Virus-PLoc: a fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Viruses can reproduce their progenies only within a host cell, and their actions depend both on its destructive tendencies toward a specific host cell and on environmental conditions. Therefore, knowledge of the subcellular localization of viral proteins in a host cell or virus-infected cell is very useful for in-depth studying of their functions and mechanisms as well as designing antiviral drugs. An analysis on the Swiss-Prot database (version 50.0, released on May 30, 2006) indicates that only 23.5% of viral protein entries are annotated for their subcellular locations in this regard. As for the gene ontology database, the corresponding percentage is 23.8%. Such a gap calls for the development of high throughput tools for timely annotating the localization of viral proteins within host and virus-infected cells. In this article, a predictor called ”Virus-PLoc” has been developed that is featured by fusing many basic classifiers with each engineered according to the K-nearest neighbor rule. The overall jackknife success rate obtained by Virus-PLoc in identifying the subcellular compartments of viral proteins was 80% for a benchmark dataset in which none of proteins has more than 25% sequence identity to any other in a same location site. Virus-PLoc will be freely available as a web-server at for the public usage. Furthermore, Virus-PLoc has been used to provide large-scale predictions of all viral protein entries in Swiss-Prot database that do not have subcellular location annotations or are annotated as being uncertain. The results thus obtained have been deposited in a downloadable file prepared with Microsoft Excel and named ”Tab_Virus-PLoc.xls.” This file is available at the same website and will be updated twice a year to include the new entries of viral proteins and reflect the continuous development of Virus-PLoc.

References in zbMATH (referenced in 14 articles )

Showing results 1 to 14 of 14.
Sorted by year (citations)

  1. Shatabda, Swakkhar; Saha, Sanjay; Sharma, Alok; Dehzangi, Abdollah: iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features (2017)
  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: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  4. 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)
  5. Chou, Kuo-Chen: Some remarks on protein attribute prediction and pseudo amino acid composition (2011)
  6. Mei, S.; Wang, F.; Zhou, S.: Gene ontology based transfer learning for protein subcellular localization (2011) ioport
  7. 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)
  8. Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra: High performance set of PseAAC and sequence based descriptors for protein classification (2010)
  9. Brown, J. B.; Akutsu, Tatsuya: Identification of novel DNA repair proteins via primary sequence, secondary structure, and homology (2009) ioport
  10. Lin, Hao: The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou’s pseudo amino acid composition (2008)
  11. Zhang, Tong-Liang; Ding, Yong-Sheng; Chou, Kuo-Chen: Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern (2008)
  12. Chen, Ying-Li; Li, Qian-Zhong: Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition (2007)
  13. Jahandideh, Samad; Sarvestani, Amir Sabet; Abdolmaleki, Parviz; Jahandideh, Mina; Barfeie, Mahdyar: (\gamma)-Turn types prediction in proteins using the support vector machines (2007)
  14. Kurgan, Lukasz A.; Stach, Wojciech; Ruan, Jishou: Novel scales based on hydrophobicity indices for secondary protein structure (2007)