iLoc-Euk

iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. Predicting protein subcellular localization is an important and difficult problem, particularly when query proteins may have the multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular location predictor can only be used to deal with the single-location or ”singleplex” proteins. Actually, multiple-location or ”multiplex” proteins should not be ignored because they usually posses some unique biological functions worthy of our special notice. By introducing the ”multi-labeled learning” and ”accumulation-layer scale”, a new predictor, called iLoc-Euk, has been developed that can be used to deal with the systems containing both singleplex and multiplex proteins. As a demonstration, the jackknife cross-validation was performed with iLoc-Euk on a benchmark dataset of eukaryotic proteins classified into the following 22 location sites: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centriole, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole, where none of proteins included has ≥25% pairwise sequence identity to any other in a same subset. The overall success rate thus obtained by iLoc-Euk was 79%, which is significantly higher than that by any of the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, iLoc-Euk is freely accessible to the public at the web-site http://icpr.jci.edu.cn/bioinfo/iLoc-Euk. It is anticipated that iLoc-Euk may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development Also, its novel approach will further stimulate the development of predicting other protein attributes.


References in zbMATH (referenced in 31 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. Akbar, Shahid; Hayat, Maqsood: iMethyl-STTNC: identification of N(^6)-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences (2018)
  3. Mei, Juan; Fu, Yi; Zhao, Ji: Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition (2018)
  4. Zhang, Shengli; Duan, Xin: Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC (2018)
  5. Zhang, Shengli; Liang, Yunyun: Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC (2018)
  6. 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)
  7. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  8. Muthu Krishnan, S.: Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach (2016)
  9. Fu, Haoyue; Yang, Lianping; Zhang, Xiangde: An RNA secondary structure prediction method based on minimum and suboptimal free energy structures (2015)
  10. Kumar, Ravindra; Srivastava, Abhishikha; Kumari, Bandana; Kumar, Manish: Prediction of (\beta)-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine (2015)
  11. Fan, Guo-Liang; Li, Qian-Zhong: Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou’s pseudo amino acid composition (2013)
  12. Feng, Peng-Mian; Ding, Hui; Chen, Wei; Lin, Hao: Naïve Bayes classifier with feature selection to identify phage virion proteins (2013)
  13. 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)
  14. Zhou, Xuan; Li, Zhanchao; Dai, Zong; Zou, Xiaoyong: Predicting promoters by pseudo-trinucleotide compositions based on discrete wavelets transform (2013)
  15. Fan, Guo-Liang; Li, Qian-Zhong: Predicting mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou’s pseudo amino acid composition (2012)
  16. Hayat, Maqsood; Khan, Asifullah: MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM (2012)
  17. Hemmateenejad, Bahram; Miri, Ramin; Elyasi, Maryam: A segmented principal component analysis -- regression approach to QSAR study of peptides (2012)
  18. Jahandideh, Samad; Mahdavi, Abbas: RFCRYS: sequence-based protein crystallization propensity prediction by means of random forest (2012)
  19. Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui: Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection (2012)
  20. Li, Tao; Li, Qian-Zhong: Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure (2012)

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