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Julio Peironcely @ ICCS 2011

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Talk given at the 9th International Conference on Chemical Structures, June 9th. How metabolomics and metabolite identification can be improved by chemoinformatics.

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Improving Metabolite Identification with Chemoinformatics Julio E. Peironcely @peyron PhD student at Leiden University and TNO June 9th 2011 ICCS 2011
Metabolome all low molecular weight molecules (metabolites) in cells, body fluids, tissues, etc. Metabolomics the quantitative and qualitative analysis of all metabolites in samples of cells, body fluids, tissues, etc. June 9th 2011 ICCS 2011 Julio E. Peironcely
Metabolomics Genome Transcripts Proteins Metabolites Phenotype June 9th 2011 ICCS 2011 Julio E. Peironcely
Metabolomics Experi- Biological Biological Sample Data Data pre- Data mental Sampling inter- question preparation acquisition processing analysis design pretation Metabolites Relevant biomolecules/ List of Samples Raw data connectivities Protocol peaks/ & biomolecules Models June 9th 2011 ICCS 2011 Julio E. Peironcely
LC-MS and Metabolite Identification RPLC-microTOF (Bruker) LC MS MS peak = m/z@rt = metabolite Kloet et al, Metabolomics 2011 June 9th 2011 ICCS 2011 Julio E. Peironcely
When all you have is a mass and a window List of Elemental Compositions ChemSpider MS Mass + KEGG HMDB Window PubChem List of Molecules Measured Mass + Mass Window = Multiple Elemental Compositions June 9th 2011 ICCS 2011 Julio E. Peironcely
n When you have MS Measured Mass + Mass Window + Fragments = Single Elemental Composition ChemSpider n Mass + KEGG HMDB MS Window 1 EC fragments PubChem List of Molecules Use the expert system June 9th 2011 ICCS 2011 Julio E. Peironcely
The expert system Dr. Ronnie van Doorn Dr. Albert Tas June 9th 2011 ICCS 2011 Julio E. Peironcely
Bottlenecks in Metabolite Identification Many metabolites in LC-MS not identified HighRes MS can obtain 1 EC = many structures No tools for automatic identification Takes long for the expert to identify metabolites Julio E. Peironcely
Challenges Analytical methods Software Databases Julio E. Peironcely
Our approach Experimental 1 Data 1.1 1.1 1.2 1.2 1.1. 1.2. 1.2. Processing 1.1.1 1 1.2.1 1 1.2.2 2 Data Trees present Identity assignment n MS Find Similar Trees Database absent Generate Elemental Formula Structure + Structures Fragments generator Filter Metabolite- Structures likeness June 9th 2011 ICCS 2011 Julio E. Peironcely
Our approach Experimental 1 Data 1.1 1.1 1.2 1.2 1.1. 1.2. 1.2. Processing 1.1.1 1 1.2.1 1 1.2.2 2 Data Trees present Identity assignment n MS Find Similar Trees Database absent Generate Elemental Formula Structure + Structures Fragments generator Filter Metabolite- Structures likeness June 9th 2011 ICCS 2011 Julio E. Peironcely
Our approach Experimental 1 Data 1.1 1.1 1.2 1.2 1.1. 1.2. 1.2. Processing 1.1.1 1 1.2.1 1 1.2.2 2 Data Trees present Identity assignment n MS Find Similar Trees Database absent Generate Elemental Formula Structure + Structures Fragments generator Filter Metabolite- Structures likeness June 9th 2011 ICCS 2011 Julio E. Peironcely
Our approach Experimental 1 Data 1.1 1.1 1.2 1.2 1.1. 1.2. 1.2. Processing 1.1.1 1 1.2.1 1 1.2.2 2 Data Trees present Identity assignment n MS Find Similar Trees Database absent Generate Elemental Formula Structure + Structures Fragments generator Filter Metabolite- Structures likeness June 9th 2011 ICCS 2011 Julio E. Peironcely
From data to information
MEF: spectral to fragmentation tree Experimental Data Processing Data Trees CML Find Similar XCMS Trees CDK Generate Structures MEF Filter Structures Rojas-Cherto et al. Bioinformatics (submitted) June 9th 2011 ICCS 2011 Julio E. Peironcely
MEF: spectral to fragmentation tree Experimental full Data Peak scan MS Parent Ion Mass EC Processing Data Trees Reaction Noise 1 Neutral Find Similar Trees Fragments loss Generate Structures MS2 1.1 1.2 1.3 1.1 1.2 1.3 Filter Structures MS3 1.1.1 1.2.1 1.2.2 1.2.3 1.3.1 1.3.2 1.1.1 1.2.1 1.2.2 1.2.3 1.3.1 1.3.2 Spectral Tree Fragmentation Tree Rojas-Cherto et al. Bioinformatics (submitted) June 9th 2011 ICCS 2011 Julio E. Peironcely
Extracting more information from your data
Fragmentation tree fingerprints Experimental 1 Data Processing Data Trees 1.1 1.1 1.2 1.2 1.3 1.3 Find Similar 1.1. 1.2. 1.2. 1.2. 1.3. 1.3. Trees 1.1.1 1 1.2.1 1 1.2.2 2 1.2.3 3 1.3.1 1 1.3.2 2 Generate Structures Filter Structures Poster 59 Miguel Rojas-Cherto, Leiden University June 9th 2011 ICCS 2011 Julio E. Peironcely
Fragmentation tree fingerprints resultsunknown Most similar trees MCS Experimental Data Processing Data Trees Find Similar Trees Generate Structures Filter Structures Poster 59 Miguel Rojas-Cherto, Leiden University June 9th 2011 ICCS 2011 Julio E. Peironcely
De-novo identification
Structure Generator Experimental Data Elemental Fragments Formula Processing Data Trees Find Similar Generate Trees Keep molecules if Generate canonical Structures augmentation CDK Nauty Filter Structures All non-duplicated molecules Poster 38 In collaboration with Jean-Loup Faulon, Evry University June 9th 2011 ICCS 2011 Julio E. Peironcely
Structure Generator Results MOLGEN same # of molecules Experimental Data Processing Data Trees p-Cresol Glycine Phenylalanine Malic acid D-Cysteine Find Similar sulfate Trees Elemental C2H5NO2 C9H11NO2 C4H6O5 C3H7NO2S C7H8O3S Composition Generate # Output 84 277,810,163 8,070 3,838 10,203,389 Structures Molecules 6 4,037,499 1,601 100 19,940 Filter 1 Fragment Structures 93,137 948 2 Fragments 584 3 Fragments 278 Poster 38 In collaboration with Jean-Loup Faulon, Evry University June 9th 2011 ICCS 2011 Julio E. Peironcely
Lots of candidates structures
Metabolite-likeness HMDB 8K ZINC 21M Experimental Data Atom Counts Physicochemical desc. Standardization Processing MDL Public Keys Data Trees FCFP_4 ECFP_4 Diversity Selection Find Similar Trees Training Set Test Set Generate 532 + 532 6.4K + 6.4K Structures 5-fold CV Metabolite Filter likeness Structures SVM RF BC In collaboration with Andreas Bender, Cambridge Univ. June 9th 2011 ICCS 2011 Julio E. Peironcely
Metabolite-likeness HMDB 8K ZINC 21M Experimental Data Atom Counts Physicochemical desc. Standardization Processing MDL Public Keys Data Trees FCFP_4 ECFP_4 Diversity Selection Find Similar Trees Training Set Test Set Generate 532 + 532 6.4K + 6.4K Structures 5-fold CV Metabolite Filter likeness Structures SVM RF BC 1st RF MDLPublicKeys 2nd RF ECFP_4 Sensitivity Specificity AUC Sensitivity Specificity AUC 99.84% 87.52% 99.20% 99.77% 86.36% 99% In collaboration with Andreas Bender, Cambridge Univ. June 9th 2011 ICCS 2011 Julio E. Peironcely
Metabolite-likeness, external validation Experimental Data HMDB External DrugBank ChEMBL validation set Processing Data Trees Random Selection Find Similar Trees Generate Standardization Structures Filter Metabolite RF MDLPublicKeys Structures likeness RF ECFP_4 In collaboration with Andreas Bender, Cambridge Univ. June 9th 2011 ICCS 2011 Julio E. Peironcely
Metabolite-likeness, external validation Experimental Data Processing Data Trees Find Similar Trees Generate Structures Filter Structures In collaboration with Andreas Bender, Cambridge Univ. June 9th 2011 ICCS 2011 Julio E. Peironcely
From information to knowledge
www.MetiTree.nl ICCS 2011 Group Database Upload and organize your own trees Visualize trees Find similar trees (to be added: Struct. Gen, Met-likeness) June 9th 2011 ICCS 2011 Julio E. Peironcely
Conclusions Chemoinformatics plays a crucial role in the metabolite identification pipeline Now it is the time to challenge this pipeline with real cases Expert is still needed June 9th 2011 ICCS 2011 Julio E. Peironcely
Acknowledgements TNO Quality of Life Leon Coulier Albert Tas Leiden University Miguel Rojas-Cherto Piotr Kasper Michael van Vliet University of Cambridge Theo Reijmers Andreas Bender Rob Vreeken Ronnie van Doorn Thomas Hankemeier Evry University Jean-Loup Faulon Davide Fichera Wageningen University/ HMP University of PRI Alberta Egon Willighangen David Wishart Justin van der Hooft Ying (Edison) Dong Ric de Vos Jacques Vervoort June 9th 2011 ICCS 2011 Julio E. Peironcely

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Julio Peironcely @ ICCS 2011

  • 1. Improving Metabolite Identification with Chemoinformatics Julio E. Peironcely @peyron PhD student at Leiden University and TNO June 9th 2011 ICCS 2011
  • 2. Metabolome all low molecular weight molecules (metabolites) in cells, body fluids, tissues, etc. Metabolomics the quantitative and qualitative analysis of all metabolites in samples of cells, body fluids, tissues, etc. June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 3. Metabolomics Genome Transcripts Proteins Metabolites Phenotype June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 4. Metabolomics Experi- Biological Biological Sample Data Data pre- Data mental Sampling inter- question preparation acquisition processing analysis design pretation Metabolites Relevant biomolecules/ List of Samples Raw data connectivities Protocol peaks/ & biomolecules Models June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 5. LC-MS and Metabolite Identification RPLC-microTOF (Bruker) LC MS MS peak = m/z@rt = metabolite Kloet et al, Metabolomics 2011 June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 6. When all you have is a mass and a window List of Elemental Compositions ChemSpider MS Mass + KEGG HMDB Window PubChem List of Molecules Measured Mass + Mass Window = Multiple Elemental Compositions June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 7. n When you have MS Measured Mass + Mass Window + Fragments = Single Elemental Composition ChemSpider n Mass + KEGG HMDB MS Window 1 EC fragments PubChem List of Molecules Use the expert system June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 8. The expert system Dr. Ronnie van Doorn Dr. Albert Tas June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 9. Bottlenecks in Metabolite Identification Many metabolites in LC-MS not identified HighRes MS can obtain 1 EC = many structures No tools for automatic identification Takes long for the expert to identify metabolites Julio E. Peironcely
  • 10. Challenges Analytical methods Software Databases Julio E. Peironcely
  • 11. Our approach Experimental 1 Data 1.1 1.1 1.2 1.2 1.1. 1.2. 1.2. Processing 1.1.1 1 1.2.1 1 1.2.2 2 Data Trees present Identity assignment n MS Find Similar Trees Database absent Generate Elemental Formula Structure + Structures Fragments generator Filter Metabolite- Structures likeness June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 12. Our approach Experimental 1 Data 1.1 1.1 1.2 1.2 1.1. 1.2. 1.2. Processing 1.1.1 1 1.2.1 1 1.2.2 2 Data Trees present Identity assignment n MS Find Similar Trees Database absent Generate Elemental Formula Structure + Structures Fragments generator Filter Metabolite- Structures likeness June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 13. Our approach Experimental 1 Data 1.1 1.1 1.2 1.2 1.1. 1.2. 1.2. Processing 1.1.1 1 1.2.1 1 1.2.2 2 Data Trees present Identity assignment n MS Find Similar Trees Database absent Generate Elemental Formula Structure + Structures Fragments generator Filter Metabolite- Structures likeness June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 14. Our approach Experimental 1 Data 1.1 1.1 1.2 1.2 1.1. 1.2. 1.2. Processing 1.1.1 1 1.2.1 1 1.2.2 2 Data Trees present Identity assignment n MS Find Similar Trees Database absent Generate Elemental Formula Structure + Structures Fragments generator Filter Metabolite- Structures likeness June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 15. From data to information
  • 16. MEF: spectral to fragmentation tree Experimental Data Processing Data Trees CML Find Similar XCMS Trees CDK Generate Structures MEF Filter Structures Rojas-Cherto et al. Bioinformatics (submitted) June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 17. MEF: spectral to fragmentation tree Experimental full Data Peak scan MS Parent Ion Mass EC Processing Data Trees Reaction Noise 1 Neutral Find Similar Trees Fragments loss Generate Structures MS2 1.1 1.2 1.3 1.1 1.2 1.3 Filter Structures MS3 1.1.1 1.2.1 1.2.2 1.2.3 1.3.1 1.3.2 1.1.1 1.2.1 1.2.2 1.2.3 1.3.1 1.3.2 Spectral Tree Fragmentation Tree Rojas-Cherto et al. Bioinformatics (submitted) June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 18. Extracting more information from your data
  • 19. Fragmentation tree fingerprints Experimental 1 Data Processing Data Trees 1.1 1.1 1.2 1.2 1.3 1.3 Find Similar 1.1. 1.2. 1.2. 1.2. 1.3. 1.3. Trees 1.1.1 1 1.2.1 1 1.2.2 2 1.2.3 3 1.3.1 1 1.3.2 2 Generate Structures Filter Structures Poster 59 Miguel Rojas-Cherto, Leiden University June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 20. Fragmentation tree fingerprints resultsunknown Most similar trees MCS Experimental Data Processing Data Trees Find Similar Trees Generate Structures Filter Structures Poster 59 Miguel Rojas-Cherto, Leiden University June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 22. Structure Generator Experimental Data Elemental Fragments Formula Processing Data Trees Find Similar Generate Trees Keep molecules if Generate canonical Structures augmentation CDK Nauty Filter Structures All non-duplicated molecules Poster 38 In collaboration with Jean-Loup Faulon, Evry University June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 23. Structure Generator Results MOLGEN same # of molecules Experimental Data Processing Data Trees p-Cresol Glycine Phenylalanine Malic acid D-Cysteine Find Similar sulfate Trees Elemental C2H5NO2 C9H11NO2 C4H6O5 C3H7NO2S C7H8O3S Composition Generate # Output 84 277,810,163 8,070 3,838 10,203,389 Structures Molecules 6 4,037,499 1,601 100 19,940 Filter 1 Fragment Structures 93,137 948 2 Fragments 584 3 Fragments 278 Poster 38 In collaboration with Jean-Loup Faulon, Evry University June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 24. Lots of candidates structures
  • 25. Metabolite-likeness HMDB 8K ZINC 21M Experimental Data Atom Counts Physicochemical desc. Standardization Processing MDL Public Keys Data Trees FCFP_4 ECFP_4 Diversity Selection Find Similar Trees Training Set Test Set Generate 532 + 532 6.4K + 6.4K Structures 5-fold CV Metabolite Filter likeness Structures SVM RF BC In collaboration with Andreas Bender, Cambridge Univ. June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 26. Metabolite-likeness HMDB 8K ZINC 21M Experimental Data Atom Counts Physicochemical desc. Standardization Processing MDL Public Keys Data Trees FCFP_4 ECFP_4 Diversity Selection Find Similar Trees Training Set Test Set Generate 532 + 532 6.4K + 6.4K Structures 5-fold CV Metabolite Filter likeness Structures SVM RF BC 1st RF MDLPublicKeys 2nd RF ECFP_4 Sensitivity Specificity AUC Sensitivity Specificity AUC 99.84% 87.52% 99.20% 99.77% 86.36% 99% In collaboration with Andreas Bender, Cambridge Univ. June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 27. Metabolite-likeness, external validation Experimental Data HMDB External DrugBank ChEMBL validation set Processing Data Trees Random Selection Find Similar Trees Generate Standardization Structures Filter Metabolite RF MDLPublicKeys Structures likeness RF ECFP_4 In collaboration with Andreas Bender, Cambridge Univ. June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 28. Metabolite-likeness, external validation Experimental Data Processing Data Trees Find Similar Trees Generate Structures Filter Structures In collaboration with Andreas Bender, Cambridge Univ. June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 29. From information to knowledge
  • 30. www.MetiTree.nl ICCS 2011 Group Database Upload and organize your own trees Visualize trees Find similar trees (to be added: Struct. Gen, Met-likeness) June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 31. Conclusions Chemoinformatics plays a crucial role in the metabolite identification pipeline Now it is the time to challenge this pipeline with real cases Expert is still needed June 9th 2011 ICCS 2011 Julio E. Peironcely
  • 32. Acknowledgements TNO Quality of Life Leon Coulier Albert Tas Leiden University Miguel Rojas-Cherto Piotr Kasper Michael van Vliet University of Cambridge Theo Reijmers Andreas Bender Rob Vreeken Ronnie van Doorn Thomas Hankemeier Evry University Jean-Loup Faulon Davide Fichera Wageningen University/ HMP University of PRI Alberta Egon Willighangen David Wishart Justin van der Hooft Ying (Edison) Dong Ric de Vos Jacques Vervoort June 9th 2011 ICCS 2011 Julio E. Peironcely
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