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bio3d (version 2.3-3)

pymol: Biomolecular Visualization with PyMOL

Description

Visualize Bio3D structure objects in PyMOL

Usage

pymol(…)

# S3 method for pdbs pymol(pdbs, col=NULL, as="ribbon", file=NULL, type="script", exefile=NULL, …)

# S3 method for nma pymol(…)

# S3 method for pca pymol(…)

# S3 method for modes pymol(modes, mode=NULL, file=NULL, scale=5, dual=FALSE, type="script", exefile=NULL, …)

# S3 method for dccm pymol(dccm, pdb, file=NULL, step=0.2, omit=0.2, radius = 0.15, type="script", exefile=NULL, …)

Arguments

pdbs

aligned C-alpha Cartesian coordinates as obtained with read.fasta.pdb or pdbaln.

col

a single element character vector specifying the coloring of the structures. Options are: ‘index’, ‘index2’, ‘gaps’, ‘rmsf’.

Special cases: Provide a ‘core’ object as obtained by core.find to color on the invariant core. Alternatively, provide a vector containing the color code for each structure in the ‘pdbs’ object.

as

show as ‘ribbon’, ‘cartoon’, ‘lines’, ‘putty’.

file

a single element character vector specifying the file name of the PyMOL session/script file.

type

a single element character vector specifying the output type: ‘script’ generates a .pml script; ‘session’ generates a .pse session file; ‘launch’ launches pymol.

exefile

file path to the ‘PYMOL’ program on your system (i.e. how is ‘PYMOL’ invoked). If NULL, use OS-dependent default path to the program.

modes

an object of class nma or pca as obtained from functions nma or pca.xyz.

mode

the mode number for which the vector field should be made.

scale

global scaling factor.

dual

logical, if TRUE mode vectors are also drawn in both direction.

dccm

an object of class dccm as obtained from function dccm.

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

numeric, radius of visualized correlation cylinders in PyMol. Alternatively, a matrix with the same dimesions as dccm can be provided, e.g. to draw cylinders with radii associated to the pairwise correlation value.

arguments passed to function pymol.modes for ‘nma’ and ‘pca’ objects.

Value

Called for its action

Details

These functions provides a convenient approach for the visualization of Bio3D objects in PyMOL. See examples for more details.

DCCM PyMOL visualization: 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 “R.py”. PyMOL will only be launched (and opened) when using argument ‘type='launch'’. 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.

NMA / PCA PyMOL vector field visualization: This function generates a PyMOL (python) script for drawing mode vectors on a PDB structure. The PyMOL script file is stored in the working directory with filename “R.py”.

References

Skjaerven, L. et al. (2014) BMC Bioinformatics 15, 399. Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696.

See Also

view

Examples

Run this code
# NOT RUN {
##- pymol with a 'pdbs' object
attach(transducin)

# build a pymol session containing all structures in the PDBs object
pymol(pdbs)

# color by invariant core (
# core <- core.find(pdbs)
pymol(pdbs, col=core)

# color by RMSF
pymol(pdbs, col="rmsf")

# color by clustering
rd <- rmsd(pdbs$xyz)
hc <- hclust(as.dist(rd))
grps <- cutree(hc, k=3)
pymol(pdbs, col=grps)



##- pymol with a 'dccm' object
## 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)

pymol(cm, modes$xyz)



##- pymol with a 'nma' or 'pca' object
pymol(modes, mode=7)

detach(transducin)
# }

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