bio3d (version 2.1-2)

dccm.pca: Dynamic Cross-Correlation from Principal Component Analysis

Description

Calculate the cross-correlation matrix from principal component analysis (PCA).

Usage

## S3 method for class 'pca':
dccm(x, nmodes = NULL, ncore = NULL, \dots)

Arguments

x

an object of class pca as obtained from function pca.xyz.

nmodes

numerical, number of modes to consider.

ncore

number of CPU cores used to do the calculation. By default (ncore = NULL), use all available cores detected.

...

additional arguments to cov2dccm.

Value

• Returns a cross-correlation matrix.

Details

This function calculates the cross-correlation matrix from principal component analysis (PCA) obtained from pca.xyz of a set of protein structures. It is an alternative way to calculate correlation in addition to the conventional way from xyz coordinates directly. But, in this new way one can freely chooses the number of modes to be included in the calculation (i.e. filter out fast modes).

References

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

See Also

pca.xyz, plot.dccm

Examples

##-- Read example trajectory file
trtfile <- system.file("examples/hivp.dcd", package="bio3d")
trj <- read.dcd(trtfile)

## Read the starting PDB file to determine atom correspondence
pdbfile <- system.file("examples/hivp.pdb", package="bio3d")
pdb <- read.pdb(pdbfile)

## Select residues 24 to 27 and 85 to 90 in both chains
inds <- atom.select(pdb,"///24:27,85:90///CA/")

## lsq fit of trj on pdb
xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz)

## Do PCA
pca <- pca.xyz(xyz)

## DCCM
cij <- dccm(pca, nmodes=10)

## Plot DCCM
plot(cij)