K.Sönmez; Discovery of Neuropeptides of Orphan GPCRs... , 30.05.2007
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  • K.Sönmez; Discovery of Neuropeptides of Orphan GPCRs... , 30.05.2007

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Faculty of Engineering and Natural Sciences

Discovery of Neuropeptides of Orphan GPCRs by Evolutionary Computational Techniques

Kemal Sönmez
SRI International, Menlo Park, CA, USA


G Protein Coupled Receptors (GPCRs) are a cell's main conduits for communicating with its environment through its membrane and constitute the starting points of many important signaling pathways in the cell. In particular, a large class of all known drugs act on GPCRs. The identification of peptide hormones that act as endogenous ligands for GPCRs has been a slow and costly process that requires the purification of an unstable peptide from homogenate tissues. Consequently, there are still a fair number of "orphan" G Protein Coupled Receptors (GPCR), i.e., their endogenous ligands (peptide hormones) are unknown. Computational identification of these short peptide hormone sequences is a hard problem. I will describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure and show how such integrated models can be used to discover new functional molecules through cross-genomic sequence comparisons. I will present experimental results with an implementation of the algorithm used to identify potential prohormones by comparing the human and mouse proteins, resulting in high accuracy identification in a known set of proteins from SwissProt and several putative novel hormones from the public genome protein set. Finally, in order to validate the computational methodology, I will describe the basic molecular biological characterization of one novel putative peptide hormone, including identification in the brain and regional localizations. Success of this approach may have a great impact on our understanding of GPCRs and associated pathways, and help us identify new targets for drug development.

Ph.D. in Electrical Engineering University of Maryland, College Park, Maryland, 1998
Dissertation: Robust Speech Recognition by Topology Preserving Adaptation
Advisor: John S. Baras
M.S. in Electrical and Computer Engineering
North Carolina State University, Raleigh, North Carolina, 1991
B.S. in Electrical Engineering
Middle East Technical University, 1989

May 30, 2007, 13:40, FENS G035