Natural diversity screening, assay development, and characterization of nylon-6 enzymatic depolymerization.

Successes in biocatalytic polyester recycling have raised the possibility of deconstructing alternative polymers enzymatically, with polyamide (PA) being a logical target due to the array of amide-cleaving enzymes present in nature. Here, we screen 40 potential natural and engineered nylon-hydrolyzing enzymes (nylonases), using mass spectrometry to quantify eight compounds resulting from enzymatic nylon-6 (PA6) hydrolysis. Comparative time-course reactions incubated at 40-70 °C showcase enzyme-dependent variations in product distributions and extent of PA6 film depolymerization, with significant nylon deconstruction activity appearing rare.

Sourcing thermotolerant poly(ethylene terephthalate) hydrolase scaffolds from natural diversity.

Enzymatic deconstruction of poly(ethylene terephthalate) (PET) is under intense investigation, given the ability of hydrolase enzymes to depolymerize PET to its constituent monomers near the polymer glass transition temperature. To date, reported PET hydrolases have been sourced from a relatively narrow sequence space. Here, we identify additional PET-active biocatalysts from natural diversity by using bioinformatics and machine learning to mine 74 putative thermotolerant PET hydrolases.

Enzyme kinetics by GH7 cellobiohydrolases on chromogenic substrates is dictated by non-productive binding: insights from crystal structures and MD simulation.

Cellobiohydrolases (CBHs) in the glycoside hydrolase family 7 (GH7) (EC3.2.1.

Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases.

Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These enzymes are often bimodular, including a catalytic domain and carbohydrate-binding module (CBM) attached via a flexible linker, and exhibit an active site that binds cello-oligomers of up to ten glucosyl moieties. GH7 cellulases consist of two major subtypes: cellobiohydrolases (CBH) and endoglucanases (EG).

Improving Enzyme Optimum Temperature Prediction with Resampling Strategies and Ensemble Learning.

Accurate prediction of the optimal catalytic temperature () of enzymes is vital in biotechnology, as enzymes with high values are desired for enhanced reaction rates. Recently, a machine learning method (temperature optima for microorganisms and enzymes, TOME) for predicting was developed. TOME was trained on a normally distributed data set with a median of 37 °C and less than 5% of values above 85 °C, limiting the method's predictive capabilities for thermostable enzymes.

Enabling microbial syringol conversion through structure-guided protein engineering.

Microbial conversion of aromatic compounds is an emerging and promising strategy for valorization of the plant biopolymer lignin. A critical and often rate-limiting reaction in aromatic catabolism is -aryl-demethylation of the abundant aromatic methoxy groups in lignin to form diols, which enables subsequent oxidative aromatic ring-opening. Recently, a cytochrome P450 system, GcoAB, was discovered to demethylate guaiacol (2-methoxyphenol), which can be produced from coniferyl alcohol-derived lignin, to form catechol.