Periscope: quantitative prediction of soluble protein expression in the periplasm of Escherichia coli.
Periscope: quantitative prediction of soluble protein expression in the periplasm of Escherichia coli.
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Periscope：定量预测大肠杆菌周质中可溶性蛋白的表达
DOI：
10.1038/srep21844
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发表时间：
2016-03-02
影响因子：
4.6
通讯作者：
Ramanan RN
中科院分区：
文献类型：
--
作者：
Chang CC;Li C;Webb GI;Tey B;Song J;Ramanan RN
可溶性蛋白质在大肠杆菌中的周质表达不仅简化了下游纯化过程，而且提高了获得正确折叠和生物活性蛋白质的可能性。信号肽和靶蛋白的不同组合导致不同的可溶性蛋白表达水平，范围从可忽略到每升几克。合理选择有前途的候选人的准确算法可以作为一个强大的工具，以补充目前的试错法。因此，蛋白质组学研究可以以更高的效率和成本效益进行。在这里，我们开发了一个两阶段结构的预测器，以预测周质中目标蛋白的真实值表达水平。第一阶段支持向量机（SVM）分类器的输出决定使用哪一个第二阶段支持向量回归（SVR）分类器。当在独立的测试数据集上进行测试时，预测器实现了78%的总体预测准确度和0.77的Pearson相关系数（PCC）。我们进一步说明了相对于不同的模型的各种功能的相对重要性。结果表明，二肽谷氨酰胺和天冬氨酸的出现是分类模型的最重要的特征。最后，我们通过Periscope Web服务器提供对实现的预测器的访问，可以在http：//www.example.com上免费访问。lightning.med.monash.edu/periscope/
Periplasmic expression of soluble proteins inEscherichia colinot only offers a much-simplified downstream purification process, but also enhances the probability of obtaining correctly folded and biologically active proteins. Different combinations of signal peptides and target proteins lead to different soluble protein expression levels, ranging from negligible to several grams per litre. Accurate algorithms for rational selection of promising candidates can serve as a powerful tool to complement with current trial-and-error approaches. Accordingly, proteomics studies can be conducted with greater efficiency and cost-effectiveness. Here, we developed a predictor with a two-stage architecture, to predict the real-valued expression level of target protein in the periplasm. The output of the first-stage support vector machine (SVM) classifier determines which second-stage support vector regression (SVR) classifier to be used. When tested on an independent test dataset, the predictor achieved an overall prediction accuracy of 78% and a Pearson’s correlation coefficient (PCC) of 0.77. We further illustrate the relative importance of various features with respect to different models. The results indicate that the occurrence of dipeptide glutamine and aspartic acid is the most important feature for the classification model. Finally, we provide access to the implemented predictor through the Periscope webserver, freely accessible athttp://lightning.med.monash.edu/periscope/.