Nuc-PLoc: A new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. The life processes of an eukaryotic cell are guided by its nucleus. In addition to the genetic material, the cellular nucleus contains many proteins located at its different compartments, called subnuclear locations. Information of their localization in a nucleus is indispensable for the in-depth study of system biology because, in addition to helping determine their functions, it can provide illuminative insights of how and in what kind of microenvironments these subnuclear proteins are interacting with each other and with other molecules. Facing the deluge of protein sequences generated in the post-genomic age, we are challenged to develop an automated method for fast and effectively annotating the subnuclear locations of numerous newly found nuclear protein sequences. In view of this, a new classifier, called Nuc-PLoc, has been developed that can be used to identify nuclear proteins among the following nine subnuclear locations: (1) chromatin, (2) heterochromatin, (3) nuclear envelope, (4) nuclear matrix, (5) nuclear pore complex, (6) nuclear speckle, (7) nucleolus, (8) nucleoplasm and (9) nuclear promyelocytic leukaemia (PML) body. Nuc-PLoc is featured by an ensemble classifier formed by fusing the evolution information of a protein and its pseudo-amino acid composition. The overall jackknife cross-validation accuracy obtained by Nuc-PLoc is significantly higher than those by the existing methods on the same benchmark data set through the same testing procedure. As a user-friendly web-server, Nuc-PLoc is freely accessible to the public at

References in zbMATH (referenced in 14 articles )

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  1. 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)
  2. 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)
  3. 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)
  4. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  5. 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)
  6. 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)
  7. Li, Tao; Li, Qian-Zhong: Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure (2012)
  8. González-Díaz, Humberto; Prado-Prado, Francisco; Sobarzo-Sánchez, Eduardo; Haddad, Mohamed; Maurel Chevalley, Séverine; Valentin, Alexis; Quetin-Leclercq, Joëlle; Dea-Ayuela, María A.; Gomez-Muños, María Teresa; Munteanu, Cristian R.; Torres-Labandeira, Juan José; García-Mera, Xerardo; Tapia, Ricardo A.; Ubeira, Florencio M.: NL MIND-BEST: a web server for ligands and proteins discovery -- theoretic-experimental study of proteins of \textitGiardialamblia and new compounds active against \textitPlasmodiumfalciparum (2011)
  9. Zakeri, Pooya; Moshiri, Behzad; Sadeghi, Mehdi: Prediction of protein submitochondria locations based on data fusion of various features of sequences (2011)
  10. Esmaeili, Maryam; Mohabatkar, Hassan; Mohsenzadeh, Sasan: Using the concept of Chou’s pseudo amino acid composition for risk type prediction of human papillomaviruses (2010)
  11. Guang, Xuanmin; Guo, Yanzhi; Xiao, Jiamin; Wang, Xia; Sun, Jing; Xiong, Wenjia; Li, Menglong: Predicting the state of cysteines based on sequence information (2010)
  12. Mei, Suyu; Fei, Wang: Amino acid classification based spectrum kernel fusion for protein subnuclear localization (2010) ioport
  13. Wang, Tong; Xia, Tian; Hu, Xiao-ming: Geometry preserving projections algorithm for predicting membrane protein types (2010)
  14. Du, Pufeng; Cao, Shengjiao; Li, Yanda: SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic (K)-nearest neighbor (ET-KNN) algorithm (2009)