1. Sense-Antisense Proteins Vision Lab Presentation Ruchir Shah April 16, 2003

2. * Peptides generated from sense and antisense DNA strands have ‘inverted hydropathies’. Although it makes no sense, it is hypothesized that S- and AS- peptides could have a high binding affinity for each other. Sense-Antisense Proteins Picture adapted from: J.R.Heal et al; ChemBioChem 2002,3,136-151.

3. S-AS Codon Table

4. Inverted Hydropathy Blue=Non Polar Pink=Polar Picture adapted from: J.R.Heal et al; ChemBioChem 2002,3,136-151.

5. S-AS Codons Degeneracy: One sense AA can have more than One antisense AA. Hydropathy: Sense & antisense AA’s have inverted hydropathy. Codon biases/codon frequencies? Sense proteins interact with Antisense proteins: Numerous experimental evidences suggest that Sense and AS peptide have specific binding Affinity.

6. Experimental evidences Picture adapted from: J.R.Heal et al; ChemBioChem 2002,3,136-151.

7. How do S-AS Amino Acids interact? Picture adapted from: J.R.Heal et al; ChemBioChem 2002,3,136-151.

8. Molecular Recognition Theory Picture adapted from: J.R.Heal et al; ChemBioChem 2002,3,136-151.

9. Tasks Literature says: –S-AS proteins exist –S-AS proteins interact specifically with each other! Tasks: –Look for S-AS protein pairs(how such many pairs exist?) –What are the biological implications? –Do they really interact?

10. How to find S-AS pairs from Sequence Db? Conventional Sequence identity tools can be used to find out ‘similar’ proteins. Example: Blast or Smith Waterman with a choice of substitution matrix Positive score for Identity or desirable substitutions. Negative score for undesirable substitutions.

11. BLOSUM 62 Source: http://www.blc.arizona.edu/courses/bioinformatics/blosum.html

12. Design of a new substitution matrix To find S-AS pairs using existing sequence identity tools I need a special matrix. New matrix should: - positively score S-AS pairs - negatively score other pairs - reflect the degeneracy of genetic code - average score should be negative to avoid false positives!!

13. S-AS Codon Table

15. Results: What does it look like? It works!!

16. Results: contd.. Low complexity regions!

17. Lots of ‘small’ hits(lessons learnt!) “get rid of noise/background” “get rid of Low complexity regions” “use a better matrix”

18. Design of a new substitution matrix New matrix should: - positively score S-AS pairs - negatively score other pairs - reflect the degeneracy of genetic code -take into account the codon biases

19. S-AS Codon Table Source: SGD(Stanford) Saccharomyces Genome Database

20. 1.Low complexity filter : SEG 2.More meaningful Matrix: Formula for new scoring scheme

21. Flow Chart Sequence database (Yeast) ~6000prtns Run Smith Waterman All against All With new matrix Look for ‘hits’ Compare it with Interaction data

22. Tasks Look for sense-antisense protein pairs in protein sequence databases. List all sense-antisense pairs Compare the list with List of interacting proteins. Example: Sense-Antisense pairs Database of Interacting Prtns P5-P99 P2-P102 P104-P4 P1-P101 P2-P102 P3-P103 P4-P104

23. Tasks Look for sense-antisense protein pairs in protein sequence databases. List all sense-antisense pairs Compare the list with List of interacting proteins. Example: Sense-Antisense pairs Database of Interacting Prtns P5-P99 P2-P102 P104-P4 P1-P101 P2-P102 P3-P103 P4-P104

24. DIP : Database of Interacting Proteins http://dip.doe-mbi.ucla.edu/dip/Main.cgi SS=small scale experiment HT=high throughput exp. Purple=overlap Bars= more than 1 exp. Proteins = 4727 Interactions= 15174

25. Work in Progress Statistics of alignment: Distinguish random from meaningful hits! Relative entropy of the matrix Gap Penalties

26. Acknowledgments Todd Vision (Biology) Alex Tropsha (Pharmacy) Dr. Falk (Nephrology) All of my lab mates.