Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai, China.
PLoS One. 2010 Jun 28;5(6):e11335. doi: 10.1371/journal.pone.0011335.
One of the fundamental goals in proteomics and cell biology is to identify the functions of proteins in various cellular organelles and pathways. Information of subcellular locations of proteins can provide useful insights for revealing their functions and understanding how they interact with each other in cellular network systems. Most of the existing methods in predicting plant protein subcellular localization can only cover three or four location sites, and none of them can be used to deal with multiplex plant proteins that can simultaneously exist at two, or move between, two or more different location sits. Actually, such multiplex proteins might have special biological functions worthy of particular notice. The present study was devoted to improve the existing plant protein subcellular location predictors from the aforementioned two aspects. A new predictor called "Plant-mPLoc" is developed by integrating the gene ontology information, functional domain information, and sequential evolutionary information through three different modes of pseudo amino acid composition. It can be used to identify plant proteins among the following 12 location sites: (1) cell membrane, (2) cell wall, (3) chloroplast, (4) cytoplasm, (5) endoplasmic reticulum, (6) extracellular, (7) Golgi apparatus, (8) mitochondrion, (9) nucleus, (10) peroxisome, (11) plastid, and (12) vacuole. Compared with the existing methods for predicting plant protein subcellular localization, the new predictor is much more powerful and flexible. Particularly, it also has the capacity to deal with multiple-location proteins, which is beyond the reach of any existing predictors specialized for identifying plant protein subcellular localization. As a user-friendly web-server, Plant-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results. It is anticipated that the Plant-mPLoc predictor as presented in this paper will become a very useful tool in plant science as well as all the relevant areas.
蛋白质组学和细胞生物学的基本目标之一是确定各种细胞细胞器和途径中蛋白质的功能。蛋白质亚细胞位置的信息可以为揭示其功能以及了解它们在细胞网络系统中如何相互作用提供有用的见解。现有的大多数预测植物蛋白质亚细胞定位的方法只能覆盖三到四个位置站点，而且没有一个方法可以用于处理可以同时存在于两个或两个以上不同位置站点的多重植物蛋白质。实际上，这种多重蛋白质可能具有特殊的生物学功能，值得特别关注。本研究从上述两个方面致力于改进现有的植物蛋白质亚细胞定位预测器。通过三种不同的伪氨基酸组成模式整合基因本体信息、功能域信息和序列进化信息，开发了一种新的预测器，称为“Plant-mPLoc”。它可以用于识别以下 12 个位置的植物蛋白质：（1）细胞膜，（2）细胞壁，（3）叶绿体，（4）细胞质，（5）内质网，（6）细胞外，（7）高尔基体，（8）线粒体，（9）细胞核，（10）过氧化物酶体，（11）质体和（12）液泡。与现有的预测植物蛋白质亚细胞定位的方法相比，新的预测器更加强大和灵活。特别是，它还具有处理多位置蛋白质的能力，这是任何专门用于识别植物蛋白质亚细胞定位的现有预测器都无法实现的。作为一个用户友好的网络服务器，Plant-mPLoc 可免费访问 http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/。此外，为了方便绝大多数实验科学家，还提供了一个逐步指南，介绍如何使用网络服务器获得所需的结果。预计本文提出的 Plant-mPLoc 预测器将成为植物科学以及所有相关领域的非常有用的工具。
