Automatic classification of fungal-fungal interactions using deep leaning models.

Fungi provide valuable solutions for diverse biotechnological applications, such as enzymes in the food industry, bioactive metabolites for healthcare, and biocontrol organisms in agriculture. Current workflows for identifying new biocontrol fungi often rely on subjective visual observations of strains' performance in microbe-microbe interaction studies, making the process time-consuming and difficult to reproduce. To overcome these challenges, we developed an AI-automated image classification approach using machine learning algorithm based on deep neural network.

Protein representations: Encoding biological information for machine learning in biocatalysis.

Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations.

Mechanism-Based Design of the First GlnA4-Specific Inhibitors.

γ-Glutamylamine synthetases are an important class of enzymes that play a key role in glutamate-based metabolism. Methionine sulfoximine (MSO) is a well-established inhibitor for the archetypal glutamine synthetase (GS) but inhibitors for most GS-like enzymes are unknown. Assuming a conserved catalytic mechanism for GS and GS-like enzymes, we explored if subtype-selective inhibitors can be obtained by merging MSO with the cognate substrates of the respective GS-like enzymes.