In huangrh/radii: Determine Hydrodynamic Radius from Gel Filtration

require(ggplot2)
require(radii)

Load and plot the gel filtration data

# load the data: the gel filtration elusion profile
file <- system.file("extdata", "160826bioradstdpH74EDTA_HiLoad.asc", package="radii")
HiLoad_std <- read.csv(file, header = TRUE, skip=2, sep = "\t"); 
HiLoad_std <- na.omit(HiLoad_std[, 1:2])

# plot the data: gel filtration profile
# with(HiLoad_std, plot(mAU ~ ml, type = "l", main = "HiLoad: Biorad STD"))
#
g <- radii:::ggplot() 
g <- g + ggplot2::geom_line(aes(ml, mAU), data=HiLoad_std)
g <- g + ggtitle("HiLoad: Biorad std") + xlab("Vol (mL)") + ylab("A280 (mAU)")
label=as.character(radii::protein_std_HiLoad$ve)
pos <- data.frame(x= as.numeric(label),y = c(42, 29, 21, 34, 20))
g <- g + annotate("text", label=label, x=pos$x, y=pos$y)
print(g)

Transform the standard and then fit to the Stokes radius model

# superdex 200 HiLoad 16/60
vt = mean(c(122.31,122.93))
vo = mean(c(47.26,46.59))

protein_std_HiLoad$sigma <- radii::partition_coef(protein_std_HiLoad$ve, vo=vo, vt=vt)
protein_std_HiLoad$erfcinv <- radii::erfcinv(protein_std_HiLoad$sigma)
# 
knitr::kable(protein_std_HiLoad, format = "markdown")
protein_std_HiLoad <- na.omit(protein_std_HiLoad)

fit <- lm (radii~erfcinv+1, data = protein_std_HiLoad)
summary(fit)
xdat <- seq(min(protein_std_HiLoad$erfcinv), max(protein_std_HiLoad$erfcinv),by = 0.01 )
pred <- predict(fit, newdata = data.frame(erfcinv=xdat))

# Plotting 
g = radii:::ggplot() + geom_point(data = protein_std_HiLoad, aes(erfcinv, radii)) 
g <- g + ggtitle(expression("Stokes raddi vs erfcinv (" ~ sigma ~ ")"))
g + geom_line(aes(xdat,pred))

Purified VWF Propeptide

# superdex 200 HiLoad 16/60
vt = mean(c(122.31,122.93))
vo = mean(c(47.26,46.59))
# sample data
ve_D1D2_pure   = 74.36
sigma_D1D2_pure=radii::partition_coef(ve_D1D2_pure, vo=vo, vt=vt)
erfcinv_D1D2_pure= radii::erfcinv(sigma_D1D2_pure)
r_D1D2_pure = predict(fit, newdata = data.frame(erfcinv=erfcinv_D1D2_pure))
print(paste("Stokes radius of the purified D1D2", r_D1D2_pure, "nm"))

Plasma D1D2

# D1D2
ve_D1D2_plasma = 81
sigma_D1D2_plasma=radii::partition_coef(ve_D1D2_plasma, vo=vo, vt=vt)
erfcinv_D1D2_plasma= radii::erfcinv(sigma_D1D2_plasma)
r_D1D2_plasma = predict(fit, newdata = data.frame(erfcinv=erfcinv_D1D2_plasma))
print(paste("Stokes radius of the purified D1D2", r_D1D2_plasma, "nm"))