Building blocks for automated elucidation of metabolites: Machine learning methods for NMR prediction

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1 Building blocks for automated elucidation of metabolites: Machine learning methods for NMR prediction Stefan Kuhn 1, Björn Egert 2, Steffen Neumann 2, Christoph Steinbeck 1European Bioinformatics Institute (EBI), Chemoinformatics and Metabolism Team, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, United Kingdom 2Research Group for Molecular Informatics, Cologne University Bioinformatics Center (CUBIC), Zuelpicher Str. 47, D Cologne, Germany, steinbeck@ebi.ac.uk, Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 1 07:05:37

2 CUBIC Experiment: Fast quenching of metabolism Cell lysis and extraction Derivation Detection via GC/MS Trehalose Lactat Glutamat Signalintensit ä t Ca compounds visible in GC 400 derivatives can be reproducibly quantified 240 compounds identified t [min] Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 2 07:05:37

3 CUBIC Gas chromatography (GC) Mass spectrometry (MS) Procedure: Extraction of bacterial cells with methanol Mass spectrometer Derivatisation Separation of compounds by gas chromatography Analysis by massspectrometry after electron impact ionization Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 3 07:05:37

4 De novo Elucidation of Biomarkers and Metabolites: Computer Assisted Structure Elucidation (CASE) Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 4 07:05:38

5 The Chemistry Development Kit (CDK) Java library for chemoinformatics, Open Source, LGPL (permits commercial use) >50 developers, core team people >50 academic and industrial projects world wide Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 5 07:05:38

6 CDK Functionality Input/Output I/O (CML, MDL Molfile, SDF, PDB) SMILES InChI Modelling 3D Model Builder Atom Typing Force Field Representation of Biomolecular Structures Chemical Graphs Isomorphism detection Maximum Common Substructure Searches SMARTS and Substructure searches Ring searches Aromaticity detection Visualization Structure Diagram Layout (SDG) 2D Rendering 3D Rendering Library Enumeration Deterministic Isomer generator Stochastic Structure Generators via Simulated Annealing Genetic Algorithms Properties Fingerprinting > 70 QSAR Descriptors QSAR model building Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 6 07:05:38

7 Characterizing Biomarkers and Metabolites NMRShiftDB ( Open Access Open Submission Open Source [1] Steinbeck, C.; Kuhn, S.; Krause, S., J. Chem. Inf. Comput. Sci. 2003, 43, [2] Steinbeck, C.; Kuhn, S. Phytochemistry 2004, 65, Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 7 07:05:38

8 2D NMR Data for CASE Steinbeck, C. Computer Assisted Structure Elucidation. In Handbook on Chemoinformatics.; Gasteiger, J. Ed.; Wiley VCH: Weinheim, 2003; Vol. 2; pp Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 8 07:05:39

9 CASE with Simulated Annealing Fitness Evaluation (Scoring) S total = S NMR HMBC + S NMR HHCOSY + S NMR Shift + S Symmetry + O H S MassSpec... + S Features H O Polycarpol (C 30 H 48 O 2 ). Steinbeck, C.; Journal of Chemical Information & Computer Sciences 2001, 41, Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 9 07:05:39

10 How far do we get with 1D NMR? Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 10 07:05:39

11 Deterministic Structure Generators work quite nicely for small molecules even with very simple fitness functions For around 10 heavy atoms, we've been able to find the correct solutions just based on 13 C shift prediction and comparison with measured spectrum. Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 11 07:05:40

12 1D Proton NMR Prediction Methods trained based on CDK descriptors (random order) J48 HOSE codes Support Vector Machines M5' PRISM naïve Bayes Linear Regression K Means Clustering Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 12 07:05:40

13 Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 13 07:05:40

14 Descriptors (416/100%) Physicochemical (242/57,93%) Spatial (105/25,24%) Topological (66/15,86%) Exp. Conditions (3/0.72%) RDF G H,G D [9] Van der Waals [11] RDF G S [9] Distance [11] RDF G H topol[9] Picontact [11] Temperature Frequency Solvent Valence Electrons[11] Heavy Atom Hydrogen BondsToAtom [11] Min Avg Electronegativity [9] Sigma Pi Period [11] Charge [9] 330 descriptors in total Hybrization [11] Sigma Pi Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 14 07:05:40

15 Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 15 07:05:41

16 Random Forest, real vs predicted, protons Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 16 07:05:41

17 Kuhn S., Egert B., Neumann S. and Steinbeck C. (2008) BMC Bioinformatics Sep 25;9(1):400. Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 17 07:05:41

18 Acknowledgement Prof. Peter Murray Rust Stefan Kuhn Steffen Neumann Bjlörn Egert Egon Willighagen (Unilever Center for Molecular Informatics, Cambridge, UK) Dr. William Hull, Dr. Willi von der Lieth (DKFZ, Heidelberg) Dr. Kämpchen (Universität Marburg) All Collaborators at Cologne University Bioinformatics Center (CUBIC), EBI and the CDK team Dr. Heinz Kolshorn (Universität Mainz) DFG, BMBF, DAAD Roche Diagnostics, Penzberg Orion Pharma, Finnland Christoph Steinbeck European Bioinformatics Institute (EBI) Slide 18 07:05:42