Skip to content
2000
image of Identifying Novel Inhibitors for Dengue NS2B-NS3 Protease by Combining Topological similarity, Molecular Dynamics, MMGBSA and SiteMap Analysis

Abstract

Introduction

DENV NS2B-NS3 protease inhibitors were designed based upon the reference molecule, 4-(1,3-dioxoisoindolin-2-yl)-N-(4-ethylphenyl) benzenesulfonamide, reported by our team with the aim to optimize lead compound rational approach. Top five best scoring molecules with zinc ids ZINC23504872, ZINC48412318, ZINC00413269, ZINC13998032 and ZINC75249613 bearing ‘pyrimidin-4(3H)-one’ basic scaffold have been identified as a promising candidate against DENV protease enzyme.

Methods

The shape and electrostatic complementary between identified HITs and reference molecules were found to be Tanimotoshape 0.453, 0.690, 0.680, 0.685 & 0.672 respectively and Tanimotoelectrostatic 0.211, 0.211, 0.441, 0.442, 0.442 and 0.442 respectively. The molecular docking studies suggested that the identified HITs displayed the good interactions with active site residues and lower binding energies. The stability of docked complexes was assessed by MD simulations studies. The RMSD values of protein backbone (1.6779, 3.1563, 3.3634, 3.3893 & 3.0960 Å) and protein backbone RMSF values (1.0126, 1.0834, 1.0890, 0.9974 & 1.0080 Å respectively) for all top five HITs were stable and molecules did not fluctuate from the active pocket during entire 100ns MD run.

Results

The druggability Dscore below 1 indicate the tightly binding of ligand at the active site. Dscore for ZINC23504872 was found to be 1.084 while for the second class of compounds ZINC48412318, ZINC00413269, ZINC13998032 and ZINC75249613, 0.503, 0.484, 0.487 and 0.501 Dscores were observed. In-silico ADMET calculations suggested that all five HITs were possessed the drug likeliness properties and did not violate the Lipinski’s rule of five.

Conclusion

Summing up, these in-silico generated data suggested that the identified molecules bearing pyrimidin-4(3H)-one would be promising scaffold for DENV protease inhibitors. However, experimental results are needed to prove the obtained results.

Loading

Article metrics loading...

/content/journals/cad/10.2174/0115734099329789240905141013
2024-10-29
2025-04-10
Loading full text...

Full text loading...

References

  1. Bhatt S. Gething P.W. Brady O.J. Messina J.P. Farlow A.W. Moyes C.L. Drake J.M. Brownstein J.S. Hoen A.G. Sankoh O. Myers M.F. George D.B. Jaenisch T. Wint G.R.W. Simmons C.P. Scott T.W. Farrar J.J. Hay S.I. The global distribution and burden of dengue. Nature 2013 496 7446 504 507 10.1038/nature12060 23563266
    [Google Scholar]
  2. Guzman M.G. Halstead S.B. Artsob H. Buchy P. Farrar J. Gubler D.J. Hunsperger E. Kroeger A. Margolis H.S. Martínez E. Nathan M.B. Pelegrino J.L. Simmons C. Yoksan S. Peeling R.W. Dengue: a continuing global threat. Nat. Rev. Microbiol. 2010 8 S12 S7 S16 10.1038/nrmicro2460 21079655
    [Google Scholar]
  3. Kautner I. Robinson M.J. Kuhnle U. Dengue virus infection: Epidemiology, pathogenesis, clinical presentation, diagnosis, and prevention. J. Pediatr. 1997 131 4 516 524 10.1016/S0022‑3476(97)70054‑4 9386651
    [Google Scholar]
  4. Simmons C.P. Farrar J.J. van Vinh Chau N. Wills B. Dengue. N. Engl. J. Med. 2012 366 15 1423 1432 10.1056/NEJMra1110265 22494122
    [Google Scholar]
  5. Dengue and severe dengue. 2021 Available from:https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue
  6. Guy B. Noriega F. Ochiai R.L. L’azou M. Delore V. Skipetrova A. Verdier F. Coudeville L. Savarino S. Jackson N. A recombinant live attenuated tetravalent vaccine for the prevention of dengue. Expert Rev. Vaccines 2017 16 7 671 684 10.1080/14760584.2017.1335201
    [Google Scholar]
  7. Bazan J.F. Fletterick R.J. Detection of a trypsin-like serine protease domain in flaviviruses and pestviruses. Virology 1989 171 2 637 639 10.1016/0042‑6822(89)90639‑9 2548336
    [Google Scholar]
  8. Chambers T.J. Grakoui A. Rice C.M. Processing of the yellow fever virus nonstructural polyprotein: a catalytically active NS3 proteinase domain and NS2B are required for cleavages at dibasic sites. J. Virol. 1991 65 11 6042 6050 10.1128/jvi.65.11.6042‑6050.1991 1833562
    [Google Scholar]
  9. Falgout B. Pethel M. Zhang Y.M. Lai C.J. Both nonstructural proteins NS2B and NS3 are required for the proteolytic processing of dengue virus nonstructural proteins. J. Virol. 1991 65 5 2467 2475 10.1128/jvi.65.5.2467‑2475.1991 2016768
    [Google Scholar]
  10. Phong W.Y. Moreland N.J. Lim S.P. Wen D. Paradkar P.N. Vasudevan S.G. Dengue protease activity: the structural integrity and interaction of NS2B with NS3 protease and its potential as a drug target. Biosci. Rep. 2011 31 5 399 409 10.1042/BSR20100142 21329491
    [Google Scholar]
  11. De Clercq E. Anti-HIV drugs: 25 compounds approved within 25 years after the discovery of HIV. Int. J. Antimicrob. Agents 2009 33 4 307 320 10.1016/j.ijantimicag.2008.10.010 19108994
    [Google Scholar]
  12. Wyles D.L. Antiviral resistance and the future landscape of hepatitis C virus infection therapy. J. Infect. Dis. 2013 207 S33 S39 10.1093/infdis/jis761 23390303
    [Google Scholar]
  13. Timiri A.K. Sinha B.N. Jayaprakash V. Progress and prospects on DENV protease inhibitors. Eur. J. Med. Chem. 2016 117 125 143 10.1016/j.ejmech.2016.04.008 27092412
    [Google Scholar]
  14. Murtuja S. Shilkar D. Sarkar B. Sinha B.N. Jayaprakash V. A short survey of dengue protease inhibitor development in the past 6 years (2015–2020) with an emphasis on similarities between DENV and SARS-CoV-2 proteases. Bioorg. Med. Chem. 2021 49 116415 10.1016/j.bmc.2021.116415 34601454
    [Google Scholar]
  15. Colwell L.J. Statistical and machine learning approaches to predicting protein–ligand interactions. Curr. Opin. Struct. Biol. 2018 49 123 128 10.1016/j.sbi.2018.01.006 29452923
    [Google Scholar]
  16. Heck G.S. Pintro V.O. Pereira R.R. Mb Levin N. Supervised machine learning methods applied to predict ligand- binding affinity. Curr. Med. Chem. 2017 24 2459 2470 28641555
    [Google Scholar]
  17. Luo Y. Wang L. Discovery and development of atp-competitive mtor inhibitors using computational approaches. Curr. Pharm. Des. 2017 23 29 4321 4331 28699534
    [Google Scholar]
  18. Badrinarayan P. Narahari Sastry G. Virtual high throughput screening in new lead identification. Comb. Chem. High Throughput Screen. 2011 14 10 840 860 10.2174/138620711797537102 21843146
    [Google Scholar]
  19. Siddique M.U. Sinha B.N. Jayaprakash V. Indian J. Indian J. Pharmaceut. Educ. Res 2018 52 159 165
    [Google Scholar]
  20. Usman M.S. Bharbhuiya T.K. Mondal S. Rani S. Kyal C. Kumari R. Gene Rep. 2018 13 212 219 10.1016/j.genrep.2018.10.011
    [Google Scholar]
  21. Siddique M.U. Ansari A. Sinha B.N. Jayaprakash V. Comparative computational studies on selective cytochromep450 1b1 inhibitors. Int. J. Bioauto. 2020 24 3 213 224 10.7546/ijba.2020.24.3.000537
    [Google Scholar]
  22. Bagchi S. Alia U. Mohammad F. Mohd Siddique M.U. High throughput virtual screening based discovery of dengue protease inhibitor. J. Pharmaceu. Chem. 2017 4 3 35 40 10.14805/jphchem.2017.art92
    [Google Scholar]
  23. Boström J. Grant J.A. Fjellström O. Thelin A. Gustafsson D. Potent fibrinolysis inhibitor discovered by shape and electrostatic complementarity to the drug tranexamic acid. J. Med. Chem. 2013 56 8 3273 3280 10.1021/jm301818g 23521080
    [Google Scholar]
  24. Bakan A. Nevins N. Lakdawala A.S. Bahar I. Druggability assessment of allosteric proteins by dynamics simulations in the presence of probe molecules. J. Chem. Theory Comput. 2012 8 7 2435 2447 10.1021/ct300117j 22798729
    [Google Scholar]
  25. Timiri A.K. Selvarasu S. Kesherwani M. Vijayan V. Sinha B.N. Devadasan V. Jayaprakash V. Synthesis and molecular modelling studies of novel sulphonamide derivatives as dengue virus 2 protease inhibitors. Bioorg. Chem. 2015 62 74 82 10.1016/j.bioorg.2015.07.005 26247308
    [Google Scholar]
  26. Irwin J.J. Shoichet B.K. ZINC--a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005 45 1 177 182 10.1021/ci049714+ 15667143
    [Google Scholar]
  27. L. LigPrep SR version 2.3. Schrödinger NY 2009
    [Google Scholar]
  28. Wizard P.P. New York, NY 2010
  29. Schrödinger L. New York. 2008
  30. Dhankhar P. Dalal V. Singh V. Tomar S. Kumar P Computational guided identification of novel potent inhibitors of N-terminal domain of nucleocapsid protein of severe acute respiratory syndrome coronavirus 2. J. Biomol. Struc. Dynam. 2022 40 9 4084 99
    [Google Scholar]
  31. Dalal V. Kumari R. Screening and identification of natural product‐like compounds as potential antibacterial agents targeting FemC of staphylococcus aureus: an in‐silico approach. ChemistrySelect 2022 7 42 e202201728 10.1002/slct.202201728
    [Google Scholar]
  32. Lipinski C.A. Lead- and drug-like compounds: The rule-of-five revolution. Drug Discov. Today. Technol. 2004 1 4 337 341 10.1016/j.ddtec.2004.11.007 24981612
    [Google Scholar]
  33. Sastry G.M. Dixon S.L. Sherman W. Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. J. Chem. Inf. Model. 2011 51 10 2455 2466 10.1021/ci2002704 21870862
    [Google Scholar]
  34. Bitencourt-Ferreira G. De Azevedo W.F. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys. Chem. 2018 240 63 69 10.1016/j.bpc.2018.05.010 29906639
    [Google Scholar]
  35. Morrone Xavier M. Sehnem Heck G. Boff de Avila M. Maria Bernhardt Levin N. Oliveira Pintro V. Lemes Carvalho N. Filgueira de Azevedo W. Computational tool for statistical analysis of docking results and development of scoring functions. Comb. Chem. High Throughput Screen. 2016 19 801 812 27686428
    [Google Scholar]
  36. Radhakrishnan M.L. Tidor B. Specificity in molecular design: a physical framework for probing the determinants of binding specificity and promiscuity in a biological environment. J. Phys. Chem. B 2007 111 47 13419 13435 10.1021/jp074285e 17979267
    [Google Scholar]
  37. Berman H.M. Westbrook J. Feng Z. Gilliland G. Bhat T.N. Weissig H. Shindyalov I.N. Bourne P.E. The Protein Data Bank. Nucleic Acids Res. 2000 28 1 235 242 10.1093/nar/28.1.235 10592235
    [Google Scholar]
  38. Halgren T.A. Identifying and characterizing binding sites and assessing druggability. J. Chem. Inf. Model. 2009 49 2 377 389 10.1021/ci800324m 19434839
    [Google Scholar]
  39. Halgren T. New method for fast and accurate binding-site identification and analysis. Chem. Biol. Drug Des. 2007 69 2 146 148 10.1111/j.1747‑0285.2007.00483.x 17381729
    [Google Scholar]
  40. Schrödinger L.L.C. DE Shaw Research. New York, NY, USA Schrödinger 2020
    [Google Scholar]
  41. Mark P. Nilsson L. Structure and dynamics of the tip3p, spc, and spc/e water models at 298 K. J. Phys. Chem. A 2001 105 43 9954 9960 10.1021/jp003020w
    [Google Scholar]
  42. Jorgensen W.L. Maxwell D.S. Tirado-Rives J. Development and testing of the opls all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 1996 118 45 11225 11236 10.1021/ja9621760
    [Google Scholar]
  43. Gibson D.A. Carter E.A. Time-reversible multiple time scale ab initio molecular dynamics. J. Phys. Chem. 1993 97 51 13429 13434 10.1021/j100153a002
    [Google Scholar]
  44. Cheng A. Merz K.M. Application of the nosé−hoover chain algorithm to the study of protein dynamics. J. Phys. Chem. 1996 100 5 1927 1937 10.1021/jp951968y
    [Google Scholar]
  45. Kalibaeva G. Ferrario M. Ciccotti G. Constant pressure-constant temperature molecular dynamics: a correct constrained NPT ensemble using the molecular virial. Mol. Phys. 2003 101 6 765 778 10.1080/0026897021000044025
    [Google Scholar]
/content/journals/cad/10.2174/0115734099329789240905141013
Loading
/content/journals/cad/10.2174/0115734099329789240905141013
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher's website along with the published article.

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test