Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms. Information on subcellular localization of proteins is important to molecular cell biology, proteomics, system biology and drug discovery. To provide the vast majority of experimental scientists with a user-friendly tool in these areas, we present a package of Web servers developed recently by hybridizing the ’higher level’ approach with the ab initio approach. The package is called Cell-PLoc and contains the following six predictors: Euk-mPLoc, Hum-mPLoc, Plant-PLoc, Gpos-PLoc, Gneg-PLoc and Virus-PLoc, specialized for eukaryotic, human, plant, Gram-positive bacterial, Gram-negative bacterial and viral proteins, respectively. Using these Web servers, one can easily get the desired prediction results with a high expected accuracy, as demonstrated by a series of cross-validation tests on the benchmark data sets that covered up to 22 subcellular location sites and in which none of the proteins included had greater than or equal to25% sequence identity to any other protein in the same subcellular-location subset. Some of these Web servers can be particularly used to deal with multiplex proteins as well, which may simultaneously exist at, or move between, two or more different subcellular locations. Proteins with multiple locations or dynamic features of this kind are particularly interesting, because they may have some special biological functions intriguing to investigators in both basic research and drug discovery. This protocol is a step-by-step guide on how to use the Web-server predictors in the Cell-PLoc package. The computational time for each prediction is less than 5 s in most cases. The Cell-PLoc package is freely accessible at http://chou.med.harvard.edu/bioinf/Cell-PLoc.

References in zbMATH (referenced in 24 articles )

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  1. 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)
  2. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  3. Ali, Farman; Hayat, Maqsood: Classification of membrane protein types using voting feature interval in combination with Chou’s pseudo amino acid composition (2015)
  4. Khan, Zaheer Ullah; Hayat, Maqsood; Khan, Muazzam Ali: Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model (2015)
  5. Kou, Gaoshan; Feng, Yonge: Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts (2015)
  6. Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan: mLASSO-Hum: a LASSO-based interpretable human-protein subcellular localization predictor (2015)
  7. Zhao, Xiaowei; Ning, Qiao; Chai, Haiting; Ma, Zhiqiang: Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique (2015)
  8. Feng, Peng-Mian; Ding, Hui; Chen, Wei; Lin, Hao: Naïve Bayes classifier with feature selection to identify phage virion proteins (2013)
  9. Zhang, Wenyi; Xu, Xin; Jia, Longjia; Ma, Zhiqiang; Luo, Na; Wang, Jianan: The prediction of calpain cleavage sites with the mrmr and IFS approaches (2013)
  10. Zhao, Xiaowei; Zhang, Jian; Ning, Qiao; Sun, Pingping; Ma, Zhiqiang; Yin, Minghao: Identification of protein pupylation sites using bi-profile Bayes feature extraction and ensemble learning (2013)
  11. Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui: Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection (2012)
  12. Li, Tao; Li, Qian-Zhong: Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure (2012)
  13. Lu, Jin-Long; Hu, Xue-Hai; Hu, Dong-Gang: A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences (2012)
  14. Mei, Suyu: Predicting plant protein subcellular multi-localization by Chou’s PseAAC formulation based multi-label homolog knowledge transfer learning (2012)
  15. Mei, Suyu: Multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization (2012)
  16. Mishra, Pooja; Nath Pandey, Paras: Elman RNN based classification of proteins sequences on account of their mutual information (2012)
  17. Qiu, Zhijun; Wang, Xicheng: Prediction of protein-protein interaction sites using patch-based residue characterization (2012)
  18. Shi, Shao-Ping; Qiu, Jian-Ding; Sun, Xing-Yu; Suo, Sheng-Bao; Huang, Shu-Yun; Liang, Ru-Ping: A method to distinguish between lysine acetylation and lysine methylation from protein sequences (2012)
  19. Huang, Yujuan; Yang, Lianping; Wang, Tianming: Phylogenetic analysis of DNA sequences based on the generalized pseudo-amino acid composition (2011)
  20. Khan, Asifullah; Majid, Abdul; Hayat, Maqsood: CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition (2011)

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