Mesostate
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==Concept== | ==Concept== | ||
- | + | *Protein structures (3D) can be parsed into a finite number of structural alphabets (e.g. a torsion angle of each residue) | |
*From these '''structural alphabets''', we will identify meaningful structural words.. | *From these '''structural alphabets''', we will identify meaningful structural words.. | ||
**Frequent structural words (motifs?) and popular sentences (super secondary structures?) | **Frequent structural words (motifs?) and popular sentences (super secondary structures?) | ||
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===Torsion angles=== | ===Torsion angles=== | ||
*Structural alphabets based on torsion angle distributions in the [[:en:ramachandran plot|ramachandran plot]] | *Structural alphabets based on torsion angle distributions in the [[:en:ramachandran plot|ramachandran plot]] | ||
- | + | [[file:ramachandran.png|thumb|left|Randomly 5000 residues picked, Red=Sheets, Green=Helices]] | |
- | + | ||
- | + | ||
====Mesostate==== | ====Mesostate==== | ||
*Data production by [[배형섭]] | *Data production by [[배형섭]] | ||
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==Status(result)== | ==Status(result)== | ||
- | *Check your data (pdbstyle-1.75.new) | + | *Check your data with Perl script (pdbstyle-1.75.new) |
**check.pl < pdbstyle-1.75.new > tmp.txt | **check.pl < pdbstyle-1.75.new > tmp.txt | ||
<pre> | <pre> | ||
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<pre> | <pre> | ||
meso = read.csv("tmp1.csv") | meso = read.csv("tmp1.csv") | ||
- | + | save(meso,"meso.rdata") | |
</pre> | </pre> | ||
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[6,] 35495 3524 13284 6641 5944 34984 | [6,] 35495 3524 13284 6641 5944 34984 | ||
</pre> | </pre> | ||
- | [[file:binsplot.png|thumb|left]] | + | [[file:binsplot.png|thumb|left|6x6 bins - heat color density]] |
==References== | ==References== | ||
- | <biblio> #FPP pmid=19188606 | + | <biblio> |
+ | #LFF pmid=14985506 | ||
+ | #FPP pmid=19188606 | ||
</biblio> | </biblio> |
Latest revision as of 02:24, 6 September 2010
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Procedure
Standard data set
- We are going to use the SCOP DB: sequences and structures in the Astral compendium
Torsion angles
- Structural alphabets based on torsion angle distributions in the ramachandran plot
Mesostate
- Data production by 배형섭
- Torsion angle mesostate by LINUS (Rose Lab)
Calculation
- Following tools can be used to calculate torsion angles of backbones
Alphabet assignment
- Information theory
Profiling
- Normalization?
- Distance metric?
Applications
Status(result)
- Check your data with Perl script (pdbstyle-1.75.new)
- check.pl < pdbstyle-1.75.new > tmp.txt
while(<>) { chomp; @tmp = split/\t/,$_; if($tmp[8]=~/_/) { next } if(scalar(@tmp)==9) { print $_,"\n" } }
- txt2csv.pl < tmp.txt > tmp1.csv
while(<>) { chomp; $_=~s/\[|\]//g; @line = split/\s+/,$_; $tmp = '"'.join("\"\,\"",@line)."\""; print $tmp,"\n"; }
- Using R
meso = read.csv("tmp1.csv") save(meso,"meso.rdata")
## load saved R data load("meso.rdata") ## analysis dim(meso) # dimension 11,810,116 residues meso[1:2,] # check first two rows in the data (list) dom = unique(meso$Domain) ndom = length(dom) # 65,485 SCOP domains nrow(meso)/ndom # average 180 residues (domain size) # consider 1st, last residues are skipped.. ## ## ramachandran plot ## # randomly picking 5,000 residue's Phi & Psi rn = sample(nrow(meso),5000) png(file="ramachandran.plot") plot(meso$Phi[rn],meso$Psi[rn],xlab="Phi",ylab="Psi",xlim=c(-180,180),ylim=c(-180,180),main="Ramachandran plot",col="gray") # randomly picking 5,000 helices rn = sample(which(meso$Structure=='H'),5000) points(meso$Phi[rn],meso$Psi[rn],col="green") # randomly picking 5,000 sheets rn = sample(which(meso$Structure=='E'),5000) points(meso$Phi[rn],meso$Psi[rn],col="red") dev.off() ##
- 6 x 6 bins
library(ash) x = as.matrix(meso[,7:8]) ab = matrix(c(-180,-180,180,180),2,2) nbin = c(6,6) bins = bin2(x,ab,nbin) ----- > print(bins) [,1] [,2] [,3] [,4] [,5] [,6] [1,] 90545 18116 83067 120228 284531 1369188 [2,] 100305 84717 3685965 485872 653175 2066098 [3,] 4849 86683 1565006 5826 39358 294463 [4,] 23516 9348 2594 135671 25882 3456 [5,] 54491 12777 109935 234363 17244 41903 [6,] 35495 3524 13284 6641 5944 34984
References
Error fetching PMID 14985506:
Error fetching PMID 19188606:
Error fetching PMID 19188606:
- Error fetching PMID 14985506:
- Error fetching PMID 19188606: