bio3d (version 2.2-4)

view.dccm: Visualization of Dynamic Cross-Correlation

Description

Structural visualization of a cross-correlation matrix.

Usage

view.dccm(dccm, pdb, step=0.2, omit=0.2, radius=0.15, type="pymol", outprefix="corr", launch=FALSE, exefile="pymol")

Arguments

dccm

an object of class dccm as obtained from function dccm or dccm.nma.

pdb

an object of class pdb as obtained from function read.pdb or a numerical vector of Cartesian coordinates.

step

binning interval of cross-correlation coefficents.

omit

correlation coefficents with values (0-omit, 0+omit) will be omitted from visualization.

radius

radius of visualized correlations.

type

character string specifying the type of visualization: ‘pymol’ or ‘pdb’.

outprefix

character string specifying the file prefix. If NULL the temp directory will be used.

launch

logical, if TRUE PyMol will be launched.

exefile

file path to the ‘PYMOL’ program on your system (i.e. how is ‘PYMOL’ invoked).

Value

Called for its action.

Details

This function generates a PyMOL (python) script that will draw colored lines between (anti)correlated residues. The PyMOL script file is stored in the working directory with filename “corr.py”, with coordinates in PDB format with filename “corr.inpcrd.pdb”. PyMOL will only be launched when using argument ‘launch=TRUE’. Alternatively a PDB file with CONECT records will be generated (when argument type='pdb'). For the PyMOL version, PyMOL CGO objects are generated - each object representing a range of correlation values (corresponding to the actual correlation values as found in the correlation matrix). E.g. the PyMOL object with name “cor_-1_-08” would display all pairs of correlations with values between -1 and -0.8.

References

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

See Also

nma, dccm

Examples

## Not run: 
# ## 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)
# 
# view.dccm(cm, modes$xyz)
# ## End(Not run)