dccm.nma: Dynamic Cross-Correlation from Normal Modes Analysis

In bio3d: Biological Structure Analysis

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 nma as obtained from function nma.
nmodes	numerical, number of modes to consider.
ncore	number of CPU cores used to do the calculation. ncore>1 requires package ‘parallel’ installed.
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

nma, plot.dccm

Examples

if(!requireNamespace("lattice", quietly=TRUE)) {
   message("Need lattice installed to run this example")
} else {

## 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))
     
}

bio3d documentation built on Oct. 30, 2024, 1:08 a.m.