Bioinformatic approaches to the identification of novel neuropeptide precursors.
With the entire genome sequence of several animals now available, it is becoming possible to identify in silico all putative peptides and their precursors in an organism. In this chapter we describe a searching algorithm that can be used to scan the genome for predicted proteins with the structural hallmarks of (neuro)peptide precursors. We also describe how to use search strings such as the presence of a glycine residue as a putative amidation site, dibasic cleavage sites, the presence of a signal peptide, and specific peptide motifs to improve a standard BLAST search and make it suitable for searching (neuro)peptides in EST data. We briefly explain how bioinformatic tools and in silico predicted peptide precursor sequences can aid experimental peptide identification with mass spectrometry.
Clynen
Elke
E
Department of Biology, Functional Genomics and Proteomics, K.U.Leuven, Leuven, Belgium.
Liu
Feng
F
Husson
Steven J
SJ
Landuyt
Bart
B
Hayakawa
Eisuke
E
Baggerman
Geert
G
Wets
Geert
G
Schoofs
Liliane
L
D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D030562:Databases, Protein; D029721:Drosophila Proteins / Q000235:genetics / Q000378:metabolism; D004331:Drosophila melanogaster / Q000235:genetics; D020224:Expressed Sequence Tags; D016678:Genome; D013058:Mass Spectrometry / Q000379:methods; D051379:Mice; D008969:Molecular Sequence Data; D009479:Neuropeptides / Q000235:genetics / Q000378:metabolism*; D011498:Protein Precursors / Q000235:genetics / Q000378:metabolism*
D029721:Drosophila Proteins; D009479:Neuropeptides; D011498:Protein Precursors