Abstract
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.
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Acknowledgments
This work was supported by grants of the Fund for Scientific Research (FWO)-Flanders (1.5.137.06) and the Institute for the Promotion of Innovation through Science and Technology (IWT)-Flanders (SBO 335605). The authors also acknowledge Prometa, the Interfacultary Centre for Proteomics and Metabolomics at K.U. Leuven. E. Clynen and S.J. Husson are postdoctoral fellows of the FWO-Flanders and B. Landuyt is a postdoctoral fellow of the IWT-Flanders.
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Clynen, E. et al. (2010). Bioinformatic Approaches to the Identification of Novel Neuropeptide Precursors. In: Soloviev, M. (eds) Peptidomics. Methods in Molecular Biology, vol 615. Humana Press. https://doi.org/10.1007/978-1-60761-535-4_25
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DOI: https://doi.org/10.1007/978-1-60761-535-4_25
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