In search of the representative pharmacophore hypotheses of the enzymatic proteome of Plasmodium falciparum: a multicomplex-based approach

  • Anu Manhas
  • Mohsin Y. Lone
  • Prakash C. JhaEmail author
Short Communication


Drug resistance has made malaria an untreatable disease and therefore intensified the need for the development of new drugs and the identification of potential drug targets. In this pursuit, in silico efforts made in the past have not shown significant responses. Therefore, in the present work, the multicomplex-based pharmacophore modeling approach was employed to construct the pharmacophores of the 16 selected Plasmodium falciparum (Pf) targets. All the constructed hypotheses (153) were screened against a focused dataset made up of experimental actives of the chosen targets (3705 inhibitors). The rationale was to check the affinity of the inhibitors for the off-targets. Subsequently, the constructed hypotheses from each target were pooled based on the feature types and the pooled-hypotheses were then clustered to offer an insight about the pharmacophore similarity. Tanimoto similarity index was also calculated to look for the similarity among the inhibitors belonging to different Pf targets. Overall, the work was accomplished to bid healthier perceptive of the pharmacophore-based virtual screening and abet in providing guiding principles for the construction of stringent pharmacophores that can be employed for the screening.

Graphical abstract


Multicomplex-based pharmacophore Enzymatic proteome Clustering Tanimoto similarity Virtual screening 



Anu Manhas and PCJ acknowledge Science and Engineering Research Board (SERB), Department of Science and Technology (DST) for project grant through grant number EMR/2016/003025. MY Lone acknowledges the University Grants Commission (UGC), Govt. of India for the financial assistance.

Compliance with ethical standards

Conflict of interest

The authors declared no competing interest.

Supplementary material

11030_2018_9885_MOESM1_ESM.docx (4.9 mb)
Supplementary material 1 (DOCX 5066 kb)


  1. 1.
    Snow RW, Guerra CA, Noor AM, Myint HY, Hay SI (2005) The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature 434:214. CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    World Health Organisation- WHO (2016) World malaria report 2015. WHO. Accessed 19 April 2016
  3. 3.
    World Health Organisation- WHO (2015) Investing to overcome the global impact of neglected tropical diseases: 3rd WHO report on neglected tropical diseases, vol 3. World Health Organization-WHO, GenevaGoogle Scholar
  4. 4.
    Bhatt S, Weiss D, Cameron E, Bisanzio D, Mappin B, Dalrymple U, Battle K, Moyes C, Henry A, Eckhoff P (2015) The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 526:207. CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Cui L, Mharakurwa S, Ndiaye D, Rathod PK, Rosenthal PJ (2015) Antimalarial drug resistance: literature review and activities and findings of the ICEMR network. Am J Trop Med Hyg 93:57–68. CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Paloque L, Ramadani AP, Mercereau-Puijalon O, Augereau J-M, Benoit-Vical F (2016) Plasmodium falciparum: multifaceted resistance to artemisinins. Malar J 15:149. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Verlinden BK, Louw A, Birkholtz L-M (2016) Resisting resistance: is there a solution for malaria? Expert Opin Drug Discov 11:395–406. CrossRefPubMedGoogle Scholar
  8. 8.
    Müller IB, Hyde JE (2010) Antimalarial drugs: modes of action and mechanisms of parasite resistance. Future Microbiol 5:1857–1873. CrossRefPubMedGoogle Scholar
  9. 9.
    World Health Organisation- WHO (2016) Malaria vaccine: WHO position paper- January 2016. Wkly Epidemiol Rec 91:33–52. Accessed Jan 2016
  10. 10.
    Leelananda SP, Lindert S (2016) Computational methods in drug discovery. Beilstein J Org Chem 12:2694. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Langer T, Hoffmann RD (2006) Pharmacophores and Pharmacophore Searches, vol 32. WILEY-VCH Verlag GmbH & Co., KGaA, WeinheimCrossRefGoogle Scholar
  12. 12.
    Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9–20. CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol 152:21–37. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Yang S-Y (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15:444–450. CrossRefPubMedGoogle Scholar
  15. 15.
    Kurogi Y, Guner OF (2001) Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr Med Chem 8:1035–1055. CrossRefPubMedGoogle Scholar
  16. 16.
    Guner O (2002) History and evolution of the pharmacophore concept in computer-aided drug design. Curr Top Med Chem 2:1321–1332. CrossRefPubMedGoogle Scholar
  17. 17.
    Xiao Z, Varma S, Xiao Y-D, Tropsha A (2004) Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst™ HypoGen and k-nearest neighbor QSAR methods. J Mol Graph Model 23:129–138. CrossRefPubMedGoogle Scholar
  18. 18.
    Kirchmair J, Laggner C, Wolber G, Langer T (2005) Comparative analysis of protein-bound ligand conformations with respect to catalyst’s conformational space subsampling algorithms. J Chem Inf Model 45:422–430. CrossRefPubMedGoogle Scholar
  19. 19.
    Kirchmair J, Wolber G, Laggner C, Langer T (2006) Comparative performance assessment of the conformational model generators omega and catalyst: a large-scale survey on the retrieval of protein-bound ligand conformations. J Chem Inf Model 46:1848–1861. CrossRefPubMedGoogle Scholar
  20. 20.
    Kirchmair J, Ristic S, Eder K, Markt P, Wolber G, Laggner C, Langer T (2007) Fast and efficient in silico 3D screening: toward maximum computational efficiency of pharmacophore-based and shape-based approaches. J Chem Inf Model 47:2182–2196. CrossRefPubMedGoogle Scholar
  21. 21.
    Kristam R, Gillet VJ, Lewis RA, Thorner D (2005) Comparison of conformational analysis techniques to generate pharmacophore hypotheses using catalyst. J Chem Inf Model 45:461–476. CrossRefPubMedGoogle Scholar
  22. 22.
    Manhas A, Patel A, Lone MY, Jha PK, Jha PC (2018) Identification of PfENR inhibitors: a hybrid structure based approach in conjunction with molecular dynamics simulations. J Cell Biochem.
  23. 23.
    Manhas A, Kumar SP, Jha PC (2016) Molecular modeling of Plasmodium falciparum peptide deformylase and structure-based pharmacophore screening for inhibitors. RSC Adv 6:29466–29485. CrossRefGoogle Scholar
  24. 24.
    Manhas A, Lone MY, Jha PC (2017) Multicomplex-based pharmacophore modeling coupled with molecular dynamics simulations: an efficient strategy for the identification of novel inhibitors of PfDHODH. J Mol Graph Model 75:413–423. CrossRefPubMedGoogle Scholar
  25. 25.
    Lone MY, Kumar SP, Athar M, Jha PC (2018) Exploration of Mycobacterium tuberculosis structural proteome: an in silico approach. J Theor Biol 439:14–23. CrossRefPubMedGoogle Scholar
  26. 26.
    Lone MY, Athar M, Gupta VK, Jha PC (2017) Prioritization of natural compounds against mycobacterium tuberculosis 3-dehydroquinate dehydratase: a combined in silico and in vitro study. Biochem Biophys Res Commun 491:1105–1111. CrossRefPubMedGoogle Scholar
  27. 27.
    Lone MY, Athar M, Gupta VK, Jha PC (2017) Identification of Mycobacterium tuberculosis enoyl-acyl carrier protein reductase inhibitors: a combined in silico and in vitro analysis. J Mol Graph Model 76:172–180. CrossRefPubMedGoogle Scholar
  28. 28.
    Lone MY, Manhas A, Athar M, Jha PC (2017) Identification of InhA inhibitors: a combination of virtual screening, molecular dynamics simulations and quantum chemical studies. J Biomol Struct Dyn. CrossRefGoogle Scholar
  29. 29.
    Accelrys Discovery Studio version 4.0, Accelrys, San Diego, USA.
  30. 30.
    Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK (2006) BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucl Acids Res 35:D198–D201. CrossRefPubMedGoogle Scholar
  31. 31.
    Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B (2011) ChEMBL: a large-scale bioactivity database for drug discovery. Nucl Acids Res 40:1100–1107. CrossRefGoogle Scholar
  32. 32.
    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 4:187–217. CrossRefGoogle Scholar
  33. 33.
    Weinstein JN, Myers TG, O’Connor PM, Friend SH, Fornace AJ, Kohn KW, Fojo T, Bates SE, Rubinstein LV, Anderson NL (1997) An information-intensive approach to the molecular pharmacology of cancer. Science 275:343–349. CrossRefPubMedGoogle Scholar
  34. 34.
    Zhou L, Griffith R, Gaeta B (2014) Combining spatial and chemical information for clustering pharmacophores. BMC Bioinformatics 15:S5. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Chemical SciencesCentral University of GujaratGandhinagarIndia
  2. 2.Department of ChemistryIndian Institute of Technology GandhinagarGandhinagarIndia
  3. 3.Centre for Applied ChemistryCentral University of GujaratGandhinagarIndia

Personalised recommendations