bio3d (version 2.1-3)

core.find: Identification of Invariant Core Positions

Description

Perform iterated rounds of structural superposition to identify the most invariant region in an aligned set of protein structures.

Usage

core.find(aln, 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)

Arguments

aln

a numeric matrix of aligned C-alpha xyz Cartesian coordinates. For example an alignment data structure obtained with read.fasta.pdb or a trajectory subset obtained from

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.

outpath

character string specifying the output directory when write.pdbs is TRUE.

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.

Value

• Returns a list of class "core" with the following components:
• volume
  total core volume at each fitting iteration/round.
• length
  core length at each round.
• resno
  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.
• step.inds
  atom indices of core atoms at each round.
• atom
  atom indices of core positions in the last round.
• xyz
  xyz indices of core positions in the last round.
• c1A.atom
  atom indices of core positions with a total volume under 1 Angstrom^3.
• c1A.xyz
  xyz indices of core positions with a total volume under 1 Angstrom^3.
• c1A.resno
  residue numbers of core positions with a total volume under 1 Angstrom^3.
• c0.5A.atom
  atom indices of core positions with a total volume under 0.5 Angstrom^3.
• c0.5A.xyz
  xyz indices of core positions with a total volume under 0.5 Angstrom^3.
• c0.5A.resno
  residue numbers of core positions with a total volume under 0.5 Angstrom^3.

Details

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.

References

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.

See Also

read.fasta.pdb, plot.core, fit.xyz

Examples

##-- 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")

##-- 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")