SCL-Epred: A generalised de novo eukaryotic protein subcellular localisation predictor

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Title: SCL-Epred: A generalised de novo eukaryotic protein subcellular localisation predictor
Authors: Mooney, CatherineCessieux, AmélieShields, Denis C.Pollastri, Gianluca
Permanent link: http://hdl.handle.net/10197/12167
Date: Aug-2013
Online since: 2021-05-18T12:16:30Z
Abstract: 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/.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Springer
Journal: Amino Acids
Volume: 45
Issue: 2
Start page: 291
End page: 299
Copyright (published version): 2013 Springer
Keywords: Subcellular fractionsEukaryotic cellsHumansProteinsMembrane proteinsProteomeComputational biologyAmino acid sequenceAlgorithms
DOI: 10.1007/s00726-013-1491-3
Language: en
Status of Item: Peer reviewed
ISSN: 0939-4451
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Conway Institute Research Collection
Computer Science Research Collection
CASL Research Collection
Medicine Research Collection

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