Peptide identification using peptide amino acid attribute vectors.
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.
Halligan
Brian D
BD
Bioinformatics Research Center, Medical College of Wisconsin, Milwaukee, Wisconsin 53213, USA. halligan@mcw.edu
Dratz
Edward A
EA
Feng
Xin
X
Twigger
Simon N
SN
Tonellato
Peter J
PJ
Greene
Andrew S
AS
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
D010455:Peptides; D011506:Proteins