dccm {bio3d}R Documentation

DCCM: Dynamical Cross-Correlation Matrix

Description

Determine the cross-correlations of atomic displacements.

Usage

dccm(xyz, reference = apply(xyz, 2, mean))

Arguments

xyz a numeric matrix of Cartesian coordinates with a row per structure/frame.
reference The reference structure about which displacements are analysed.

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.

Value

Returns a cross-correlation matrix.

Note

This function is currently very basic i.e. inefficient and SLOW.

Author(s)

Barry Grant

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.

See Also

cor for examining xyz cross-correlations, pca.xyz.

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,"///24:27,85:90///CA/")

## lsq fit of trj on pdb
fit.xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz)

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

## Plot DCCM
library(lattice)
contourplot(cij, region = TRUE, labels=F, 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 1.0-5 Index]