Plant-mPLoc

Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. 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.


References in zbMATH (referenced in 27 articles )

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  1. Shen, Yinan; Tang, Jijun; Guo, Fei: Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC (2019)
  2. Zhang, Shengli; Duan, Xin: Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC (2018)
  3. Jiao, Ya-Sen; Du, Pu-Feng: Prediction of Golgi-resident protein types using general form of Chou’s pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection (2016)
  4. Kou, Gaoshan; Feng, Yonge: Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts (2015)
  5. Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan: mLASSO-Hum: a LASSO-based interpretable human-protein subcellular localization predictor (2015)
  6. Mei, Suyu: \textitSVMensemble based transfer learning for large-scale membrane proteins discrimination (2014)
  7. Yang, Lei; Lv, Yingli; Li, Tao; Zuo, Yongchun; Jiang, Wei: Human proteins characterization with subcellular localizations (2014)
  8. Huang, Chao; Yuan, Jing-Qi: Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions (2013)
  9. Hayat, Maqsood; Khan, Asifullah: MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM (2012)
  10. Li, Tao; Li, Qian-Zhong: Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure (2012)
  11. Liu, Guoqing; Liu, Jia; Cui, Xiangjun; Cai, Lu: Sequence-dependent prediction of recombination hotspots in \textitSaccharomycescerevisiae (2012)
  12. Lu, Jin-Long; Hu, Xue-Hai; Hu, Dong-Gang: A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences (2012)
  13. Mei, Suyu: Predicting plant protein subcellular multi-localization by Chou’s PseAAC formulation based multi-label homolog knowledge transfer learning (2012)
  14. Mei, Suyu: Multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization (2012)
  15. Mishra, Pooja; Nath Pandey, Paras: Elman RNN based classification of proteins sequences on account of their mutual information (2012)
  16. Qiu, Zhijun; Wang, Xicheng: Prediction of protein-protein interaction sites using patch-based residue characterization (2012)
  17. Wang, Yongcui; Ren, Xianwen; Zhang, Chunhua; Deng, Naiyang; Zhang, Xiangsun: Interrogating noise in protein sequences from the perspective of protein-protein interactions prediction (2012)
  18. Chou, Kuo-Chen: Some remarks on protein attribute prediction and pseudo amino acid composition (2011)
  19. de Avila e Silva, Scheila; Echeverrigaray, Sergio; Gerhardt, Günther J. L.: BacPP: bacterial promoter prediction -- a tool for accurate sigma-factor specific assignment in enterobacteria (2011)
  20. Hayat, Maqsood; Khan, Asifullah: Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition (2011)

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