title: Identification of Functionally Related Enzymes by Learning-to-Rank Methods.
abstract: Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work, we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes.
authors.forename: Michiel
authors.initials: M
authors.lastname: Stock
authors.forename: Thomas
authors.initials: T
authors.lastname: Fober
authors.forename: Eyke
authors.initials: E
authors.lastname: Hüllermeier
authors.forename: Serghei
authors.initials: S
authors.lastname: Glinca
authors.forename: Gerhard
authors.initials: G
authors.lastname: Klebe
authors.forename: Tapio
authors.initials: T
authors.lastname: Pahikkala
authors.forename: Antti
authors.initials: A
authors.lastname: Airola
authors.forename: Bernard
authors.initials: B
authors.lastname: De Baets
authors.forename: Willem
authors.initials: W
authors.lastname: Waegeman
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry*; D000069550:Machine Learning; D011487:Protein Conformation
chemical_list: D004798:Enzymes