iLoc-Virus

iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. In the last two decades or so, although many computational methods were developed for predicting the subcellular locations of proteins according to their sequence information, it is still remains as a challenging problem, particularly when the system concerned contains both single- and multiple-location proteins. Also, among the existing methods, very few were developed specialized for dealing with viral proteins, those generated by viruses. Actually, knowledge of the subcellular localization of viral proteins in a host cell or virus-infected cell is very important because it is closely related to their destructive tendencies and consequences. In this paper, by introducing the ”multi-label scale” and by hybridizing the gene ontology information with the sequential evolution information, a predictor called iLoc-Virus is developed. It can be utilized to identify viral proteins among the following six locations: (1) viral capsid, (2) host cell membrane, (3) host endoplasmic reticulum, (4) host cytoplasm, (5) host nucleus, and (6) secreted. The iLoc-Virus predictor not only can more accurately predict the location sites of viral proteins in a host cell, but also have the capacity to deal with virus proteins having more than one location. As a user-friendly web-server, iLoc-Virus is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc-Virus. Meanwhile, a step-by-step guide is provided on how to use the web-server to get the desired results. Furthermore, for the user’s convenience, the iLoc-Virus web-server also has the function to accept the batch job submission. It is anticipated that iLoc-Virus may become a useful high throughput tool for both basic research and drug development.


References in zbMATH (referenced in 31 articles , 1 standard article )

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  1. Butt, Ahmad Hassan; Rasool, Nouman; Khan, Yaser Daanial: Prediction of antioxidant proteins by incorporating statistical moments based features into Chou’s PseAAC (2019)
  2. Hussain, Waqar; Khan, Yaser Daanial; Rasool, Nouman; Khan, Sher Afzal; Chou, Kuo-Chen: SPrenylC-PseAAC: a sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins (2019)
  3. Shen, Yinan; Tang, Jijun; Guo, Fei: Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC (2019)
  4. Tian, Baoguang; Wu, Xue; Chen, Cheng; Qiu, Wenying; Ma, Qin; Yu, Bin: Predicting protein-protein interactions by fusing various Chou’s pseudo components and using wavelet denoising approach (2019)
  5. Zhang, Shengli; Duan, Xin: Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC (2018)
  6. Shatabda, Swakkhar; Saha, Sanjay; Sharma, Alok; Dehzangi, Abdollah: iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features (2017)
  7. Jia, Jianhua; Liu, Zi; Xiao, Xuan; Liu, Bingxiang; Chou, Kuo-Chen: pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach (2016)
  8. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  9. 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)
  10. Fatemi, Mohammad H.; Heidari, Afsane; Gharaghani, Sajjad: QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors (2015)
  11. Golzari, Fahimeh; Jalili, Saeed: VR-BFDT: a variance reduction based binary fuzzy decision tree induction method for protein function prediction (2015)
  12. Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan: mLASSO-Hum: a LASSO-based interpretable human-protein subcellular localization predictor (2015)
  13. Lyons, James; Biswas, Neela; Sharma, Alok; Dehzangi, Abdollah; Paliwal, Kuldip K.: Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping (2014)
  14. Mei, Suyu: \textitSVMensemble based transfer learning for large-scale membrane proteins discrimination (2014)
  15. Tahir, Muhammad; Khan, Asifullah; Kaya, Hüseyin: Protein subcellular localization in human and hamster cell lines: employing local ternary patterns of fluorescence microscopy images (2014)
  16. Yang, Lei; Lv, Yingli; Li, Tao; Zuo, Yongchun; Jiang, Wei: Human proteins characterization with subcellular localizations (2014)
  17. Feng, Peng-Mian; Ding, Hui; Chen, Wei; Lin, Hao: Naïve Bayes classifier with feature selection to identify phage virion proteins (2013)
  18. 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)
  19. Li, Yao-Wang; Li, Bo: Characterization of structure-antioxidant activity relationship of peptides in free radical systems using QSAR models: key sequence positions and their amino acid properties (2013)
  20. Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen: iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints (2013)

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