Intracellular removal of acetyl, feruloyl and p-coumaroyl decorations on arabinoxylo-oligosaccharides imported from lignocellulosic biomass degradation by Ruminiclostridium cellulolyticum.

BACKGROUND: Xylans are polysaccharides that are naturally abundant in agricultural by-products, such as cereal brans and straws. Microbial degradation of arabinoxylan is facilitated by extracellular esterases that remove acetyl, feruloyl, and p-coumaroyl decorations. The bacterium Ruminiclostridium cellulolyticum possesses the Xua (xylan utilization associated) system, which is responsible for importing and intracellularly degrading arabinoxylodextrins. This system includes an arabinoxylodextrins importer, four intracellular glycosyl hydrolases, and two intracellular esterases, XuaH and XuaJ which are encoded at the end of the gene cluster. RESULTS: Genetic studies demonstrate that the genes xuaH and xuaJ are part of the xua operon, which covers xuaABCDD'EFGHIJ. This operon forms a functional unit regulated by the two-component system XuaSR. The esterases encoded at the end of the cluster have been further characterized: XuaJ is an acetyl esterase active on model substrates, while XuaH is a xylan feruloyl- and p-coumaryl-esterase. This latter is active on oligosaccharides derived from wheat bran and wheat straw. Modelling studies indicate that XuaH has the potential to interact with arabinoxylobiose acylated with mono- or diferulate. The intracellular esterases XuaH and XuaJ are believed to allow the cell to fully utilize the complex acylated arabinoxylo-dextrins imported into the cytoplasm during growth on wheat bran or straw. CONCLUSIONS: This study reports for the first time that a cytosolic feruloyl esterase is part of an intracellular arabinoxylo-dextrin import and degradation system, completing its cytosolic enzymatic arsenal. This system represents a new pathway for processing highly-decorated arabinoxylo-dextrins, which could provide a competitive advantage to the cell and may have interesting biotechnological applications.

GASP: A Pan-Specific Predictor of Family 1 Glycosyltransferase Acceptor Specificity Enabled by a Pipeline for Substrate Feature Generation and Large-Scale Experimental Screening.

Glycosylation represents a major chemical challenge; while it is one of the most common reactions in Nature, conventional chemistry struggles with stereochemistry, regioselectivity, and solubility issues. In contrast, family 1 glycosyltransferase (GT1) enzymes can glycosylate virtually any given nucleophilic group with perfect control over stereochemistry and regioselectivity. However, the appropriate catalyst for a given reaction needs to be identified among the tens of thousands of available sequences. Here, we present the glycosyltransferase acceptor specificity predictor (GASP) model, a data-driven approach to the identification of reactive GT1:acceptor pairs. We trained a random forest-based acceptor predictor on literature data and validated it on independent in-house generated data on 1001 GT1:acceptor pairs, obtaining an AUROC of 0.79 and a balanced accuracy of 72%. The performance was stable even in the case of completely new GT1s and acceptors not present in the training data set, highlighting the pan-specificity of GASP. Moreover, the model is capable of parsing all known GT1 sequences, as well as all chemicals, the latter through a pipeline for the generation of 153 chemical features for a given molecule taking the CID or SMILES as input (freely available at https://github.com/degnbol/GASP). To investigate the power of GASP, the model prediction probability scores were compared to GT1 substrate conversion yields from a newly published data set, with the top 50% of GASP predictions corresponding to reactions with >50% synthetic yields. The model was also tested in two comparative case studies: glycosylation of the antihelminth drug niclosamide and the plant defensive compound DIBOA. In the first study, the model achieved an 83% hit rate, outperforming a hit rate of 53% from a random selection assay. In the second case study, the hit rate of GASP was 50%, and while being lower than the hit rate of 83% using expert-selected enzymes, it provides a reasonable performance for the cases when an expert opinion is unavailable. The hierarchal importance of the generated chemical features was investigated by negative feature selection, revealing properties related to cyclization and atom hybridization status to be the most important characteristics for accurate prediction. Our study provides a GT1:acceptor predictor which can be trained on other data sets enabled by the automated feature generation pipelines. We also release the new in-house generated data set used for testing of GASP to facilitate the future development of GT1 activity predictors and their robust benchmarking.