OwlGems team is currently comprised of high-school, undergraduate, graduate, and mentoring professionals whose aim is to help create microbial innovations for global improvement. Our team has chosen to tackle the global public health problem of counterfeit medications. These fake drugs not only prevent the sick from receiving treatment, but can also lead to the evolution of drug-resistant pathogens. Detection methods do exist currently, but they are expensive, time consuming, and lack either sensitivity or specificity. In order to overcome these current detection shortfalls, we are developing a system to rapidly create drug biosensors that are inexpensive, specific, and sensitive to the presence of their target drug. Our current focus is directed at using bioinformatics techniques to select a viable protein to be integrated to a host replication cell that will bind to artemisinin to produce a color indicator to test the strength of the possible counterfeit.
With machine learning in the form of a Long Short Term Memory (LSTM) neural network, our team will be creating a novel artemisinin-binding protein using a set of known proteins that bind to artemisinin. This protein will then be reverse translated and inserted into a bacterial host, along with an operon that causes autolysis. The protein will then be extracted and used to detect the presence and concentration of artemisinin in solution.
Should this protein prove sensitive and specific enough to reliably bind to artemisinin, the next step would be to construct a calibration curve to see how effectively we can measure the concentration of artemisinin in solution, as well as the speed at which the result can be determined. From there, we can either refine the artemisinin system or begin work on a different drug biosensor. At the end stage, this technique could be refined to produce a simple, self-contained system that allows labs or end users to determine if drug samples are genuine or counterfeit.