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 alo...

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Autores principales:	Kemal Sonmez, Naunihal T Zaveri, Ilan A Kerman, Sharon Burke, Charles R Neal, Xinmin Xie, Stanley J Watson, Lawrence Toll
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/572c5aef0083490bb4779bddd0dea9aa
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spelling	oai:doaj.org-article:572c5aef0083490bb4779bddd0dea9aa2021-11-25T05:41:53ZEvolutionary sequence modeling for discovery of peptide hormones.1553-734X1553-735810.1371/journal.pcbi.1000258https://doaj.org/article/572c5aef0083490bb4779bddd0dea9aa2009-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19132080/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358There 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 hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including its identification and regional localization in the brain. This species comparison, HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development.Kemal SonmezNaunihal T ZaveriIlan A KermanSharon BurkeCharles R NealXinmin XieStanley J WatsonLawrence TollPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 1, p e1000258 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Kemal Sonmez Naunihal T Zaveri Ilan A Kerman Sharon Burke Charles R Neal Xinmin Xie Stanley J Watson Lawrence Toll Evolutionary sequence modeling for discovery of peptide hormones.
description	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 hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including its identification and regional localization in the brain. This species comparison, HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development.
format	article
author	Kemal Sonmez Naunihal T Zaveri Ilan A Kerman Sharon Burke Charles R Neal Xinmin Xie Stanley J Watson Lawrence Toll
author_facet	Kemal Sonmez Naunihal T Zaveri Ilan A Kerman Sharon Burke Charles R Neal Xinmin Xie Stanley J Watson Lawrence Toll
author_sort	Kemal Sonmez
title	Evolutionary sequence modeling for discovery of peptide hormones.
title_short	Evolutionary sequence modeling for discovery of peptide hormones.
title_full	Evolutionary sequence modeling for discovery of peptide hormones.
title_fullStr	Evolutionary sequence modeling for discovery of peptide hormones.
title_full_unstemmed	Evolutionary sequence modeling for discovery of peptide hormones.
title_sort	evolutionary sequence modeling for discovery of peptide hormones.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/572c5aef0083490bb4779bddd0dea9aa
work_keys_str_mv	AT kemalsonmez evolutionarysequencemodelingfordiscoveryofpeptidehormones AT naunihaltzaveri evolutionarysequencemodelingfordiscoveryofpeptidehormones AT ilanakerman evolutionarysequencemodelingfordiscoveryofpeptidehormones AT sharonburke evolutionarysequencemodelingfordiscoveryofpeptidehormones AT charlesrneal evolutionarysequencemodelingfordiscoveryofpeptidehormones AT xinminxie evolutionarysequencemodelingfordiscoveryofpeptidehormones AT stanleyjwatson evolutionarysequencemodelingfordiscoveryofpeptidehormones AT lawrencetoll evolutionarysequencemodelingfordiscoveryofpeptidehormones
_version_	1718414517427240960

Evolutionary sequence modeling for discovery of peptide hormones.

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