Abstract
BACKGROUND: Due to the impending depletion of fossil fuels, it has become important to identify alternative energy sources. The biofuel industry has proven to be a promising alternative. However, owing to the complex nature of plant biomass, hence the degradation, biofuel production remains a challenge. The copper-dependent Auxiliary Activity family 9 (AA9) proteins have been found to act synergistically with other cellulose-degrading enzymes resulting in an increased rate of cellulose breakdown. AA9 proteins are lytic polysaccharide monooxygenase (LPMO) enzymes, otherwise known as polysaccharide monooxygenases (PMOs). They are further classified as Type 1, 2 or 3 PMOs, depending on the different cleavage products formed. As AA9 proteins are known to exhibit low sequence conservation, the analysis of unique features of AA9 domains of these enzymes should provide insights for the better understanding of how different AA9 PMO types function. RESULTS: Bioinformatics approaches were used to identify features specific to the catalytic AA9 domains of each type of AA9 PMO. Sequence analysis showed the N terminus to be highly variable with type-specific inserts evident in this region. Phylogenetic analysis was performed to cluster AA9 domains based on their types. Motif analysis enabled the identification of sub-groups within each AA9 PMO type with the majority of these motifs occurring within the highly variable N terminus of AA9 domains. AA9 domain structures were manually docked to crystalline cellulose and used to analyze both the type-specific inserts and motifs at a structural level. The results indicated that these regions influence the AA9 domain active site topology and may contribute to the regioselectivity displayed by different AA9 PMO types. Physicochemical property analysis was performed and detected significant differences in aromaticity, isoelectric point and instability index between certain AA9 PMO types. CONCLUSIONS: In this study, a type-specific characterisation of AA9 domains was performed using various bioinformatics approaches. These highly variable proteins were found to have a greater degree of conservation within their respective types. Type-specific features were identified for AA9 domains, which could be observed at a sequence, structural and physicochemical level. This provides a basis under which to identify and group new AA9 LPMOs in future.