Advertisement

3 Biotech

, 8:385 | Cite as

In silico insights into prediction and analysis of potential novel pyrrolopyridine analogs against human MAPKAPK-2: a new SAR-based hierarchical clustering approach

  • Kranthi Kumar Konidala
  • Uma Devi Bommu
  • Suneetha Yeguvapalli
  • Neeraja Pabbaraju
Original Article
  • 35 Downloads

Abstract

In the present study, we have focused on to elucidate potential bioactive pyrrolopyridine (PYP23) analogs against human mitogen-activated protein kinase-activated protein kinase-2 (MK-2). Here, in silico methods and computational systems biology tools were used as rational strategies to predict novel PYP23 analogs against the MK-2. Initially, crystal structure (PDB-ID: 2P3G) consists steriochemical conflicts were rectified by structure-optimization approaches using the Modeller program, and a new optimized-high resolution model was generated. The stereochemical qualities of the predicted MK-2 model were judged; these showed that the model was reliable for docking assessments. SAR-based bioactivity analysis showed that among the 197 datasets only 15 candidates contained bioactivity data and were accepted as probable MK-2 inhibitors. Virtual screening and docking strategies of dataset compounds against the ligand-binding domain of MK-2 recognized 13 composites containing high binding affinity than known compounds. Furthermore, the comparative structure clustering, in silico toxicogenomics and QSAR-based anticancer properties prediction approaches were successful in the recognition of five best potential compounds such as 60118340, 60118338, 60117736, 60118473 and 60118322, which have great anticancer and drug-likeness with non-toxicity class indices. Leu70, Glu139, Leu141, Glu145, Glu190, Thr206 and Asp207 were found to be novel hotspot residues prominently involved in H-bonds framing with ligands. Interestingly, they have shown better molecular similarity with known bioactive PYP inhibitors. Thus, predicted five compounds can useful as possible chemotherapeutic agents for MK-2 and show similar molecular actions like known PYP inhibitors. Overall, these streamlined new methods may have great potential to reveal possible ligands toward other molecular targets and biomarkers.

Keywords

MAP kinase-activated protein kinase-2 Pyrrolopyrimidine inhibitors SAR-based bioassay clustering Virtual screening Ensemble docking ADMET analysis 

Notes

Acknowledgements

K. Kranthi Kumar (F1-17.1/2013-14/RGNF-2013-14-SC-AND-40670/(SAIII/Website)) and B. Uma Devi (F1–17.1/2012–13/RGNF-2012–13-SC-AND-32761/(SA-III/WEBSITE)) are thankful to University Grants Commission (UGC) New Delhi, India, for awarding research fellowship, Rajiv Gandhi National Fellowship (RGNF/SRF), to carry out this work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Supplementary material

13205_2018_1405_MOESM1_ESM.docx (26 kb)
Supplementary material 1 (DOCX 25 KB)

References

  1. Anderson DR, Meyers MJ, Vernier WF et al (2007) Pyrrolopyridine Inhibitors of mitogen-activated protein kinase-activated protein kinase 2 (MK-2). J Med Chem 50:2647–2654CrossRefPubMedGoogle Scholar
  2. Anton R, Bauer SM, Peter RWEF, Keck et al (2014) A p38 substrate-specific MK2-EGFP translocation assay for identification and validation of new p38 inhibitors in living cells: a comprising alternative for acquisition of cellular p38 inhibition Data. PLoS One.  https://doi.org/10.1371/journal.pone.0095641 CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bommu UD, Konidala KK, Pabbaraju N, Yeguvapalli S (2017) Ligand-based virtual screening, molecular docking, QSAR and pharmacophore analysis of quercetin-associated potential novel analogs against epidermal growth factor receptor. J Recept Signal Transduct Res 37:600–610CrossRefPubMedGoogle Scholar
  4. Cargnello M, Roux PP (2011) Activation and function of the MAPKs and their substrates, the MAPK-activated protein kinases. Microbiol Mol Biol Rev 75:50–83CrossRefPubMedPubMedCentralGoogle Scholar
  5. Chen K, Huang J, Gong W et al (2007) Toll-like receptors in inflammation, infection and cancer. Int Immunopharmacol 7:1271–1285CrossRefPubMedGoogle Scholar
  6. Cheng F, Li W, Zhou Y et al (2012) admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 52:3099–3105CrossRefPubMedGoogle Scholar
  7. Chikanza IC (2004) Corticosteroid resistance in rheumatoid arthritis: molecular and cellular perspectives. Rheumatology 43:1337–1345CrossRefPubMedGoogle Scholar
  8. Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2:1511–1519CrossRefPubMedPubMedCentralGoogle Scholar
  9. Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717CrossRefPubMedPubMedCentralGoogle Scholar
  10. Dallakyan S, Olson AJ (2014) Small-molecule library screening by docking with PyRx. Methods Mol Biol 1263:243–250CrossRefGoogle Scholar
  11. Dobbelstein M, Moll U (2014) Targeting tumour-supportive cellular machineries in anticancer drug development. Nat Rev Drug Discov 13:179–196CrossRefPubMedGoogle Scholar
  12. Fiore M, Forli S, Manetti F (2015) Targeting mitogen-activated protein kinase-activated protein kinase 2 (MAPKAPK2, MK2): medicinal chemistry efforts to lead small molecule inhibitors to clinical trials. J Med Chem 59:3609–3634CrossRefPubMedPubMedCentralGoogle Scholar
  13. Gurgis FMS, Ziaziaris W, Munoz L (2013) Mitogen-activated protein kinase-activated protein kinase 2 in neuroinflammation, heat shock protein 27 phosphorylation, and cell cycle: role and targeting. Mol Pharmacol 85(2):345–356CrossRefPubMedGoogle Scholar
  14. Höpker K, Hagmann H, Khurshid S et al (2012) AATF/Che-1 acts as a phosphorylation-dependent molecular modulator to repress p53-driven apoptosis. EMBO J 31:3961–3975CrossRefPubMedPubMedCentralGoogle Scholar
  15. Kaplan W (2001) Swiss-PDB viewer (deep view). Brief Bioinform 2:195–197CrossRefPubMedGoogle Scholar
  16. Kim S, Han L, Yu B et al (2015a) PubChem structure–activity relationship (SAR) clusters. J Chem Inform.  https://doi.org/10.1186/s13321-015-0070-x CrossRefGoogle Scholar
  17. Kim S, Thiessen PA, Bolton EE et al (2015b) PubChem substance and compound databases. Nucleic Acids Res.  https://doi.org/10.1093/nar/gkv951 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Kumar KK, Devi BU, Neeraja P (2017) Integration of in silico approaches to determination of endocrine-disrupting perfluorinated chemicals binding potency with steroidogenic acute regulatory protein. Biochem Biophys Res Commun 491:1007–1014CrossRefGoogle Scholar
  19. Laskowski RA, Macarthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291CrossRefGoogle Scholar
  20. Leelananda SP, Lindert S (2016) Computational methods in drug discovery. Beilstein J Org Chem 12:2694–2718CrossRefPubMedPubMedCentralGoogle Scholar
  21. Lüthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356:83–85CrossRefPubMedGoogle Scholar
  22. Manke IA, Nguyen A, Lim D et al (2005) MAPKAP kinase-2 is a cell cycle checkpoint kinase that regulates the G2/M transition and S phase progression in response to UV irradiation. Mol Cell 17:37–48CrossRefGoogle Scholar
  23. Mourey RJ, Burnette BL, Brustkern SJ et al (2010) A benzothiophene inhibitor of mitogen-activated protein kinase-activated protein kinase 2 inhibits tumor necrosis factor production and has oral anti-inflammatory efficacy in acute and chronic models of inflammation. J Pharmacol Exp Ther 333:797–807CrossRefPubMedGoogle Scholar
  24. Phatak SS, Stephan CC, Cavasotto CN (2009) High-throughput and in silico screenings in drug discovery. Expert Opin Drug Discov 4:947–959CrossRefPubMedGoogle Scholar
  25. Putz M, Duda-Seiman C, Duda-Seiman D et al (2016) Chemical structure-biological activity models for pharmacophores’ 3D-interactions. Int J Mol Sci 17:1087CrossRefPubMedCentralGoogle Scholar
  26. Šali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815CrossRefPubMedGoogle Scholar
  27. Sancar A, Lindsey-Boltz LA, Gaddameedhi S et al (2014) Circadian clock, cancer, and chemotherapy. Biochemistry 54:110–123CrossRefPubMedPubMedCentralGoogle Scholar
  28. Singh H, Kumar R, Singh S et al (2016) Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines. BMC Cancer.  https://doi.org/10.1186/s12885-016-2082-y CrossRefPubMedPubMedCentralGoogle Scholar
  29. Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2013) Computational methods in drug discovery. Pharmacol Rev 66:334–395CrossRefPubMedGoogle Scholar
  30. Trempolec N, Dave-Coll N, Nebreda AR (2013) SnapShot: p38 MAPK substrates. Cell.  https://doi.org/10.1016/j.cell.2013.01.047 CrossRefPubMedGoogle Scholar
  31. Tusar M, Minovski N, Fjodorova N, Novic M (2012) In silico assessment of adverse effects of a large set of 6-fluoroquinolones obtained from a study of tuberculosis chemotherapy. Curr Drug Saf 7:313–320CrossRefPubMedGoogle Scholar
  32. Xu L, Chen S, Bergan RC (2006) MAPKAPK2 and HSP27 are downstream effectors of p38 MAP kinase-mediated matrix metalloproteinase type 2 activation and cell invasion in human prostate cancer. Oncogene 25:2987–2998CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Kranthi Kumar Konidala
    • 1
  • Uma Devi Bommu
    • 2
  • Suneetha Yeguvapalli
    • 2
  • Neeraja Pabbaraju
    • 1
  1. 1.Division of Molecular Physiology, Department of ZoologySri Venkateswara UniversityTirupatiIndia
  2. 2.Division of Cancer Informatics, Department of ZoologySri Venkateswara UniversityTirupatiIndia

Personalised recommendations