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首页> 外文期刊>Genomics >pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC
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pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC

机译：pLoc-mEuk：通过将关键的GO信息提取到一般的PseAAC中，预测多标签真核蛋白的亚细胞定位

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Many efforts have been made in predicting the subcellular localization of eukaryotic proteins, but most of the existing methods have the following two limitations: (1) their coverage scope is less than ten locations and hence many organelles in an eukaryotic cell cannot be covered, and (2) they can only be used to deal with single-label systems in which each of the constituent proteins has one and only one location. Actually, proteins with multiple locations are particularly interesting since they may have some exceptional functions very important for in-depth understanding the biological process in a cell and for selecting drug target as well. Although several predictors (such as “Euk-mPLoc”, “Euk-PLoc 2.0” and “iLoc-Euk”) can cover up to 22 different location sites, and they also have the function to treat multi-labeled proteins, further efforts are needed to improve their prediction quality, particularly in enhancing the absolute true rate and in reducing the absolute false rate. Here we propose a new predictor called “pLoc-mEuk” by extracting the key GO (Gene Ontology) information into the general PseAAC (Pseudo Amino Acid Composition). Rigorous cross-validations on a high-quality and stringent benchmark dataset have indicated that the proposed pLoc-mEuk predictor is remarkably superior to iLoc-Euk, the best of the aforementioned three predictors. To maximize the convenience of most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc-mEuk/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.
机译：在预测真核蛋白的亚细胞定位方面已经做出了许多努力，但是大多数现有方法具有以下两个局限性：（1）它们的覆盖范围小于十个位置，因此不能覆盖真核细胞中的许多细胞器，并且（2）它们只能用于处理其中每个组成蛋白只有一个且只有一个位置的单标记系统。实际上，具有多个位置的蛋白质特别有趣，因为它们可能具有一些特殊功能，这些功能对于深入了解细胞中的生物学过程以及选择药物靶标也非常重要。尽管几种预测因子（例如“ Euk-mPLoc”，“ Euk-PLoc 2.0”和“ iLoc-Euk”）可以覆盖多达22个不同的位置，并且它们还具有治疗多标记蛋白质的功能，但仍需进一步努力需要提高其预测质量，特别是在提高绝对真实率和降低绝对错误率方面。在这里，我们通过将关键的GO（基因本体论）信息提取到一般的PseAAC（伪氨基酸组成）中，提出了一种称为“ pLoc-mEuk”的新预测因子。在高质量和严格的基准数据集上进行严格的交叉验证表明，提出的pLoc-mEuk预测因子明显优于上述三个预测因子中最好的iLoc-Euk。为了最大程度地提高大多数实验科学家的便利性，已在http://www.jci-bioinfo.cn/pLoc-mEuk/上建立了用于新预测变量的用户友好型Web服务器，用户可以通过该服务器轻松获得所需的结果。无需进行复杂的数学运算。

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