Computational Prediction of Protein Subcellular Locations in Eukaryotes: an Experience Report  

John Meinken1 , Jack Min2
1 Department of Computer Science and Information Systems, Youngstown State University, Youngstown, OH 44555, USA
2 Center for Applied Chemical Biology, Department of Biological Sciences, Youngstown State University, Youngstown, OH 44555, USA
Author    Correspondence author
Computational Molecular Biology, 2012, Vol. 2, No. 1   doi: 10.5376/cmb.2012.02.0001
Received: 02 Mar., 2012    Accepted: 10 Apr., 2012    Published: 24 Apr., 2012
© 2012 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Meinken and Min, 2012, Computational Prediction of Protein Subcellular Locations in Eukaryotes:  an Experience Report, Computational Molecular Biology, Vol.2, No.1 1-7 (doi: 10.5376/cmb.2012.02.0001)


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.      


Eukaryotes; Protein subcellular location; Secretome; Computational prediction
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