Demos for canprot
The canprot package calculates chemical metrics of proteins from amino acid compositions. This vignette was compiled on 2025-02-07 with canprot version 2.0.0-2.
Next vignette: Introduction to canprot
canprot demo #1: Thermophiles
Run the demo using demo("thermophiles"). For this demo,
just the output is shown below.
The canprot functions used are:
calc_metrics(): Calculates metrics named in an argument. The metrics calculated here are:S0g: standard specific entropyZc: carbon oxidation state
cplab: This is not a function, but an object that has formatted text labels for each metric.add_hull(): Adds a convex hull around data points
The data are from Dick et al. (2023) for methanogen genomes (amino acid composition and optimal growth temperature) and from Luo et al. (2024) for Nitrososphaeria MAGs (genome assemblies and habitat and respiration types). The plots reveal that proteins tend to have higher specific entropy in thermophilic genomes and MAGs from thermal habitats compared to mesophilic genomes and MAGs from nonthermal habitats, for a given carbon oxidation state. This implies that, after correcting for ZC, proteins in thermophiles have a more negative derivative of the standard Gibbs energy per gram of protein with respect to temperature.
canprot demo #2: Subcellular locations
Run the demo using demo("locations"). The code and
output of the demo are shown below.
The canprot functions used are:
human_aa(): Gets amino acid compositions of human proteins from UniProt IDsplength(): Calculates protein length (this line is commented out)Zc(): Calculates carbon oxidation statepI(): Calculates isoelectric pointadd_cld(): Adds compact letter display to a boxplot
# Read SI table
file <- system.file("extdata/protein/TAW+17_Table_S6_Validated.csv", package = "canprot")
dat <- read.csv(file)
# Keep only proteins with validated location
dat <- dat[dat$Reliability == "Validated", ]
# Keep only proteins with one annotated location
dat <- dat[rowSums(dat[, 4:32]) == 1, ]
# Get the amino acid compositions
aa <- human_aa(dat$Uniprot)
# Put the location into the amino acid data frame
aa$location <- dat$IF.main.protein.location
# Use top locations (and their colors) from Fig. 2B of Thul et al., 2017
locations <- c("Cytosol","Mitochondria","Nucleoplasm","Nucleus","Vesicles","Plasma membrane")
col <- c("#194964", "#2e6786", "#8a2729", "#b2333d", "#e0ce1d", "#e4d71c")
# Keep the proteins in these locations
aa <- aa[aa$location %in% locations, ]
## Keep only proteins with length between 100 and 2000
#aa <- aa[plength(aa) >= 100 & plength(aa) <= 2000, ]
# Get amino acid composition for proteins in each location
# (Loop over groups by piping location names into lapply)
aalist <- lapply(locations, function(location) aa[aa$location == location, ] )
# Setup plot
par(mfrow = c(1, 2))
titles <- c(Zc = "Carbon oxidation state", pI = "Isoelectric point")
# Calculate Zc and pI
for(metric in c("Zc", "pI")) {
datlist <- lapply(aalist, metric)
bp <- boxplot(datlist, ylab = cplab[[metric]], col = col, show.names = FALSE)
add_cld(datlist, bp)
# Make rotated labels
x <- (1:6) + 0.1
y <- par()$usr[3] - 1.5 * strheight("A")
text(x, y, locations, srt = 25, adj = 1, xpd = NA)
axis(1, labels = FALSE)
title(titles[metric], font.main = 1)
}
The plots show carbon oxidation state (ZC) and isoelectric point (pI) for human proteins in different subcellular locations. The localization data is from Table S6 of Thul et al. (2017), filtered to include proteins that have both a validated location and only one predicted location.
canprot demo #3: Redoxins
Run the demo using demo("redoxins"). For this demo, just
the output is shown below.
The canprot functions used are:
read_fasta(): Reads a FASTA sequence file and returns amino acid compositions of proteins. Additional processing is performed by using the following arguments:typeto read header linesiseqto read specific sequencesstartandstopto read segments of the sequences
Zc(): Calculates carbon oxidation state
This is an exploratory analysis for hypothesis generation about evolutionary links between midpoint reduction potential and ZC of proteins. The reduction potential data was taken from Åslund et al. (1997) and Hirasawa et al. (1999) for E. coli and spinach proteins, respectively. This plot is modified from Fig. 5 of this preprint; the figure did not appear in the published paper.