#' Quasi-Sequence-Order (QSO) Descriptor
#'
#' This function calculates the Quasi-Sequence-Order (QSO) descriptor
#' (dim: \code{20 + 20 + (2 * nlag)}, default is 100).
#'
#' @param x A character vector, as the input protein sequence.
#'
#' @param nlag The maximum lag, defualt is 30.
#'
#' @param w The weighting factor, default is 0.1.
#'
#' @return A length \code{20 + 20 + (2 * nlag)} named vector
#'
#' @author Nan Xiao <\url{https://nanx.me}>
#'
#' @seealso See \code{\link{extractSOCN}} for sequence-order-coupling numbers.
#'
#' @export extractQSO
#'
#' @references
#' Kuo-Chen Chou. Prediction of Protein Subcellar Locations by
#' Incorporating Quasi-Sequence-Order Effect.
#' \emph{Biochemical and Biophysical Research Communications},
#' 2000, 278, 477-483.
#'
#' Kuo-Chen Chou and Yu-Dong Cai. Prediction of Protein Sucellular Locations by
#' GO-FunD-PseAA Predictor.
#' \emph{Biochemical and Biophysical Research Communications},
#' 2004, 320, 1236-1239.
#'
#' Gisbert Schneider and Paul Wrede. The Rational Design of
#' Amino Acid Sequences by Artifical Neural Networks and Simulated
#' Molecular Evolution: Do Novo Design of an Idealized Leader Cleavge Site.
#' \emph{Biophys Journal}, 1994, 66, 335-344.
#'
#' @examples
#' x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
#' extractQSO(x)
extractQSO <- function(x, nlag = 30, w = 0.1) {
if (protcheck(x) == FALSE) {
stop("x has unrecognized amino acid type")
}
N <- nchar(x)
if (N <= nlag) {
stop('Length of the protein sequence must be greater than "nlag"')
}
DistMat1 <- read.csv(system.file(
"sysdata/Schneider-Wrede.csv",
package = "protr"
), header = TRUE)
DistMat2 <- read.csv(system.file(
"sysdata/Grantham.csv",
package = "protr"
), header = TRUE)
row.names(DistMat1) <- as.character(DistMat1[, 1])
DistMat1 <- DistMat1[, -1]
row.names(DistMat2) <- as.character(DistMat2[, 1])
DistMat2 <- DistMat2[, -1]
xSplitted <- strsplit(x, split = "")[[1]]
# Compute Schneider.tau_d
tau1 <- vector("list", nlag)
for (d in 1:nlag) {
for (i in 1:(N - d)) {
tau1[[d]][i] <- (DistMat1[xSplitted[i], xSplitted[i + d]])^2
}
}
tau1 <- sapply(tau1, sum)
# Compute Grantham.tau_d
tau2 <- vector("list", nlag)
for (d in 1:nlag) {
for (i in 1:(N - d)) {
tau2[[d]][i] <- (DistMat2[xSplitted[i], xSplitted[i + d]])^2
}
}
tau2 <- sapply(tau2, sum)
# Compute fr
AADict <- c(
"A", "R", "N", "D", "C", "E", "Q", "G", "H", "I",
"L", "K", "M", "F", "P", "S", "T", "W", "Y", "V"
)
fr <- summary(factor(xSplitted, levels = AADict), maxsum = 21)
# Compute Schneider.Xr Grantham.Xr Schneider.Xd Grantham.Xd
Xr1 <- fr / (1 + (w * sum(tau1)))
names(Xr1) <- paste("Schneider.Xr.", names(Xr1), sep = "")
Xr2 <- fr / (1 + (w * sum(tau2)))
names(Xr2) <- paste("Grantham.Xr.", names(Xr2), sep = "")
Xd1 <- (w * tau1) / (1 + (w * sum(tau1)))
names(Xd1) <- paste("Schneider.Xd.", 1:nlag, sep = "")
Xd2 <- (w * tau2) / (1 + (w * sum(tau2)))
names(Xd2) <- paste("Grantham.Xd.", 1:nlag, sep = "")
QSO <- c(Xr1, Xr2, Xd1, Xd2)
QSO
}
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