nma.pdbs {bio3d}

Ensemble Normal Mode Analysis

Description

Perform normal mode analysis (NMA) on an ensemble of aligned protein structures.

Usage

## S3 method for class 'pdbs'
nma(pdbs, fit = TRUE, full = FALSE, subspace = NULL,
         rm.gaps = TRUE, varweight=FALSE, 
         outpath = NULL, ncore = 1, progress = NULL, ...)

## S3 method for class 'enma'
print(x, ...)

Arguments

pdbs

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

fit

logical, if TRUE coordinate superposition is performed prior to normal mode calculations.

full

logical, if TRUE return the complete, full structure, ‘nma’ objects.

subspace

number of eigenvectors to store for further analysis.

rm.gaps

logical, if TRUE obtain the hessian matrices for only atoms in the aligned positions (non-gap positions in all aligned structures). Thus, gap positions are removed from output.

varweight

logical, if TRUE perform weighing of the pair force constants. Alternatively, provide a NxN matrix containing the weights. See function var.xyz.

outpath

character string specifing the output directory to which the PDB structures should be written.

ncore

number of CPU cores used to do the calculation. ncore>1 requires package ‘parallel’ installed.

x

an enma object obtained from nma.pdbs.

progress

progress bar for use with shiny web app.

...

additional arguments to nma, aa2mass, and print.

Details

This function performs normal mode analysis (NMA) on a set of aligned protein structures obtained with function read.fasta.pdb or pdbaln. The main purpose is to provide aligned atomic fluctuations and mode vectors in an automated fashion.

The normal modes are calculated on the full structures as provided by object ‘pdbs’. With the input argument ‘full=TRUE’ the full ‘nma’ objects are returned together with output ‘U.subs’ providing the aligned mode vectors. When ‘rm.gaps=TRUE’ the unaligned atoms are ommited from output. With default arguments ‘rmsip’ provides RMSIP values for all pairwise structures.

See examples for more details.

Value

Returns an ‘enma’ object with the following components:

fluctuations

a numeric matrix containing aligned atomic fluctuations with one row per input structure.

rmsip

a numeric matrix of pair wise RMSIP values (only the ten lowest frequency modes are included in the calculation).

U.subspace

a three-dimensional array with aligned eigenvectors (corresponding to the subspace defined by the first N non-trivial eigenvectors (‘U’) of the ‘nma’ object).

L

numeric matrix containing the raw eigenvalues with one row per input structure.

xyz

an object of class ‘xyz’ containing the Cartesian coordinates in which the calculation was performed. Coordinates are superimposed to the first structure of the pdbs object when ‘fit=TRUE’.

full.nma

a list with a nma object for each input structure.

Author(s)

Lars Skjaerven

References

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

See Also

For normal mode analysis on single structure PDB: nma.pdb

For the analysis of the resulting ‘eNMA’ object: mktrj.enma, dccm.enma, plot.enma, cov.enma.

Similarity measures: sip, covsoverlap, bhattacharyya, rmsip.

Related functionality: pdbaln, read.fasta.pdb.

Examples

# Needs MUSCLE installed - testing excluded

if(check.utility("muscle")) {

## Fetch PDB files and split to chain A only PDB files
ids <- c("1a70_A", "1czp_A", "1frd_A", "1fxi_A", "1iue_A", "1pfd_A")
files <- get.pdb(ids, split = TRUE, path = tempdir())

## Sequence Alignement
pdbs <- pdbaln(files, outfile = tempfile())

## Normal mode analysis on aligned data
modes <- nma(pdbs, rm.gaps=FALSE)

## Plot fluctuation data
plot(modes, pdbs=pdbs)

## Cluster on Fluctuation similariy
sip <- sip(modes)
hc <- hclust(dist(sip))
col <- cutree(hc, k=3)

## Plot fluctuation data
plot(modes, pdbs=pdbs, col=col)

## Remove gaps from output
modes <- nma(pdbs, rm.gaps=TRUE)

## RMSIP is pre-calculated
heatmap(1-modes$rmsip)

## Bhattacharyya coefficient
bc <- bhattacharyya(modes)
heatmap(1-bc)

}