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  • 标题:Computational Prediction of Protein Subcellular Locations in Eukaryotes: an Experience Report
  • 本地全文:下载
  • 作者:John Meinken ; Jack Min
  • 期刊名称:Computational Molecular Biology
  • 电子版ISSN:1927-5587
  • 出版年度:2012
  • 卷号:2
  • 期号:1
  • DOI:10.5376/cmb.2012.02.0001
  • 语种:English
  • 出版社:Sophia Publications
  • 摘要:Computational prediction of protein subcellular locations in eukaryotes facilitates experimental design and proteome analysis. We provide a short review on recent development of computational tools and our experience in evaluating some of these tools. Classical secretomes can be relatively accurately predicted using computational tools to predict existence of a secretory signal peptide and to remove transmembrane proteins and endoplasmic reticulum (ER) proteins. The protocols of differentially combining SignalP, Phobius, WoLFPSORT, and TargetP for identifying a secretory signal peptide in different kingdom of eukaryotes, with TMHMM for removing transmembrane proteins and PS-Scan for removing ER proteins significantly improve the secretome prediction accuracies. Our evaluation showed that current computational tools for predicting other subcellular locations, including mitochondrial or chloroplast localization, still need to be improved.
  • 关键词:Eukaryotes; Protein subcellular location; Secretome; Computational prediction
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