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Gpos-mPLoc: A Top-Down Approach to Improve the Quality of Predicting Subcellular Localization of Gram-Positive Bacterial Proteins

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Abstract:

In this paper, a new predictor called “Gpos-mPLoc”, is developed for identifying the subcellular localization of Gram positive bacterial proteins by fusing the information of gene ontology, as well as the functional domain information and sequential evolution information. Compared with the old Gpos-PLoc, the new predictor is much more powerful and flexible. Particularly, it also has the capacity to deal with multiple-location proteins as indicated by the character “m” in front of “PLoc” of its name. For a newlyconstructed stringent benchmark dataset in which none of included proteins has ≥ 25% pairwise sequence identity to any other in a same subset (location), the overall jackknife success rate achieved by Gpos-mPLoc was 82.2%, which was about 10% higher than the corresponding rate by the Gpos-PLoc. As a user friendly web-server, Gpos-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/Gpos-multi/.





Keywords: Multiplex protein; ensemble classifier; functional domain; fusion approach; gene ontology; homology search; representative proteins; sequential evolution

Document Type: Research Article

DOI: https://doi.org/10.2174/092986609789839322

Publication date: 2009-12-01

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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