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Abstract
A set of 29 flavonoid molecules are used to generate comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. The best CoMFA model showed a cross-validated correlation coefficient (q2) = 0.762, non-cross-validated correlation coefficient (r2) = 0.939, standard error of estimate (S) = 0.038 and F = 396. And that for CoMSIA model were q2 = 0.758, r2 = 0.957, S = 0.063 and F = 236. The models show a high predictive ability, validated by 11 favonoid molecules. The docking studies shows the hydrogen bonding interaction is mostly responsible for binding of the flavonoids molecules in the binding pocket of HIV 1-RT protein (3HVT.pdb).
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References
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- J.B. Harborne and C.A. Williams, Advances in Flavonoid Research Since 1992, Phytochemistry, 55, 481 (2000); https://doi.org/10.1016/S0031-9422(00)00235-1
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- SYBYL-X 2.0, Tripos Inc, St. Louis (2013).
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- A. Sarkar, T.R. Middya and A.D. Jana, A QSAR Study of Radical Scavenging Antioxidant Activity of a Series of Flavonoids using Dft Based Quantum Chemical Descriptors-The Importance of Group Frontier Electron Density, J. Mol. Model., 18, 2621 (2012); https://doi.org/10.1007/s00894-011-1274-2
- S.J. Smerdon, J. Jäger, J. Wang, L.A. Kohlstaedt, A.J. Chirino, J.M. Friedman, P.A. Rice and T.A. Steitz, Structure of the Binding Site for Nonnucleoside Inhibitors of the Reverse Transcriptase of Human Immunodeficiency Virus Type 1, Proc. Natl. Acad. Sci. (USA), 91, 3911 (1994); https://doi.org/10.1073/pnas.91.9.3911
- A.N. Jain, Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine, J. Med. Chem., 46, 499 (2003); https://doi.org/10.1021/jm020406h
- W. Welch, J. Rupper and A.N. Jain, Hammerhead: Fast, Fully Automated Docking of Flexible Ligands to Protein Binding Sites, Chem. Biol., 3, 449 (1996); https://doi.org/10.1016/s1074-5521(96)90093-9
- P.M. Marsden, D. Puvanendrampillai, J.B.O. Mitchell and R.C. Glen, Predicting Protein-Ligand Binding Affinities: A Low Scoring Game? Org. Biomol. Chem., 2, 3267 (2004); https://doi.org/10.1039/b409570g
References
D. Baltimore, Viral RNA-Dependent DNA Polymerase: RNA-Dependent DNA Polymerase in Virions of RNA Tumour Viruses, Nature, 226, 1209 (1970); https://doi.org/10.1038/2261209a0
F. Barre-Sinoussi, J.C. Chermann, F. Rey, M.T. Nugeyre, S. Chamaret, J. Gruest, C. Dauguet, C. Axler-Blin, F. Vezinet-Brun, C. Rouzioux, W. Rozenbaum and L. Montagnier, Isolation of a T-lymphotropic Retrovirus from a Patient at Risk for Acquired Immune Deficiency Syndrome (AIDS), Science, 220, 868 (1983); https://doi.org/10.1126/science.6189183
D.S. Auld, H. Kawaguchi, D.M. Livingston and B.L. Vallee, RNA-Dependent DNA Polymerase (Reverse Transcriptase) from Avian Myeloblastosis Virus: A Zinc Metalloenzyme, Proc. Natl. Acad. Sci. USA, 71, 2091 (1974); https://doi.org/10.1073/pnas.71.5.2091
C.K. Tan, J. Zhang, Z.Y. Li, W.G. Tarpley, K.M. Downey and A.G. So, Functional Characterization of RNA-Dependent DNA Polymerase and RNase H Activities of a Recombinant HIV Reverse Transcriptase, Biochemistry, 30, 2651 (1991); https://doi.org/10.1021/bi00224a013
T. Pengsuparp, L. Cai, H. Constant, H.H.S. Fong, L.Z. Lin, A.D. Kinghorn, J.M. Pezzuto, G.A. Cordell, K. Ingolfsdöttir, H. Wagner and S.H. Hughes, Mechanistic Evaluation of New Plant-Derived Compounds that Inhibit HIV-1 Reverse Transcriptase, J. Nat. Prod., 58, 1024 (1995); https://doi.org/10.1021/np50121a006
B. Havsteen, Flavonoids, A Class of Natural Products of High Pharmacological Potency, Biochem. Pharmacol., 32, 1141 (1983); https://doi.org/10.1016/0006-2952(83)90262-9
J.B. Harborne and C.A. Williams, Advances in Flavonoid Research Since 1992, Phytochemistry, 55, 481 (2000); https://doi.org/10.1016/S0031-9422(00)00235-1
C.Q. Hu, K. Chen, Q. Shi, R.E. Kilkuskie, Y.C. Cheng and K.-H. Lee, Anti-AIDS Agents, 10. Acacetin-7-O-b-D-galactopyranoside, an Anti-HIV Principle from Chrysanthemum morifolium and a Structure-Activity Correlation with Some Related Flavonoids, J. Nat. Prod., 57, 42 (1994); https://doi.org/10.1021/np50103a006
M. Gabor, Anti-Inflammatory and Anti-Allergic Properties of Flavonoids, Prog. Clin. Biol. Res., 213, 471 (1986).
G. Spedding, A. Ratty and E. Middleton Jr., Inhibition of Reverse Transcriptases by Flavonoids, Antiviral Res., 12, 99 (1989); https://doi.org/10.1016/0166-3542(89)90073-9
I.T. Kusumoto, M. Hattori, Y. Miyaichi, T. Tomimori, M. Hanaoka and T. Namba, Effect of Flavonoids and Alkaloids on Reverse Transcriptase, Shoyakugaku Zasshi, 45, 240 (1991).
R.D. Cramer, D.E. Patterson and J.D. Bunce, Comparative Molecular Field Analysis (CoMFA). 1. Effect of Shape on Binding of Steroids to Carrier Proteins, J. Am. Chem. Soc., 110, 5959 (1988); https://doi.org/10.1021/ja00226a005
S. Srivastava, W.W. Richardson, M.P. Bradley and G.M. Crippen, eds.: H. Kubinyi, Three-Dimensional Receptor Modeling using Distance Geometry and Voronoi polyhedra. In: 3D QSAR in Drug Design, Theory, Methods and Applications, ESCOM, Leiden, p. 409 (1993).
G. Klebe, U. Abraham and T. Mietzner, Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity, J. Med. Chem., 37, 4130 (1994); https://doi.org/10.1021/jm00050a010
M. Bohm, J. Sturzebecher and G. Klebe, Three-Dimensional Quanti-tative Structure-Activity Relationship Analyses Using Comparative Molecular Field Analysis and Comparative Molecular Similarity Indices Analysis To Elucidate Selectivity Differences of Inhibitors Binding to Trypsin, Thrombin, and Factor Xa, J. Med. Chem., 42, 458 (1999); https://doi.org/10.1021/jm981062r
SYBYL-X 2.0, Tripos Inc, St. Louis (2013).
M.D.M. Abdul Hameed, A. Hamza, J. Liu and C.G. Zhan, Combined 3D-QSAR Modeling and Molecular Docking Study on Indolinone Derivatives as Inhibitors of 3-Phosphoinositide-Dependent Protein Kinase-1, J. Chem. Inf. Model., 48, 1760 (2008); https://doi.org/10.1021/ci800147v
D.M. Hawkins, S.C. Basak and D. Mills, Assessing Model Fit by Cross-Validation, J. Chem. Inf. Comput. Sci., 43, 579 (2003); https://doi.org/10.1021/ci025626i
A. Sarkar and A.D. Jana, Molecular Modeling of 4',5-Disubstituted Biphenyl Acetic Acid Molecules for their Anti-inflammatory Activity through 3D-QSAR, Docking and Molecular Dynamics Simulation, Asian J. Chem., 30, 2437 (2018); https://doi.org/10.14233/ajchem.2018.21434
A. Sarkar, T.R. Middya and A.D. Jana, A QSAR Study of Radical Scavenging Antioxidant Activity of a Series of Flavonoids using Dft Based Quantum Chemical Descriptors-The Importance of Group Frontier Electron Density, J. Mol. Model., 18, 2621 (2012); https://doi.org/10.1007/s00894-011-1274-2
S.J. Smerdon, J. Jäger, J. Wang, L.A. Kohlstaedt, A.J. Chirino, J.M. Friedman, P.A. Rice and T.A. Steitz, Structure of the Binding Site for Nonnucleoside Inhibitors of the Reverse Transcriptase of Human Immunodeficiency Virus Type 1, Proc. Natl. Acad. Sci. (USA), 91, 3911 (1994); https://doi.org/10.1073/pnas.91.9.3911
A.N. Jain, Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine, J. Med. Chem., 46, 499 (2003); https://doi.org/10.1021/jm020406h
W. Welch, J. Rupper and A.N. Jain, Hammerhead: Fast, Fully Automated Docking of Flexible Ligands to Protein Binding Sites, Chem. Biol., 3, 449 (1996); https://doi.org/10.1016/s1074-5521(96)90093-9
P.M. Marsden, D. Puvanendrampillai, J.B.O. Mitchell and R.C. Glen, Predicting Protein-Ligand Binding Affinities: A Low Scoring Game? Org. Biomol. Chem., 2, 3267 (2004); https://doi.org/10.1039/b409570g