Fast structural search for classification of gut bacterial mucin O-glycan degrading enzymes

The useful part

Cowen, Kyongbum Lee The Enzyme Commission (EC) numbering scheme provides a hierarchical way to classify enzymes according to their catalytic functions. While recent protein language model (PLM) based approaches like CLEAN and ProteInter have improved sequence-based EC number prediction, they struggle with fine-grained classification at the deepest hierarchical level. Given an enzyme of interest, DEFT first uses a PLM based method to assign the first two levels of the enzyme's EC number, and then uses a structure-based method to predict the remaining two levels of the EC number.

How it works

• Using benchmarking datasets, we demonstrate that DEFT achieves superior accuracy compared with current state-of-the-art tools for EC number prediction.
• we show that DEFT's computational efficiency enables high-throughput, genome-wide annotations of total enzyme repertoires in organisms.
• We illustrate this capability by experimentally validating DEFT predicted glycoside hydrolase (GH) profiles of intestinal mucus associated bacteria.
• We introduce Deep Enzyme Function Transfer (DEFT) that harmonizes sequence- and structure-based approaches through the key insight that PLM based annotations of the first two EC number hierarchy levels...

Details worth keeping

by Mert Erden, Tyler Schult, Karin Yanagi, Jugal Kishore Sahoo, David L.