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

* 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,

S-AS Codon Table

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

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.

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

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

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

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?

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.

BLOSUM 62 Source:

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!!

S-AS Codon Table

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

Results: contd.. Low complexity regions!

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

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

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

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

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

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

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

DIP : Database of Interacting Proteins SS=small scale experiment HT=high throughput exp. Purple=overlap Bars= more than 1 exp. Proteins = 4727 Interactions= 15174

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

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