While brainstorming project ideas at the beginning of the summer, we were initially interested in research that would involve bioremediation for management of pollutants and waste. Our inspiration came from an interest in conducting meaningful research work with environmental applications, especially projects that combined our skill sets of computational and wet lab work. Our principal investigator, Dr. Christopher Rao, recommended that we investigate PET degradation as a potential avenue as it is currently a hot topic in research. We found that bioremediation of PET plastics is a project topic that other iGEM teams have explored before, and believed there was a lot of opportunity for collaboration to build upon prior research.
Polyethylene terephthalate, or PET, is a type of plastic that is widely used for packaging food and beverages, including soft drinks, juices, and water. 3.1 million tons of PET plastic are produced every year in North America alone, and the use of single-use plastics has surged during the pandemic. Optimizing recycling processes of PET plastic is integral to managing PET waste especially because of current standards of mass production of PET. Although PET is the most recycled plastic in the U.S., its current recycling rate is only 31%, which leads to a massive environmental impact of PET waste being disposed of into garbage, drains, and rivers. In our community, there is no industrially-scaled manner to bioremediate these plastics.
Biodegradation is a promising method of managing PET waste as it uses enzymes to break down PET without the production of secondary pollutants, which is a concern associated with other waste management processes like incineration. This year's UIUC iGEM team aims to improve that by tweaking PETase, a naturally-occurring enzyme found in Ideonella sakaiensis, a bacterium discovered in 2016 as the world's first PET-eating bacteria. Project apPETite is an attempt to provide a framework for developing an enzyme with enhanced PET plastic degradation.
Figure 1: Our experimental design pathway
As shown in Figure 1, our drylab team and wetlab teams worked together to propose optimized candidate enzymes and test their level of catalytic efficiency of PET. To generate enzymes with higher degradation rates and thermostability, we developed machine learning algorithms to produce a number of candidate enzyme sequences. These candidate enzymes were then expressed and purified from E. coli BL21 and tested for the improved degradation of PET plastic using the NanoDrop method for validation.