MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition
| Autor: | Pierre Dönnes, Hans-Werner Adolph, Oliver Kohlbacher, Annette Höglund, Torsten Blum |
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| Rok vydání: | 2006 |
| Předmět: |
Statistics and Probability
Proteome Sequence analysis Amino Acid Motifs Molecular Sequence Data Computational biology Biology Proteomics medicine.disease_cause Models Biological Biochemistry Pattern Recognition Automated Protein sequencing Artificial Intelligence Sequence Analysis Protein Protein methods Protein targeting medicine Computer Simulation Amino Acid Sequence Molecular Biology Peptide sequence Genetics Binding Sites Subcellular localization Protein subcellular localization prediction Computer Science Applications Computational Mathematics Models Chemical Computational Theory and Mathematics Algorithms Software Protein Binding Subcellular Fractions |
| Zdroj: | Bioinformatics. 22:1158-1165 |
| ISSN: | 1367-4811 1367-4803 |
| DOI: | 10.1093/bioinformatics/btl002 |
| Popis: | Motivation: Functional annotation of unknown proteins is a major goal in proteomics. A key annotation is the prediction of a protein's subcellular localization. Numerous prediction techniques have been developed, typically focusing on a single underlying biological aspect or predicting a subset of all possible localizations. An important step is taken towards emulating the protein sorting process by capturing and bringing together biologically relevant information, and addressing the clear need to improve prediction accuracy and localization coverage. Results: Here we present a novel SVM-based approach for predicting subcellular localization, which integrates N-terminal targeting sequences, amino acid composition and protein sequence motifs. We show how this approach improves the prediction based on N-terminal targeting sequences, by comparing our method TargetLoc against existing methods. Furthermore, MultiLoc performs considerably better than comparable methods predicting all major eukaryotic subcellular localizations, and shows better or comparable results to methods that are specialized on fewer localizations or for one organism. Availability: Contact: hoeglund@informatik.uni-tuebingen.de |
| Databáze: | OpenAIRE |
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