title: Peptide identification using peptide amino acid attribute vectors.
abstract: We describe the theoretical basis for a peptide identification method wherein peptides are represented as vectors based on their amino acid composition and grouped into clusters. Unknown peptides are identified by finding the database cluster and peptide entries with the shortest Euclidian distance. We demonstrate that the amino acid composition of peptides is virtually as informative as the sequence and allows rapid peptide identification more accurately than peptide mass alone.
authors.affiliation: Bioinformatics Research Center, Medical College of Wisconsin, Milwaukee, Wisconsin 53213, USA. halligan@mcw.edu
authors.forename: Brian D
authors.initials: BD
authors.lastname: Halligan
authors.forename: Edward A
authors.initials: EA
authors.lastname: Dratz
authors.forename: Xin
authors.initials: X
authors.lastname: Feng
authors.forename: Simon N
authors.initials: SN
authors.lastname: Twigger
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Tonellato
authors.forename: Andrew S
authors.initials: AS
authors.lastname: Greene
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods; D006801:Humans; D010455:Peptides / Q000032:analysis* / Q000737:chemistry; D011506:Proteins / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D051381:Rats; D020539:Sequence Analysis, Protein
chemical_list: D010455:Peptides; D011506:Proteins