Download Sense antisense proteins & Protein interaction databases

Sense-Antisense Proteins
Vision Lab Presentation
Ruchir Shah
April 16, 2003
Sense-Antisense Proteins
* Peptides generated from sense
and antisense DNA strands have
‘inverted hydropathies’.
Although it makes no sense, it is
hypothesized that S- and ASpeptides could have a high
binding affinity for each other.
Picture adapted from: J.R.Heal et al; ChemBioChem 2002,3,136-151.
S-AS Codon Table
Inverted Hydropathy
Blue=Non Polar
Pink=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
S-AS Codon Table
Codon
AmAcid /1000
5'Sense3' Sense
GGG
Gly
5.98
GGA
Gly
10.92
GGT
Gly
23.9
GGC
Gly
9.71
Freq.
Sense
0.00598
0.01092
0.0239
0.00971
0.05051
Codon
5 AS 3'
CCC
TCC
ACC
GCC
AA
Anti S
Pro
Ser
Thr
Ala
/1000
6.78
14.22
12.56
12.54
Freq.
Anti S
0.00678
0.01422
0.01256
0.01254
GAG
GAA
Glu
Glu
19.14
45.92
0.01914
0.04592
0.06506
CTC
TTC
Leu
Phe
5.38
18.21
0.00538
0.01821
GAT
GAC
Asp
Asp
37.84
20.26
0.03784
0.02026
0.0581
ATC
GTC
Ile
Val
17.07
11.59
0.01707
0.01159
GTG
GTA
GTT
GTC
Val
Val
Val
Val
10.66
11.78
22.01
11.59
0.01066
0.01178
0.02201
0.01159
0.05604
CAC
TAC
AAC
GAC
His
Tyr
Asn
Asp
7.77
14.67
24.94
20.26
0.00777
0.01467
0.02494
0.02026
GCG
GCA
GCT
GCC
Ala
Ala
Ala
Ala
6.15
16.16
21.09
12.54
0.00615
0.01616
0.02109
0.01254
0.05594
CGC
TGC
AGC
GGC
Arg
Cys
Ser
Gly
2.58
4.67
9.68
9.71
0.00258
0.00467
0.00968
0.00971
0.00924
0.0213
0.00173
0.00301
0.00648
0.00258
0.04434
CCT
TCT
CCG
TCG
ACG
GCG
AGG
AGA
CGG
CGA
CGT
CGC
Arg
Arg
Arg
Arg
Arg
Arg
9.24
21.3
1.73
3.01
6.48
2.58
Pro
Ser
Pro
Ser
Thr
Ala
13.58
23.55
5.27
8.56
7.95
6.15
0.01358
0.02355
0.00527
0.00856
0.00795
0.00615
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
P1-P101
P2-P102
P3-P103
P4-P104
Database of Interacting Prtns
P5-P99
P2-P102
P104-P4
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
P1-P101
P2-P102
P3-P103
P4-P104
Database of Interacting Prtns
P5-P99
P2-P102
P104-P4
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
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.