Motivation: Subcellular localization is a key feature in the process of functional annotation of both globular and membrane proteins. In the absence of experimental data, protein localization is inferred on the basis of annotation transfer upon sequence similarity search. However predictive tools are necessary when the localization of homologs is not known. This is so particularly for membrane proteins. Furthermore, most of the available predictors of subcellular localization are specifically trained on globular proteins and poorly perform on membrane proteins. Results: Here we develop MemLoci, a new SVM-based tool that discriminates three membrane protein localizations: plasma, internal and organelle membrane. When tested on an independent set, MemLoci outperforms existing methods, reaching an overall accuracy of 70% on predicting the location in the three membrane types, with a generalized correlation coefficient as high as 0.50. Availability: The MemLoci server is freely available on the web at: http://mu2py.biocomp.unibo.it/memloci. Data sets described in the paper can be downloaded at the same site.

MemLoci: predicting subcellular localization of membrane proteins in Eukaryotes

PIERLEONI, ANDREA;MARTELLI, PIER LUIGI;CASADIO, RITA
2011

Abstract

Motivation: Subcellular localization is a key feature in the process of functional annotation of both globular and membrane proteins. In the absence of experimental data, protein localization is inferred on the basis of annotation transfer upon sequence similarity search. However predictive tools are necessary when the localization of homologs is not known. This is so particularly for membrane proteins. Furthermore, most of the available predictors of subcellular localization are specifically trained on globular proteins and poorly perform on membrane proteins. Results: Here we develop MemLoci, a new SVM-based tool that discriminates three membrane protein localizations: plasma, internal and organelle membrane. When tested on an independent set, MemLoci outperforms existing methods, reaching an overall accuracy of 70% on predicting the location in the three membrane types, with a generalized correlation coefficient as high as 0.50. Availability: The MemLoci server is freely available on the web at: http://mu2py.biocomp.unibo.it/memloci. Data sets described in the paper can be downloaded at the same site.
Pierleoni A.; Martelli P.L.; Casadio R.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/101381
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