Mesostate
From CSBLwiki
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- | *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> |
Revision as of 08:10, 30 August 2010
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Concept
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
images to be posted...
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 $tmp,"\n"; }
- Using R
meso = read.csv("tmp1.csv") table(meso$Mesostate) # space & underbar remove
## 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 19188606:
- Error fetching PMID 19188606: