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首页> 外文期刊>Genomics >pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
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pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC

机译：PLOC-MGNEG：通过PSEAAC通过深基因本体学习预测革兰阴性细菌蛋白的亚细胞定位

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Information of the proteins' subcellular localization is crucially important for revealing their biological functions in a cell, the basic unit of life. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop computational tools for timely identifying their subcellular locations based on the sequence information alone. The current study is focused on the Gram-negative bacterial proteins. Although considerable efforts have been made in protein subcellular prediction, the problem is far from being solved yet. This is because mounting evidences have indicated that many Gram-negative bacterial proteins exist in two or more location sites. Unfortunately, most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions important for both basic research and drug design. In this study, by using the multi-label theory, we developed a new predictor called "pLoc-mGneg" for predicting the subcellular localization of Gram-negative bacterial proteins with both single and multiple locations. Rigorous cross-validation on a high quality benchmark dataset indicated that the proposed predictor is remarkably superior to "iLoc-Gneg", the state-of-the-art predictor for the same purpose. For the convenience of most experimental scientists, a user-friendly web-server for the novel predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGneg/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.
机译：蛋白质亚细胞定位的信息对于揭示其在细胞中的生物学功能，生命基本单位是至关重要的。利用在后一组年龄中产生的蛋白质序列的雪崩，强度希望开发用于及时基于单独信息的序列信息来及时识别其亚细胞位置的计算工具。目前的研究专注于革兰氏阴性细菌蛋白质。虽然在蛋白质亚细胞预测中取代了相当大的努力，但问题远未解决。这是因为安装证据表明，许多革兰氏阴性细菌蛋白存在于两个或更多个位置位点。遗憾的是，大多数现有方法只能用于处理单一位置蛋白。实际上，具有多个位置的蛋白质可能对基本研究和药物设计具有一些特殊的生物学功能。在本研究中，通过使用多标签理论，我们开发了一种称为“PLoC-Mgneg”的新预测因子，用于预测单个和多个位置的革兰阴性细菌蛋白的亚细胞定位。高质量的基准数据集上的严格交叉验证表明，所提出的预测器非常优于“ILOC-GNEG”，最先进的预测器以相同的目的。为了便于大多数实验性科学家，在http://www.jci-bioinfo.cn/ploc-mgneg/中建立了一个用于小说预测器的用户友好的网页服务器，用户可以轻松地获得所需的结果需要通过所涉及的复杂数学。

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