dccm                  package:bio3d                  R Documentation

_D_C_C_M: _D_y_n_a_m_i_c_a_l _C_r_o_s_s-_C_o_r_r_e_l_a_t_i_o_n _M_a_t_r_i_x

_D_e_s_c_r_i_p_t_i_o_n:

     Determine the cross-correlations of atomic displacements.

_U_s_a_g_e:

     dccm(xyz, reference = apply(xyz, 2, mean))

_A_r_g_u_m_e_n_t_s:

     xyz: a numeric matrix of Cartesian coordinates with a row per
          structure/frame. 

reference: The reference structure about which displacements are
          analysed. 

_D_e_t_a_i_l_s:

     The extent to which the atomic fluctuations/displacements of a
     system are correlated with one another can be assessed by
     examining the magnitude of all pairwise cross-correlation
     coefficients (see McCammon and Harvey, 1986).

     This function returns a matrix of all atom-wise cross-correlations
     whose elements, Cij, may be displayed in a graphical
     representation frequently termed a dynamical cross-correlation
     map, or DCCM.

     If Cij = 1 the fluctuations of atoms i and j are completely
     correlated (same period and same phase), if Cij = -1 the
     fluctuations of atoms i and j are completely anticorrelated (same
     period and opposite phase), and if Cij = 0 the fluctuations of i
     and j are not correlated.

     Typical characteristics of DCCMs include a line of strong
     cross-correlation along the diagonal, cross-correlations emanating
     from the diagonal, and off-diagonal cross-correlations. The high
     diagonal values occur where i = j, where Cij is always equal to
     1.00. Positive correlations emanating from the diagonal indicate
     correlations between contiguous residues, typically within a
     secondary structure element or other tightly packed unit of
     structure. Typical secondary structure patterns include a
     triangular pattern for helices and a plume for strands. 
     Off-diagonal positive and negative correlations may indicate
     potentially interesting correlations between domains of
     non-contiguous residues.

_V_a_l_u_e:

     Returns a cross-correlation matrix.

_N_o_t_e:

     This function is currently very basic i.e. inefficient and *SLOW*.

_A_u_t_h_o_r(_s):

     Gisle Saelensminde

_R_e_f_e_r_e_n_c_e_s:

     Grant, B.J. et al. (2006) _Bioinformatics_ *22*, 2695-2696.

     McCammon, A. J. and Harvey, S. C. (1986) _Dynamics of Proteins and
     Nucleic Acids_, Cambridge University Press, Cambridge.

_S_e_e _A_l_s_o:

     'cor' for examining xyz cross-correlations, 'pca.xyz'.

_E_x_a_m_p_l_e_s:

     ## Not run: 
     ##-- Read example trajectory file
     trtfile <- system.file("examples/hivp.dcd", package="bio3d")
     trj <- read.dcd(trtfile)

     ## Read the starting PDB file to determine atom correspondence
     pdbfile <- system.file("examples/hivp.pdb", package="bio3d")
     pdb <- read.pdb(pdbfile)

     ## select residues 24 to 27 and 85 to 90 in both chains
     inds <- atom.select(pdb,"///24:27,85:90///CA/")

     ## lsq fit of trj on pdb
     fit.xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz)

     ## DCCM (slow to run so restrict to Calpha)
     cij <- dccm(fit.xyz)

     ## Plot DCCM
     library(lattice)
     contourplot(cij, region = TRUE, labels=F, col="gray40",
                 at=c(-1, -0.75, -0.5, -0.25, 0.25, 0.5, 0.75, 1),
                 xlab="Residue No.", ylab="Residue No.",
                 main="DCCM: dynamic cross-correlation map")
     ## End(Not run)

