R: Dynamic Cross-Correlation from Normal Modes Analysis

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 `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

    ## 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))

[Package bio3d version 2.4-2 Index]