Suboptimal Path Analysis for Correlation Networks

Usage

cnapath(cna, from, to=NULL, k=10, collapse=TRUE, ncore=NULL, ...)
"summary"(object, ..., pdb = NULL, label = NULL, col = NULL, plot = FALSE, concise = FALSE, cutoff = 0.1, normalize = TRUE, weight = FALSE)
"print"(x, ...)

Arguments

cna

A ‘cna’ object obtained from cna.

from

Integer vector or matrix indicating node id(s) of source. If is matrix and to is NULL, the first column represents source and the second sink.

to

Integer vector indicating node id(s) of sink. All combinations of from and to values will be used as source/sink pairs.

k

Integer, number of suboptimal paths to identify.

collapse

Logical, if TRUE results from all source/sink pairs are merged with a single ‘cnapath’ object returned.

ncore

Number of CPU cores used to do the calculation. By default (NULL), use all detected CPU cores.

object

A ‘cnapath’ class of object obtained from cnapath. Multiple ‘object’ input is allowed for comparing paths from different networks.

pdb

A ‘pdb’ class of object obtained from read.pdb and is used as the reference for node residue ids (in summary.cnapath) or for molecular visulaization with VMD (in vmd.cnapath).

label

Character, label for paths identified from different networks.

col

Colors for plotting statistical results for paths identified from different networks.

plot

Logical, if TRUE path length distribution and node degeneracy will be plotted.

concise

Logical, if TRUE only ‘on path’ residues will be displayed in the node degeneracy plot.

cutoff

Numeric, nodes with node degeneracy larger than cutoff are shown in the output.

normalize

Logical, if TRUE node degeneracy is divided by the total (weighted) number of paths.

weight

Logical, if TRUE each path is weighted by path length in calculating the node degeneracty.

x

A 'cnapath' class object as obtained from function cna.

...

Additional arguments passed to igraph function get.shortest.paths (in the function cnapath), passed to summary.cnapath (in print.cnapath), as additional paths for comparison (in summary.cnapath).

Examples

# Redundant testing excluded

attach(transducin)
inds = match(c("1TND_A", "1TAG_A"), pdbs$id)

npdbs <- trim(pdbs, row.inds=inds)
gaps.res <- gap.inspect(npdbs$ali)

modes <- nma(npdbs)


Details of Scheduled Calculation:
  ... 2 input structures 
  ... storing 936 eigenvectors for each structure 
  ... dimension of x$U.subspace: ( 942x936x2 )
  ... coordinate superposition prior to NM calculation 
  ... aligned eigenvectors (gap containing positions removed)  
  ... estimated memory usage of final 'eNMA' object: 13.5 Mb 


  |======================================================================| 100%

cij <- dccm(modes)
net <- cna(cij, cutoff.cij=0.3)

# get paths
pa1 <- cnapath(net[[1]], from = 314, to=172, k=50)
pa2 <- cnapath(net[[2]], from = 314, to=172, k=50)

# print the information of a path
pa1

Number of paths in network(s):
     50 

Path length distribution: 

[4.77,4.86] (4.86,4.94] (4.94,5.03] (5.03,5.12]  (5.12,5.2] 
          8          11          11           9          11 

Node degeneracy table: 

 5    164  165  166  167  168  169  170  171  172  314 
 0.12 0.88 0.50 0.40 0.56 0.70 0.72 1.00 0.40 1.00 1.00


# print two paths simultaneously
pas <- list(pa1, pa2)
names(pas) <- c("GTP", "GDP")
print.cnapath(pas)

Number of networks:  2 ( GTP, GDP )
Number of paths in network(s):
   GTP: 50
   GDP: 50

Path length distribution: 
   ---  GTP  ---
[4.77,4.86] (4.86,4.94] (4.94,5.03] (5.03,5.12]  (5.12,5.2] 
          8          11          11           9          11 

   ---  GDP  ---
[7.27,7.32] (7.32,7.37] (7.37,7.43] (7.43,7.48] (7.48,7.54] 
          1           5          10          13          21 

Node degeneracy table: 

    5    10   11   14   164  165  166  167  168  169  170  171  172  194  195 
GTP 0.12 0.00 0.00 0.00 0.88 0.50 0.40 0.56 0.70 0.72 1.00 0.40 1.00 0.00 0.00
GDP 0.00 0.42 0.58 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.10 0.30
    196  239  295  301  302  303  304  305  306  307  308  310  311  314 
GTP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00
GDP 0.42 0.52 0.96 0.34 0.68 0.14 0.24 0.12 0.32 0.54 0.22 0.40 0.64 1.00


# Or, for the same effect,
# summary(pa1, pa2, label=c("GTP", "GDP"))

# replace node numbers with residue name and residue number in the PDB file
pdb <- read.pdb("1tnd")

  Note: Accessing on-line PDB file

pdb <- trim.pdb(pdb, atom.select(pdb, chain="A", resno=npdbs$resno[1, gaps.res$f.inds]))
print.cnapath(pas, pdb=pdb)

Number of networks:  2 ( GTP, GDP )
Number of paths in network(s):
   GTP: 50
   GDP: 50

Path length distribution: 
   ---  GTP  ---
[4.77,4.86] (4.86,4.94] (4.94,5.03] (5.03,5.12]  (5.12,5.2] 
          8          11          11           9          11 

   ---  GDP  ---
[7.27,7.32] (7.32,7.37] (7.37,7.43] (7.43,7.48] (7.48,7.54] 
          1           5          10          13          21 

Node degeneracy table: 

    K31  G36  A37  S40  L190 N191 F192 R193 M194 F195 D196 V197 G198 I220 A221
GTP 0.12 0.00 0.00 0.00 0.88 0.50 0.40 0.56 0.70 0.72 1.00 0.40 1.00 0.00 0.00
GDP 0.00 0.42 0.58 0.60 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.10 0.30
    A222 N265 C321 N327 V328 K329 F330 V331 F332 D333 A334 T336 D337 I340
GTP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00
GDP 0.42 0.52 0.96 0.34 0.68 0.14 0.24 0.12 0.32 0.54 0.22 0.40 0.64 1.00


# plot path length distribution and node degeneracy
print.cnapath(pas, pdb = pdb, col=c("red", "darkgreen"), plot=TRUE)

# View paths in 3D molecular graphic with VMD
#vmd.cnapath(pa1, pdb, launch = TRUE)
#vmd.cnapath(pa1, pdb, colors = 7, launch = TRUE)
#vmd.cnapath(pa1, pdb, spline=TRUE, colors=c("pink", "red"), launch = TRUE)
#pdb2 <- read.pdb("1tag")
#pdb2 <- trim.pdb(pdb2, atom.select(pdb2, chain="A", resno=npdbs$resno[2, gaps.res$f.inds]))
#vmd.cnapath(pa2, pdb2, launch = TRUE)

detach(transducin)

See also

Author