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This paper was part of my journal club recently. I touched upon LPMOs, short for Lytic Polysaccharide Monooxygenases, in my previous post that are basically oxidative enzymes.

These interesting group of enzymes have three basic types: Type I, Type II, and Type III, classified based on the site of attack, namely LPMO1 (Type I) when oxidation occurs at C1 carbon, LPMO2 (Type II) when oxidation occurs at C4 carbon, and LPMO3 (Type III) if either C1 or C4 carbons are attacked. These subtypes are part of four CAZy families to which LPMOs are categorized into (AA9, AA10, AA11, and AA13).

Having said this, the identification of the types in LPMO is not a trivial task. This specificity to cleave a particular bond, or regiospecificity, is characterized by time-consuming chromatography experiments (HPAEC-PAD), as they are time course studies that involve incubating with the substrate for longer periods. If aldonic acids are discovered in the experiments, then it is C1 cleaving or Type I; and if 4-gemdiol-aldose is detected than it is C4 cleaving of Type II LPMOs.

Given this complex identification protocol, any shortcut to identify the regiospecificity is welcome and that’s what Danneels et al have attempted in their paper published in PLOS ONE. Specifically, using an indicator diagram based identification, they give a solution to identify regiospecificity.

To test they used Hypocrea jecorina‘s LPMO9A (having both C1/C4 cleavage) and did site-directed mutagenesis on key aromatic residues that are involved in substrate binding to create mutants that are either selective to C1 or C4 cleavage. Comparing the activity of the wildtype with the mutants by plotting the speed of release of aldonic acids with respect to 4-gemdiol-aldose the authors plot it as an indicator diagram. Basically, if one calculate the slope of the line, and it is closer to x-axis (release of aldonic acid) then the enzyme’s regiospecificity is for C1 oxidation or consisting of Type I LPMO activity. If closer to y-axis, then Type II LPMO activity.

It would be interesting to see this type of indicator diagram applied for enzyme activity identification for new LPMO enzymes, and also for enzyme engineering studies on LPMO.

Reference: B. Danneels, M. Tanghe, H. Joosten, T. Gundinger, O. Spadiut, I. Stals and T. Desmet, “A quantitative indicator diagram for lytic polysaccharide monooxygenases reveals the role of aromatic surface residues in HjLPMO9A regioselectivity“, 2017. .

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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.