extractProtPSSMAcc: Profile-based protein representation derived by PSSM...

In road2stat/Rcpi: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery

Profile-based protein representation derived by PSSM (Position-Specific Scoring Matrix) and auto cross covariance

Description

Profile-based protein representation derived by PSSM (Position-Specific Scoring Matrix) and auto cross covariance

Usage

extractProtPSSMAcc(pssmmat, lag)

Arguments

pssmmat	The PSSM computed by extractProtPSSM.
lag	The lag parameter. Must be less than the number of amino acids in the sequence (i.e. the number of columns in the PSSM matrix).

Details

This function calculates the feature vector based on the PSSM by running PSI-Blast and auto cross covariance tranformation.

Value

A length lag * 20^2 named numeric vector, the element names are derived by the amino acid name abbreviation (crossed amino acid name abbreviation) and lag index.

References

Wold, S., Jonsson, J., Sj\"orstr\"om, M., Sandberg, M., & R\"annar, S. (1993). DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Analytica chimica acta, 277(2), 239–253.

See Also

extractProtPSSM extractProtPSSMFeature

Examples

x = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]

dbpath = tempfile('tempdb', fileext = '.fasta')
invisible(file.copy(from = system.file('protseq/Plasminogen.fasta', package = 'Rcpi'), to = dbpath))
pssmmat = extractProtPSSM(seq = x, database.path = dbpath)
pssmacc = extractProtPSSMAcc(pssmmat, lag = 3)
tail(pssmacc)

road2stat/Rcpi documentation built on July 9, 2023, 1:43 a.m.