dccm.xyz {bio3d} | R Documentation

## DCCM: Dynamical Cross-Correlation Matrix

### Description

Determine the cross-correlations of atomic displacements.

### Usage

    ## S3 method for class 'xyz'
    dccm(x, reference = NULL, grpby=NULL,
    ncore=1, nseg.scale=1, ...)
    cov2dccm(vcov, method = c("pearson", "lmi"), ncore = NULL)

### Arguments

`x` | a numeric matrix of Cartesian coordinates with a row per structure/frame.
`reference` | The reference structure about which displacements are analysed.
`grpby` | a vector counting connective duplicated elements that indicate the elements of `xyz` that should be considered as a group (e.g. atoms from a particular residue).
`ncore` | number of CPU cores used to do the calculation. `ncore>1` requires package ‘parallel’ installed.
`nseg.scale` | split input data into specified number of segments prior to running multiple core calculation. See `fit.xyz`.
`...` | hmm.
`vcov` | numeric variance-covariance matrix.
`method` | method to calculate the cross-correlation.

### Details

The extent to which the atomic fluctuations/displacements of a system are correlated with one another can be assessed by examining the magnitude of all pairwise cross-correlation coefficients (see McCammon and Harvey, 1986).

This function returns a matrix of all atom-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 atoms i and j are completely correlated (same period and same phase), if Cij = -1 the fluctuations of atoms i and j are completely anticorrelated (same period and opposite phase), and if Cij = 0 the fluctuations of i and j are not correlated.

Typical characteristics of DCCMs include a line of strong cross-correlation along the diagonal, cross-correlations emanating from the diagonal, and off-diagonal cross-correlations. The high diagonal values occur where i = j, where Cij is always equal to 1.00. Positive correlations emanating from the diagonal indicate correlations between contiguous residues, typically within a secondary structure element or other tightly packed unit of structure. Typical secondary structure patterns include a triangular pattern for helices and a plume for strands. Off-diagonal positive and negative correlations may indicate potentially interesting correlations between domains of non-contiguous residues.

`cov2dccm` function calculates the N-by-N cross-correlation matrix directly from a 3N-by-3N variance-covariance matrix. If `method = "pearson"`, the conventional Pearson's inner-product correlaiton calculation will be invoked, in which only the diagnol of each residue-residue covariance sub-matrix is considered.

If `method = "lmi"`, then the linear mutual information cross-correlation will be calculated. ‘LMI’ considers both diagnol and off-diagnol entries in sub-matrices, and so even grabs the correlation of residues moving on orthognal directions. (See more details in `lmi`.)

### Value

Returns a cross-correlation matrix.

### Author(s)

Xin-Qiu Yao and Gisle Saelensminde

### References

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

McCammon, A. J. and Harvey, S. C. (1986) Dynamics of Proteins and Nucleic Acids, Cambridge University Press, Cambridge.

Lange, O.F. and Grubmuller, H. (2006) PROTEINS: Structure, Function, and Bioinformatics 62:1053–1061.

### See Also

`cor` for examining xyz cross-correlations, `dccm`, `dccm.nma`, `dccm.pca`, `lmi`, `dccm.enma`.

### Examples

    ## Not run:
    ##-- Read example trajectory file
    trtfile <- system.file("examples/hivp.dcd", package="bio3d")
    trj <- read.dcd(trtfile)

    ## 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)

    ## DCCM (slow to run so restrict to Calpha)
    cij <- dccm(xyz)

    ## Plot DCCM
    plot(cij)

    ## Or
    library(lattice)
    contourplot(cij, region = TRUE, labels=FALSE, col="gray40",
                at=c(-1, -0.75, -0.5, -0.25, 0.25, 0.5, 0.75, 1),
                xlab="Residue No.", ylab="Residue No.",
                main="DCCM: dynamic cross-correlation map")

    ## End(Not run)

[Package bio3d version 2.3-4 Index]