title: Application of pattern recognition techniques to mass spectrometric data for sequencing C-terminal peptide residue series.
abstract: The application of pattern recognition to sequence elucidation from fast atom bombardment (FAB), collisionally activated dissociation (CAD), and tandem mass spectrometric data of peptides was investigated. Learning machine techniques for pattern recognition were applied to detect C-terminal series amino acid sequences up to the pentapeptide Try-Gly-Gly-Phe-Leu (YGGFL). The approach conditions the data by building upon known fragmentation pathways of peptides in FAB/CAD-related analysis. The intensities of critical sequence ion peaks are used to describe each pattern in the training set. A well-defined training set is then made use of to classify unknown species. The FORTRAN-77 program is adapted from one first developed by P.C. Jurs. It requires only the input of the critical peak intensities and the length of the unit to be tested. The method has potential in applications to larger peptides provided a database for the training set can be constructed.
authors.affiliation: Department of Chemistry, University of Papua New Guinea, National Capital District.
authors.forename: R
authors.initials: R
authors.lastname: Delgoda
authors.forename: J D
authors.initials: JD
authors.lastname: Pulfer
mesh_terms: D000595:Amino Acid Sequence; D003627:Data Interpretation, Statistical; D004743:Enkephalin, Leucine / Q000032:analysis*; D008969:Molecular Sequence Data; D009842:Oligopeptides / Q000032:analysis; D010363:Pattern Recognition, Automated; D017421:Sequence Analysis / Q000379:methods*; D012984:Software; D016339:Spectrometry, Mass, Fast Atom Bombardment / Q000379:methods
chemical_list: D009842:Oligopeptides; D004743:Enkephalin, Leucine