When collecting protein-ligand binding data using a technique such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance (SPR), it is useful to simulate binding curves to help optimise your experiments. After initial binding parameters are known, binding curves can be simulated and parameters such as: analyte concentration, time of association, dissociation etc. can be varied. The models within this package may also be used to fit a curve to measured binding data using a non-linear regression.

Currently, two binding models are included with this package:

• 1:1 binding
• 2:1 heterogeneous binding.

1:1 Binding

library(pbm)
time <- seq(0,1000)
response <- binding1to1(time,500,6e-7,10000,0.01,0.8)
plot(time, response, type="l")

Optional drift parameter

library(pbm)
time <- seq(0,1000)
response <- binding1to1(time,500,6e-7,10000,0.01,0.8, drift = 1e-04)
#> Warning in binding1to1(time, 500, 6e-07, 10000, 0.01, 0.8, drift = 1e-04):
#> Drift parameter set
plot(time, response, type="l")

2:1 Binding

library(pbm)
time <- seq(0,1000)
response <- binding2to1(time,500,6e-7,10000,0.01,0.5,2500,0.001,0.3)
plot(time, response, type="l")

Non-linear Regression

library(pbm)

# Generate example binding data with noise
time <- seq(0,1000)
response <- binding2to1(time,500,6e-7,10000,0.01,0.5,2500,0.001,0.3)
noisyresponse <- jitter(response,amount=0.02)
data <- data.frame(time, noisyresponse)
names(data) <- c("x","y")

# Fit a nlm to binding data
startingvalues <- list(kon1=70000,koff1=0.01,rmax1=0.3,kon2=9000,koff2=0.004,rmax2=0.3)
fit <- nls(y ~ binding2to1(x,500,6e-7,kon1,koff1,rmax1,kon2,koff2,rmax2),
  data=data,
  start=startingvalues)

# Plot the fitted model
plot(data$x, data$y, type="p", pch=4, cex=.5)
par(col="red",lwd=3)
lines(data$x,predict(fit, list(x=data$x)))

Parameters predicted from fitted model: