GeoStaS Domain Finder

Identifies geometrically stable domains in biomolecules

geostas(...)

# S3 method for default
geostas(...)

# S3 method for xyz
geostas(xyz, amsm = NULL, k = 3, pairwise = TRUE,
      clustalg = "kmeans", fit = TRUE, ncore = NULL, verbose=TRUE, ...)

# S3 method for nma
geostas(nma, m.inds = 7:11, verbose=TRUE, ...)

# S3 method for enma
geostas(enma, pdbs = NULL, m.inds = 1:5, verbose=TRUE, ...)

# S3 method for pdb
geostas(pdb, inds = NULL, verbose=TRUE, ...)

# S3 method for pdbs
geostas(pdbs, verbose=TRUE, ...)

amsm.xyz(xyz, ncore = NULL)

# S3 method for geostas
print(x, ...)

Arguments

...	arguments passed to and from functions, such as `kmeans`, and `hclust` which are called internally in `geostas.xyz`.
xyz	numeric matrix of xyz coordinates as obtained e.g. by `read.ncdf`, `read.dcd`, or `mktrj`.
amsm	a numeric matrix as obtained by `amsm.xyz` (convenient e.g. for re-doing only the clustering analysis of the ‘AMSM’ matrix).
k	an integer scalar or vector with the desired number of groups.
pairwise	logical, if TRUE use pairwise clustering of the atomic movement similarity matrix (AMSM), else columnwise.
clustalg	a character string specifing the clustering algorithm. Allowed values are ‘kmeans’ and ‘hclust’.
fit	logical, if TRUE coordinate superposition on identified core atoms is performed prior to the calculation of the AMS matrix.
ncore	number of CPU cores used to do the calculation. `ncore>1` requires package ‘parallel’ installed.
verbose	logical, if TRUE details of the geostas calculations are printed to screen.
nma	an ‘nma’ object as obtained from function `nma`. Function `mktrj` is used internally to generate a trajectory based on the normal modes.
m.inds	the mode number(s) along which trajectory should be made (see function `mktrj`).
enma	an ‘enma’ object as obtained from function `nma.pdbs`. Function `mktrj` is used internally to generate a trajectory based on the normal modes.
pdbs	a ‘pdbs’ object as obtained from function `pdbaln` or `read.fasta.pdb`.
pdb	a ‘pdb’ object as obtained from function `read.pdb`.
inds	a ‘select’ object as obtained from function `atom.select` giving the atomic indices at which the calculation should be based. By default the function will attempt to locate C-alpha atoms using function `atom.select`.
x	a ‘geostas’ object as obtained from function `geostas`.

Details

This function attempts to identify rigid domains in a protein (or nucleic acid) structure based on an structural ensemble, e.g. obtained from NMR experiments, molecular dynamics simulations, or normal mode analysis.

The algorithm is based on a geometric approach for comparing pairwise traces of atomic motion and the search for their best superposition using a quaternion representation of rotation. The result is stored in a NxN atomic movement similarity matrix (AMSM) describing the correspondence between all pairs of atom motion. Rigid domains are obtained by clustering the elements of the AMS matrix (pairwise=TRUE), or alternatively, the columns similarity (pairwise=FALSE), using either K-means (kmeans) or hierarchical (hclust) clustering.

Compared to the conventional cross-correlation matrix (see function dccm) the “geostas” approach provide functionality to also detect domains involved in rotational motions (i.e. two atoms located on opposite sides of a rotating domain will appear as anti-correlated in the cross-correlation matrix, but should obtain a high similarity coefficient in the AMS matrix).

See examples for more details.

Note

The current implementation in Bio3D uses a different fitting and clustering approach than the original Java implementation. The results will therefore differ.

Value

Returns a list object of type ‘geostas’ with the following components:

amsm

a numeric matrix of atomic movement similarity (AMSM).

fit.inds

a numeric vector of xyz indices used for fitting.

grps

a numeric vector containing the domain assignment per residue.

atomgrps

a numeric vector containing the domain assignment per atom (only provided for geostas.pdb).

inds

a list of atom ‘select’ objects with indices to corresponding to the identified domains.

References

Romanowska, J. et al. (2012) JCTC 8, 2588--2599. Skjaerven, L. et al. (2014) BMC Bioinformatics 15, 399. Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696.

Author

Julia Romanowska and Lars Skjaerven

Examples

# \donttest{
# PDB server connection required - testing excluded

#### NMR-ensemble example
## Read a multi-model PDB file 
pdb <- read.pdb("1d1d", multi=TRUE)
#>   Note: Accessing on-line PDB file

## Find domains and write PDB
gs  <- geostas(pdb, fit=TRUE)
#>   .. 220 'calpha' atoms selected
#>   .. 'xyz' coordinate data with 20 frames 
#>   .. 'fit=TRUE': running function 'core.find'
#>   .. coordinates are superimposed to core region
#>   .. calculating atomic movement similarity matrix ('amsm.xyz()') 
#>   .. dimensions of AMSM are 220x220
#>   .. clustering AMSM using 'kmeans' 
#>   .. converting indices to match input 'pdb' object 
#>      (additional attribute 'atomgrps' generated) 

## Plot a atomic movement similarity matrix
plot.geostas(gs, contour=FALSE)

## Fit all frames to the 'first' domain
domain.inds <- gs$inds[[1]]

xyz <- pdbfit(pdb, inds=domain.inds)

#write.pdb(pdb, xyz=xyz, chain=gs$atomgrps)

# }

if (FALSE) {
#### NMA example
## Fetch stucture
pdb <- read.pdb("1crn")

## Calculate (vibrational) normal modes
modes <- nma(pdb)

## Find domains
gs <- geostas(modes, k=2)

## Write NMA trajectory with domain assignment
mktrj(modes, mode=7, chain=gs$grps)

## Redo geostas domain clustering 
gs <- geostas(modes, amsm=gs$amsm, k=5)




#### Trajectory example
## Read inn DCD trajectory file, fit coordinates
dcdfile <- system.file("examples/hivp.dcd", package = "bio3d")
trj <- read.dcd(dcdfile)
xyz <- fit.xyz(trj[1,], trj)

## Find domains
gs <- geostas(xyz, k=3, fit=FALSE)

## Principal component analysis 
pc.md <- pca.xyz(xyz)

## Visualize PCs with colored domains (chain ID)
mktrj(pc.md, pc=1, chain=gs$grps)




#### X-ray ensemble GroEL subunits
# Define the ensemble PDB-ids
ids <- c("1sx4_[A,B,H,I]", "1xck_[A-B]", "1sx3_[A-B]", "4ab3_[A-B]")

# Download and split PDBs by chain ID
raw.files <- get.pdb(ids, path = "raw_pdbs", gzip = TRUE)
files <- pdbsplit(raw.files, ids, path = "raw_pdbs/split_chain/")

# Align structures
pdbs <- pdbaln(files)

# Find domains
gs <- geostas(pdbs, k=4, fit=TRUE)

# Superimpose to core region
pdbs$xyz <- pdbfit(pdbs, inds=gs$fit.inds)

# Principal component analysis 
pc.xray <- pca(pdbs)

# Visualize PCs with colored domains (chain ID)
mktrj(pc.xray, pc=1, chain=gs$grps)


##- Same, but more manual approach 
gaps.pos <- gap.inspect(pdbs$xyz)

# Find core region
core <- core.find(pdbs)

# Fit to core region
xyz <- fit.xyz(pdbs$xyz[1, gaps.pos$f.inds],
               pdbs$xyz[, gaps.pos$f.inds],
               fixed.inds=core$xyz,
               mobile.inds=core$xyz)

# Find domains
gs <- geostas(xyz, k=4, fit=FALSE)

# Perform PCA
pc.xray <- pca.xyz(xyz)

# Make trajectory
mktrj(pc.xray, pc=1, chain=gs$grps)

}