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Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization

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  • Kuo-Chen Chou
  • Hong-Bin Shen

Abstract

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 sites. 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.

Suggested Citation

  • Kuo-Chen Chou & Hong-Bin Shen, 2010. "Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization," PLOS ONE, Public Library of Science, vol. 5(6), pages 1-11, June.
    • Handle: RePEc:plo:pone00:0011335
    DOI: 10.1371/journal.pone.0011335
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    1. Jason R. Schnell & James J. Chou, 2008. "Structure and mechanism of the M2 proton channel of influenza A virus," Nature, Nature, vol. 451(7178), pages 591-595, January.
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    1. Bi-Qing Li & Le-Le Hu & Lei Chen & Kai-Yan Feng & Yu-Dong Cai & Kuo-Chen Chou, 2012. "Prediction of Protein Domain with mRMR Feature Selection and Analysis," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-14, June.
    2. Junzhe Cao & Wenqi Liu & Jianjun He & Hong Gu, 2013. "Mining Proteins with Non-Experimental Annotations Based on an Active Sample Selection Strategy for Predicting Protein Subcellular Localization," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-9, June.
    3. Xiao Wang & Guo-Zheng Li, 2012. "A Multi-Label Predictor for Identifying the Subcellular Locations of Singleplex and Multiplex Eukaryotic Proteins," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
    4. Yan Tong & Hui Huang & YuHua Wang, 2021. "Genome-Wide Analysis of the Trihelix Gene Family and Their Response to Cold Stress in Dendrobium officinale," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    5. Shibiao Wan & Man-Wai Mak & Sun-Yuan Kung, 2014. "HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-12, March.
    6. Le-Le Hu & Tao Huang & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Prediction of Body Fluids where Proteins are Secreted into Based on Protein Interaction Network," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-8, July.
    7. Lili Liu & Zijun Zhang & Qian Mei & Ming Chen, 2013. "PSI: A Comprehensive and Integrative Approach for Accurate Plant Subcellular Localization Prediction," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-9, October.
    8. Xuan Xiao & Zhi-Cheng Wu & Kuo-Chen Chou, 2011. "A Multi-Label Classifier for Predicting the Subcellular Localization of Gram-Negative Bacterial Proteins with Both Single and Multiple Sites," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-10, June.

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