Enzyme discovery is always a hot topic for industry and biochemists, since there is huge commercial benefit and advancement in current knowledge-base. Unlike, early days where enzyme discovery relied upon assays, now it is a bioinformatics approach to speed up the process.
Lytic polysaccharide monooxygenases (LPMO) are the latest family of enzymes that have affected the biofuel industry, specifically cellulosic bioethanol industry. These enzymes, previously thought to bind to polymers of carbohydrates, are now understood to boost the enzymatic process of degrading recalcitrant crystalline polysaccharides. LPMOs are probably one of the enzymes that bind to crystalline surfaces of polymers and thus hopefully reducing the cost and time in the pre-processing step of biomass to bioethanol conversion.
As of now, there are 4 families that CAZy identifies to consist of LPMOs (AA9, AA10, AA11, and AA13), where they are now called as Auxillary Activity enzymes. However, there is the possibility that newer families of LPMOs may exist and it depends on how or when we identify them. Voshol et al in their recent paper discuss a bionnformatics based approach and validation using expression data for discovery of novel LPMO families, and report the existence of “LPMO14” family that are active on sugars such as beat-1,3-glucans.
In short, they took 14 known LPMOs from the four families and generated a HMM profile, using which they scanned six genomes and identified 7 LPMO14 genes. They were also able to find LPMO genes in other organisms (such as Drosophila, Bivalves, corals, etc) where the function of LPMO is not known.
While, this data sounds promising, further identification and characterization using standard assays would ensure that this is indeed a new family of LPMOs. Also, the authors do not mention as to why they stuck to 14 known LPMO structures to generate the HMM profile, while there are nearly 50 structures currently deposited in PDB.
Reference: G. Voshol, E. Vijgenboom and P. Punt, “The discovery of novel LPMO families with a new Hidden Markov model“, BMC Research Notes, vol. 10, no. 1, 2017.