ModEnzA: Accurate Identification of Metabolic Enzymes Using Function Specific Profile HMMs with Optimised Discrimination Threshold and Modified Emission Probabilities.

Various enzyme identification protocols involving homology transfer by sequence-sequence or profile-sequence comparisons have been devised which utilise Swiss-Prot sequences associated with EC numbers as the training set. A profile HMM constructed for a particular EC number might select sequences which perform a different enzymatic function due to the presence of certain fold-specific residues which are conserved in enzymes sharing a common fold. We describe a protocol, ModEnzA (HMM-ModE Enzyme Annotation), which generates profile HMMs highly specific at a functional level as defined by the EC numbers by incorporating information from negative training sequences. We enrich the training dataset by mining sequences from the NCBI Non-Redundant database for increased sensitivity. We compare our method with other enzyme identification methods, both for assigning EC numbers to a genome as well as identifying protein sequences associated with an enzymatic activity. We report a sensitivity of 88% and specificity of 95% in identifying EC numbers and annotating enzymatic sequences from the E. coli genome which is higher than any other method. With the next-generation sequencing methods producing a huge amount of sequence data, the development and use of fully automated yet accurate protocols such as ModEnzA is warranted for rapid annotation of newly sequenced genomes and metagenomic sequences.