Evolutionary sequence modeling for discovery of peptide hormones.

PLoS Computational Biology
Kemal SonmezLawrence Toll

Abstract

There are currently a large number of "orphan" G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide hormones) are unknown. Identification of these peptide hormones is a difficult and important problem. We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species and show how such models can be used to discover new functional molecules, in particular peptide hormones, via cross-genomic sequence comparisons. The computational framework incorporates a priori high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic models. This computational method was used for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. Experimental results with an initial implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human proteins in the Swiss-Prot database of known annotated proteins. In this proof of concept, we identified 45 out of 54 prohormones with only 44 false positives. The comparison of known and hypot...Continue Reading

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Citations

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Methods Mentioned

BETA
amidation
PCR
electrophoresis

Software Mentioned

MP
phylo
Photoshop
Adobe Photoshop
Celera
GUI SequenceMatcher
ENSEMBL
BLAST
ClustalW
HMM

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