Literature DB >> 21919864

PSCL: predicting protein subcellular localization based on optimal functional domains.

Kai Wang1, Le-Le Hu, Xiao-He Shi, Ying-Song Dong, Hai-Peng Li, Tie-Qiao Wen.   

Abstract

It is well known that protein subcellular localizations are closely related to their functions. Although many computational methods and tools are available from Internet, it is still necessary to develop new algorithms in this filed to gain a better understanding of the complex mechanism of plant subcellular localization. Here, we provide a new web server named PSCL for plant protein subcellular localization prediction by employing optimized functional domains. After feature optimization, 848 optimal functional domains from InterPro were obtained to represent each protein. By calculating the distances to each of the seven categories, PSCL showing the possibilities of a protein located into each of those categories in ascending order. Toward our dataset, PSCL achieved a first-order predicted accuracy of 75.7% by jackknife test. Gene Ontology enrichment analysis showing that catalytic activity, cellular process and metabolic process are strongly correlated with the localization of plant proteins. Finally, PSCL, a Linux Operate System based web interface for the predictor was designed and is accessible for public use at http://pscl.biosino.org/.

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Year:  2012        PMID: 21919864     DOI: 10.2174/092986612798472820

Source DB:  PubMed          Journal:  Protein Pept Lett        ISSN: 0929-8665            Impact factor:   1.890


  4 in total

1.  System approaches reveal the molecular networks involved in neural stem cell differentiation.

Authors:  Kai Wang; Haifeng Wang; Jiao Wang; Yuqiong Xie; Jun Chen; Huang Yan; Zengrong Liu; Tieqiao Wen
Journal:  Protein Cell       Date:  2012-04-10       Impact factor: 14.870

2.  Prediction of protein domain with mRMR feature selection and analysis.

Authors:  Bi-Qing Li; Le-Le Hu; Lei Chen; Kai-Yan Feng; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2012-06-15       Impact factor: 3.240

3.  REGULATOR: a database of metazoan transcription factors and maternal factors for developmental studies.

Authors:  Kai Wang; Hiroki Nishida
Journal:  BMC Bioinformatics       Date:  2015-04-10       Impact factor: 3.169

4.  Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.

Authors:  Lisa M Breckels; Sean B Holden; David Wojnar; Claire M Mulvey; Andy Christoforou; Arnoud Groen; Matthew W B Trotter; Oliver Kohlbacher; Kathryn S Lilley; Laurent Gatto
Journal:  PLoS Comput Biol       Date:  2016-05-13       Impact factor: 4.475

  4 in total

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