Title
In silico identification of new secretory peptide genes in Drosophila melanogaster
Author
Faculty/Department
Faculty of Sciences. Biology
Publication type
article
Publication
Birmingham, Ala ,
Subject
Chemistry
Biology
Source (journal)
Molecular and cellular proteomics. - Birmingham, Ala
Volume/pages
5(2006) :3 , p. 510-522
ISSN
1535-9476
ISI
000236142800008
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
Bioactive peptides play critical roles in regulating most biological processes in animals. The elucidation of the amino acid sequence of these regulatory peptides is crucial for our understanding of animal physiology. Most of the (neuro) peptides currently known were identified by purification and subsequent amino acid sequencing. With the entire genome sequence of some animals now available, it has become possible to predict novel putative peptides. In this way, BLAST (Basic Local Alignment Searching Tool) analysis of the Drosophila melanogaster genome has allowed annotation of 36 secretory peptide genes so far. Peptide precursor genes are, however, poorly predicted by this algorithm, thus prompting an alternative approach described here. With the described searching program we scanned the Drosophila genome for predicted proteins with the structural hallmarks of neuropeptide precursors. As a result, 76 additional putative secretory peptide genes were predicted in addition to the 43 annotated ones. These putative (neuro) peptide genes contain conserved motifs reminiscent of known neuropeptides from other animal species. Peptides that display sequence similarities to the mammalian vasopressin, atrial natriuretic peptide, and prolactin precursors and the invertebrate peptides orcokinin, prothoracicotropic hormones, trypsin modulating oostatic factor, and Drosophila immune induced peptides (DIMs) among others were discovered. Our data hence provide further evidence that many neuropeptide genes were already present in the ancestor of Protostomia and Deuterostomia prior to their divergence. This bioinformatic study opens perspectives for the genome-wide analysis of peptide genes in other eukaryotic model organisms.
E-info
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