Functional significance of fructan biosynthesis in halophilic archaea and eubacteria

Fruktanlar, fruktozdan oluşan homopolimerleri kapsayan bir polisakkarit grubunun genel adıdır ve mikrobiyal olanları fruktoz birimleri arasındaki bağlara göre levan (β-2,6 bağlı) ve inülinler (β-2,1 bağlı) olarak iki temel gruba ayrılırlar. Fruktanların

A life cycle assessment of early-stage enzyme manufacturing simulations from sustainable feedstocks

Enzyme-catalyzed reactions have relatively small environmental footprints. However, enzyme manufacturing significantly impacts the environment through dependence on traditional feedstocks. With the objective of determining the environmental impacts of enzyme production, the sustainability potential of six cradle-to-gate enzyme manufacturing systems focusing on glucose, sea lettuce, acetate, straw, and phototrophic growth, was thoroughly evaluated. Human and ecosystem toxicity categories dominated the overall impacts. Sea lettuce, straw, or phototrophic growth reduces fermentation-based emissions by 51.0, 63.7, and 79.7%, respectively. Substituting glucose-rich media demonstrated great potential to reduce marine eutrophication, land use, and ozone depletion. Replacing organic nitrogen sources with inorganic ones could further lower these impacts. Location-specific differences in electricity result in a 14% and a 27% reduction in the carbon footprint for operation in Denmark compared to the US and China. Low-impact feedstocks can be competitive if they manage to achieve substrate utilization rates and productivity levels of conventional enzyme production processes.</p

GASP: A pan-specific predictor of family 1 glycosyltransferase 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%. GASP 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). GASP had an 83% hit rate in a comparative case study for the glycosylation of the anti-helminth drug niclosamide, significantly outperforming a hit rate of 53% from a random selection assay. However, it was unable to compete with a hit rate of 83% for the glycosylation of the plant defensive compound DIBOA using expert-selected enzymes, with GASP achieving a hit rate of 50%. 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 ready-to-use GT1:acceptor predictor which in addition can be trained on other datasets enabled by the automated feature generation pipelines