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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...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2004
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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. |
format | Text |
id | pubmed-449705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2004 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT podellsheila predictingnterminalmyristoylationsitesinplantproteins AT gribskovmichael predictingnterminalmyristoylationsitesinplantproteins |