General Information
| Description | Directing Viridiplantae proteins to six chloroplastic sub-compartments |
| Homepage | http://schloro.biocomp.unibo.it |
Publications
SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments
Motivation:
Chloroplasts are organelles found in plants and involved in several important cell processes. Similarly to other compartments in the cell, chloroplasts have an internal structure comprising several sub-compartments, where different proteins are targeted to perform their functions. Given the relation between protein function and localization, the availability of effective computational tools to predict protein sub-organelle localizations is crucial for large-scale functional studies.
Results:
In this paper we present SChloro, a novel machine-learning approach to predict protein sub-chloroplastic localization, based on targeting signal detection and membrane protein information. The proposed approach performs multi-label predictions discriminating six chloroplastic sub-compartments that include inner membrane, outer membrane, stroma, thylakoid lumen, plastoglobule and thylakoid membrane. In comparative benchmarks, the proposed method outperforms current state-of-the-art methods in both single- and multi-compartment predictions, with an overall multi-label accuracy of 74%. The results demonstrate the relevance of the approach that is eligible as a good candidate for integration into more general large-scale annotation pipelines of protein subcellular localization.
Availability and Implementation:
The method is available as web server at http://schloro.biocomp.unibo.it
Contact:
gigi@biocomp.unibo.it.
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