1. Improving Genome Annotation using Proteomics Nathan Edwards Center for Bioinformatics and Computational Biology University of Maryland, College Park

2. 2 Mass Spectrometry for Proteomics Measure mass of many (bio)molecules simultaneously High bandwidth Mass is an intrinsic property of all (bio)molecules No prior knowledge required

3. 3 Mass Spectrometer Ionizer Sample + _ Mass Analyzer Detector MALDI Electro-Spray Ionization (ESI) Time-Of-Flight (TOF) Quadrapole Ion-Trap Electron Multiplier (EM)

4. 4 High Bandwidth

5. 5 Mass is fundamental!

6. 6 Mass Spectrometry for Proteomics Measure mass of many molecules simultaneously...but not too many, abundance bias Mass is an intrinsic property of all (bio)molecules...but need a reference to compare to

7. 7 Mass Spectrometry for Proteomics Mass spectrometry has been around since the turn of the century......why is MS based Proteomics so new? Ionization methods MALDI, Electrospray Protein chemistry & automation Chromatography, Gels, Computers Protein / genome sequences A reference for comparison

8. 8 Sample Preparation for Peptide Identification Enzymatic Digest and Fractionation

9. 9 Single Stage MS MS m/z

10. 10 Tandem Mass Spectrometry (MS/MS) Precursor selection m/z

11. 11 Tandem Mass Spectrometry (MS/MS) Precursor selection + collision induced dissociation (CID) MS/MS m/z

12. 12 Peptide Identification For each (likely) peptide sequence 1. Compute fragment masses 2. Compare with spectrum 3. Retain those that match well Peptide sequences from (any) sequence database Swiss-Prot, IPI, NCBI’s nr, ESTs, genomes,... Automated, high-throughput peptide identification in complex mixtures

13. 13 Peptide Identification...can provide direct experimental evidence for the amino-acid sequence of functional proteins. Evidence for: Functional protein isoforms Translation start and frame Proteins with short open-reading-frames

14. 14 How could this help? Evidence for SNPs and alternative splicing stops with transcription No genomic or transcript evidence for translation start-site. Conservation doesn’t stop at coding bases! Statistical gene-finders struggle with micro- exons, translation start-site, and short ORFs.

15. 15 What can be observed? Known coding SNPs Novel coding mutations Alternative splicing isoforms Microexons ( non-cannonical splice-sites ) Alternative translation start-sites ( codons ) Alternative translation frames “Dark” open-reading-frames

16. 16 Splice Isoform Human Jurkat leukemia cell-line Lipid-raft extraction protocol, targeting T cells von Haller, et al. MCP 2003. LIME1 gene: LCK interacting transmembrane adaptor 1 LCK gene: Leukocyte-specific protein tyrosine kinase Proto-oncogene Chromosomal aberration involving LCK in leukemias. Multiple significant peptide identifications

17. 17 Splice Isoform

18. 18 Novel Splice Isoform

19. 19 Translation Start-Site Human erythroleukemia K562 cell-line Depth of coverage study Resing et al. Anal. Chem. 2004. THOC2 gene: Part of the heteromultimeric THO/TREX complex. Initially believed to be a “novel” ORF RefSeq mRNA in Jun 2007, no RefSeq protein TrEMBL entry Feb 2005, no SwissProt entry Genbank mRNA in May 2002 (complete CDS) Plenty of EST support ~ 100,000 bases upstream of other isoforms

20. 20 Translation Start-Site

21. 21 Translation Start-Site

22. 22 Translation Start-Site

23. 23 Translation Start-Site

24. 24 Easily distinguish minor sequence variations Two B. anthracis Sterne α/β SASP annotations RefSeq/Gb: MVMARN... (7441 Da) CMR: MARN... (7211 Da) Intact proteins differ by 230 Da 7441 Da vs 7211 Da N-terminal tryptic peptides: MVMAR (606.3 Da), MVMARNR (876.4 Da), vs MARNR (646.3 Da) Very different MS/MS spectra

25. 25 Bacterial Gene-Finding …TAGAAAAATGGCTCTTTAGATAAATTTCATGAAAAATATTGA… Stop codon Find all the open-reading-frames......courtesy of Art Delcher

26. 26 Bacterial Gene-Finding …TAGAAAAATGGCTCTTTAGATAAATTTCATGAAAAATATTGA… Stop codon …ATCTTTTTACCGAGAAATCTATTTAAAGTACTTTTTATAACT… Shifted Stop Stop codon Reverse strand Find all the open-reading-frames......but they overlap – which ones are correct?...courtesy of Art Delcher

27. 27 Coding-Sequence “Score”...courtesy of Art Delcher

28. 28 Glimmer3 Performance Glimmer3 trained & compared to RefSeq genes with annotated function Correct STOP: 99.6% Correct START: 84.3% “Not all the genomes necessarily have carefully/accurately annotated start sites, so the results for number of correct starts may be suspect.”

29. 29 N-terminal peptides (Protein) N-terminal peptides establish start-site of known & unexpected ORFs Use: Directly to annotate genomes Evaluate and improve algorithms Map cross-species

30. 30 N-terminal peptide workflows Typical proteomics workflows sample peptides from the proteome “randomly” Caulobacter crescentus (70%) 3733 Proteins (RefSeq Genome annot.) 66K tryptic peptides (600 Da to 3000 Da) 2085 N-terminal tryptic peptides (3%)

31. 31 N-terminal peptide workflow Protect protein N-terminus Digest to peptides Chemically modify free peptide N-term Use chem. mod. to capture unwanted peptides Nat Biotech, Vol. 21, pp. 566-569, 2003.

32. 32 Increasing N-terminal peptide coverage Multiple (digest) enzymes: trypsin-R: 60% (80%) acid + lys-C + trypsin: 85% (94%) Repeated LC-MS/MS Precursor Exclusion / Inclusion lists MALDI / ESI Protein separation and/or orthogonal fractionation Anal Chem, Vol. 76, pp. 4193-4201, 2004.

33. 33 Proteomics Informatics Search spectra against: Entire bacterial genome; All Met initiated peptides; or Statistically likely Met initiated peptides. Easily consider initial Met loss PTM, too Off-the-shelf MS/MS search engines (Mascot / X!Tandem / OMSSA)

34. 34 Other Practical Issues Suitable for commonly available instrumentation Only the sample prep. is (somewhat) novel. Need living organism Stage of life-cycle? Bang for buck? N-terminal peptides / $$$$

35. 35 Other Research Projects Alternative splicing and coding SNPs in clinical cancer samples MS/MS spectral matching using HMMs Combining MS/MS search engine results using machine learning Microorganism identification using MS (www.RMIDb.org) Gapped/spaced seeds for inexact sequence alignment. Applications of SBH-graphs and Eulerian paths

36. 36 Hidden Markov Models for Spectral Matching Capture statistical variation and consensus in peak intensity Capture semantics of peaks Extrapolate model to other peptides Good specificity with superior sensitivity for peptide detection Assign 1000’s of additional spectra (w/ p-value < 10 -5 )

37. 37 Peptide DLATVYVDVLK

38. 38 Peptide DLATVYVDVLK

39. 39 Acknowledgements Catherine Fenselau, Steve Swatkoski UMCP Biochemistry Chau-Wen Tseng, Xue Wu UMCP Computer Science Cheng Lee Calibrant Biosystems PeptideAtlas, HUPO PPP, X!Tandem Funding: NIH/NCI, USDA/ARS