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In silico prediction of the peroxisomal proteome in fungi, plants and animals.
Stockholms unviersitet.ORCID iD: 0000-0002-8879-9245
2003 (English)In: Journal of Molecular Biology, ISSN 0022-2836, E-ISSN 1089-8638, Vol. 330, no 2, 443-456 p.Article in journal (Refereed) Published
Abstract [en]

In an attempt to improve our abilities to predict peroxisomal proteins, we have combined machine-learning techniques for analyzing peroxisomal targeting signals (PTS1) with domain-based cross-species comparisons between eight eukaryotic genomes. Our results indicate that this combined approach has a significantly higher specificity than earlier attempts to predict peroxisomal localization, without a loss in sensitivity. This allowed us to predict 430 peroxisomal proteins that almost completely lack a localization annotation. These proteins can be grouped into 29 families covering most of the known steps in all known peroxisomal pathways. In general, plants have the highest number of predicted peroxisomal proteins, and fungi the smallest number.

Place, publisher, year, edition, pages
2003. Vol. 330, no 2, 443-456 p.
Keyword [en]
ACYL-COA OXIDASE, TARGETING SIGNAL, BETA-OXIDATION, SACCHAROMYCES-CEREVISIAE, ISOPRENOID BIOSYNTHESIS, PHYLOGENETIC PROFILES, SUBCELLULAR LOCATION, MEMBRANE-PROTEINS, PSI-BLAST, IMPORT
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:kth:diva-50444DOI: 10.1016/S0022-2836(03)00553-9ISI: 000183824900023PubMedID: 12823981OAI: oai:DiVA.org:kth-50444DiVA: diva2:461895
Available from: 2011-12-05 Created: 2011-12-05 Last updated: 2017-12-08Bibliographically approved

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Emanuelsson, Olof

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