Skip to main content
SpringerLink
Log in

Linked Open Data: Ligand-Transporter Interaction Profiling and Beyond

  • Protocol
  • First Online:
Multi-Target Drug Design Using Chem-Bioinformatic Approaches

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

  • 1237 Accesses

Abstract

Multi-target drug design is an innovative new paradigm in the drug development process. With the help of growing open data sources, in silico modeling approaches have become successful tools to discover and investigate multi-target drugs. In this chapter, we describe a workflow for retrieving and curating information for multiple drug targets from the open domain, provide insights into how the retrieved data can be employed in ligand and structure-based approaches, and discuss the hurdles to consider with respect to data analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Morphy R, Rankovic Z (2005) Designed multiple ligands. An emerging drug discovery paradigm. J Med Chem 48:6523–6543

    Article  CAS  Google Scholar 

  2. Hopkins AL, Mason JS, Overington JP (2006) Can we rationally design promiscuous drugs? Curr Opin Struct Biol 16:127–136

    Article  CAS  Google Scholar 

  3. Peters J-U (2013) Polypharmacology – foe or friend? J Med Chem 56:8955–8971

    Article  CAS  Google Scholar 

  4. Anighoro A, Bajorath J, Rastelli G (2014) Polypharmacology: challenges and opportunities in drug discovery. J Med Chem 57:7874–7887

    Article  CAS  Google Scholar 

  5. Bolognesi ML, Cavalli A (2016) Multitarget drug discovery and polypharmacology. ChemMedChem 11:1190–1192

    Article  CAS  Google Scholar 

  6. Lu J-J, Pan W, Hu Y-J, Wang Y-T (2012) Multi-target drugs: the trend of drug research and development. PLoS One 7:e40262

    Article  CAS  Google Scholar 

  7. Harrison RK (2016) Phase II and phase III failures: 2013–2015. Nat Rev Drug Discov 15:817–818

    Article  CAS  Google Scholar 

  8. Zhang W, Pei J, Lai L (2017) Computational multitarget drug design. J Chem Inf Model 57:403–412

    Article  CAS  Google Scholar 

  9. Ma XH, Shi Z, Tan C, Jiang Y, Go ML, Low BC, Chen YZ (2010) In-silico approaches to multi-target drug discovery. Pharm Res 27:739–749

    Article  CAS  Google Scholar 

  10. 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

    Article  CAS  Google Scholar 

  11. Lavecchia A, Cerchia C (2016) In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discov Today 21:288–298

    Article  CAS  Google Scholar 

  12. Taboureau O, Baell JB, Fernández-Recio J, Villoutreix BO (2012) Established and emerging trends in computational drug discovery in the structural genomics era. Chem Biol 19:29–41

    Article  CAS  Google Scholar 

  13. Kuyoc-Carrillo VF, Medina-Franco JL (2014) Progress in the analysis of multiple activity profile of screening data using computational approaches. Drug Dev Res 75:313–323

    Article  CAS  Google Scholar 

  14. Ellingson SR, Smith JC, Baudry J (2014) Polypharmacology and supercomputer-based docking: opportunities and challenges. Mol Simul 40:848–854

    Article  CAS  Google Scholar 

  15. Reker D, Rodrigues T, Schneider P, Schneider G (2014) Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus. Proc Natl Acad Sci U S A 111:4067–4072

    Article  CAS  Google Scholar 

  16. Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25:197–206

    Article  CAS  Google Scholar 

  17. Li H, Gao Z, Kang L et al (2006) TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Res 34:W219–W224

    Article  CAS  Google Scholar 

  18. Chen YZ, Zhi DG (2001) Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins 43:217–226

    Article  CAS  Google Scholar 

  19. Meslamani J, Rognan D, Kellenberger E (2011) sc-PDB: a database for identifying variations and multiplicity of “druggable” binding sites in proteins. Bioinformatics 27:1324–1326

    Article  CAS  Google Scholar 

  20. Meslamani J, Li J, Sutter J, Stevens A, Bertrand H-O, Rognan D (2012) Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling. J Chem Inf Model 52:943–955

    Article  CAS  Google Scholar 

  21. Williams AJ, Harland L, Groth P et al (2012) Open PHACTS: semantic interoperability for drug discovery. Drug Discov Today 17:1188–1198

    Article  Google Scholar 

  22. Bento AP, Gaulton A, Hersey A et al (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42:D1083–D1090

    Article  CAS  Google Scholar 

  23. Montanari F, Zdrazil B, Digles D, Ecker GF (2016) Selectivity profiling of BCRP versus P-gp inhibition: from automated collection of polypharmacology data to multi-label learning. J Cheminform. https://doi.org/10.1186/s13321-016-0121-y

  24. Berthold MR et al (2008) KNIME: the Konstanz information miner. In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R (eds) Data analysis, machine learning and applications, Studies in classification, data analysis, and knowledge organization. Springer, Berlin, Heidelberg

    Google Scholar 

  25. Pipeline pilot. http://accelrys.com/products/collaborative-science/biovia-pipeline-pilot/

  26. Montanari F, Zdrazil B (2017) How open data shapes in silico transporter modeling. Molecules. https://doi.org/10.3390/molecules22030422

    Article  Google Scholar 

  27. César-Razquin A, Snijder B, Frappier-Brinton T et al (2015) A call for systematic research on solute carriers. Cell 162:478–487

    Article  Google Scholar 

  28. Kristensen AS, Andersen J, Jørgensen TN, Sørensen L, Eriksen J, Loland CJ, Strømgaard K, Gether U (2011) SLC6 neurotransmitter transporters: structure, function, and regulation. Pharmacol Rev 63:585–640

    Article  CAS  Google Scholar 

  29. Koldsø H, Christiansen AB, Sinning S, Schiøtt B (2013) Comparative modeling of the human monoamine transporters: similarities in substrate binding. ACS Chem Neurosci 4:295–309

    Article  Google Scholar 

  30. Sitte HH, Freissmuth M (2015) Amphetamines, new psychoactive drugs and the monoamine transporter cycle. Trends Pharmacol Sci 36:41–50

    Article  CAS  Google Scholar 

  31. Schultz W (2010) Dopamine signals for reward value and risk: basic and recent data. Behav Brain Funct 6:24

    Article  Google Scholar 

  32. Webb B, Sali A (2016) Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci 86:2.9.1–2.9.37

    Google Scholar 

  33. (2015) Schrödinger Release 2015-2. Schrödinger, LLC, New York, NY

    Google Scholar 

  34. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759

    Article  CAS  Google Scholar 

  35. Molecular Operating Environment (MOE), 2013.08. Chemical Computing Group Inc., Montreal, Canada

    Google Scholar 

  36. Bowers K, Chow E, Xu H, et al (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: ACM/IEEE SC 2006 conference (SC’06). https://doi.org/10.1109/sc.2006.54

  37. Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY, 2017. Maestro-Desmond interoperability tools, Schrödinger, New York, NY, 2017

    Google Scholar 

  38. 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

    Article  CAS  Google Scholar 

  39. Aoki-Kinoshita KF, Kanehisa M (2007) KEGG primer: an introduction to pathway analysis using KEGG. NCI Nature Pathway Interaction Database. https://doi.org/10.1038/pid.2007.2

  40. Kramer C, Fuchs JE, Whitebread S, Gedeck P, Liedl KR (2014) Matched molecular pair analysis: significance and the impact of experimental uncertainty. J Med Chem 57:3786–3802

    Article  CAS  Google Scholar 

  41. Hu Y, Bajorath J (2014) Influence of search parameters and criteria on compound selection, promiscuity, and pan assay interference characteristics. J Chem Inf Model 54:3056–3066

    Article  CAS  Google Scholar 

  42. Hu Y, Bajorath J (2015) Structural and activity profile relationships between drug scaffolds. AAPS J 17:609–619

    Article  CAS  Google Scholar 

  43. Zdrazil B, Hellsberg E, Viereck M, Ecker GF (2016) From linked open data to molecular interaction: studying selectivity trends for ligands of the human serotonin and dopamine transporter. Medchemcomm 7:1819–1831

    Article  CAS  Google Scholar 

  44. Bemis GW, Murcko MA (1996) The properties of known drugs. 1. Molecular frameworks. J Med Chem 39:2887–2893

    Article  CAS  Google Scholar 

  45. myExperiment – Workflows – KNIME workflow without hERG labels included from Zdrazil et al., MedChemComm, 2016: “From linked open data to molecular interaction: studying selectivity trends for ligands of the human serotonin and dopamine transporter” (Barbara Zdrazil) [KNIME Workflow]. https://www.myexperiment.org/workflows/4911.html. Accessed 5 Feb 2018

  46. Berman HM (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242

    Article  CAS  Google Scholar 

  47. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38

    Article  CAS  Google Scholar 

  48. PyMOL, The PyMOL Molecular Graphics System, version 2.0. Schrödinger, LLC

    Google Scholar 

  49. Conroy MJ, Sehnal D, Deshpande M, Svobodova R, Mir S, Berka K, Midlik A, Velankar S, Koca J (2017) LiteMol: web-based three-dimensional visualization of macromolecular structure data. Acta Crystallogr A 73:C669

    Article  Google Scholar 

  50. Sehnal D, Deshpande M, Vařeková RS, Mir S, Berka K, Midlik A, Pravda L, Velankar S, Koča J (2017) LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data. Nat Methods 14:1121–1122

    Article  CAS  Google Scholar 

  51. Mir S, Alhroub Y, Anyango S et al (2018) PDBe: towards reusable data delivery infrastructure at protein data bank in Europe. Nucleic Acids Res 46:D486–D492

    Article  CAS  Google Scholar 

  52. Pozharski E, Weichenberger CX, Rupp B (2013) Techniques, tools and best practices for ligand electron-density analysis and results from their application to deposited crystal structures. Acta Crystallogr D Biol Crystallogr 69:150–167

    Article  CAS  Google Scholar 

  53. Emsley P, Lohkamp B, Scott WG, Cowtan K (2010) Features and development of Coot. Acta Crystallogr D Biol Crystallogr 66:486–501

    Article  CAS  Google Scholar 

  54. Qu X, Swanson R, Day R, Tsai J (2009) A guide to template based structure prediction. Curr Protein Pept Sci 10:270–285

    Article  CAS  Google Scholar 

  55. Singh SK, Piscitelli CL, Yamashita A, Gouaux E (2008) A competitive inhibitor traps LeuT in an open-to-out conformation. Science 322:1655–1661

    Article  CAS  Google Scholar 

  56. Penmatsa A, Wang KH, Gouaux E (2013) X-ray structure of dopamine transporter elucidates antidepressant mechanism. Nature 503:85–90

    Article  CAS  Google Scholar 

  57. Thompson JD, Gibson TJ, Higgins DG (2002) Multiple sequence alignment using ClustalW and ClustalX. Curr Protoc Bioinformatics. Chapter 2:Unit 2.3

    Google Scholar 

  58. Shen M-Y, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15:2507–2524

    Article  CAS  Google Scholar 

  59. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291

    Article  CAS  Google Scholar 

  60. Ray A, Lindahl E, Wallner B (2010) Model quality assessment for membrane proteins. Bioinformatics 26:3067–3074

    Article  CAS  Google Scholar 

  61. Lushington GH (2015) Comparative modeling of proteins. Methods Mol Biol 1215:309–330

    Article  CAS  Google Scholar 

  62. Chen Y-C (2015) Beware of docking! Trends Pharmacol Sci 36:78–95

    Article  Google Scholar 

  63. Richter L, de Graaf C, Sieghart W, Varagic Z, Mörzinger M, de Esch IJP, Ecker GF, Ernst M (2012) Diazepam-bound GABAA receptor models identify new benzodiazepine binding-site ligands. Nat Chem Biol 8:455–464

    Article  CAS  Google Scholar 

  64. Kukol A (2017) Molecular modeling of proteins. Humana Press, New York

    Google Scholar 

  65. Schuetz DA, de Witte WEA, Wong YC et al (2017) Kinetics for drug discovery: an industry-driven effort to target drug residence time. Drug Discov Today 22:896–911

    Article  CAS  Google Scholar 

  66. De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035–4061

    Article  Google Scholar 

  67. Huang D, Caflisch A (2011) The free energy landscape of small molecule unbinding. PLoS Comput Biol 7:e1002002

    Article  CAS  Google Scholar 

  68. Schrödinger Release 2015-2: Maestro, version 10.2. Schrödinger, LLC, New York, NY

    Google Scholar 

  69. Schrödinger Release 2015-2: Desmond Molecular Dynamics System, version 4.2. Schrödinger, LLC, New York, NY

    Google Scholar 

  70. Wang H, Gouaux E (2012) Substrate binds in the S1 site of the F253A mutant of LeuT, a neurotransmitter sodium symporter homologue. EMBO Rep 13:861–866

    Article  CAS  Google Scholar 

  71. Jurik A, Zdrazil B, Holy M, Stockner T, Sitte HH, Ecker GF (2015) A binding mode hypothesis of tiagabine confirms liothyronine effect on γ-aminobutyric acid transporter 1 (GAT1). J Med Chem 58:2149–2158

    Article  CAS  Google Scholar 

  72. Saha K (2015) “Second generation” mephedrone analogs, 4-MEC and 4-MePPP, differentially affect monoamine transporter function. Intrinsic Activity 3:A2.18

    Article  Google Scholar 

Download references

Acknowledgment

We gratefully acknowledge financial support provided by the Austrian Science Fund, grants #F03502 (SFB35) and W1232 (MolTag). Stefanie Kickinger, Eva Hellsberg, and Sankalp Jain contributed equally to this chapter.

Author information

Author notes

  1. Stefanie Kickinger, Eva Hellsberg, and Sankalp Jain contributed equally to this work.

Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria

    Stefanie Kickinger, Eva Hellsberg, Sankalp Jain & Gerhard F. Ecker

Authors
  1. Stefanie Kickinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eva Hellsberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sankalp Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gerhard F. Ecker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerhard F. Ecker .

Editor information

Editors and Affiliations

  1. Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India

    Kunal Roy

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Kickinger, S., Hellsberg, E., Jain, S., Ecker, G.F. (2018). Linked Open Data: Ligand-Transporter Interaction Profiling and Beyond. In: Roy, K. (eds) Multi-Target Drug Design Using Chem-Bioinformatic Approaches. Methods in Pharmacology and Toxicology. Humana Press, New York, NY. https://doi.org/10.1007/7653_2018_13

Download citation

  • DOI: https://doi.org/10.1007/7653_2018_13

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8732-0

  • Online ISBN: 978-1-4939-8733-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation