Bios2cor (version 2.2.1)

dynamic_mip: Mutual Information Product (MIP) function applied to rotamers in molecular dynamics simulations

Description

Calculates a corrected mutual information score (MIP), by substraction of the average product from the probability of joint occurrence of events.

Usage

dynamic_mip(
    dynamic_structure,
    rotamers,
    res_selection=
      c("C","I","L","M","V","R","H","K","D","E","N","Q","F","Y","W","T","S","P")
  )

Value

returns a list of four elements which are numeric matrices containing (1) the correlation/covariation scores for each pair of rotamers (score), (2) the Z-scores for each pair of rotamers (Zscore), (3) the correlation/covariation scores for each pair of rotamers with zero values for autocorrelation (correlation within the same side chain) (score_noauto) and (4) the Z-scores calculated without autocorrelation pairs and zero values for autocorrelation pairs (Zscore_noauto).

Arguments

dynamic_structure

An object of class 'structure' that is created by the dynamic_structure function

rotamers

A character matrix of type 'rotamers' that is produced by the angle2rotamer function. The matrix indicates the rotameric state of each side chain dihedral angle for each frame of the trajectory.

res_selection

List of amino acids that will be taken into account in the correlation/covariation matrix. By default, all the amino acids are taken into account except Gly and Ala, with no side chain dihedral angles.

Author

Antoine GARNIER and Marie CHABBERT

Details

The MIP score at position [i,j] has been computed with the following formula :

$${MIP(i,j)} = MI(i,j) - \frac{MI(i,\bar{j})MI(\bar{i},j)}{<MI>}$$

with :

• \({MI(i,j) = \sum_{x,y}^{ } p_{x,y}(i,j) ln\frac{p_{x,y}(i,j)}{p_{x}(i)p_{y}(j)}}\)

• \(MI(i,\bar{j}) = \frac{1}{n-1} \sum_{j \neq i}^{ } MI(i,j)\)

• \(MI(\bar{i},j) = \frac{1}{n-1} \sum_{i \neq j}^{ } MI(i,j)\)

• \(<MI> = \frac{2}{n(n-1)} \sum_{i,j}^{ }MI(i,j)\)

and where \(p_{x,y}(i,j)\) is the frequency of the rotamer pair (x,y) at dihedral angles i and j.

References

Dunn SD, Wahl LM, Gloor GB. Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinfor;atics 2008;24:333-340. Martin LC, Gloor GB, Dunn SD, Wahl LM. Using infor;ation theory to search for co-evolving residues in proteins. Bioinformatics 2005;21:4116-4124.

Examples

  #Reading pdb and dcd files
  pdb <- system.file("rotamer/tiny_toy_coordinates.pdb", package= "Bios2cor")
  trj <- system.file("rotamer/tiny_toy_dynamics.dcd", package= "Bios2cor")

  #Creating dynamic_structure object
  wanted_frames <- seq(from = 5, to = 40, by = 15)
  dynamic_structure <- dynamic_structure(pdb, trj, wanted_frames)

  #Creating rotamers object using conversion_file
  conversion_file <- system.file("rotamer/dynameomics_rotamers.csv", package= "Bios2cor")
  rotamers <- angle2rotamer(dynamic_structure, conversion_file)

  #Creating correlation object for selected residues with MIP method
  wanted_residues <- c("H","N")
  mip_corr <- dynamic_mip(dynamic_structure, rotamers, wanted_residues)