sense-antisense proteins vision lab presentation ruchir shah april 16, 2003

Sense-Antisense Proteins

Vision Lab Presentation

Ruchir ShahApril 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 2002,3,136-151.

S-AS Codon Table

Inverted Hydropathy

Blue=Non PolarPink=Polar

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

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 2002,3,136-151.

How do S-AS Amino Acids interact?

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

Molecular Recognition Theory

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

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: http://www.blc.arizona.edu/courses/bioinformatics/blosum.html

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

Codon AmAcid /1000 Freq. Codon AA /1000 Freq.5'Sense3' Sense Sense 5 AS 3' Anti S Anti SGGG Gly 5.98 0.00598 CCC Pro 6.78 0.00678GGA Gly 10.92 0.01092 TCC Ser 14.22 0.01422GGT Gly 23.9 0.0239 ACC Thr 12.56 0.01256GGC Gly 9.71 0.00971 GCC Ala 12.54 0.01254

0.05051

GAG Glu 19.14 0.01914 CTC Leu 5.38 0.00538GAA Glu 45.92 0.04592 TTC Phe 18.21 0.01821

0.06506

GAT Asp 37.84 0.03784 ATC Ile 17.07 0.01707GAC Asp 20.26 0.02026 GTC Val 11.59 0.01159

0.0581

GTG Val 10.66 0.01066 CAC His 7.77 0.00777GTA Val 11.78 0.01178 TAC Tyr 14.67 0.01467GTT Val 22.01 0.02201 AAC Asn 24.94 0.02494GTC Val 11.59 0.01159 GAC Asp 20.26 0.02026

0.05604

GCG Ala 6.15 0.00615 CGC Arg 2.58 0.00258GCA Ala 16.16 0.01616 TGC Cys 4.67 0.00467GCT Ala 21.09 0.02109 AGC Ser 9.68 0.00968GCC Ala 12.54 0.01254 GGC Gly 9.71 0.00971

0.05594

AGG Arg 9.24 0.00924 CCT Pro 13.58 0.01358AGA Arg 21.3 0.0213 TCT Ser 23.55 0.02355CGG Arg 1.73 0.00173 CCG Pro 5.27 0.00527CGA Arg 3.01 0.00301 TCG Ser 8.56 0.00856CGT Arg 6.48 0.00648 ACG Thr 7.95 0.00795CGC Arg 2.58 0.00258 GCG Ala 6.15 0.00615

0.04434

S-AS Codon Table

Source:SGD(Stanford)SaccharomycesGenomeDatabase

1. Low complexity filter : SEG2. More meaningful Matrix: Formula for new scoring

scheme

Flow Chart

Sequence database(Yeast) ~6000prtns

Run Smith WatermanAll against AllWith 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 PrtnsP5-P99P2-P102P104-P4

P1-P101P2-P102P3-P103P4-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 PrtnsP5-P99P2-P102P104-P4

P1-P101P2-P102P3-P103P4-P104

DIP : Database of Interacting Proteinshttp://dip.doe-mbi.ucla.edu/dip/Main.cgi

SS=small scale experimentHT=high throughput exp.Purple=overlapBars= more than 1 exp.

Proteins = 4727Interactions= 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.