Filter for Cross-correlation Matrices (Cij)

Usage

filter.dccm(x, cutoff.cij = 0.4, cmap = NULL, xyz = NULL, fac = NULL,  cutoff.sims = NULL, collapse = TRUE, extra.filter = NULL, ...)

Arguments

x

A matrix (nXn), a numeric array with 3 dimensions (nXnXm), a list with m cells each containing nXn matrix, or a list with ‘all.dccm’ component, containing atomic correlation values, where "n" is the number of residues and "m" the number of calculations. The matrix elements should be in between -1 and 1. See ‘dccm’ function in bio3d package for further details.

cutoff.cij

Threshold for each individual correlation value. See below for details.

cmap

logical or numerical matrix indicating the contact map. If logical and TRUE, contact map will be calculated with input xyz.

xyz

XYZ coordinates for distance matrix calculation.

fac

factor indicating distinct categories of input correlation matrices.

cutoff.sims

Threshold for the number of simulations with observed correlation value above cutoff.cij for the same residue/atomic pairs. See below for details.

collapse

logical, if TRUE the mean matrix will be returned.

extra.filter

Filter to apply in addition to the model chosen.

...

extra arguments passed to function cmap.

Examples

# Example of transducin
attach(transducin)

gaps.pos <- gap.inspect(pdbs$xyz)
modes <- nma.pdbs(pdbs, ncore=NULL)

Warning message:
3QI2_B might have missing residue(s) in structure:
   Fluctuations at neighboring positions may be affected.

Details of Scheduled Calculation:
  ... 53 input structures 
  ... storing 909 eigenvectors for each structure 
  ... dimension of x$U.subspace: ( 915x909x53 )
  ... coordinate superposition prior to NM calculation 
  ... aligned eigenvectors (gap containing positions removed)  
  ... estimated memory usage of final 'eNMA' object: 336.8 Mb 


  |                                                                            
  |                                                                      |   0%

dccms <- dccm.enma(modes, ncore=NULL)

cij <- filter.dccm(dccms, xyz=pdbs)


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


# Example protein kinase
# Select Protein Kinase PDB IDs
ids <- c("4b7t_A", "2exm_A", "1opj_A", "4jaj_A", "1a9u_A",
                 "1tki_A", "1csn_A", "1lp4_A")

# Download and split by chain ID
files <- get.pdb(ids, path = "raw_pdbs", split=TRUE)


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


# Alignment of structures
pdbs <- pdbaln(files) # Sequence identity

Reading PDB files:
raw_pdbs/split_chain/4b7t_A.pdb
raw_pdbs/split_chain/2exm_A.pdb
raw_pdbs/split_chain/1opj_A.pdb
raw_pdbs/split_chain/4jaj_A.pdb
raw_pdbs/split_chain/1a9u_A.pdb
raw_pdbs/split_chain/1tki_A.pdb
raw_pdbs/split_chain/1csn_A.pdb
raw_pdbs/split_chain/1lp4_A.pdb
........

Extracting sequences

pdb/seq: 1   name: raw_pdbs/split_chain/4b7t_A.pdb 
pdb/seq: 2   name: raw_pdbs/split_chain/2exm_A.pdb 
pdb/seq: 3   name: raw_pdbs/split_chain/1opj_A.pdb 
pdb/seq: 4   name: raw_pdbs/split_chain/4jaj_A.pdb 
pdb/seq: 5   name: raw_pdbs/split_chain/1a9u_A.pdb 
pdb/seq: 6   name: raw_pdbs/split_chain/1tki_A.pdb 
pdb/seq: 7   name: raw_pdbs/split_chain/1csn_A.pdb 
pdb/seq: 8   name: raw_pdbs/split_chain/1lp4_A.pdb 

summary(c(seqidentity(pdbs)))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1520  0.2010  0.2530  0.3402  0.3150  1.0000 


# NMA on all structures
modes <- nma.pdbs(pdbs, ncore=NULL)


Details of Scheduled Calculation:
  ... 8 input structures 
  ... storing 711 eigenvectors for each structure 
  ... dimension of x$U.subspace: ( 717x711x8 )
  ... coordinate superposition prior to NM calculation 
  ... aligned eigenvectors (gap containing positions removed)  
  ... estimated memory usage of final 'eNMA' object: 31.2 Mb 


  |                                                                            
  |                                                                      |   0%


# Calculate correlation matrices for each structure
cij <- dccm(modes)

# Set DCCM plot panel names for combined figure
dimnames(cij$all.dccm) = list(NULL, NULL, ids)
plot.dccm(cij$all.dccm)

# Filter to display only correlations present in all structures
cij.all <- filter.dccm(cij, cutoff.sims = 8, cutoff.cij = 0)


plot.dccm(cij.all, main = "Consensus Residue Cross Correlation")



detach(transducin)

See also

Author