NDLI: Predicting N-terminal myristoylation sites in plant proteins

Content Provider	Springer Nature : BioMed Central
Author	Podell, Sheila Gribskov, Michael
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 . 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.
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/1471-2164-5-37.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/1471-2164-5-37
Journal	BMC Genomics
Issue Number	1
Volume Number	5
Language	English
Publisher	BioMed Central
Publisher Date	2004-06-17
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Hide Markov Model Training Sequence Amino Acid Frequency Position Specific Score Matrix Negative Sequence
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

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