R: Dynamic Cross-Correlation from Gaussian Network Model

dccm.gnm {bio3d} | R Documentation

## Dynamic Cross-Correlation from Gaussian Network Model

### Description

Calculate the cross-correlation matrix from Gaussian network model normal modes analysis.

### Usage

    ## S3 method for class 'gnm'
    dccm(x, ...)

    ## S3 method for class 'egnm'
    dccm(x, ...)

### Arguments

`x` | an object of class ‘gnm’ or ‘egnm’ as obtained from `gnm`.
`...` | additional arguments (currently ignored).

### Details

This function calculates the cross-correlation matrix from Gaussian network model (GNM) normal modes analysis (NMA) obtained from `gnm`. 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. (See more details in `help(dccm.nma)`).

### Value

Returns a cross-correlation matrix.

### Author(s)

Xin-Qiu Yao & Lars Skjaerven

### References

Bahar, I. et al. (1997) Folding Des. 2, 173.

### See Also

`gnm`, `dccm.nma`, `dccm.enma`, `plot.dccm`.

### Examples

    ## Fetch stucture
    pdb <- read.pdb( system.file("examples/1hel.pdb", package="bio3d") )

    ## Calculate normal modes
    modes <- gnm(pdb)

    ## Calculate correlation matrix
    cm <- dccm(modes)

    ## Plot correlation map
    plot(cm, sse = pdb, contour = FALSE, col.regions = bwr.colors(20),
         at = seq(-1, 1, 0.1))

[Package bio3d version 2.3-4 Index]