SACCHARIS: An automated pipeline to streamline discovery of carbohydrate active enzyme activities within polyspecific families and de novo sequence datasets


Jones, D.R., Thomas, D., Alger, N., Ghavidel, A., Douglas Inglis, G., Wade Abbott, D. (2018). SACCHARIS: An automated pipeline to streamline discovery of carbohydrate active enzyme activities within polyspecific families and de novo sequence datasets. Biotechnology for Biofuels, [online] 11(1),

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Improved feed utilization, repurposing of agricultural residues; and sustainable production of energy, fuels, and bioproducts are escalating priorities for Canada. To address these issues, next-generation approaches will help stimulate discovery-based science and innovation. In this regard, ‘carbohydrate active enzymes’ (i.e. CAZymes) are protein catalysts that modify carbohydrates. CAZymes fulfill essential and diverse roles in nature ranging from synthesizing polysaccharides that fortify the cell walls of plants to releasing simple sugars from complex polysaccharides during animal digestion. Importantly, CAZymes can be harnessed for diverse applications in agriculture and represent sustainable, green technologies for the conversion of biomass into useful products.

Discovering new enzymes that will improve the efficiency of a catalytic process (i.e. generate a product faster) or catalyse a new reaction are challenging feats. This challenge is compounded by the abundance of genetic information that has been sequenced and the rate at which it is accumulating. Currently, databases are stocked full of putative protein sequences, genomes, and metagenomes. In particular, cataloging ‘microbiomes’ (i.e. entire collection of genes with a community) and reporting the changes that result under host or diet induced change are not common practices. Sorting through this deep pool of genetic information to determine what unknown functions are present or determine what changes in community structure mean for animal performance still remains a difficult and relatively slow task. Indeed, assigning function to sequence has created a bottle neck in the CAZyme discovery pipeline. New approaches that help streamline this process will likely lead to breakthrough discoveries, applications for the microbiome, and innovations for Canadian agriculture.

As part of the AAFC Clean tech network, Dr. Abbott and colleagues at the Lethbridge Research and Development Centre have been trying to ‘widen the bottleneck’ for CAZyme discovery. His team has developed a new bioinformatic pipeline called ‘SACCHARIS’ (Sequence Analysis and Clustering of CarboHydrate Active enzymes for Rapid Informed prediction of Specificity), derived from the Greek word ‘sákkʰaris’ or ‘sugar’. SACCHARIS identifies proteins predicted to encode CAZymes with new activities from sequence datasets. This tool can be used to explore genomes and even metagenomes for CAZyme discovery. Additionally, SACCHARIS can also generate CAZome fingerprints for predicting the signature metabolic potential of two or more organisms. Although only recently published, the pipeline has already led to successful patent protection of two unique enzyme activities and discovered several protein functions that have not been described previously. Currently, efforts to harness SACCHARIS for other research priorities in AAFC, such as engineering the cell walls of biorefinery crops, programming gut microbiomes, or illuminating the roles of CAZymes in crop diseases. In 2018, members of the SACCHARIS team will be visiting several AAFC centres across Canada to train interested researchers and develop new potential applications for the bioinformatic pipeline.


Background: Deposition of new genetic sequences in online databases is expanding at an unprecedented rate. As a result, sequence identification continues to outpace functional characterization of carbohydrate active enzymes (CAZymes). In this paradigm, the discovery of enzymes with novel functions is often hindered by high volumes of uncharacterized sequences particularly when the enzyme sequence belongs to a family that exhibits diverse functional specificities (i.e., polyspecificity). Therefore, to direct sequence-based discovery and characterization of new enzyme activities we have developed an automated in silico pipeline entitled: Sequence Analysis and Clustering of CarboHydrate Active enzymes for Rapid Informed prediction of Specificity (SACCHARIS). This pipeline streamlines the selection of uncharacterized sequences for discovery of new CAZyme or CBM specificity from families currently maintained on the CAZy website or within user-defined datasets. Results: SACCHARIS was used to generate a phylogenetic tree of a GH43, a CAZyme family with defined subfamily designations. This analysis confirmed that large datasets can be organized into sequence clusters of manageable sizes that possess related functions. Seeding this tree with a GH43 sequence from Bacteroides dorei DSM 17855 (BdGH43b, revealed it partitioned as a single sequence within the tree. This pattern was consistent with it possessing a unique enzyme activity for GH43 as BdGH43b is the first described α-glucanase described for this family. The capacity of SACCHARIS to extract and cluster characterized carbohydrate binding module sequences was demonstrated using family 6 CBMs (i.e., CBM6s). This CBM family displays a polyspecific ligand binding profile and contains many structurally determined members. Using SACCHARIS to identify a cluster of divergent sequences, a CBM6 sequence from a unique clade was demonstrated to bind yeast mannan, which represents the first description of an α-mannan binding CBM. Additionally, we have performed a CAZome analysis of an in-house sequenced bacterial genome and a comparative analysis of B. thetaiotaomicron VPI-5482 and B. thetaiotaomicron 7330, to demonstrate that SACCHARIS can generate "CAZome fingerprints", which differentiate between the saccharolytic potential of two related strains in silico. Conclusions: Establishing sequence-function and sequence-structure relationships in polyspecific CAZyme families are promising approaches for streamlining enzyme discovery. SACCHARIS facilitates this process by embedding CAZyme and CBM family trees generated from biochemically to structurally characterized sequences, with protein sequences that have unknown functions. In addition, these trees can be integrated with user-defined datasets (e.g., genomics, metagenomics, and transcriptomics) to inform experimental characterization of new CAZymes or CBMs not currently curated, and for researchers to compare differential sequence patterns between entire CAZomes. In this light, SACCHARIS provides an in silico tool that can be tailored for enzyme bioprospecting in datasets of increasing complexity and for diverse applications in glycobiotechnology.

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