Systematic identification of biocatalysts through a combined informatics approach
by James Skelton
16:00 (40 min) in USB 5.008
Biocatalysts are increasingly used for a range of purposes, including industrial applications (e.g., synthesising pharmaceutical agents) and bioremediation. Compared to traditional chemical engineering approaches, biocatalysts have advantages including improved selectivity, the ability to act in milder conditions, and reduced production of toxic by-products. One aim of the synthetic biology strand of the Newcastle University Frontiers in Engineering Biology (NUFEB) project seeks to identify biocatalytic candidates for involvement in the estrogen metabolism. Currently, in silico enzyme discovery in (meta)genomes is primarily achieved through homology searches (e.g. BLAST). However, there are no existing workflows that are able to directly take a desired compound / metabolic transformation and identify biocatalytic candidates from a set of user defined proteins.
In this talk, I describe my work in using predefined transformation patterns in reaction SMARTS, a chemoinformatics transform language, to classify reactions. Using the MetaCyc database, I then demonstrate how this workflow can be used to systematically identify seeds for homology searches. Further, we demonstrate that, for many of the predefined transformation patterns, reactant similarity may provide a means of ranking enzymatic candidates that is superior to random searching.