title: MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc/
authors.affiliation: Division for Simulation of Biological Systems, WSI/ZBIT, Eberhard Karls University Tübingen Sand 14, D-72076 Tübingen, Germany. hoeglund@informatik.uni-guebingen.de
authors.forename: Annette
authors.initials: A
authors.lastname: Höglund
authors.forename: Pierre
authors.initials: P
authors.lastname: Dönnes
authors.forename: Torsten
authors.initials: T
authors.lastname: Blum
authors.forename: Hans-Werner
authors.initials: HW
authors.lastname: Adolph
authors.forename: Oliver
authors.initials: O
authors.lastname: Kohlbacher
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001665:Binding Sites; D003198:Computer Simulation; D008954:Models, Biological; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D011485:Protein Binding; D020543:Proteome / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D013347:Subcellular Fractions / Q000737:chemistry / Q000378:metabolism*
chemical_list: D020543:Proteome