1. ProReP - Protein Results Parser v3.0©
A Tool For Handling Tandem Mass Spectrometer Protein Database Search Results
Capstone Presentation
Kiran Annaiah (M.S Bioinformatics)
Advisors
Dr. Randy Arnold Dr. Haixu Tang

2. Outline Background Data generation from Mass Spec Experiment
Mascot Search Engine
Why to parse Mascot results?
Parser features
Results
Conclusions
Acknowledgments

3. Background High-throughput “shotgun” Proteomics Mass Spectrometry
Identify, characterize and quantify all expressed proteins simultaneously in a mixture.
Mass Spectrometry
Peptide mass fingerprinting
Collision Induced Dissociation (CID) spectra from MS/MS analysis
LC/MS/MS approach used to identify protein components in a complex mixture
Tandem mass spectra helps in inferring amino acid sequences of peptides

4. Peptide Mass Fingerprinting vs. MS/MS protein identification
James S. Eddes et.al., 2002, Proteomics

5. Database Searching L M G S E I P K b1 b2 b3 b4 b5 b6 b7 NH2 CO2 y7 y6
m/z y7 y6 y5 y4 y3 y2 y1
Database searching software
Results
MASCOT®
Proteins found
Hemoglobin, beta chain
Pept. Mass Score Sequence
HLDNLK
VHLTDAEK
AAVNGLWGK
VINAFNDGLK
VVAGVASALAHK
LVINAFNDGLK …
Database (SwissProt)
Actin
MYTCVPIASEQUENCEMIMEWTPQSDLIRPTVCIMNERCVGGPYILCMTEND
Amylase
DSLIKRNYTIPMCSQIRECNHIPLMTRCHGYYKWSIALAINTQSFGIVRIVAMNKLPSSCRTIVGHWEDRICTMQNCISPPEKELIAVARGTSP …

6. Mascot Search Engine
Uses mass spectrometry data to identify proteins from primary sequence databases
MS/MS ion search
Enzyme cleavage rules applied to sequences in the protein databases
Experimental mass values compared with calculated fragment ion mass values
Use scoring algorithm to identify the closest match or matches
Probability based MOWSE scoring algorithm
Databases
MSDB – non-identical protein sequence DB
NCBInr
SwissProt
dbEST – “single-pass” cDNA sequences or EST’s

7. A Typical Experiment Analysis of Liver / Brain Tissue
Digest with Trypsin
Liquid Chromatography
LC eluting sample electrosprayed into Mass Spec
APAAIGAYSQAVLVDR from 14.5 kDa translational inhibitor protein
MS-MS on intense peak of a parent ion
Raw data converted to a DTA file
Mascot Search
Generates Html file

8. Mascot output – Html file (avg. size 5 MB)

9. Motivation Mass spectrometry generates enormous amount of data
Mascot returns on an average hundreds of proteins matching the mass spectral data
Time consuming to analyze the mascot results manually
Need different ways of looking at data
Comparison of various data sets (experiments)
No tools were available in public domain to analyze Mascot results

10. Protein Results Parser v3.0
Features
Single File parsing
Sequence coverage - with single file parsing
Two-file comparison
Multiple files
Compare
Combine
Tool was developed using Perl/Tk
Windows application

11. Single File Parsing

12. Screened Html Result (smaller file size)

13. Sequence Coverage

14. Two file Comparison

15. Results – Comparison of Two Experiments

16. Combine and Compare Feature
Drug A
Treatments
(protein digest)
Drug B
Fractions
(SCX)
Triplicates
(LC/MS/MS)
15 data files
15 data files
Combine
Combine
Compare

17. Multiple File Comparison

18. Results – Multiple file comparison (sequential display)

19. Results – Multiple file comparison (tabular display)

20. Combine – Merging of multiple experiments

21. Results – combining multiple experiments+ +

22. Conclusion Decreased data analysis and processing time.
Search results reduced using user specified criteria in an automated way.
Removal of low-scoring peptide matched greatly improves the accuracy of data interpretation
A single result file can be processed multiple times, using a different set of parsing criteria each time, without the need to repeat the database search.
The ability to compare two or more result files in an automated fashion makes determination of sample similarity a nearly effortless endeavor

23. Acknowledgements Dr. Randy Arnold – Manager and Research Scientist
(Proteomics Research and Development Facility – Dept. of Chemistry)
Dr. Haixu Tang – Asst. Prof, School of Informatics
Abhijit Mahabal – Grad student, CS Dept.
Kranthi Varala – Grad Student, Bioinformatics