SCL-Epred: a generalised de novo eukaryotic protein subcellular localisation predictor
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Knowledge of the subcellular location of a protein provides valuable information about its function, possible interaction with other proteins and drug targetability, among other things. The experimental determination of a protein’s location in the cell is expensive, time consuming and open to human error. Fast and accurate predictors of subcellular location have an important role to play if the abundance of sequence data which is now available is to be fully exploited. In the post-genomic era, genomes in many diverse organisms are available. Many of these organisms are important in human and veterinary disease and fall outside of the well-studied plant, animal and fungi groups. We have developed a general eukaryotic subcellular localisation predictor (SCL-Epred) which predicts the location of eukaryotic proteins into three classes which are important, in particular, for determining the drug targetability of a protein—secreted proteins, membrane proteins and proteins that are neither secreted nor membrane. The algorithm powering SCL-Epred is a N-to-1 neural network and is trained on very large non-redundant sets of protein sequences. SCL-Epred performs well on training data achieving a Q of 86 % and a generalised correlation of 0.75 when tested in tenfold cross-validation on a set of 15,202 redundancy reduced protein sequences. The three class accuracy of SCL-Epred and LocTree2, and in particular a consensus predictor comprising both methods, surpasses that of other widely used predictors when benchmarked using a large redundancy reduced independent test set of 562 proteins. SCL-Epred is publicly available at http://distillf.ucd.ie/distill/.
KeywordsSubcellular localisation prediction Eukaryotes N-to-1 neural network SCL-Epred
The work was funded through a Science Foundation Ireland principal investigator grant (08/IN.1/B1864) to D. C. Shields and a Science Foundation Ireland research frontiers grant (10/RFP/GEN2749) to G. Pollastri. The authors wish to acknowledge UCD IT Services, and in particular the Phaeton administrators, for the provision of computational facilities and support. We thank Tatyana Goldberg from the Rost Lab at TU Munich for providing LocTree2 predictions.
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