title: Function prediction of hypothetical proteins without sequence similarity to proteins of known function.
abstract: Function prediction by sequence-similarity based methods identifies only approximately 50% of the proteins deduced from newly sequenced genomes. We have developed an approach to annotate the 'leftover proteins' i.e., those which cannot be assigned function using sequence similarity. Our method (MOPS) is pan-taxonomic, predicting fine-grained molecular function (rather than a broad functional category) with high performance. In addition, we developed a validation scheme that assesses predictions using domain-specific knowledge.
authors.affiliation: Center Robert Cedergren for Bioinformatics and Genomics, Département de Biochimie, Université de Montréal, 2900 Edouard-Montpetit, Montréal, Québec, H3T 1J4, Canada. sivakumar.kannan@umontreal.ca
authors.forename: S
authors.initials: S
authors.lastname: Kannan
authors.forename: A M
authors.initials: AM
authors.lastname: Hauth
authors.forename: G
authors.initials: G
authors.lastname: Burger
mesh_terms: D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D019295:Computational Biology; D030562:Databases, Protein; D008928:Mitochondria / Q000235:genetics / Q000378:metabolism; D016366:Open Reading Frames; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins