Evolutionary Sequence Modeling for Discovery of Peptide Hormones

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 ide...

• Type: Conference
• Year: 2007
• Where: ICASSP
• Authors: M. Kemal Sönmez, Lawrence Toll, Nina Zaveri
• Keywords: Computational Framework, ICASSP 2007, Peptide Hormone, Putative Peptide Hormone, Signal Processing