plot.core {bio3d}

Plot Core Fitting Progress

Description

Plots the total ellipsoid volume of core positions versus core size at each iteration of the core finding process.

Usage

## S3 method for class 'core'
plot(x, y = NULL, type = "h", main = "", sub = "", 
   xlim = NULL, ylim = NULL, xlab = "Core Size (Number of Residues)", 
   ylab = "Total Ellipsoid Volume (Angstrom^3)", axes = TRUE, 
   ann = par("ann"), col = par("col"), ...)

Arguments

x

a list object obtained with the function core.find from which the ‘volume’ component is taken as the x coordinates for the plot.

y

the y coordinates for the plot.

type

one-character string giving the type of plot desired.

main

a main title for the plot, see also ‘title’.

sub

a sub-title for the plot.

xlim

the x limits of the plot.

ylim

the y limits of the plot.

xlab

a label for the x axis.

ylab

a label for the y axis.

axes

a logical value indicating whether both axes should be drawn.

ann

a logical value indicating whether the default annotation (title and x and y axis labels) should appear on the plot.

col

The colors for lines and points. Multiple colours can be specified so that each point is given its own color. If there are fewer colors than points they are recycled in the standard fashion.

...

extra plotting arguments.

Value

Called for its effect.

Note

The produced plot can be useful for deciding on the core/non-core boundary.

Author(s)

Barry Grant

References

Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.

See Also

core.find, print.core

Examples

## Not run: 

##-- Generate a small kinesin alignment and read corresponding structures
pdbfiles <- get.pdb(c("1bg2","2ncd","1i6i","1i5s"), URLonly=TRUE)
pdbs <- pdbaln(pdbfiles)

##-- Find 'core' positions
core <- core.find(pdbs)
plot(core)

##-- Fit on these relatively invarient subset of positions 
core.inds <- print(core)
xyz <- pdbfit(pdbs, core.inds, outpath="corefit_structures")

##-- Compare to fitting on all equivalent positions
xyz2 <- pdbfit(pdbs)

## Note that overall RMSD will be higher but RMSF will
##  be lower in core regions, which may equate to a
##  'better fit' for certain applications
gaps <- gap.inspect(pdbs$xyz)
rmsd(xyz[,gaps$f.inds])
rmsd(xyz2[,gaps$f.inds])

plot(rmsf(xyz[,gaps$f.inds]), typ="l", col="blue", ylim=c(0,9))
points(rmsf(xyz2[,gaps$f.inds]), typ="l", col="red")


## End(Not run)