dccm.nma {bio3d} | R Documentation |
Dynamic Cross-Correlation from Normal Modes Analysis
Description
Calculate the cross-correlation matrix from Normal Modes Analysis.
Usage
## S3 method for class 'nma'
dccm(x, nmodes = NULL, ncore = NULL, progress = NULL, ...)
Arguments
x |
an object of class |
nmodes |
numerical, number of modes to consider. |
ncore |
number of CPU cores used to do the calculation.
|
progress |
progress bar for use with shiny web app. |
... |
additional arguments ? |
Details
This function calculates the cross-correlation matrix from Normal
Modes Analysis (NMA) obtained from nma
of a protein
structure. It returns a matrix of residue-wise cross-correlations
whose elements, Cij, may be displayed in a graphical
representation frequently termed a dynamical cross-correlation
map, or DCCM.
If Cij = 1 the fluctuations of residues i and j are completely correlated (same period and same phase), if Cij = -1 the fluctuations of residues i and j are completely anticorrelated (same period and opposite phase), and if Cij = 0 the fluctuations of i and j are not correlated.
Value
Returns a cross-correlation matrix.
Author(s)
Lars Skjaerven
References
Wynsberghe. A.W.V, Cui, Q. Structure 14, 1647–1653. Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.
See Also
Examples
## Fetch stucture
pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") )
## Calculate normal modes
modes <- nma(pdb)
## Calculate correlation matrix
cm <- dccm.nma(modes)
## Plot correlation map
plot(cm, sse = pdb, contour = FALSE, col.regions = bwr.colors(20),
at = seq(-1, 1, 0.1))