Enzyme classification by ligand binding.
The problem of assigning a biochemical function to newly discovered proteins has been traditionally approached by expert enzymological analysis, sequence analysis, and structural modeling. In recent years, the appearance of databases containing protein-ligand interaction data for large numbers of protein classes and chemical compounds have provided new ways of investigating proteins for which the biochemical function is not completely understood. In this work, we introduce a method that utilizes ligand-binding data for functional classification of enzymes. The method makes use of the existing Enzyme Commission (EC) classification scheme and the data on interactions of small molecules with enzymes from the BRENDA database. A set of ligands that binds to an enzyme with unknown biochemical function serves as a query to search a protein-ligand interaction database for enzyme classes that are known to interact with a similar set of ligands. These classes provide hypotheses of the query enzyme's function and complement other computational annotations that take advantage of sequence and structural information. Similarity between sets of ligands is computed using point set similarity measures based upon similarity between individual compounds. We present the statistics of classification of the enzymes in the database by a cross-validation procedure and illustrate the application of the method on several examples.
Johnson & Johnson Pharmaceutical Research and Development, Cranbury, New Jersey 08512, USA. sizraile@prdus.jnj.com
Sergei
S
Izrailev
Michael A
MA
Farnum
D015720:5'-Nucleotidase; D048908:Aliivibrio fischeri / Q000201:enzymology; D004798:Enzymes / Q000145:classification* / Q000378:metabolism*; D008024:Ligands; D011485:Protein Binding
D004798:Enzymes; D008024:Ligands; D015720:5'-Nucleotidase