pLoc-mVirus

pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Knowledge of subcellular locations of proteins is crucially important for in-depth understanding their functions in a cell. With the explosive growth of protein sequences generated in the postgenomic age, it is highly demanded to develop computational tools for timely annotating their subcellular locations based on the sequence information alone. The current study is focused on virus proteins. Although considerable efforts have been made in this regard, the problem is far from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions. This kind of multiplex proteins is particularly important for both basic research and drug design. Using the multi-label theory, we present a new predictor called ”pLoc-mVirus” by extracting the optimal GO (Gene Ontology) information into the general PseAAC (Pseudo Amino Acid Composition). Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mVirus predictor is remarkably superior to iLoc-Virus, the state-of-the-art method in predicting virus protein subcellular localization. To maximize the convenience of most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc-mVirus/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.


References in zbMATH (referenced in 24 articles )

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  1. Adilina, Sheikh; Farid, Dewan Md; Shatabda, Swakkhar: Effective DNA binding protein prediction by using key features via Chou’s general PseAAC (2019)
  2. Ahmad, Jamal; Hayat, Maqsood: MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components (2019)
  3. Chen, Guodong; Cao, Man; Yu, Jialin; Guo, Xinyun; Shi, Shaoping: Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou’s general PseAAC (2019)
  4. Ghosh, Avirup; Yan, Hong: Hydrogen bond analysis of the EGFR-ErbB3 heterodimer related to non-small cell lung cancer and drug resistance (2019)
  5. 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)
  6. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  7. Khan, Yaser Daanial; Jamil, Mehreen; Hussain, Waqar; Rasool, Nouman; Khan, Sher Afzal; Chou, Kuo-Chen: pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments (2019)
  8. Lu, Fuhua; Zhu, Maoshu; Lin, Ying; Zhong, Hongbin; Cai, Lei; He, Lin; Chou, Kuo-Chen: The preliminary efficacy evaluation of the CTLA-4-ig treatment against lupus nephritis through \textitin-silico analyses (2019)
  9. Ning, Qiao; Ma, Zhiqiang; Zhao, Xiaowei: Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou’s 5-step rule and pseudo components (2019)
  10. Pan, Yi; Wang, Shiyuan; Zhang, Qi; Lu, Qianzi; Su, Dongqing; Zuo, Yongchun; Yang, Lei: Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions (2019)
  11. Shen, Yinan; Tang, Jijun; Guo, Fei: Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC (2019)
  12. 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)
  13. Zhao, Xiaowei; Zhang, Ye; Ning, Qiao; Zhang, Hongrui; Ji, Jinchao; Yin, Minghao: Identifying N(^6)-methyladenosine sites using extreme gradient boosting system optimized by particle swarm optimizer (2019)
  14. Arif, Muhammad; Hayat, Maqsood; Jan, Zahoor: IMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition (2018)
  15. Chiu, Jimmy Ka Ho; Dillon, Tharam S.; Chen, Yi-Ping Phoebe: Large-scale frequent stem pattern mining in RNA families (2018)
  16. 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)
  17. 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)
  18. Qiu, Wenying; Li, Shan; Cui, Xiaowen; Yu, Zhaomin; Wang, Minghui; Du, Junwei; Peng, Yanjun; Yu, Bin: Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou’s pseudo-amino acid composition (2018)
  19. Sabooh, M. Fazli; Iqbal, Nadeem; Khan, Mukhtaj; Khan, Muslim; Maqbool, H. F.: Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC (2018)
  20. Sankari, E. Siva; Manimegalai, D.: Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC (2018)

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