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.

Chemoenzymatic indican for light-driven denim dyeing.

Blue denim, a billion-dollar industry, is currently dyed with indigo in an unsustainable process requiring harsh reducing and alkaline chemicals. Forming indigo directly in the yarn through indican (indoxyl-ß-glucoside) is a promising alternative route with mild conditions. Indican eliminates the requirement for reducing agent while still ending as indigo, the only known molecule yielding the unique hue of blue denim. However, a bulk source of indican is missing. Here, we employ enzyme and process engineering guided by techno-economic analyses to develop an economically viable drop-in indican synthesis technology. Rational engineering of PtUGT1, a glycosyltransferase from the indigo plant, alleviated the severe substrate inactivation observed with the wildtype enzyme at the titers needed for bulk production. We further describe a mild, light-driven dyeing process. Finally, we conduct techno-economic, social sustainability, and comparative life-cycle assessments. These indicate that the presented technologies have the potential to significantly reduce environmental impacts from blue denim dyeing with only a modest cost increase.

An improved integrative GFP-based vector for genetic engineering of Parageobacillus thermoglucosidasius facilitates the identification of a key sporulation regulator.

Parageobacillus thermoglucosidasius is a thermophilic Gram-positive bacterium, which is a promising host organism for sustainable bio-based production processes. However, to take full advantage of the potential of P. thermoglucosidasius, more efficient tools for genetic engineering are required. The present study describes an improved shuttle vector, which speeds up recombination-based genomic modification by incorporating a thermostable sfGFP variant into the vector backbone. This additional selection marker allows for easier identification of recombinants, thereby removing the need for several culturing steps. The novel GFP-based shuttle is therefore capable of facilitating faster metabolic engineering of P. thermoglucosidasius through genomic deletion, integration, or exchange. To demonstrate the efficiency of the new system, the GFP-based vector was utilised for deletion of the spo0A gene in P. thermoglucosidasius DSM2542. This gene is known to be a key regulator of sporulation in Bacillus subtilis, and it was therefore hypothesised that the deletion of spo0A in P. thermoglucosiadius would produce an analogous sporulation-inhibited phenotype. Subsequent analyses of cell morphology and culture heat resistance suggests that the P. thermoglucosidasius ∆spo0A strain is sporulation-deficient. This strain may be an excellent starting point for future cell factory engineering of P. thermoglucosidasius, as the formation of endospores is normally not a desired trait in large-scale production.

Family 1 glycosyltransferases (GT1, UGTs) are subject to dilution-induced inactivation and low chemo stability toward their own acceptor substrates.

Glycosylation reactions are essential but challenging from a conventional chemistry standpoint. Conversely, they are biotechnologically feasible as glycosyltransferases can transfer sugar to an acceptor with perfect regio- and stereo-selectivity, quantitative yields, in a single reaction and under mild conditions. Low stability is often alleged to be a limitation to the biotechnological application of glycosyltransferases. Here we show that these enzymes are not necessarily intrinsically unstable, but that they present both dilution-induced inactivation and low chemostability towards their own acceptor substrates, and that these two phenomena are synergistic. We assessed 18 distinct GT1 enzymes against three unrelated acceptors (apigenin, resveratrol, and scopoletin-respectively a flavone, a stilbene, and a coumarin), resulting in a total of 54 enzymes: substrate pairs. For each pair, we varied catalyst and acceptor concentrations to obtain 16 different reaction conditions. Fifteen of the assayed enzymes (83%) displayed both low chemostability against at least one of the assayed acceptors at submillimolar concentrations, and dilution-induced inactivation. Furthermore, sensitivity to reaction conditions seems to be related to the thermal stability of the enzymes, the three unaffected enzymes having melting temperatures above 55°C, whereas the full enzyme panel ranged from 37.4 to 61.7°C. These results are important for GT1 understanding and engineering, as well as for discovery efforts and biotechnological use.