aanma.pdbs {bio3d}

Ensemble Normal Mode Analysis with All-Atom ENM

Description

Perform normal mode analysis (NMA) on an ensemble of aligned protein structures using all-atom elastic network model (aaENM).

Usage

## S3 method for class 'pdbs'
aanma(pdbs, fit = TRUE, full = FALSE, subspace = NULL,
  rm.gaps = TRUE, ligand = FALSE, outpath = NULL, gc.first = TRUE,
  ncore = NULL, ...)

Arguments

pdbs

an ‘pdbs’ object as obtained from read.all.

fit

logical, if TRUE C-alpha coordinate based 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.

ligand

logical, if TRUE ligand molecules are also included in the calculation.

outpath

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

gc.first

logical, if TRUE will call gc() first before mode calculation for each structure. This is to avoid memory overload when ncore > 1.

ncore

number of CPU cores used to do the calculation.

...

additional arguments to aanma.

Details

This function builds elastic network model (ENM) using all heavy atoms and performs subsequent normal mode analysis (NMA) on a set of aligned protein structures obtained with function read.all. The main purpose is to automate ensemble normal mode analysis using all-atom ENMs.

By default, the effective Hessian for all C-alpha atoms is calculated based on the Hessian built from all heavy atoms (including ligand atoms if ligand=TRUE). Returned values include aligned mode vectors and (when full=TRUE) a list containing the full ‘nma’ objects one per each structure. When ‘rm.gaps=TRUE’ the unaligned atoms are ommited from output. With default arguments ‘rmsip’ provides RMSIP values for all pairwise structures.

When outmodes is provided and is not ‘calpha’ (e.g. ‘noh’. See aanma for more details), the function simply returns a list of ‘nma’ objects, one per each structure, and no aligned mode vector is returned. In this case, the arguments full, subspace, and rm.gaps are ignored. This is equivalent to a wrapper function repeatedly calling aanma.

Value

Returns a list of ‘nma’ objects (outmodes is provided and is not ‘calpha’) or 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.

full.nma

a list with a nma object for each input structure (available only when full=TRUE).

Author(s)

Xin-Qiu Yao & Lars Skjaerven

See Also

For normal mode analysis on single structure PDB: aanma

For conventional C-alpha based normal mode analysis: nma, nma.pdbs.

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

Similarity measures: sip, covsoverlap, bhattacharyya, rmsip.

Related functionality: read.all.

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
    aln <- pdbaln(files, outfile = tempfile())
    
    ## Read all pdb coordinates
    pdbs <- read.all(aln)

    ## Normal mode analysis on aligned data
    modes <- aanma(pdbs, rm.gaps=TRUE)
    
    ## 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)
    
    ## RMSIP is pre-calculated
    heatmap(1-modes$rmsip)
    
    ## Bhattacharyya coefficient
    bc <- bhattacharyya(modes)
    heatmap(1-bc)

  }