Bios2cor (version 1.2)

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

Description

Calculates a mutual information score (MI) based on the probability of joint occurrence of events and corrects it with the average product which is subtracted from the MI score.

Usage

rotamer_mip(
    dynamic_struct,
    rotamers,
    res_selection=
      c("C","I","L","M","V","R","H","K","D","E","N","Q","F","Y","W","T","S","P"),
    z_score= TRUE,
    auto_pairing= FALSE
  )

Arguments

dynamic_struct

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

rotamers

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

res_selection

Selection of amino acid types that will be taken into account in the covariation matrix. Allows to limit the analysis to a limited selection of amino acid types.

z_score

A logical value for Z-score normalisation of the covariation matrix. Default is TRUE.

auto_pairing

A logical value that maintains (TRUE) or removes (FALSE) covariation scores between dihedral angles within a same residue in the covariation matrix. DEFAULT is FALSE.

Value

A list of two elements : a matrix containing a correlation score for each pair of rotamer and its normalized version

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.

N.B. this formula has been widely applied in the field of sequence covariation but favors pairs with high entropy.

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

# NOT RUN {
  #Calculating rotamers
  pdb <- system.file("rotamer/tiny_toy_coordinates.pdb", package= "Bios2cor")
  trj <- system.file("rotamer/tiny_toy_dynamics.dcd", package= "Bios2cor")

  conversion_file <- system.file("rotamer/dynameomics_rotameres.csv", package= "Bios2cor")

  wanted_residues <- c("H","N")
  
  wanted_frames <- seq(from= 5, to= 40, by= 15)
  dynamic_struct <- dynamic_struct(pdb, trj, wanted_frames)
  
  my_angles <- angle_conversion(dynamic_struct, conversion_file)
  
  #Creating MIP object
  mip_corr <- rotamer_mip(dynamic_struct, my_angles, wanted_residues)
# }