R/522-extractProtPSSMFeature.R

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

#' Profile-based protein representation derived by PSSM 
#' (Position-Specific Scoring Matrix)
#'
#' Profile-based protein representation derived by PSSM 
#' (Position-Specific Scoring Matrix)
#'
#' This function calculates the profile-based protein representation 
#' derived by PSSM. The feature vector is based on the PSSM computed by 
#' \code{\link{extrProtPSSM}}. For a given sequence, 
#' The PSSM feature represents the log-likelihood of the substitution of the 
#' 20 types of amino acids at that position in the sequence.
#' Each PSSM feature value in the vector represents the degree of conservation 
#' of a given amino acid type. The value is normalized to 
#' interval (0, 1) by the transformation 1/(1+e^(-x)).
#'
#' @param pssmmat The PSSM computed by \code{\link{extrProtPSSM}}.
#'
#' @return A numeric vector which has \code{20 x N} named elements, 
#' where \code{N} is the size of the window (number of rows of the PSSM).
#'
#' @seealso \link{extrProtPSSM} \link{extrProtPSSMAcc}
#'
#' @keywords extract PSSM Blast Alignment
#'
#' @aliases extrProtPSSMFeature
#'
#' @author Min-feng Zhu <\email{wind2zhu@@163.com}>, 
#'         Nan Xiao <\url{http://r2s.name}>
#'
#' @export extrProtPSSMFeature
#'
#' @references
#' Ye, Xugang, Guoli Wang, and Stephen F. Altschul. 
#' "An assessment of substitution scores for protein profile-profile comparison." 
#' \emph{Bioinformatics} 27.24 (2011): 3356--3363.
#'
#' Rangwala, Huzefa, and George Karypis. 
#' "Profile-based direct kernels for remote homology detection and fold recognition." 
#' \emph{Bioinformatics} 21.23 (2005): 4239--4247.
#'
#' @examples
#' \donttest{
#' x = readFASTA(system.file('protseq/P00750.fasta', package = 'BioMedR'))[[1]]
#' dbpath = tempfile('tempdb', fileext = '.fasta')
#' invisible(file.copy(from = system.file('protseq/Plasminogen.fasta', 
#' package = 'BioMedR'), to = dbpath))
#' pssmmat = extractProtPSSM(seq = x, database.path = dbpath)
#' pssmfeature = extrProtPSSMFeature(pssmmat)
#' head(pssmfeature)
#' }
#'

extrProtPSSMFeature = function(pssmmat) {

    # Normalize PSSM scores to (0, 1)
    result = as.vector(1/(1 + exp(pssmmat)))

    AADict = c('A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 
               'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V')
    names(result) = paste0('PSSM_', paste0(rep(1L:ncol(pssmmat), each = 20L), 
                                           '_', AADict))

    return(result)

}