 17. Rasool, N.; Ashraf, A.; Waseem, M.; Hussain, W.; Mahmood, S. Computational Exploration of Antiviral Activity of Phytochemicals Against NS2B/NS3 Proteases From Dengue Virus. Turk. J. Biochem, 2019, 44 (3), 261–277. CrossrefGoogle Scholar
    • 18. Rasool, N.; Hussain, W.; Mahmood, S. Prediction of Protein Solubility Using Primary Structure Compositional Features: A Machine Learning Perspective. J. Proteomics Bioinform 2017, 10 (12), 324–328. CrossrefGoogle Scholar
    • 19. Rasool, N.; Jalal, A.; Amjad, A.; Hussain, W. Probing the Pharmacological Parameters, Molecular Docking and Quantum Computations of Plant Derived Compounds Exhibiting Strong Inhibitory Potential Against NS5 from Zika Virus. Braz. Arch. Biol. Technol. 2018, 61, e18180004. CrossrefGoogle Scholar
    • 20. Costa, G.; Rocca, R.; Corona, A.; Grandi, N.; Moraca, F.; Romeo, I.; Talarico, C.; Gagliardi, M. G.; Ambrosio, F. A.; Ortuso, F. Novel Natural Non-nucleoside Inhibitors of HIV-1 Reverse Transcriptase Identified by Shape -and Structure-based Virtual Screening Techniques. Eur. J. Med. Chem. 2019, 161, 1–10. CrossrefGoogle Scholar
    • 21. Ullah, A.; Prottoy, N. I.; Araf, Y.; Hossain, S.; Sarkar, B.; Saha, A. Molecular Docking and Pharmacological Property Analysis of Phytochemicals From Clitoria Ternatea as Potent Inhibitors of Cell Cycle Checkpoint Proteins in the Cyclin/CDK Pathway in Cancer Cells. Comput. Mol. Biosci. 2019, 9 (3), 81. CrossrefGoogle Scholar
    • 22. Hussain, W.; Amir, A.; Rasool, N. Computer-aided Study of Selective Flavonoids Against Chikungunya Virus Replication Using Molecular Docking and DFT-based Approach. Struct. Chem. 2020, 31, 1–12. CrossrefGoogle Scholar
    • 23. Rasool, N.; Bakht, A.; Hussain, W, Analysis of Inhibitor Binding Combined with Reactivity Studies to Discover the Potentially Inhibiting Phytochemicals Targeting Chikungunya Viral Replication. Curr. Drug. Discov. Technol. 2020. Google Scholar
    • 24. Ragno, R. www.3d-qsar.com: A Web Portal that Brings 3-D QSAR to all Electronic Devices—the Py-CoMFA Web Application as Tool to Build Models from pre-Aligned Datasets. J. Comput-Aided Mol. Des. 2019, 33 (9), 855–864. CrossrefGoogle Scholar
    • 25. Spessard, G. O. ACD Labs/LogP dB 3.5 and ChemSketch 3.5. J. Chem. Inf. Comput. Sci. 1998, 38 (6), 1250–1253. CrossrefGoogle Scholar
    • 26. Tosco, P.; Balle, T. Open3DQSAR: A New Open-source Software Aimed at High-throughput Chemometric Analysis of Molecular Interaction Fields. J. Mol. Model 2011, 17 (1), 201–208. CrossrefGoogle Scholar
    • 27. Tosco, P.; Balle, T.; Shiri, F. Open3DALIGN: An Open-source Software Aimed at Unsupervised Ligand Alignment. J. Comput-aided Mol. Des. 2011, 25 (8), 777. CrossrefGoogle Scholar
    • 28. Ståhle, L.; Wold, S, Partial Least Squares Analysis with Cross-validation for the Two-class Problem: A Monte Carlo Study. J. Chemometr. 1987, 1 (3), 185–196. CrossrefGoogle Scholar
    • 29. Liton, M. A. K.; Bhowmick, A.; Ali, M. A. 3D-QSAR MIFs Studies on 3, 5-substituted-1, 4, 2-dioxazoles Derivatives Using Open3DQSAR Tools. Univ. J. Chem. 2013, 1, 71–76. CrossrefGoogle Scholar
    • 30. Cramer R. D III.; Bunce, J. D.; Patterson, D. E.; Frank, I. E. Crossvalidation, Bootstrapping, and Partial Least Squares Compared with Multiple Regression in Conventional QSAR Studies. Quant. Struct-Activ. Relat. 1988, 7 (1), 18–25. CrossrefGoogle Scholar
    • 31. Centner, V.; Massart, D-L.; de Noord, O. E.; de Jong, S.; Vandeginste, B. M.; Sterna, C. Elimination of Uninformative Variables for Multivariate Calibration. Anal. Chem. 1996, 68 (21), 3851–3858. CrossrefGoogle Scholar
    • 32. Adhikari, N.; Amin, S. A.; Jha, T.; Gayen, S. Integrating Regression and Classification-based QSARs with Molecular Docking Analyses to Explore the Structure-antiaromatase Activity Relationships of Letrozole-based Analogs. Canad. J. Chem. 2017, 95 (12), 1285–1295. CrossrefGoogle Scholar
    • 33. Sepehri, B.; Omidikia, N.; Kompany-Zareh, M.; Ghavami, R. Predictive and Descriptive CoMFA Models: The Effect of Variable Selection. Combin. Chem. High Throughput Screen 2018, 21 (2), 117–124. CrossrefGoogle Scholar
    • 34. Ballante, F.; Ragno, R. 3-D QSAutogrid/R: An Alternative Procedure to Build 3-D QSAR Models. Methodologies and Applications. J. Chem. Inf. Model 2012, 52 (6), 1674–1685. CrossrefGoogle Scholar
    • 35. Edrada, R. A.; Proksch, P.; Wray, V.; Witte, L.; Müller, W.; Van Soest, R. W. Four New Bioactive Manzamine-type Alkaloids from the Philippine Marine Sponge Xestospongia Ashmorica. J. Nat. Prod. 1996, 59 (11), 1056–1060. CrossrefGoogle Scholar
    • 36. El Sayed, K. A.; Dunbar, D. C.; Perry, T. L.; Wilkins, S. P.; Hamann, M. T.; Greenplate, J. T.; Wideman, M. A. Marine Natural Products as Prototype Insecticidal Agents. J. Agric. Food Chem. 1997, 45 (7), 2735–2739. CrossrefGoogle Scholar
    • 37. Penta, A.; Chander, S.; Ganguly, S.; Murugesan, S. De Novo Design and In-silico Studies of Novel 1-phenyl-2, 3, 4, 9-tetrahydro-1H-pyrido [3, 4-b] Indole-3-Carboxylic Acid Derivatives as HIV-1 Reverse Transcriptase Inhibitors. Med. Chem. Res. 2014, 23 (8), 3662–3670. CrossrefGoogle Scholar
    • 38. Himes, R. H.; Kersey, R. N.; Heller-Bettinger, I.; Samson, F. E. Action of the Vinca Alkaloids Vincristine, Vinblastine, and Desacetyl Vinblastine Amide on Microtubules in vitro. Cancer Res. 1976, 36 (10), 3798–3802. Google Scholar
    • 39. Cui, C-B.; Kakeya, H.; Osada, H. Novel Mammalian Cell Cycle Inhibitors, Spirotryprostatins A and B, Produced by Aspergillus Fumigatus, which Inhibit Mammalian Cell Cycle at G2/M Phase. Tetrahedron 1996, 52 (39), 12651–12666. CrossrefGoogle Scholar
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