TY - GEN
T1 - Evolutionary sequence modeling for discovery of peptide hormones
AU - Sönmez, Kemal
AU - Toll, Lawrence
AU - Zaveri, Nina
PY - 2007
Y1 - 2007
N2 - We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure and show how such models can be used to discover new functional molecules through cross-genomic sequence comparisons. The framework incorporates a priori high-level knowledge of structural and evolutionary constraints in terms of a hierarchical grammar of evolutionary probabilistic models. In particular, we demonstrate a novel computational method for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. We present experimental results with an initial 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 and a putative novel hormone from an unknown set. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including identification in the brain and regional localizations. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways, and help us identify new targets for drug development.
AB - We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure and show how such models can be used to discover new functional molecules through cross-genomic sequence comparisons. The framework incorporates a priori high-level knowledge of structural and evolutionary constraints in terms of a hierarchical grammar of evolutionary probabilistic models. In particular, we demonstrate a novel computational method for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. We present experimental results with an initial 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 and a putative novel hormone from an unknown set. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including identification in the brain and regional localizations. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways, and help us identify new targets for drug development.
KW - Evolutionary HMM
KW - Hierarchical grammar
KW - Peptide hormone
UR - http://www.scopus.com/inward/record.url?scp=34547518271&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34547518271&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2007.366695
DO - 10.1109/ICASSP.2007.366695
M3 - Conference contribution
AN - SCOPUS:34547518271
SN - 1424407281
SN - 9781424407286
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - I377-I380
BT - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
T2 - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Y2 - 15 April 2007 through 20 April 2007
ER -