Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours

Abstract

Abstract Despite the current wealth of sequencing data, one‐third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently are not amenable to modern systemic analyses. As 555 of these orphan enzymes have metabolic pathway neighbours, we developed a global framework that utilizes the pathway and (meta)genomic neighbour information to assign candidate sequences to orphan enzymes. For 131 orphan enzymes (37% of those for which (meta)genomic neighbours are available), we associate sequences to them using scoring parameters with an estimated accuracy of 70%, implying functional annotation of 16 345 gene sequences in numerous (meta)genomes. As a case in point, two of these candidate sequences were experimentally validated to encode the predicted activity. In addition, we augmented the currently available genome‐scale metabolic models with these new sequence–function associations and were able to expand the models by on average 8%, with a considerable change in the flux connectivity patterns and improved essentiality prediction.

Keywords

• genomics
• metabolic pathways
• metagenomics
• neighbourhood information
• orphan enzymes