R/desc-09-CTDDClass.R

In nanxstats/protr: Generating Various Numerical Representation Schemes for Protein Sequences

#' 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.
#'
#' @author Nan Xiao <\url{https://nanx.me}>
#'
#' @seealso See \code{\link{extractCTDCClass}} and
#' \code{\link{extractCTDTClass}} for Composition and Transition of
#' the CTD descriptors with customized amino acid classification support.
#'
#' @export extractCTDDClass
#'
#' @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 = "protr"))[[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")
#' )
#'
#' extractCTDDClass(x, aagroup1 = group1, aagroup2 = group2, aagroup3 = group3)
extractCTDDClass <- function(x, aagroup1, aagroup2, aagroup3) {
  if (protcheck(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 = ""
  )

  D
}