Functional and informatics analysis enables glycosyltransferase activity prediction

The elucidation and prediction of how changes in a protein result in altered activities and selectivities remain a major challenge in chemistry. Two hurdles have prevented accurate family-wide models: obtaining (i) diverse datasets and (ii) suitable parameter frameworks that encapsulate activities in large sets. Here, we show that a relatively small but broad activity dataset is sufficient to train algorithms for functional prediction over the entire glycosyltransferase superfamily 1 (GT1) of the plant Arabidopsis thaliana. Whereas sequence analysis alone failed for GT1 substrate utilization patterns, our chemical–bioinformatic model, GT-Predict, succeeded by coupling physicochemical features with isozyme-recognition patterns over the family. GT-Predict identified GT1 biocatalysts for novel substrates and enabled functional annotation of uncharacterized GT1s. Finally, analyses of GT-Predict decision pathways revealed structural modulators of substrate recognition, thus providing information on mechanisms. This multifaceted approach to enzyme prediction may guide the streamlined utilization (and design) of biocatalysts and the discovery of other family-wide protein functions

Isoelectrically trapped enzymatic bioreactors in a multimembrane cell coupled to an electric field: theoretical modelling and experimental validation with urease.

A novel type of immobilized enzyme reactor operating under an electric field is here reported: a multicompartment immobilized enzyme reactor (MIER). In this experimental set-up, the enzyme and zwitterionic buffering ions are trapped in between two isoelectric membranes, having isoelectric point (pl) values so far apart as to trap the enzyme by an isoelectric mechanism, while allowing operation at pH optima, even when the latter pH value is quite removed from the enzyme pl. As an example, urease (pl 4.9) is trapped between a pl 4.0 and a pl 8.0 membranes, thus permitting operation (via suitable amphoteric ions buffering at pH 7.5) at the pH of optimum of activity (pH 7.5). The charged product (ammonium ions) quickly leaves the enzyme chamber under the influence of the electric field, thus allowing sustained activity for much longer time periods than in conventional reactors. As an example, while in a batch reactor 90% of original enzyme activity is lost in 200 min, only 2% activity is lost in the same period in the MIER reactor. As an additional bonus, the MIER reactor allows conversion rates of approximately 95% in a wide range of substrate concentrations, whereas batch-type reactors rarely achieve better than 50% conversion under comparable experimental conditions

Molecular docking for predictive toxicology

Molecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology