dccm.pca. bio3d 2.3-0

Usage

"dccm"(x, pc = NULL, ncore = NULL, ...)

Arguments

x: an object of class pca as obtained from function pca.xyz.
pc: numerical, indices of PCs to be included in the calculation. If all negative, PCs complementary to abs(pc) are included.
ncore: number of CPU cores used to do the calculation. By default (ncore = NULL), use all available cores detected.
...: additional arguments to cov2dccm.

Description

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

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 PCs to be included in the calculation (e.g. filter PCs with small eigenvalues).

Value

Returns a cross-correlation matrix.

References

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

Examples

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

 NATOM = 198 
 NFRAME= 117 
 ISTART= 0 
 last  = 117 
 nstep = 117 
 nfile = 117 
 NSAVE = 1 
 NDEGF = 0 
 version 24 
Reading (x100)
  |======================================================================| 100%


## 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, resno=c(24:27,85:90), elety='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: only use first 10 PCs
cij <- dccm(pca, pc = c(1:10))

## Plot DCCM
plot(cij)

## DCCM: remove first 10 PCs
cij <- dccm(pca, pc = -c(1:10))



## Plot DCCM
plot(cij)

Author

Xin-Qiu Yao

Dynamic Cross-Correlation from Principal Component Analysis