core.find.RdPerform iterated rounds of structural superposition to identify the most invariant region in an aligned set of protein structures.
core.find(...) # S3 method for pdbs core.find(pdbs, shortcut = FALSE, rm.island = FALSE, verbose = TRUE, stop.at = 15, stop.vol = 0.5, write.pdbs = FALSE, outpath="core_pruned", ncore = 1, nseg.scale = 1, progress = NULL, ...) # S3 method for default core.find(xyz, ...) # S3 method for pdb core.find(pdb, verbose=TRUE, ...)
| pdbs | a numeric matrix of aligned C-alpha xyz Cartesian
coordinates. For example an alignment data structure obtained with
|
|---|---|
| shortcut | if TRUE, remove more than one position at a time. |
| rm.island | remove isolated fragments of less than three residues. |
| verbose | logical, if TRUE a “core\_pruned” directory containing ‘core structures’ for each iteraction is written to the current directory. |
| stop.at | minimal core size at which iterations should be stopped. |
| stop.vol | minimal core volume at which iterations should be stopped. |
| write.pdbs | logical, if TRUE core coordinate files, containing
only core positions for each iteration, are written to a location
specified by |
| outpath | character string specifying the output directory when
|
| ncore | number of CPU cores used to do the calculation.
|
| nseg.scale | split input data into specified number of segments
prior to running multiple core calculation. See |
| progress | progress bar for use with shiny web app. |
| xyz | a numeric matrix of xyz Cartesian coordinates,
e.g. obtained from |
| pdb | an object of type |
| ... | arguments passed to and from functions. |
This function attempts to iteratively refine an initial structural superposition determined from a multiple alignment. This involves iterated rounds of superposition, where at each round the position(s) displaying the largest differences is(are) excluded from the dataset. The spatial variation at each aligned position is determined from the eigenvalues of their Cartesian coordinates (i.e. the variance of the distribution along its three principal directions). Inspired by the work of Gerstein et al. (1991, 1995), an ellipsoid of variance is determined from the eigenvalues, and its volume is taken as a measure of structural variation at a given position.
Optional “core PDB files” containing core positions, upon which
superposition is based, can be written to a location specified by
outpath by setting write.pdbs=TRUE. These files are
useful for examining the core filtering process by visualising them in a
graphics program.
Returns a list of class "core" with the following components:
total core volume at each fitting iteration/round.
core length at each round.
residue number of core residues at each round (taken from the first aligned structure) or, alternatively, the numeric index of core residues at each round.
atom indices of core atoms at each round.
atom indices of core positions in the last round.
xyz indices of core positions in the last round.
atom indices of core positions with a total volume under 1 Angstrom\^3.
xyz indices of core positions with a total volume under 1 Angstrom\^3.
residue numbers of core positions with a total volume under 1 Angstrom\^3.
atom indices of core positions with a total volume under 0.5 Angstrom\^3.
xyz indices of core positions with a total volume under 0.5 Angstrom\^3.
residue numbers of core positions with a total volume under 0.5 Angstrom\^3.
Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696.
Gerstein and Altman (1995) J. Mol. Biol. 251, 161--175.
Gerstein and Chothia (1991) J. Mol. Biol. 220, 133--149.
The relevance of the ‘core positions’ identified by this procedure is dependent upon the number of input structures and their diversity.
Barry Grant
if (FALSE) { ##-- Generate a small kinesin alignment and read corresponding structures pdbfiles <- get.pdb(c("1bg2","2ncd","1i6i","1i5s"), URLonly=TRUE) pdbs <- pdbaln(pdbfiles) ##-- Find 'core' positions core <- core.find(pdbs) plot(core) ##-- Fit on these relatively invarient subset of positions #core.inds <- print(core, vol=1) core.inds <- print(core, vol=0.5) xyz <- pdbfit(pdbs, core.inds, outpath="corefit_structures") ##-- Compare to fitting on all equivalent positions xyz2 <- pdbfit(pdbs) ## Note that overall RMSD will be higher but RMSF will ## be lower in core regions, which may equate to a ## 'better fit' for certain applications gaps <- gap.inspect(pdbs$xyz) rmsd(xyz[,gaps$f.inds]) rmsd(xyz2[,gaps$f.inds]) plot(rmsf(xyz[,gaps$f.inds]), typ="l", col="blue", ylim=c(0,9)) points(rmsf(xyz2[,gaps$f.inds]), typ="l", col="red") } if (FALSE) { ##-- Run core.find() on a multimodel PDB file pdb <- read.pdb('1d1d', multi=TRUE) core <- core.find(pdb) ##-- Run core.find() on a trajectory trtfile <- system.file("examples/hivp.dcd", package="bio3d") trj <- read.dcd(trtfile) ## Read the starting PDB file to determine atom correspondence pdbfile <- system.file("examples/hivp.pdb", package="bio3d") pdb <- read.pdb(pdbfile) ## select calpha coords from a manageable number of frames ca.ind <- atom.select(pdb, "calpha")$xyz frames <- seq(1, nrow(trj), by=10) core <- core.find( trj[frames, ca.ind], write.pdbs=TRUE ) ## have a look at the various cores "vmd -m core_pruned/*.pdb" ## Lets use a 6A^3 core cutoff inds <- print(core, vol=6) write.pdb(xyz=pdb$xyz[inds$xyz],resno=pdb$atom[inds$atom,"resno"], file="core.pdb") ##- Fit trj onto starting structure based on core indices xyz <- fit.xyz( fixed = pdb$xyz, mobile = trj, fixed.inds = inds$xyz, mobile.inds = inds$xyz) ##write.pdb(pdb=pdb, xyz=xyz, file="new_trj.pdb") ##write.ncdf(xyz, "new_trj.nc") }