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Structural Chemistry

, Volume 28, Issue 6, pp 1663–1678 | Cite as

An integrated ligand-based modelling approach to explore the structure-property relationships of influenza endonuclease inhibitors

Original Research

Abstract

Influenza endonuclease plays important role in the viral transcription and translation processes. Inhibition of endonuclease enzyme may be an interesting choice to restrict influenza infection. This current study deals with validated multi-chemometric modelling approaches namely regression-based and classification-based quantitative structure-activity relationships (QSARs), hologram QSAR, comparative molecular similarity analysis (CoMSIA), Open3DQSAR study and pharmacophore mapping to identify the structural and physicochemical requirements along with the chemico-biological interactions of pyridinones and pyranones for anti-endonuclease activity. The results suggest that the pyridinone scaffold is more preferable than the pyranone ring. The keto function at 4th position and aryl tetrazole substitution at 1st position of the parent moiety may be important for endonuclease inhibition. Hydroxyl group at 5th position of the parent ring may act as hydrogen bond acceptor feature. The steric substituent is suitable at 2nd position whereas hydrophobic substitution is found to be unfavourable at this position. Bulky hydrophobic substituents are not preferred at the 3rd position of the parent moiety. The information revealed from these integrated ligand-based modelling methods may provide useful informations for designing newer potential anti-influenza agents in future.

Keywords

Influenza endonuclease inhibitors Bayesian classification modelling HQSAR CoMSIA Open3DQSAR Pharmacophore mapping 

Notes

Acknowledgements

Authors are thankful to the All India Council for Technical Education (AICTE) (Grant No. 8023/RID/RPS-53/2011-12), New Delhi and the Council of Scientific and Industrial Research (CSIR) (Grant No. 02(0037)/11/EMR-II), New Delhi as well as University Grants Commission (UGC) (Grant No. 41-747/2012(SR) and Grant No. No.F.30-106/2015-BSR), New Delhi for providing financial assistance. NA is grateful to UGC, New Delhi for providing Rajiv Gandhi National Fellowship (Grant No. F1-17.1/2014-15/RGNF-2014-15-SC-WES-73725/SA-III/Website). Authors are thankful to the authority of Jadavpur University, India and Dr. Harisingh Gour University, India for providing research facilities.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests.

Supplementary material

11224_2017_933_MOESM1_ESM.docx (115 kb)
ESM 1 (DOCX 114 kb)

References

  1. 1.
    Harper SA, Bradley JS, Englund JA, File TM, Gravenstein S, Hayden FG, AJ MG, Neuzil KM, Pavia AT, Tapper ML, Uyeki TM, Zimmerman RK, Expert Panel of the Infectious Diseases Society of America (2009) Seasonal influenza in adults and children--diagnosis, treatment, chemoprophylaxis, and institutional outbreak management: clinical practice guidelines of the Infectious Diseases Society of America. Clin Infect Dis 48:1003–1032CrossRefGoogle Scholar
  2. 2.
    Lamb RA, Krug RM (2001) Orthomyxoviridae: the viruses and their replication. In: Knipe DM, Howely PM, Griffin DE, Lamb RA, Martin MA, Roizman B, Straus SE (eds) Fields virology. Lippincott Williams & Wilkins, Philadelphia, pp. 1487–1531Google Scholar
  3. 3.
    Dunning J, Baillie JK, Cao B, Hayden FG, International Severe Acute Respiratory and Emerging Infection Consortium (2014) Antiviral combinations for severe influenza. Lancet Infect Dis 14:1259–1270CrossRefGoogle Scholar
  4. 4.
    Huang TS, Palese P, Krystal M (1990) Determination of influenza virus proteins required for genome replication. J Virol 64:5669–5673Google Scholar
  5. 5.
    Honda A, Ishihama A (1997) The molecular anatomy of influenza virus RNA polymerase. Biol Chem 378:483–488Google Scholar
  6. 6.
    Honda A, Mizumoto K, Ishihama A (2002) Minimum molecular architectures for transcription and replication of the influenza virus. Proc Natl Acad Sci U S A 99:13166–13171CrossRefGoogle Scholar
  7. 7.
    Rogolino D, Bacchi A, De Luca L, Rispoli G, Sechi M, Stevaert A, Naesens L, Carcelli M (2015) Investigation of the salicylaldehyde thiosemicarbazone scaffold for inhibition of influenza virus PA endonuclease. J Biol Inorg Chem 20:1109–1121CrossRefGoogle Scholar
  8. 8.
    Pala N, Stevaert A, Dallocchio R, Dessì A, Rogolino D, Carcelli M, Sanna V, Sechi M, Naesens L (2015) Virtual screening and biological validation of novel influenza virus PA endonuclease inhibitors. ACS Med Chem Lett 6:866–871CrossRefGoogle Scholar
  9. 9.
    Plotch SJ, Bouloy M, Ulmanen I, Krug RM (1981) A unique cap (m7GpppXm)-dependent influenza virion endonuclease cleaves capped RNAs to generate the primers that initiate viral RNA transcription. Cell 23:847–858CrossRefGoogle Scholar
  10. 10.
    Dias A, Bouvier D, Crépin T, McCarthy AA, Hart DJ, Baudin F, Cusack S, Ruigrok RW (2009) The cap-snatching endonuclease of influenza virus polymerase resides in the PA subunit. Nature 458:914–918CrossRefGoogle Scholar
  11. 11.
    Yuan P, Bartlam M, Lou Z, Chen S, Zhou J, He X, Lv Z, Ge R, Li X, Deng T, Fodor E, Rao Z, Liu Y (2009) Crystal structure of an avian influenza polymerase PA (N) reveals an endonuclease active site. Nature 458:909–913CrossRefGoogle Scholar
  12. 12.
    Yan Z, Zhang L, Fu H, Wang Z, Lin J (2014) Design of the influenza virus inhibitors targeting the PA endonuclease using 3D-QSAR modeling, side-chain hopping, and docking. Bioorg Med Chem Lett 24:539–547CrossRefGoogle Scholar
  13. 13.
    Sanz-Ezquerro JJ, Zürcher T, de la Luna S, Ortín J, Nieto A (1996) The amino-terminal one-third of the influenza virus PA protein is responsible for the induction of proteolysis. J Virol 70:1905–1911Google Scholar
  14. 14.
    Deng T, Sharps J, Fodor E, Brownlee GG (2005) In vitro assembly of PB2 with a PB1-PA dimer supports a new model of assembly of influenza a virus polymerase subunits into a functional trimeric complex. J Virol 79:8669–8674CrossRefGoogle Scholar
  15. 15.
    Hara K, Schmidt FI, Crow M, Brownlee GG (2006) Amino acid residues in the N-terminal region of the PA subunit of influenza a virus RNA polymerase play a critical role in protein stability, endonuclease activity, cap binding, and virion RNA promoter binding. J Virol 80:7789–7798CrossRefGoogle Scholar
  16. 16.
    Maier HJ, Kashiwagi T, Hara K, Brownlee GG (2008) Differential role of the influenza a virus polymerase PA subunit for vRNA and cRNA promoter binding. Virology 370:194–204CrossRefGoogle Scholar
  17. 17.
    Crépin T, Dias A, Palencia A, Swale C, Cusack S, Ruigrok RWH (2008) Mutational and metal binding analysis of the endonuclease domain of the influenza virus polymerase PA subunit. J Virol 84:9096–9104CrossRefGoogle Scholar
  18. 18.
    Cianci C, Chung TDY, Meanwell N, Putz H, Hagen M, Colonno RJ, Krystal M (1996) Identification of N-hydroxamic acid and Nhydroxy-imide compounds that inhibit the influenza virus polymerase. Antiviral Chem Chemother 7:353–360CrossRefGoogle Scholar
  19. 19.
    Singh SB, Tomassini JE (2001) Synthesis of natural flutimide and analogous fully substituted pyrazine-2, 6-diones, endonuclease inhibitors of influenza virus. J Org Chem 66:5504–5516CrossRefGoogle Scholar
  20. 20.
    Kuzuhara T, Iwai Y, Takahashi H, Hatakeyama D, Echigo N (2009) Green tea catechins inhibit the endonuclease activity of influenza A virus RNA polymerase. PLoS Curr 1RRN1052Google Scholar
  21. 21.
    Credille CV, Chen Y, Cohen SM (2016) Fragment-based identification of influenza endonuclease inhibitors. J Med Chem 59:6444–6454CrossRefGoogle Scholar
  22. 22.
    Adhikari N, Jana D, Halder AK, Mondal C, Maiti MK, Jha T (2012) Chemometric modeling of 5-phenylthiophenecarboxylic acid derivatives as anti-rheumatic agents. Curr Comput Aided Drug Des 8:182–195CrossRefGoogle Scholar
  23. 23.
    Amin SA, Gayen S (2016) Modeling cytotoxic activity of some pyrazolo-triazole hybrids using descriptors calculated from open source tool "PaDEL-descriptor". J Taibah Univ Sci DOI. doi: 10.1016/j.jtusci.2016.04.009 Google Scholar
  24. 24.
    Amin SA, Adhikari N, Gayen S, Jha T (2016) Insight into the structural requirements of theophylline-based aldehyde dehydrogenase 1A1 (ALDH1A1) inhibitors through multi-QSAR modeling and molecular docking approaches. Curr Drug Dis Tech 13:84–100CrossRefGoogle Scholar
  25. 25.
    Mondal C, Halder AK, Adhikari N, Saha A, Saha KD, Gayen S, Jha T (2015) Comparative validated molecular modeling of p53–HDM2 inhibitors as antiproliferative agents. Eur J Med Chem 90:860–875CrossRefGoogle Scholar
  26. 26.
    Accelrys Inc., Discovery Studio 3.0, San Diego, USA 2015Google Scholar
  27. 27.
    Yap CW (2011) PaDEL–descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474CrossRefGoogle Scholar
  28. 28.
    DRAGON Web version 2.1 is a software developed by Milano Chemometrics and QSAR Research group, Dipartimento di scienzedell’Ambiente e del TerritorioUniversitadegliStudi di Milano-BicoccaGoogle Scholar
  29. 29.
    Ambure P, Aher RB, Gajewicz A, Puzyn T, Roy K (2015) “NanoBRIDGES” software: open access tools to perform QSAR and nano-QSAR modeling. Chemometr Intell Lab Syst 147:1–13CrossRefGoogle Scholar
  30. 30.
    STATISTICA version 7 is statistical software of StatSoft, Inc., Tulsa, USAGoogle Scholar
  31. 31.
    Sendecor GW, Cochran WG (1967) Multiple regression in statistical methods, 6th edn. Oxford & IBH, New DelhiGoogle Scholar
  32. 32.
    Tetko IV, Tanchuk VY, Villa AE (2001) Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices. J Chem Inf Comput Sci 41:1407–1421CrossRefGoogle Scholar
  33. 33.
    Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20:269–276CrossRefGoogle Scholar
  34. 34.
    Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2012) Comparative studies on some metrics for external validation of QSPR models. J Chem Inf Model 52:396–408CrossRefGoogle Scholar
  35. 35.
    Klon AE, Lowrie JF, Diller DJ (2006) Improved naive Bayesian modeling of numerical data for absorption, distribution, metabolism and excretion (ADME) property prediction. J Chem Inf Model 46:1945–1956CrossRefGoogle Scholar
  36. 36.
    Prathipati P, Ma NL, Keller TH (2008) Global Bayesian models for the prioritization of antitubercular agents. J Chem Inf Model 48:2363–2370CrossRefGoogle Scholar
  37. 37.
    Adhikari N, Halder AK, Mallick S, Saha A, Saha KD, Jha T (2016) Robust design of some selective matrix metalloproteinase-2 inhibitors over matrix metalloproteinase-9 through in silico/fragment-based lead identification and de novo lead modification: syntheses and biological assays. Bioorg Med Chem 24:4291–4309CrossRefGoogle Scholar
  38. 38.
    Waller CL (2004) A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds. J Chem Inf Comput Sci 44:758–765CrossRefGoogle Scholar
  39. 39.
    Yu S, Yuan J, Shi J, Ruan X, Zhang T, Wang Y, Du Y (2015) HQSAR and topomer CoMFA for predicting melanocortin-4 receptor binding affinities of trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides. Chemometr Intell Lab Sys 146:34–41CrossRefGoogle Scholar
  40. 40.
    Sainy J, Sharma R (2015) QSAR analysis of thiolactone derivatives using HQSAR, CoMFA and CoMSIA. SAR QSAR Environ Res 26:873–892CrossRefGoogle Scholar
  41. 41.
    SYBYL-X 2.0, Tripos Inc. 1699 South Hanley Road. St Louis, MO 63144, USAGoogle Scholar
  42. 42.
    Cramer RD, Patterson DE, Bunce JD (1989) Recent advances in comparative molecular field analysis (CoMFA). Prog Clin Biol Res 29:161–165Google Scholar
  43. 43.
    Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967CrossRefGoogle Scholar
  44. 44.
    Ghasemi JB, Aghaee E, Jabbari A (2013) Docking, CoMFA and CoMSIA studies of a series of N-benzoylated phenoxazines and phenothiazines derivatives as antiproliferative agents. Bull Kor Chem Soc 34:899–906CrossRefGoogle Scholar
  45. 45.
    Hu S, Yu H, Zhao L, Liang A, Liu Y, Zhang H (2013) Molecular docking and 3D-QSAR studies on checkpoint kinase 1 inhibitors. Med Chem Res 22:4992–5013CrossRefGoogle Scholar
  46. 46.
    Curpăn RF, Halip L, Borota A, Mracec M, Mracec M (2016) Modeling of dexmedetomidine conformers and their interactions with alpha2 adrenergic receptor subtypes. Struct Chem 27:871–881CrossRefGoogle Scholar
  47. 47.
    Amin SA, Adhikari N, Jha T, Gayen S (2016) Exploring structural requirements of unconventional Knoevenagel-type indole derivatives as cytotoxic agents through comparative QSAR modeling approaches. Can J Chem 94:637–644CrossRefGoogle Scholar
  48. 48.
    Tosco P, Balle T (2011) Open3DQSAR: a new open–source software aimed at high-throughput chemometric analysis of molecular interaction fields. J Mol Model 17:201–208CrossRefGoogle Scholar
  49. 49.
    Kumar SP, Jha PC, Jasrai YT, Pandya HA (2016) The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models. J Biomol Struct Dyn 34:540–559CrossRefGoogle Scholar
  50. 50.
    Pastor M, Cruciani G, Clementi S (1997) Smart region definition: a new way to improve the predictive ability and interpretability of three-dimensional quantitative structure activity relationships. J Med Chem 40:1455–1464CrossRefGoogle Scholar
  51. 51.
    Centner V, Massart DL, de Noord OE, de Jong S, Vandeginste BM, Sterna C (1996) Elimination of uninformative variables for multivariate calibration. Anal Chem 68:3851–3858CrossRefGoogle Scholar
  52. 52.
    The PyMOL Molecular Graphics System, Version 1.7.0.4 Schro¨dinger, LLC, USAGoogle Scholar
  53. 53.
    Debnath AK (2002) Pharmacophore mapping of a series of 2, 4-diamino-5-deazapteridine inhibitors of Mycobacterium Avium Complex dihydrofolate reductase. J Med Chem 45:41–53CrossRefGoogle Scholar
  54. 54.
    Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M (1983) Charmm - a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 4:187–217CrossRefGoogle Scholar
  55. 55.
    Calvin YCC (2009) Pharmacoinformatics approach for mPGES-1 in anti-inflammation by 3D-QSAR pharmacophore mapping. J Tai Instit Chem Eng 40:155–161CrossRefGoogle Scholar
  56. 56.
    Pavadai E, El Mazouni F, Wittlin S, de Kock C, Phillips MA, Chibale K (2016) Identification of new human malaria parasite plasmodium falciparum dihydroorotate dehydrogenase inhibitors by pharmacophore and structure-based virtual screening. J Chem Inf Model 56:548–562CrossRefGoogle Scholar
  57. 57.
    Fisher SRA (1960) The design of experiments; Oliver and Boyd Edinburgh. Vol 12Google Scholar
  58. 58.
    Ojha PK, Mitra I, Das RN, Roy K (2011) Further exploring rm 2 metrics for validation of QSPR models dataset. Chemo Intell Lab Sys 107:194–205CrossRefGoogle Scholar
  59. 59.
    Schüürmann G, Ebert RU, Chen J, Wang B, Kühne R (2008) External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean. J Chem Inf Model 48:2140–2145CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Pharmaceutical Technology, Natural Science Laboratory, Division of Medicinal and Pharmaceutical ChemistryJadavpur UniversityKolkataIndia
  2. 2.Department of Pharmaceutical Sciences, Laboratory of Drug Design and DiscoveryDr. Harisingh Gour University (A Central University)SagarIndia

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