R/509-extractProtCTDDClass.R

In BioMedR: Generating Various Molecular Representations for Chemicals, Proteins, DNAs/RNAs and Their Interactions

#' CTD Descriptors - Distribution (with Customized Amino Acid Classification Support)
#'
#' CTD Descriptors - Distribution (with Customized Amino Acid Classification Support)
#'
#' This function calculates the Distribution descriptor of the
#' CTD descriptors, with customized amino acid classification support.
#'
#' @param x A character vector, as the input protein sequence.
#' @param aagroup1 A named list which contains the first group of customized
#' amino acid classification. See example below.
#' @param aagroup2 A named list which contains the second group of customized
#' amino acid classification. See example below.
#' @param aagroup3 A named list which contains the third group of customized
#' amino acid classification. See example below.
#'
#' @return A length \code{k * 15} named vector, \code{k} is the number of
#' amino acid properties used.
#'
#' @keywords extract CTD Composition
#'
#' @aliases extrProtCTDDClass
#'
#' @author Min-feng Zhu <\email{wind2zhu@@163.com}>, 
#'         Nan Xiao <\url{http://nanx.me}>
#'
#' @seealso See \code{\link{extrProtCTDCClass}} and \code{\link{extrProtCTDTClass}}
#'          for Composition and Transition of the CTD descriptors with
#'          customized amino acid classification support.
#'
#' @export extrProtCTDDClass
#'
#' @note For this descriptor type, users need to intelligently evaluate
#' the underlying details of the descriptors provided, instead of using
#' this function with their data blindly. It would be wise to use some
#' negative and positive control comparisons where relevant to help guide
#' interpretation of the results.
#'
#' @references
#' Inna Dubchak, Ilya Muchink, Stephen R. Holbrook and Sung-Hou Kim.
#' Prediction of protein folding class using global description of
#' amino acid sequence. \emph{Proceedings of the National Academy of Sciences}.
#' USA, 1995, 92, 8700-8704.
#'
#' Inna Dubchak, Ilya Muchink, Christopher Mayor, Igor Dralyuk and Sung-Hou Kim.
#' Recognition of a Protein Fold in the Context of the SCOP classification.
#' \emph{Proteins: Structure, Function and Genetics}, 1999, 35, 401-407.
#'
#' @examples
#' x = readFASTA(system.file('protseq/P00750.fasta', package = 'BioMedR'))[[1]]
#'
#' # using five customized amino acid property classification
#' group1 = list(hydrophobicity  = c('R', 'K', 'E', 'D', 'Q', 'N'),
#'               normwaalsvolume = c('G', 'A', 'S', 'T', 'P', 'D', 'C'),
#'               polarizability  = c('G', 'A', 'S', 'D', 'T'),
#'               secondarystruct = c('E', 'A', 'L', 'M', 'Q', 'K', 'R', 'H'),
#'               solventaccess   = c('A', 'L', 'F', 'C', 'G', 'I', 'V', 'W'))
#'
#' group2 = list(hydrophobicity  = c('G', 'A', 'S', 'T', 'P', 'H', 'Y'),
#'               normwaalsvolume = c('N', 'V', 'E', 'Q', 'I', 'L'),
#'               polarizability  = c('C', 'P', 'N', 'V', 'E', 'Q', 'I', 'L'),
#'               secondarystruct = c('V', 'I', 'Y', 'C', 'W', 'F', 'T'),
#'               solventaccess   = c('R', 'K', 'Q', 'E', 'N', 'D'))
#'
#' group3 = list(hydrophobicity  = c('C', 'L', 'V', 'I', 'M', 'F', 'W'),
#'               normwaalsvolume = c('M', 'H', 'K', 'F', 'R', 'Y', 'W'),
#'               polarizability  = c('K', 'M', 'H', 'F', 'R', 'Y', 'W'),
#'               secondarystruct = c('G', 'N', 'P', 'S', 'D'),
#'               solventaccess   = c('M', 'S', 'P', 'T', 'H', 'Y'))
#'
#' extrProtCTDDClass(x, aagroup1 = group1, aagroup2 = group2, aagroup3 = group3)

extrProtCTDDClass = function (x, aagroup1, aagroup2, aagroup3) {

  if (checkProt(x) == FALSE) stop('x has unrecognized amino acid type')

  if ((length(aagroup1) != length(aagroup2) |
       length(aagroup1) != length(aagroup3)) |
      (length(aagroup2) != length(aagroup3)))
    stop('The three groups must have the same property numbers')

  xSplitted = strsplit(x, split = '')[[1]]
  n  = nchar(x)

  propnum = length(aagroup1)

  G = vector('list', propnum)
  for (i in 1L:propnum) G[[i]] = rep(NA, n)

  # Get groups for each property & each amino acid

  for (i in 1L:propnum) {
    try(G[[i]][which(xSplitted %in% aagroup1[[i]])] <- 'G1')
    try(G[[i]][which(xSplitted %in% aagroup2[[i]])] <- 'G2')
    try(G[[i]][which(xSplitted %in% aagroup3[[i]])] <- 'G3')
  }

  # Compute Distribution

  D = vector('list', propnum)
  for (i in 1L:propnum) D[[i]] = matrix(ncol = 5L, nrow = 3L)

  for (i in 1:propnum) {
    inds = which(G[[i]] == 'G1')
    D[[i]][1, ] = (inds[c(1, floor(length(inds) * c(0.25, 0.5, 0.75)), length(inds))])*100/n
    inds = which(G[[i]] == 'G2')
    D[[i]][2, ] = (inds[c(1, floor(length(inds) * c(0.25, 0.5, 0.75)), length(inds))])*100/n
    inds = which(G[[i]] == 'G3')
    D[[i]][3, ] = (inds[c(1, floor(length(inds) * c(0.25, 0.5, 0.75)), length(inds))])*100/n
  }

  D = do.call(rbind, D)
  D = as.vector(t(D))

  names(D) = paste(rep(paste('prop', 1L:propnum, sep = ''), each = 15L),
                   rep(rep(c('.G1', '.G2', '.G3'), each = 5L), times = propnum),
                   rep(paste('.residue', c('0', '25', '50', '75', '100'),
                             sep = ''), times = 3L * propnum), sep = '')

  return(D)

}