Preprint Open Access
The HIV-1 and its variants have claimed more than 32.7 million lives since its emergence in 1981, while many highly/ active antiretroviral therapies are available but most of these therapeutics have long-term side effects. In this study, genomic analysis was performed on 98 HIV-1 genomes to determine the most coherent target, which could be utilized for termination of the viral replication and the reverse transcriptase enzyme. Following the identification of the target protein, the RNase H activity of the reverse transcriptase was selected as the potential target based on its low mutation rate and high conservation determined using MAUVE analysis. Afterwards, a library of around 94.000 small molecule inhibitors was investigated and virtual screening was performed against the RNase domain of the reverse transcriptase to identify potential hits. Four compounds with the best scores were considered and their interaction within the active site was analysed. Subsequently, all-atom molecular dynamics simulations and MM-PBSA was performed to validate the stability and binding free energy of the hits within the RNase H active site. In computational analyses, ADMET assays were performed on the hit compounds to analyse their drug candidacy based on their physicochemical and pharmacological properties. Phomoarcherin B, a pentacyclic aromatic sesquiterpene naturally found in the endophytic fungus Phomopsis archeri, known for its anticancer properties scored the best in all the experiments and was nominated as a potential inhibitor of the HIV-1 reverse transcriptase RNase H activity.
Hemelaar, J. The origin and diversity of the HIV-1 pandemic. Trends Mol. Med. 18, 182–192 (2012).
WHO. WHO HIV data and statistics. https://www.who.int/teams/global-hiv-hepatitis-and-stis-programmes/data-use/hiv-data-and-statistics (2019).
UNAIDS. Global HIV & AIDS statistics - 2020 fact sheet. https://www.unaids.org/en/resources/fact-sheet (2020).
Cohen, M. S., Hellmann, N., Levy, J. A., Decock, K. & Lange, J. The spread, treatment, and prevention of HIV-1: evolution of a global pandemic. J. Clin. Invest. 118, 1244-1254. (2008).
Kirchhoff, F. HIV Life Cycle: Overview in Encyclopedia of AIDS. (eds. Hope, T., Stevenson, M., Richman, D.) (Springer, 2016) https://doi.org/10.1007/978-1-4614-9610-6_60-1
Swanson, C. M. & Malim, M. H. SnapShot: HIV-1 Proteins. Cell. 133, 9–10 (2008).
Fanales-Belasio, E., Raimondo, M., Suligoi, B. & Buttò, S. HIV virology and pathogenetic mechanisms of infection: a brief overview. Ann. Ist. Super. Sanita. 46, 5-14 (2010).
Ruelas, D. S. & Greene, W. C. An integrated overview of HIV-1 latency. Cell. 155, 519-529. (2013).
Volberding, P. A. & Deeks, S. G. Antiretroviral therapy and management of HIV infection. Lancet 376, 49-62 (2010).
Poongavanam, V. & Kongsted, J. Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition. PLoS One 8, e73478; 10.1371/journal.pone.0073478 (2013).
Esposito, F. et al. Kuwanon-L as a new allosteric HIV-1 integrase inhibitor: molecular modeling and biological evaluation. Chembiochem 16, 2507–2512 (2015).
Pintro, V. O. & de Azevedo, W. F. Optimized virtual screening workflow: towards target-based polynomial scoring functions for HIV-1 protease. Comb. Chem. High Throughput Screen. 20, 820–827 (2017).
Zhang, B., D'Erasmo, M. P., Murelli, R. P. & Gallicchio, E. Free energy-based virtual screening and optimization of RNase H inhibitors of HIV-1 reverse transcriptase. ACS Omega 1, 435–447 (2016).
Baysal, Ö., Abdul Ghafoor, N., Silme, R. S., Ignatov, A. N. & Kniazeva, V. Molecular dynamics analysis of N-acetyl-D-glucosamine against specific SARS-CoV-2's pathogenicity factors. PLoS ONE 16, e0252571; 10.1371/journal.pone.0252571 (2021).
Baysal, Ö., Silme, R. S., Karaaslan, C. & Ignatov, A. Genetic uniformity of a specific region in SARS-CoV-2 genome and repurposing of N-Acetyl-D-Glucosamine. Fresenius Environ. Bull. 30, 2848-2857 (2021).
Baysal, Ö. & Silme, R. S. Utilization from computational methods and omics data for antiviral drug discovery to control of SARS-CoV-2 [Online First]. IntechOpen http://doi.org/10.5772/intechopen.98319 https://www.intechopen.com/online-first/76991 (2021).
Kuiken, C., Korber, B. & Shafer, R. W. HIV sequence databases. AIDS Reviews 5, 52–61 (2003).
Darling, A. C. E., Mau, B., Blattner, F. R. & Perna, N. T. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 14, 1394–1403 (2004).
Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5, 113 (2004).
Geneious Prime 2020.2.4. https://www.geneious.com (2020).
Waterhouse, A. M., Procter, J. B., Martin, D. M. A., Clamp, M. & Barton, G. J. Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics 25, 1189–1191 (2009).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
States, D. J. & Gish, W. Combined use of sequence similarity and codon bias for coding region identification. J. Comput. Biol. 1, 39–50 (1994).
Himmel, D. M. et al. Structure of HIV-1 reverse transcriptase with the inhibitor & B-Thujaplicinol bound at the RNase H active site. Structure 17, 1625–1635 (2009).
Berman, H., Henrick, K. & Nakamura, H. Announcing the worldwide protein data bank. Nat. Struct. Mol. Biol. 10, 980 (2003).
Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinforma. 54, 5.6.1-5.6.37 (2016).
Fiser, A., Do, R. K. & Sali, A. modeling of loops in protein structures. Protein Sci. 9, 1753–1773 (2000).
Schrödinger, LLC. The PyMOL Molecular Graphics System, Version~1.8. (2015).
Morris, G. M. et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791 (2009).
Sterling, T. & Irwin, J. J. ZINC 15 – Ligand discovery for everyone. J. Chem. Inf. Model. 55, 2324–2337 (2015).
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).
Salentin, S., Schreiber, S., Haupt, V. J., Adasme, M. F. & Schroeder, M. PLIP: fully automated protein–ligand interaction profiler. Nucleic Acids Res. 43, W443–W447 (2015).
Phillips, J. C. et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J. Chem. Phys. 153, 044130 (2020).
Robertson, M. J., Tirado-Rives, J. & Jorgensen, W. L. Improved peptide and protein torsional energetics with the OPLS-AA force field. J. Chem. Theory Comput. 11, 3499–3509 (2015).
Dodda, L. S., Cabeza de Vaca, I., Tirado-Rives, J. & Jorgensen, W. L. LigParGen web server: an automatic OPLS-AA parameter generator for organic ligands. Nucleic Acids Res. 45, W331–W336 (2017).
Dodda, L. S., Vilseck, J. Z., Tirado-Rives, J. & Jorgensen, W. L. 1.14*CM1A-LBCC: Localized bond-charge corrected CM1A charges for condensed-phase simulations. J. Phys. Chem. B 121, 3864–3870 (2017).
Jorgensen, W. L. & Tirado-Rives, J. Potential energy functions for atomic-level simulations of water and organic and biomolecular systems. Proc. Natl. Acad. Sci. 102, 6665–6670 (2005).
Waskom, M. L. Seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. & Eng. 9, 90–95 (2007).
Liu, H. & Hou, T. CaFE: a tool for binding affinity prediction using end-point free energy methods. Bioinformatics 32, 2216–2218 (2016).
Hou, T., Wang, J., Li, Y. & Wang, W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model. 51, 69–82 (2011).
Singh, N. & Warshel, A. Absolute binding free energy calculations: On the accuracy of computational scoring of protein–ligand interactions. Proteins Struct. Funct. Bioinforma. 78, 1705–1723 (2010).
Daina, A., Michielin, O. & Zoete, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 7, 42717 (2017).
Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46, 3–26 (2001).
Ghose, A. K., Viswanadhan, V. N. & Wendoloski, J. J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem. 1, 55–68 (1999).
Veber, D. F. et al. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45, 2615–2623 (2002).
Egan, W. J., Merz Kenneth M. & Baldwin, J. J. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 43, 3867–3877 (2000).
Muegge, I., Heald, S. L. & Brittelli, D. Simple selection criteria for drug-like chemical matter. J. Med. Chem. 44, 1841–1846 (2001).
Cheng, F. et al. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 52, 3099–3105 (2012).
Xiong, G. et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 49, W5–W14; 10.1093/nar/gkab255 (2021).
Galilee, M. & Alian, A. The structure of FIV reverse transcriptase and its implications for non-nucleoside inhibitor resistance. PLOS Pathog. 14, e1006849; 10.1371/journal.ppat.1006849 (2018).
Mueser, T. C., Nossal, N. G. & Hyde, C. C. Structure of bacteriophage T4 RNase H, a 5′ to 3′ RNA–DNA and DNA–DNA exonuclease with sequence similarity to the RAD2 family of eukaryotic proteins. Cell 85, 1101–1112 (1996).
Das, D. & Georgiadis, M. M. The crystal structure of the monomeric reverse transcriptase from Moloney Murine Leukemia virus. Structure 12, 819–829 (2004).
Chapter 26 - Acquired Immune Deficiency Syndrome in Immunology for Pharmacy (ed. Flaherty, D. K.) 214–223 (Mosby, 2012). https://doi.org/10.1016/B978-0-323-06947-2.10026-4
Daina, A. & Zoete, V. A BOILED-egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem 11, 1117–1121 (2016).
Yeo, J. Y., Goh, G.-R., Su, C. T.-T. & Gan, S. K.-E. The determination of HIV-1 RT mutation rate, its possible allosteric effects, and its implications on drug resistance. Viruses 12, 297 (2020).
Fournier, P.-E. et al. Comparative genomics of multidrug resistance in Acinetobacter baumannii. PLOS Genet. 2, e7; 10.1371/journal.pgen.0020007 (2006).
Hardison, R. C. Comparative Genomics. PLOS Biol. 1, E58; 10.1371/journal.pbio.0000058 (2003).
De Clercq, E. Non-nucleoside reverse transcriptase inhibitors (NNRTIs): past, present, and future. Chem. Biodivers. 1, 44–64 (2004).
King, R. W., Klabe, R. M., Reid, C. D. & Erickson-Viitanen, S. K. Potency of nonnucleoside reverse transcriptase inhibitors (NNRTIs) used in combination with other Human Immunodeficiency Virus NNRTIs, NRTIs, or protease inhibitors. Antimicrob. Agents Chemother. 46, 1640 LP – 1646 (2002).
De Clercq, E. Perspectives of non-nucleoside reverse transcriptase inhibitors (NNRTIs) in the therapy of HIV-1 infection. Farm. 54, 26–45 (1999).
Melikian, G. L. et al. Non-nucleoside reverse transcriptase inhibitor (NNRTI) cross-resistance: implications for preclinical evaluation of novel NNRTIs and clinical genotypic resistance testing. J. Antimicrob. Chemother. 69, 12–20 (2014).
Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2017).
Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).
Hemtasin, C. et al. Cytotoxic Pentacyclic and Tetracyclic Aromatic Sesquiterpenes from Phomopsis archeri. J. Nat. Prod. 74, 609–613 (2011).
Bedi, A., Adholeya, A. & Deshmukh, S. K. Novel anticancer compounds from endophytic fungi. Curr. Biotechnol. 7, 168–184 (2018).