Skip to main content
SpringerLink
Log in
Book cover

Data Mining for Systems Biology pp 181–193Cite as

Sparse Modeling to Analyze Drug–Target Interaction Networks

  • Protocol
  • First Online:

  • 1355 Accesses

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1807))

Abstract

Most drugs produce their phenotypic effects by interacting with target proteins, and understanding the molecular features that underpin drug–target interactions is crucial when designing a novel drug. In this chapter, we introduce the protocols that have driven recent advances in sparse modeling methods for analyzing drug–target interaction networks within a chemogenomic framework. In this approach, the chemical structures of candidate drug compounds are correlated with the genomic sequences of the candidate target proteins. We demonstrate the use of sparse canonical correspondence analysis and sparsity-induced binary classifiers to extract the underlying molecular features that are most strongly involved in drug–target interactions. We focus on drug chemical substructures and protein domains. Workflows for applying these methods are presented, and an application is described in detail. We consider the characteristics of each method and suggest possible directions for future research.

This is a preview of subscription content, 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   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
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

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Butina D, Segall M, Frankcombe K (2002) Predicting ADME properties in silico: methods and models. Drug Discov Today 7:S83–S88

    Article  CAS  PubMed  Google Scholar 

  2. Byvatov E, Fechner U, Sadowski J, Schneider G (2003) Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci 43:1882–1889

    Article  CAS  PubMed  Google Scholar 

  3. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489

    Article  CAS  PubMed  Google Scholar 

  4. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita K, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34:D354–357

    Article  CAS  PubMed  Google Scholar 

  5. Stockwell B (2000) Chemical genetics: ligand-based discovery of gene function. Nat Rev Genet 1:116–125

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Dobson C (2004) Chemical space and biology. Nature 432:824–828

    Article  CAS  PubMed  Google Scholar 

  7. Erhan D, LÕheureux P-J, Yue SY, Bengio Y (2006) Collaborative filtering on a family of biological targets. J Chem Inf Model 46:626–635

    Article  CAS  PubMed  Google Scholar 

  8. Nagamine N, Sakakibara Y (2007) Statistical prediction of protein–chemical interactions based on chemical structure and mass spectrometry data. Bioinformatics 23:2004–2012

    Article  CAS  PubMed  Google Scholar 

  9. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24:i232–i240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Faulon J, Misra M, Martin S, Sale K, Sapra R (2008) Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor. Bioinformatics 24:225–233

    Article  CAS  PubMed  Google Scholar 

  11. Jacob L, Vert J-P (2008) Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 24:2149–2156

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Yamanishi Y (2009) Supervised bipartite graph inference. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) Advances in neural information processing systems, vol 21. MIT Press, Cambridge, pp 1841–1848

    Google Scholar 

  13. Bleakley K, Yamanishi Y (2009) Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25:2397–2403

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Takigawa I, Tsuda K, Mamitsuka H (2011) Mining significant substructure pairs for interpreting polypharmacology in drug-target network. PloS One 6:e16999

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yamanishi Y, Pauwels E, Saigo H, Stoven V (2011) Extracting sets of chemical substructures and protein domains governing drug-target interactions. J Chem Inf Model 51:1183–1194

    Article  CAS  PubMed  Google Scholar 

  16. Tabei Y, Pauwels E, Stoven V, Takemoto K, Yamanishi Y (2012) Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers. Bioinformatics 28:i487–i494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Tabei Y, Yamanishi Y (2013) Scalable prediction of compound-protein interactions using minwise hashing. BMC Syst Biol 7:S3

    Article  PubMed  PubMed Central  Google Scholar 

  18. Iwata H, Mizutani S, Tabei Y, Kotera M, Goto S, Yamanishi Y (2013) Inferring protein domains associated with drug side effects based on drug-target interaction network. BMC Syst Biol 7:S18

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wishart D, Knox C, Guo A, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J (2006) Drugbank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34:D668–D672

    Article  CAS  PubMed  Google Scholar 

  20. The Uniprot Consortium (2010) The universal protein resource (UniProt) in 2010. Nucleic Acids Res 38:D142–D148

    Article  CAS  Google Scholar 

  21. Finn R, Tate J, Mistry J, Coggill P, Sammut J, Hotz H, Ceric G, Forslund K, Eddy S, Sonnhammer E, Bateman A (2008) The Pfam protein families database. Nucleic Acids Res 36:D281–D288

    Article  CAS  PubMed  Google Scholar 

  22. Wang Y, Xiao J, Suzek T, Zhang J, Wang J, Bryant S (2009) Pubchem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 37:D623–D633

    Article  CAS  Google Scholar 

  23. Greenacre M (1984) Theory and applications of correspondence analysis. Academic Press, New York

    Google Scholar 

  24. Dudoit S, Fridlyand J, Speed T (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 97:77–87

    Article  CAS  Google Scholar 

  25. Tibshirani R, Hastie T, Narasimhan B, Chu G (2003) Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Stat Sci 18:104–117

    Article  Google Scholar 

  26. Witten D, Tibshirani R, Hastie T (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10:515–534

    Article  PubMed  PubMed Central  Google Scholar 

  27. Mahe P, Ralaivola L, Stoven V, Vert J (2006) The pharmacophore kernel for virtual screening with support vector machines. J Chem Inf Model 46:2003–2014

    Article  CAS  PubMed  Google Scholar 

  28. Kratochwil N, Malherbe P, Lindemann L, Ebeling M, Hoener M, Muhlemann A, Porter R, Stahl M, Gerber P (2005) An automated system for the analysis of g protein-coupled receptor transmembrane binding pockets: Alignment, receptor-based pharmacophores, and their application. J Chem Inf Model 45:1324–1336

    Article  CAS  PubMed  Google Scholar 

  29. Jacob L, Hoffmann B, Stoven V, Vert J-P (2009) Virtual screening of GPCRs: an in silico chemogenomics approach. BMC Bioinf 9:363

    Article  CAS  Google Scholar 

  30. Campillos M, Kuhn M, Gavin A, Jensen L, Bork P (2008) Drug target identification using side-effect similarity. Science 321(5886):263–266

    Article  CAS  PubMed  Google Scholar 

  31. Takarabe M, Kotera M, Nishimura Y, Goto S, Yamanishi Y (2012) Drug target prediction using adverse event report systems: a pharmacogenomic approach. Bioinformatics 28:i611–i618

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yamanishi Y, Kotera M, Kanehisa M, Goto S (2010) Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics 26:i246–i254

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Atias N, Sharan R (2011) An algorithmic framework for predicting side-effects of drugs. J Comput Biol 18:207–218

    Article  CAS  PubMed  Google Scholar 

  34. Iorio F, Tagliaferri R, di Bernardo D (2009) Identifying network of drug mode of action by gene expression profiling. J Comput Biol 16:241–251

    Article  CAS  PubMed  Google Scholar 

  35. Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero, R, Murino L, Tagliaferri R, Brunetti-Pierri N, Isacchi A et al (2010) Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci 107:14621–14626

    Article  PubMed  Google Scholar 

  36. Wang K, Sun J, Zhou S, Wan C, Qin S, Li C, He L, Yang L (2013) Prediction of drug-target interactions for drug repositioning only based on genomic expression similarity. PLoS Comput Biol 9:e1003315

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hizukuri Y, Sawada R, Yamanishi Y (2015) Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner. BMC Med Genomics 8:1

    Article  CAS  Google Scholar 

  38. Iwata M, Sawada R, Kotera M, Yamanishi Y (2017) Elucidating the modes of action of bioactive compounds by large-scale compound-induced transcriptomics: toward drug discovery and repositioning. Sci Rep 7:40164

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hotelling, H (1936) Relation between two sets of variates. Biometrika 28:322–277

    Article  Google Scholar 

  40. Fan RE, Chang KW, Hsieh CJ, Wang X, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–1874

    Google Scholar 

Download references

Acknowledgements

This work is supported by JST PRESTO Grant Number JPMJPR15D8.

Author information

Authors and Affiliations

  1. Division of System Cohort, Medical Institute of Bioregulation, Kyushu University, Maidashi, Higashi-ku, Fukuoka, Fukuoka, Japan

    Yoshihiro Yamanishi

  2. PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan

    Yoshihiro Yamanishi

Authors
  1. Yoshihiro Yamanishi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yoshihiro Yamanishi .

Editor information

Editors and Affiliations

  1. Bioinformatics Center, Kyoto University, Uji, Kyoto, Japan

    Hiroshi Mamitsuka

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

Yamanishi, Y. (2018). Sparse Modeling to Analyze Drug–Target Interaction Networks. In: Mamitsuka, H. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 1807. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8561-6_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8561-6_13

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8560-9

  • Online ISBN: 978-1-4939-8561-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation