0
Article Tier 2 Detection Methods Environmental Sources Human Health Effects Marine & Wildlife Remediation Sign in to save

Targeted Genome Mining Facilitates the Discovery of a Promiscuous, Hyperthermostable Amidase from Thermovenabulum gondwanense with Notable Nylon-Degrading Capacity

2024

AI summary Read the abstract

Researchers used targeted genome mining to discover a promiscuous, hyperthermostable enzyme capable of degrading multiple polymer substrates, expanding the toolkit for biological plastic degradation at industrially relevant temperatures.

Polymers

Plastics are ubiquitous in our ecosystems, and microplastic accumulation in the environment is an emerging global health concern. Since available recycling technologies are not economically competitive with primary plastic production, global use is expected to reach 1231 megatons by 2060, with 493 megatons leeching into the environment each year. To identify new nylon-recycling biotechnologies, targeted genome mining was used to identify thermostable enzymes capable of degrading polyamides. Here, we describe the characterization of a novel protein sourced from Thermovenabulum gondwanense: TvgC. TvgC is extremely stable, exhibiting a melting temperature of 93 °C and no detectable losses in hydrolytic activity after one week at 60 °C. While nylonases primarily process nylon-6, TvgC catalysed the degradation of both nylon-6 and nylon-6,6 films, exhibiting marked preference for nylon-6,6, which is considered more difficult to degrade. Finally, conversion experiments demonstrate that TvgC achieves a 1.2 wt% conversion of nylon-6 film, outcompeting the most highly engineered nylonases. This novel hyperthermostable protein represents an excellent starting point for future engineering of increasingly efficient nylonases.

More Papers Like This

Article Tier 2

Targeted Genome Mining Facilitates the Discovery of a Promiscuous, Hyperthermostable Amidase from Thermovenabulum Gondwanense with Notable Nylon‐Degrading Capacity

AI summary Read the abstract

Researchers used targeted genome mining to discover a heat-stable amidase enzyme from the thermophilic bacterium Thermovenabulum gondwanense that can degrade nylon. The enzyme showed broad substrate versatility and maintained its activity at high temperatures, making it a promising candidate for industrial plastic recycling applications. The study demonstrates that mining genomes of heat-loving microorganisms can yield new biotechnologies for breaking down synthetic polymers that contribute to microplastic pollution.

Article Tier 2

Targeted Genome Mining Facilitates the Discovery of a Promiscuous, Hyperthermostable Amidase from Thermovenabulum Gondwanense with Notable Nylon‐Degrading Capacity

AI summary Read the abstract

Scientists used genome mining to discover a heat-resistant enzyme from a hot-spring bacterium that can break down nylon-6 and nylon-6,6, two of the most common nylon plastics. The enzyme remains active even after a week at 60 degrees Celsius, making it a promising candidate for industrial plastic recycling. Developing better ways to recycle nylon could help reduce the amount of nylon microplastics that accumulate in the environment and ultimately enter the human body.

Article Tier 2

Switched reaction specificity in polyesterases towards amide bond hydrolysis by enzyme engineering

AI summary Read the abstract

Researchers engineered enzymes that can break down polyamide plastics like nylon, which are normally very resistant to degradation. This could open new pathways for enzymatic recycling of synthetic fabrics and help address nylon microplastic pollution.

Article Tier 2

Discovery and Biochemical Characterization of a Novel Polyesterase for the Degradation of Synthetic Plastics

AI summary Read the abstract

Researchers used bioinformatics to discover a new enzyme from soil bacteria capable of breaking down synthetic plastics like PET and polyurethane. The enzyme was successfully expressed and characterized in the lab, offering a promising lead for developing biological plastic recycling approaches.

Article Tier 2

Machine Learning-Driven Multi-Objective Optimization of Enzyme Combinations for Plastic Degradation: An Ensemble Framework Integrating Sequence Features and Network Topology

AI summary Read the abstract

Researchers developed a machine learning framework to identify optimal enzyme combinations for breaking down polyester plastics. The study integrated kinetic data, protein sequence features, and network analysis to predict effective enzyme-substrate relationships, offering a computational approach to accelerating the discovery of enzymatic solutions for plastic waste degradation.

Research digests by email

When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.

Email me about

Share this paper