Predicting subcellular localization via protein motif co-occurrence
Predicting subcellular localization via protein motif co-occurrence
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DOI：
10.1101/gr.2650004
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发表时间：
2004-10-01
期刊：
影响因子：
7
通讯作者：
Hallett, MT
中科院分区：
文献类型：
--
作者：
Scott, MS;Thomas, DY;Hallett, MT
从蛋白质的一级序列预测蛋白质的亚细胞定位是生物信息学中一个具有挑战性的问题。我们已经创建了一个称为PSLT的贝叶斯网络定位预测因子，它基于人类蛋白质中InterPro基序和特定膜结构域的组合存在。这个概率框架产生了定位到所有细胞器的可能性，并允许预测多间隔蛋白质。当用于九个隔室的预测时，PSLT通过10次交叉验证测试获得了78%的准确率，覆盖率为74%。当用于预测其他密切相关物种的蛋白质定位时，它达到了预测的准确性和80%的覆盖率。我们将PSLT的定位预测与通过GFP标记和显微镜对一组人类蛋白质确定的定位预测进行了比较。我们发现了两类蛋白质，它们被GFP标签策略错误定位，但被PSLT正确定位。这表明，PSLT可以与定位的实验方法结合使用，以识别需要额外实验验证的蛋白质。我们使用我们的预测器注释了Swiss-Prot版本41.25中的所有9793个人类蛋白质，其中16%被PSLT预测存在于多个隔室中。
The prediction of subcellular localization of proteins from their primary sequence is a challenging problem in bioinformatics. We have created a Bayesian network localization predictor called PSLT that is based on the combinatorial presence of InterPro motifs and specific membrane domains in human proteins. This probabilistic framework generates a likelihood of localization to all organelles and allows to predict multicompartmental proteins. When used to predict on nine compartments, PSLT achieves an accuracy of 78% as estimated by using a 10-fold cross-validation test and a coverage of 74%. When used to predict the localization of proteins from other closely related species, it achieves a prediction accuracy and a coverage >80%. We compared the localization predictions of PSLT to those determined through GFP-tagging and microscopy for a group of human proteins. We found two general classes of proteins that are mislocalized by the GFP-tagging strategy but are correctly localized by PSLT. This suggests that PSLT can be used in combination with experimental approaches for localization to identify proteins for which additional experimental validation is required. We used our predictor to annotate all 9793 human proteins from SWISS-PROT release 41.25, 16% of which are predicted by PSLT to be present in more than one compartment.