Predicting N-terminal myristoylation sites in plant proteins
Predicting N-terminal myristoylation sites in plant proteins
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DOI：
10.1186/1471-2164-5-37
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发表时间：
2004-06-17
期刊：
影响因子：
4.4
通讯作者：
Gribskov, M
中科院分区：
文献类型：
--
作者：
Podell, S;Gribskov, M
背景：N端豆蔻酰化在植物逆境胁迫的膜靶向和信号转导中起着重要作用。虽然N-肉豆蔻酰转移酶的酶功能是保守的植物，动物和真菌王国，确切的底物特异性不同，使其难以预测蛋白豆蔻酰化准确地在特定的分类groups.Results：一种新的方法，用于预测N-末端豆蔻酰化位点，特别是在植物中已经开发和统计测试的灵敏度，特异性和鲁棒性。与以前可用的方法相比，新模型在检测已知阳性方面更敏感，在避免假阳性方面更具选择性。肉豆蔻酰化和非肉豆蔻酰化蛋白质的分数比其他方法更广泛地分离，大大减少了模糊性和序列的数量，从而得到中间的、无信息的结果。该预测模型可在http://plantsp.sdsc.edu/myrist.html.Conclusion上获得：新模型的上级性能是由于选择了植物特异性训练集，涵盖了来自40个不同物种的266个独特序列示例，使用基于概率的隐马尔可夫模型获得预测分数，以及选择阈值截止值以提供最大的正负区分。新模型已被用来预测589植物蛋白可能含有N-末端豆蔻酰化信号，并分析这些蛋白质发生的功能家族。
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.