BioPhysConnectoR (version 1.6-10)

sims: Apply a List of Different Amino Acid Sequences

Description

For a set of sequences given in an alignment and a corresponding PDB file the covariance matrix for each sequence is computed based on the given molecular structure. The latter can be directed into a file. The Frobenius norms are computed upon request.

Usage

sims(pdb, alignment, mj1 = NULL, mj2 = NULL, mj.avg = FALSE, alpha = 82, cuts = 169, path = getwd(), mimethod = "ORMI", gapchar = "NOGAPCHAR", inv2file = FALSE, bfacs = TRUE, frob = TRUE, loc = NULL, norm = FALSE, cluster = NULL)

Arguments

pdb

PDB file

alignment

alignment file in fasta format

mj1

matrix for the intrachain interaction strengths

mj2

matrix for the interchain interaction strengths

mj.avg

logical, if TRUE only the average value of each matrix is used as value for the interaction of any two amino acids

alpha

strength of the peptide bond

cuts

squared distance cutoff

path

path to the output files

mimethod

method for the computation of the mutual information

gapchar

character vector denoting gaps in the alignment

inv2file

logical, if TRUE, the inverse Hessian matrix is written to a file

bfacs

logical, if TRUE, the B factors are written to a file

frob

logical, if TRUE, the Frobenius norm is computed

loc

dimensions i1, j1, i2, j2 for a matrix subset of which the Frobenius norm should be computed

norm

logical, if TRUE the Frobenius norm is computed for the normalized matrices

cluster

snow cluster object created with makeCluster()

Value

Return value is a list with the following components:

$entropy

sequence entropy of the alignment

$mi

mutual information of the alignment

If the Frobenius norm is computed, the value(s) will be returned as well:

$res

vector consisting of: row number, sequence name, Frobenius norm

Details

For an alignment the sequence entropy and mutual information is computed. Furthermore for each sequence in the alignment and the structure information from the PDB file the covariance matrix and B factors can be computed and the output can be written to files. The computation is parallelized using parLapply() from the package snow. If cluster is left at its default value the computation is carried out in serial.

References

Hamacher and McCammon (2005) Journal of Chemical Theory and Computation 2, 873. Grant, Rodrigues, ElSawy, McCammon, Caves, (2006) Bioinformatics 22, 2695--2696. Tierney, Rossini, Li (2009) Int J Parallel Proc 37, 78--90.

See Also

get.entropy, get.mie, sim, simc

Examples

## Not run: 
# sims(system.file("1KZK.pdb",package="BioPhysConnectoR"),system.file("align.fasta",
#      package="BioPhysConnectoR"))
# 
# ## Cluster example
# clu<-makeCluster(2)
# sims(system.file("1KZK.pdb",package="BioPhysConnectoR"),system.file("align.fasta",
#      package="BioPhysConnectoR",cluster = clu))
# stopCluster(clu)
# 
# ## End(Not run)