pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC
复制标题
pLoc-mGneg：通过通用 PseAAC 进行深度基因本体学习来预测革兰氏阴性细菌蛋白的亚细胞定位
DOI：
10.1016/j.ygeno.2017.10.002
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发表时间：
2018-07-01
期刊：
影响因子：
4.4
通讯作者：
Chou, Kuo-Chen
中科院分区：
文献类型：
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作者：
Cheng, Xiang;Xiao, Xuan;Chou, Kuo-Chen
蛋白质的亚细胞定位信息对于揭示其在细胞（生命的基本单位）中的生物学功能至关重要。随着后基因组时代产生的大量蛋白质序列，人们非常希望开发出基于序列信息及时识别其亚细胞位置的计算工具。目前的研究主要集中在革兰氏阴性菌蛋白上。尽管人们在蛋白质亚细胞预测方面做了大量的工作，但这一问题远未得到解决。这是因为越来越多的证据表明，许多革兰氏阴性菌蛋白存在于两个或多个定位位点。不幸的是，大多数现有的方法只能用于处理单一位置的蛋白质。事实上，具有多个位置的蛋白质可能具有一些特殊的生物学功能，这对于基础研究和药物设计都是重要的。在这项研究中，通过使用多标记理论，我们开发了一个新的预测称为“pLoc-mGneg”的革兰氏阴性菌蛋白质的亚细胞定位与单一和多个位置的预测。在高质量基准数据集上进行的严格交叉验证表明，所提出的预测器明显上级用于相同目的的最先进预测器“iLoc-Gneg”。为了方便大多数实验科学家，已经在http：//www.jci-bioinfo.cn/pLoc-mGlucose/建立了一个用户友好的网络服务器，用户可以很容易地得到他们想要的结果，而不需要通过复杂的数学涉及。
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.