Development of a Web-Server for Identification of Common Lead Molecules for Multiple Protein Targets

Part of the Methods in Pharmacology and Toxicology book series (MIPT)


Due to increasing unresponsiveness of drugs to single targets in the form of resistance or presence of alternate mechanisms in case of complex diseases and disorders, etc., the focus is shifting towards polypharmacology. It is desirable that a drug works on multiple targets to elicit guaranteed/multiplier effect. Here, we provide a one stop solution to the quest of finding common leads for multiple protein targets. The computational protocol designed involves screening, docking, and scaffold-based optimization of hit molecules from a variety of compound libraries against any two specified protein targets. The protocol is validated with five case studies involving five pairs of proteins with varying active site similarities. The methodology is able to recover the known common FDA approved drugs against them. A web-server named “Multi-Target Ligand Design” is created and made freely accessible at


Multi-target drug design Polypharmacology Scaffold-based optimization Screening and docking Structure based ligand design 



Funding from the Department of Biotechnology, Govt. of India, to SCFBio is gratefully acknowledged. A.J. and A.P. are Institute Fellows. R.B. is a DST INSPIRE Fellow.

Author contributions: B.J. conceived the project. A.J., R.B., A.P. carried out the computational development. All authors analyzed the results and wrote the manuscript. M.S. helped in web enabling of the server. All authors have given approval to the final version of the manuscript.


  1. 1.
    Winau F, Westphal O, Winau R (2004) Paul Ehrlich – in search of the magic bullet. Microbes Infect 6:786–789. Scholar
  2. 2.
    Koutsoukas A, Simms B, Kirchmair J et al (2011) From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteome 74:2554–2574. Scholar
  3. 3.
    DiMasi J (2001) New drug development in the United States from 1963 to 1999. Clin Pharmacol Ther 69:286–296. Scholar
  4. 4.
    Adams CP, Brantner VV (2006) Estimating the cost of new drug development: is it really $802 million? Health Aff 25:420–428. Scholar
  5. 5.
    Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3:673–683. Scholar
  6. 6.
    Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4:682–690. Scholar
  7. 7.
    Hopkins AL (2009) Drug discovery: predicting promiscuity. Nature 462:167–168. Scholar
  8. 8.
    Apsel B, Blair JA, Gonzalez BZ et al (2008) NIH public access. Nat Chem Biol 4:691–699. Scholar
  9. 9.
    Simon Z, Peragovics Á, Vigh-Smeller M et al (2012) Drug effect prediction by polypharmacology-based interaction profiling. J Chem Inf Model 52:134–145. Scholar
  10. 10.
    Briansó F, Carrascosa MC, Oprea TI, Mestres J (2011) Cross-pharmacology analysis of G protein-coupled receptors. Curr Top Med Chem 11:1956–1963. Scholar
  11. 11.
    Paolini GV, Shapland RHB, Van Hoorn WP et al (2006) Global mapping of pharmacological space. Nat Biotechnol 24:805–815. Scholar
  12. 12.
    Oprea TI, Mestres J (2012) Drug repurposing: far beyond new targets for old drugs. AAPS J 14:759–763. Scholar
  13. 13.
    Durrant JD, Amaro RE, Xie L et al (2010) A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. PLoS Comput Biol 6:e1000648. Scholar
  14. 14.
    Boran ADW, Iyengar R (2010) Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Devel 13:297–309. Scholar
  15. 15.
    Reddy AS, Zhang S (2013) Polypharmacology: drug discovery for the future. Expert Rev Clin Pharmacol 6:41–47. Scholar
  16. 16.
    Zimmermann GR, Lehár J, Keith CT (2007) Multi-target therapeutics: when the whole is greater than the sum of the parts. Drug Discov Today 12:34–42. Scholar
  17. 17.
    Timson D (2017) Dicoumarol: a drug which hits at least two very different targets in vitamin K metabolism. Curr Drug Targets 18:500–510. Scholar
  18. 18.
    Giordano S, Petrelli A (2008) From single- to multi-target drugs in cancer therapy: when aspecificity becomes an advantage. Curr Med Chem 15:422–432. Scholar
  19. 19.
    Kropeit D, Scheuenpflug J, Erb-Zohar K et al (2017) Pharmacokinetics and safety of letermovir, a novel anti-human cytomegalovirus drug, in patients with renal impairment. Br J Clin Pharmacol 83:1944–1953. Scholar
  20. 20.
    Goldner T, Hewlett G, Ettischer N et al (2011) The novel anticytomegalovirus compound AIC246 (Letermovir) inhibits human cytomegalovirus replication through a specific antiviral mechanism that involves the viral terminase. J Virol 85:10884–10893. Scholar
  21. 21.
    Razonable R, Melendez D (2015) Letermovir and inhibitors of the terminase complex: a promising new class of investigational antiviral drugs against human cytomegalovirus. Infect Drug Resist 8:269. Scholar
  22. 22.
    Chou S (2017) A third component of the human cytomegalovirus terminase complex is involved in letermovir resistance. Antivir Res 148:1–4. Scholar
  23. 23.
    Neuber S, Wagner K, Goldner T et al (2017) Mutual interplay between the human cytomegalovirus terminase subunits pUL51, pUL56, and pUL89 promotes terminase complex formation. J Virol 91:e02384–e02316. Scholar
  24. 24.
    Lin H-H, Zhang L-L, Yan R et al (2017) Network analysis of drug–target interactions: a study on FDA-approved new molecular entities between 2000 to 2015. Sci Rep 7:12230. Scholar
  25. 25.
    Van der Schyf CJ (2011) The use of multi-target drugs in the treatment of neurodegenerative diseases. Expert Rev Clin Pharmacol 4:293–298. Scholar
  26. 26.
    Das T, Sa G, Saha B, Das K (2010) Multifocal signal modulation therapy of cancer: ancient weapon, modern targets. Mol Cell Biochem 336:85–95. Scholar
  27. 27.
    Gupta SC, Prasad S, Kim JH et al (2011) Multitargeting by curcumin as revealed by molecular interaction studies. Nat Prod Rep 28:1937. Scholar
  28. 28.
    Gupta SC, Patchva S, Koh W, Aggarwal BB (2012) Discovery of curcumin, a component of golden spice, and its miraculous biological activities. Clin Exp Pharmacol Physiol 39:283–299. Scholar
  29. 29.
    Tan W, Lu J, Huang M et al (2011) Anti-cancer natural products isolated from Chinese medicinal herbs. Chin Med 6:1–15. Scholar
  30. 30.
    Gupta SC, Kim JH, Prasad S, Aggarwal BB (2010) Regulation of survival, proliferation, invasion, angiogenesis, and metastasis of tumor cells through modulation of inflammatory pathways by nutraceuticals. Cancer Metastasis Rev 29:405–434. Scholar
  31. 31.
    Hoda N, Naz H, Jameel E et al (2016) Curcumin specifically binds to the human calcium–calmodulin-dependent protein kinase IV: fluorescence and molecular dynamics simulation studies. J Biomol Struct Dyn 34:572–584. Scholar
  32. 32.
    Srinivas NR (2010) Baicalin, an emerging multi-therapeutic agent: pharmacodynamics, pharmacokinetics, and considerations from drug development perspectives. Xenobiotica 40:357–367. Scholar
  33. 33.
    Jayaram B, Singh T, Mukherjee G et al (2012) Sanjeevini: a freely accessible web-server for target directed lead molecule discovery. BMC Bioinformatics 13:S7. Scholar
  34. 34.
    Meng X-Y, Zhang H-X, Mezei M, Cui M (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided-Drug Des 7:146–157. Scholar
  35. 35.
    Holdeman R, Nehrt S, Strome S (1998) MES-2, a maternal protein essential for viability of the germline in Caenorhabditis elegans, is homologous to a Drosophila Polycomb group protein. Development 125:2457–2467Google Scholar
  36. 36.
    Lionta E, Spyrou G, Vassilatis D, Cournia Z (2014) Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem 14:1923–1938. Scholar
  37. 37.
    Konc J, Hodošček M, Ogrizek M et al (2013) Structure-based function prediction of uncharacterized protein using binding sites comparison. PLoS Comput Biol 9:e1003341. Scholar
  38. 38.
    Kumar P, Kaalia R, Srinivasan A, Ghosh I (2018) Multiple target-based pharmacophore design from active site structures. SAR QSAR Environ Res 29:1–19. Scholar
  39. 39.
    Ploemen JHTM, Johnson WW, Jespersen S et al (1994) Active-site tyrosyl residues are targets in the irreversible inhibition of a class Mu glutathione transferase by 2-(S-glutathionyl)-3,5,6-trichloro-1,4-benzoquinone. J Biol Chem 269:26890–26897PubMedGoogle Scholar
  40. 40.
    Ramsay RR, Majekova M, Medina M, Valoti M (2016) Key targets for multi-target ligands designed to combat neurodegeneration. Front Neurosci 10.
  41. 41.
    Csermely P, Agoston V, Pongor S (2005) The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol Sci 26:178–182. Scholar
  42. 42.
    Puls LN, Eadens M, Messersmith W (2011) Current status of Src inhibitors in solid tumor malignancies. Oncologist 16:566–578. Scholar
  43. 43.
    Stella GM, Luisetti M, Inghilleri S et al (2012) Targeting EGFR in non-small-cell lung cancer: lessons, experiences, strategies. Respir Med 106:173–183. Scholar
  44. 44.
    Yildirim MA, Goh KI, Cusick ME et al (2007) Drug-target network. Nat Biotechnol 25:1119–1126. Scholar
  45. 45.
    Zhang W, Pei J, Lai L (2017) Computational multitarget drug design. J Chem Inf Model 57:403–412. Scholar
  46. 46.
    Knox C, Law V, Jewison T et al (2011) DrugBank 3.0: a comprehensive resource for “Omics” research on drugs. Nucleic Acids Res 39:D1035–D1041. Scholar
  47. 47.
    Jayaram B, Bhushan K, Shenoy SR et al (2006) Bhageerath: an energy based web enabled computer software suite for limiting the search space of tertiary structures of small globular proteins. Nucleic Acids Res 34:6195–6204. Scholar
  48. 48.
    Jayaram B, Dhingra P, Mishra A et al (2014) Bhageerath-H: a homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins. BMC Bioinformatics 15:S7. Scholar
  49. 49.
    DasGupta D, Kaushik R, Jayaram B (2015) From Ramachandran maps to tertiary structures of proteins. J Phys Chem B 119:11136–11145. Scholar
  50. 50.
    Kaushik R, Singh A, Jayaram B (2018) Where informatics lags chemistry leads. Biochemistry 57:503–506. Scholar
  51. 51.
    Singh A, Kaushik R, Mishra A et al (2016) ProTSAV: a protein tertiary structure analysis and validation server. Biochim Biophys Acta 1864:11–19. Scholar
  52. 52.
    Singh T, Biswas D, Jayaram B (2011) AADS – an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors. J Chem Inf Model 51:2515–2527. Scholar
  53. 53.
    Mukherjee G, Jayaram B (2013) A rapid identification of hit molecules for target proteins via physico-chemical descriptors. Phys Chem Chem Phys 15:9107. Scholar
  54. 54.
    Irwin JJ, Shoichet BK (2005) ZINC – a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182. Scholar
  55. 55.
  56. 56.
    Wishart DS, Knox C, Guo AC et al (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36:D901–D906. Scholar
  57. 57.
    Wishart DS, Feunang YD, Guo AC et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–D1082. Scholar
  58. 58.
    Gupta A, Gandhimathi A, Sharma P, Jayaram B (2007) ParDOCK: an all atom energy based Monte Carlo docking protocol for protein-ligand complexes. Protein Pept Lett 14:632–646. Scholar
  59. 59.
    Jain T, Jayaram B (2005) An all atom energy based computational protocol for predicting binding affinities of protein-ligand complexes. FEBS Lett 579:6659–6666. Scholar
  60. 60.
    Berman HM (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242. Scholar
  61. 61.
    Liu Z, Su M, Han L et al (2017) Forging the basis for developing protein–ligand interaction scoring functions. Acc Chem Res 50:302–309. Scholar
  62. 62.
    Yeturu K, Chandra N (2008) PocketMatch: a new algorithm to compare binding sites in protein structures. BMC Bioinformatics 9:543. Scholar
  63. 63.
    Nagarajan D, Chandra N (2013) PocketMatch (version 2.0): a parallel algorithm for the detection of structural similarities between protein ligand binding-sites. In: 2013 national conference on parallel computing technologies (PARCOMPTECH). IEEE, pp 1–6.
  64. 64.
    Sim L, Jayakanthan K, Mohan S et al (2010) New glucosidase inhibitors from an ayurvedic herbal treatment for type 2 diabetes: structures and inhibition of human intestinal maltase-glucoamylase with compounds from Salacia reticulata. Biochemistry 49:443–451. Scholar
  65. 65.
    Roig-Zamboni V, Cobucci-Ponzano B, Iacono R et al (2017) Structure of human lysosomal acid α-glucosidase—a guide for the treatment of Pompe disease. Nat Commun 8.
  66. 66.
    Wallace AC, Laskowski RA, Thornton JM (1995) Ligplot – a program to generate schematic diagrams of protein ligand interactions. Protein Eng 8:127–134. Scholar
  67. 67.
    Bledsoe RK, Madauss KP, Holt JA et al (2005) A ligand-mediated hydrogen bond network required for the activation of the mineralocorticoid receptor. J Biol Chem 280:31283–31293. Scholar
  68. 68.
    Colucci JK, Ortlund EA (2013) X-ray crystal structure of the ancestral 3-ketosteroid receptor-progesterone-mifepristone complex shows mifepristone bound at the coactivator binding interface. PLoS One 8:1–12. Scholar
  69. 69.
    Cui JJ, Tran-Dubé M, Shen H et al (2011) Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK). J Med Chem 54:6342–6363. Scholar
  70. 70.
    Huang Q, Johnson TW, Bailey S et al (2014) Design of potent and selective inhibitors to overcome clinical anaplastic lymphoma kinase mutations resistant to crizotinib. J Med Chem 57:1170–1187. Scholar
  71. 71.
    Mol CD, Dougan DR, Schneider TR et al (2004) Structural basis for the autoinhibition and STI-571 inhibition of c-kit tyrosine kinase. J Biol Chem 279:31655–31663. Scholar
  72. 72.
    Zhou M, Dong X, Baldauf C et al (2011) A novel calcium-binding site of von Willebrand factor A2 domain regulates its cleavage by ADAMTS13. Blood 117:4623–4631. Scholar
  73. 73.
    Murray CW, Berdini V, Buck IM et al (2015) Fragment-based discovery of potent and selective DDR1/2 inhibitors. ACS Med Chem Lett 6:798–803. Scholar
  74. 74.
    Heinzlmeir S, Kudlinzki D, Sreeramulu S et al (2016) Chemical proteomics and structural biology define EPHA2 inhibition by clinical kinase drugs. ACS Chem Biol 11:3400–3411. Scholar

Copyright information

© Springer Science+Business Media New York 2018

Authors and Affiliations

  1. 1.Department of ChemistryIndian Institute of Technology DelhiNew DelhiIndia
  2. 2.Supercomputing Facility for Bioinformatics & Computational BiologyIndian Institute of Technology DelhiNew DelhiIndia
  3. 3.Kusuma School of Biological SciencesIndian Institute of Technology DelhiNew DelhiIndia

Personalised recommendations