data(thermo)
## basic examples of get.protein
# amino acid composition of two proteins
get.protein(c('YML020W','YBR051W'),'SGD')
# average composition of proteins
get.protein(c('YML020W','YBR051W'),'SGD',
abundance=1,pname='PROT1_NEW')
# 1 of one and 1/2 of the other
get.protein(c('YML020W','YBR051W'),'SGD',
abundance=c(1,0.5),average=FALSE,pname='PROT2_NEW')
# compositions of proteins induced in carbon limitation
get.protein('low.C','SGD')
## overall composition of proteins exclusively localized
## to cytoplasm of S. cerevisiae with reported expression levels
y <- yeastgfp('cytoplasm')
p <- get.protein(y$yORF,'SGD',y$abundance,'cytoplasm')
# add the proteolog and calculate its properties
i <- add.protein(p)
protein(i)
## Chemical activities of model subcellular proteins
# (one-dimensional speciation diagram as a function of logfO2)
basis('CHNOS')
names <- colnames(thermo$yeastgfp)[6:28]
# calculate amino acid compositions using 'get.protein' function
for(i in 1:length(names)) {
y <- yeastgfp(names[i])
p <- get.protein(y$yORF,'SGD',y$abundance,names[i])
add.protein(p)
}
species(names,'SGD')
res <- 200
t <- affinity(O2=c(-77,-72,res))
mycolor <- topo.colors(6)[1:4]
mycolor <- rep(mycolor,times=rep(6,4))
oldpar <- par(bg='black',fg='white')
logact <- diagram(t,balance='PBB',names=names,ylim=c(-4,-1.9),legend.x=NULL,
color=mycolor,lwd=2,cex.axis=1.5,residue=TRUE)$logact
# so far good, but how about labels on the plot?
for(i in 1:length(logact)) {
imax <- which.max(as.numeric(logact[[i]]))
adj <- 0.5
if(imax > 180) adj <- 1
if(imax < 20) adj <- 0
text(seq(-77,-72,length.out=res)[imax],logact[[i]][imax],
labels=names[i],adj=adj)
}
title(main=paste('Subcellular proteologs of S. cerevisiae<n>',
describe(thermo$basis[-5,])),col.main=par('fg'))
par(oldpar)
## Oxygen fugacity - activity of H2O predominance
## diagrams for proteomes in 23 YeastGFP localizations
# arranged by decreasing metastability:
# order of this list of locations is based on the
# (dis)appearance of species on the current set of diagrams
names <- c('actin','early.Golgi','ER','vacuolar.membrane',
'cell.periphery','nucleolus','Golgi','lipid.particle',
'punctate.composite','peroxisome','bud','ER.to.Golgi',
'nuclear.periphery','ambiguous','late.Golgi','cytoplasm',
'nucleus','mitochondrion','endosome','vacuole',
'spindle.pole','bud.neck','microtubule')
nloc <- c(5,5,5,3,2,3)
inames <- 1:length(names)
# define the system
basis('CHNOS+')
# calculate amino acid compositions using 'get.protein' function
for(i in 1:length(names)) {
y <- yeastgfp(names[i])
p <- get.protein(y$yORF,'SGD',y$abundance,names[i])
add.protein(p)
}
species(names,'SGD')
t <- affinity(H2O=c(-5,0,256),O2=c(-80,-66,256))
# the plot setup
layout(matrix(c(1,1,2:7),byrow=TRUE,nrow=4),heights=c(0.7,3,3,3))
# a title
par(mar=c(0,0,0,0))
plot.new()
text(0.5,0.5,paste('Proteologs for subcellular locations of',
'S. cerevisiae<n>',describe(thermo$basis[-c(2,5),])),cex=1.5)
opar <- par(mar=c(3,4,1,1),xpd=TRUE)
for(i in 1:length(nloc)) {
diagram(t,balance='PBB',names=names[inames],
ispecies=inames,cex.axis=1.1)
label.plot(letters[i])
title(main=paste(length(inames),'locations'))
# take out the stable species
inames <- inames[-(1:nloc[i])]
}
layout(matrix(1))
par(opar)
### examples for stress response experiments
# coefficient of variation of relative
# abundances of proteins induced in heat
# response experiments (Richmond et al., 1999)
# as a function of fO2 and temperature
a <- get.protein("heat","ECO")
add.protein(a)
basis('CHNOS+')
species(a$protein,"ECO")
a <- affinity(T=c(0,150),O2=c(-90,-40))
d <- diagram(a,residue=TRUE,do.plot=FALSE,mam=FALSE)
draw.diversity(d)
title(main="Coefficient of variation of relative abundances
of proteins in E. coli observed at 50 degC heat shock",
cex.main=0.9)
# predominance fields for overall protein
# compositions induced by
# carbon, sulfur and nitrogen limitation
# (experimental data from Boer et al., 2003)
expt <- c('low.C','low.N','low.S')
for(i in 1:length(expt)) {
p <- get.protein(expt[i],"SGD",abundance=1)
add.protein(p)
}
# thermo set-up
basis("CHNOS+")
basis("O2",-75.29)
species(expt,"SGD")
a <- affinity(CO2=c(-5,0),H2S=c(-10,0))
diagram(a,balance="PBB",names=expt,color=NULL,residue=TRUE)
title(main=paste("Metastabilities of proteins induced by",
"carbon, sulfur and nitrogen limitation",sep="<n>"))
# predominance fields for overall protein
# compositions induced and repressed in
# an/aerobic carbon-limited experiments
# (Tai et al., 2005)
# the activities of glucose, ammonium and sulfate
# are similar to the non-growth-limiting concentrations
# used by Boer et al., 2003
basis(c("glucose","H2O","NH4+","H2","SO4-2","H+"),
c(-1,0,-1.3,999,-1.4,-7))
# the names of the experiments in thermo$stress
expt <- c("Clim.aerobic.down","Clim.aerobic.up",
"Clim.anaerobic.down","Clim.anaerobic.up")
# here we use abundance to indicate that the protein
# compositions should be summed together in equal amounts
for(i in 1:length(expt)) {
p <- get.protein(expt[i],"SGD",abundance=1)
add.protein(p)
}
species(expt,"SGD")
a <- affinity(C6H12O6=c(-35,-20),H2=c(-20,0))
diagram(a,residue=TRUE,color=NULL,as.residue=TRUE)
title(main="Metastabilities of average protein residues in
an/aerobic carbon limitation in yeast")</n>
<references>Boer, V. M., de Winde, J. H., Pronk, J. T. and Piper, M. D. W., 2003. The genome-wide transcriptional responses of <em>Saccharomyces cerevisiae</em> grown on glucose in aerobic chemostat cultures limited for carbon, nitrogen, phosphorus, or sulfur. <em>J. Biol. Chem.</em>, 278, 3265-3274.
Richmond, C. S., Glasner, J. D., Mau, R., Jin, H. F. and Blattner, F. R., 1999. Genome-wide expression profiling in <em>Escherichia coli</em> K-12. <em>Nucleic Acids Res.</em>, 27, 3821-3835.
Tai, S. L., Boer, V. M., Daran-Lapujade, P., Walsh, M. C., de Winde, J. H., Daran, J.-M. and Pronk, J. T., 2005. Two-dimensional transcriptome analysis in chemostat cultures: Combinatorial effects of oxygen availability and macronutrient limitation in <em>Saccharomyces cerevisiae</em>. <em>J. Biol. Chem.</em>, 280, 437-447.</references>
<keyword>misc</keyword></n></n>Run the code above in your browser using DataLab