Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins. By incorporating the information of gene ontology, functional domain, and sequential evolution, a new predictor called Gneg-mPLoc was developed. It can be used to identify Gram-negative bacterial proteins among the following eight locations: (1) cytoplasm, (2) extracellular, (3) fimbrium, (4) flagellum, (5) inner membrane, (6) nucleoid, (7) outer membrane, and (8) periplasm. It can also be used to deal with the case when a query protein may simultaneously exist in more than one location. Compared with the original predictor called Gneg-PLoc, the new predictor is much more powerful and flexible. For a newly constructed stringent benchmark dataset in which none of proteins included has >or=25% pairwise sequence identity to any other in a same subset (location), the overall jackknife success rate achieved by Gneg-mPLoc was 85.5%, which was more than 14% higher than the corresponding rate by the Gneg-PLoc. As a user friendly web-server, Gneg-mPLoc is freely accessible at

References in zbMATH (referenced in 16 articles )

Showing results 1 to 16 of 16.
Sorted by year (citations)

  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. Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)
  3. Chiu, Jimmy Ka Ho; Dillon, Tharam S.; Chen, Yi-Ping Phoebe: Large-scale frequent stem pattern mining in RNA families (2018)
  4. Liang, Yunyun; Zhang, Shengli: Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence (2018)
  5. Zhang, Shengli; Duan, Xin: Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC (2018)
  6. Khan, Muslim; Hayat, Maqsood; Khan, Sher Afzal; Ahmad, Saeed; Iqbal, Nadeem: Bi-PSSM: position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins (2017)
  7. Shatabda, Swakkhar; Saha, Sanjay; Sharma, Alok; Dehzangi, Abdollah: iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features (2017)
  8. Mei, Suyu: \textitSVMensemble based transfer learning for large-scale membrane proteins discrimination (2014)
  9. 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)
  10. Hu, Yinxia; Li, Tonghua; Sun, Jiangming; Tang, Shengnan; Xiong, Wenwei; Li, Dapeng; Chen, Guanyan; Cong, Peisheng: Predicting Gram-positive bacterial protein subcellular localization based on localization motifs (2012)
  11. Chou, Kuo-Chen: Some remarks on protein attribute prediction and pseudo amino acid composition (2011)
  12. 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)
  13. Mei, S.; Wang, F.; Zhou, S.: Gene ontology based transfer learning for protein subcellular localization (2011) ioport
  14. Xiao, Xuan; Wu, Zhi-Cheng; Chou, Kuo-Chen: \textbfiLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites (2011)
  15. Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra: High performance set of PseAAC and sequence based descriptors for protein classification (2010)
  16. Shen, Hong-Bin; Chou, Kuo-Chen: Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins (2010)