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Predicting N-terminal myristoylation sites in plant proteins

BACKGROUND: N-terminal myristoylation plays a vital role in membrane targeting and signal transduction in plant responses to environmental stress. Although N-myristoyltransferase enzymatic function is conserved across plant, animal, and fungal kingdoms, exact substrate specificities vary, making it...

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Autores principales: Podell, Sheila, Gribskov, Michael
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC449705/
https://www.ncbi.nlm.nih.gov/pubmed/15202951
http://dx.doi.org/10.1186/1471-2164-5-37
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author Podell, Sheila
Gribskov, Michael
author_facet Podell, Sheila
Gribskov, Michael
author_sort Podell, Sheila
collection PubMed
description BACKGROUND: N-terminal myristoylation plays a vital role in membrane targeting and signal transduction in plant responses to environmental stress. Although N-myristoyltransferase enzymatic function is conserved across plant, animal, and fungal kingdoms, exact substrate specificities vary, making it difficult to predict protein myristoylation accurately within specific taxonomic groups. RESULTS: A new method for predicting N-terminal myristoylation sites specifically in plants has been developed and statistically tested for sensitivity, specificity, and robustness. Compared to previously available methods, the new model is both more sensitive in detecting known positives, and more selective in avoiding false positives. Scores of myristoylated and non-myristoylated proteins are more widely separated than with other methods, greatly reducing ambiguity and the number of sequences giving intermediate, uninformative results. The prediction model is available at . CONCLUSION: Superior performance of the new model is due to the selection of a plant-specific training set, covering 266 unique sequence examples from 40 different species, the use of a probability-based hidden Markov model to obtain predictive scores, and a threshold cutoff value chosen to provide maximum positive-negative discrimination. The new model has been used to predict 589 plant proteins likely to contain N-terminal myristoylation signals, and to analyze the functional families in which these proteins occur.
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spelling pubmed-4497052004-07-10 Predicting N-terminal myristoylation sites in plant proteins Podell, Sheila Gribskov, Michael BMC Genomics Methodology Article BACKGROUND: N-terminal myristoylation plays a vital role in membrane targeting and signal transduction in plant responses to environmental stress. Although N-myristoyltransferase enzymatic function is conserved across plant, animal, and fungal kingdoms, exact substrate specificities vary, making it difficult to predict protein myristoylation accurately within specific taxonomic groups. RESULTS: A new method for predicting N-terminal myristoylation sites specifically in plants has been developed and statistically tested for sensitivity, specificity, and robustness. Compared to previously available methods, the new model is both more sensitive in detecting known positives, and more selective in avoiding false positives. Scores of myristoylated and non-myristoylated proteins are more widely separated than with other methods, greatly reducing ambiguity and the number of sequences giving intermediate, uninformative results. The prediction model is available at . CONCLUSION: Superior performance of the new model is due to the selection of a plant-specific training set, covering 266 unique sequence examples from 40 different species, the use of a probability-based hidden Markov model to obtain predictive scores, and a threshold cutoff value chosen to provide maximum positive-negative discrimination. The new model has been used to predict 589 plant proteins likely to contain N-terminal myristoylation signals, and to analyze the functional families in which these proteins occur. BioMed Central 2004-06-17 /pmc/articles/PMC449705/ /pubmed/15202951 http://dx.doi.org/10.1186/1471-2164-5-37 Text en Copyright © 2004 Podell and Gribskov; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
spellingShingle Methodology Article
Podell, Sheila
Gribskov, Michael
Predicting N-terminal myristoylation sites in plant proteins
title Predicting N-terminal myristoylation sites in plant proteins
title_full Predicting N-terminal myristoylation sites in plant proteins
title_fullStr Predicting N-terminal myristoylation sites in plant proteins
title_full_unstemmed Predicting N-terminal myristoylation sites in plant proteins
title_short Predicting N-terminal myristoylation sites in plant proteins
title_sort predicting n-terminal myristoylation sites in plant proteins
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC449705/
https://www.ncbi.nlm.nih.gov/pubmed/15202951
http://dx.doi.org/10.1186/1471-2164-5-37
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