1. Mass Spectrometry in a drug discovery setting Claus Andersen Senior Scientist Sienabiotech Spa

2. Bioinformatics and statistics in a drug discovery companyClaus Andersen Overview From genes to phenotype Proteins an introduction Mass Spec for protein Mass Spec data Mass Spec data analysis Mass Spec database searching Recent advances identification quantification characterization

3. Bioinformatics and statistics in a drug discovery companyClaus Andersen From genes to phenotype genes proteins functions pathways metabolites phenotypes mRNA expression Regulation Degradation Activation/inactivation Interactions Kinematics Protein abundance Metabolite levels ADME/Tox Structure Pharmacophore Genome comparison mRNA expression Activation/inactivation Protein abundance

4. Bioinformatics and statistics in a drug discovery companyClaus Andersen Proteins as functional units Glucose ATP D.S. Goodsell pdb.org Vale and Milligan Science 2000 Myosin

5. Bioinformatics and statistics in a drug discovery companyClaus Andersen What affects the proteome Cellular proteome Interactions Temperature Stress Environment Physiological role Pharmaceutical substances Proteasome protein degradation mRNA Ribosome protein production Genome

6. Bioinformatics and statistics in a drug discovery companyClaus Andersen Protein extraction and digestion Mass Spec on proteins Treated/Sick Control/Healthy Mass Spectrometer Protein peptides identification MS spectra quantification characterization KKYAAELHLV P O Phosphorylation KAVQQPDGLA Oxidation … post translational modifications (PTM) QFHFHWGSLDQPDGLA Peptides and MS/MS spectra HPLC

7. Bioinformatics and statistics in a drug discovery companyClaus Andersen Mass Spec data 5  g 3000 MS spectra 500 MB Total 700 MB Gygi et al. Mol. Cell Bio. (1999) 400 MS/MS spectra 200 MB

8. Bioinformatics and statistics in a drug discovery companyClaus Andersen Mass Spec data analysis Fourier transformation (noise filtering) Gaussian peak fitting (peak detection) Generation of theoretical spectra (sequence  spectra) Large scale spectral comparison (DB searching) Spectral deconvolution (de-novo sequencing) Large scale sequence searching (DB searching) Data fitting (quantitation) Statistics and probability theory (reliability estimation) Linear discriminant analysis (quality assessment) …. and lots more Large scale spectral comparison (DB searching)

9. Bioinformatics and statistics in a drug discovery companyClaus Andersen Large scale spectral comparison Mass spec data MS spectrum FLIDSSRFSYPERPIIFLSMCYNIYSIAYIVRLTVGRERISCDFEEAAEPVLIQEGLKNT Protein sequence DB~2 mil Protein peptides~60 mil Peptide fragments~2000 mil ERPIIFLSMCYNIYSIAYIV etc. etc… ERPIIFLSMCYNIYSIAYIV ERPIIFLSMCYNIYSIAYI ERPIIFLSMCYNIYSIAY ERPIIFLSMCYNIYSIA ERPIIFLSMCYNIYSI ERPIIFLSMCYNIYS ERPIIFLSMCYNIY ERPIIFLSMCYNI ERPIIFLSMCYN ERPIIFLSMCY ERPIIFLSMC ERPIIFLSM … In-silico data MS/MS Spectrum (M peptide +H) + ±Δ i NiNi KiKi { V IV YIV AYIV IAYIV SIAYIV YSIAYIV IYSIAYIV NIYSIAYIV …

10. Bioinformatics and statistics in a drug discovery companyClaus Andersen Large scale spectral comparison PEP_PROBE by Sadygov and Yates Anal. Chem. 75 2003 Hypergeometric probability model where is the binomial coefficient

11. Bioinformatics and statistics in a drug discovery companyClaus Andersen where is the cumulative distribution function given by the hypergeometric model, is the number of all peptides in the database matching the (M+H) + mass value. Sadygov and Yates Anal. Chem. 2003 Expectation value (E-value) Large scale spectral comparison The E-value tells you how many peptides from the database are expected to have the same or better matches to the experimental spectrum by chance alone.

12. Bioinformatics and statistics in a drug discovery companyClaus Andersen Large scale spectral comparison Sadygov and Yates Anal. Chem. 2003 An example from yeast (Saccharomyces cerevisiae) MS/MS spectrum (M+H) + = 2076.010 ± 0.002 AMU Yeast proteins6 200 Yeast peptides~200 000 Peptide fragments ~5 mil N=569 160 K= 84 150 ATHILDFGPGGASGLGVLTHR Top candidate peptides K1K1 N1N1 LTPPQLPPQLENVILNKY 4034 E-value 3415 10 -26.62 10 -5.25 FAS1 SIP2 PeptideProtein name

13. Bioinformatics and statistics in a drug discovery companyClaus Andersen Large scale spectral comparison The protein FAS1 is part of the fatty acid biosynthesis of yeast. Its enzyme classification number is (EC 2.3.1.86) FAS1 Protein identification In general several peptides are found for each protein (3-10) www.kegg.org

14. Bioinformatics and statistics in a drug discovery companyClaus Andersen Inverted sequence DB used for background distribution estimation (PRISM) Emili’s group Mol. Cell Proteomics, 2(2), p96-106, 2003 Number of Sibling peptides (ProteinProphet) Aebersold’s group Anal. Chem. 74, p5383-5392, 2004 Suffix tree searching: Lu and Chen Bioinformatics 19(2), pii113-ii121, 2003 Bayesian approach: Chen Biosilico in press 2004 Most recent advances Large scale spectral comparison An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. Yates’ group J.Am.Soc.Mass Spec. 5(11) 1994 ProbID: a probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data. Aebersold’s group Proteomics 2(10) 2002 Other approaches