Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Methodology article

Predicting N-terminal myristoylation sites in plant proteins

Sheila Podell12* and Michael Gribskov12

Author Affiliations

1 San Diego Supercomputer Center, University of California San Diego, La Jolla CA 92093-0537, USA

2 Department of Biology, University of California San Diego, La Jolla CA 92093-0537, USA

For all author emails, please log on.

BMC Genomics 2004, 5:37  doi:10.1186/1471-2164-5-37

Published: 17 June 2004

Abstract

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 http://plantsp.sdsc.edu/myrist.html webcite.

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.