Partnership with UIUC
During NEGEM 2020, several teams such as MIT, Purdue, and Cornell presented their iGEM projects thus far and engaged in discussion with us and judges about their designs, motivations, and plans. However, UIUC iGEM caught our eye when they presented their project on utilizing machine learning to help aid in the search for effective SARS-CoV-2 antibodies.
Immediately, we knew that we both had similar goals: we wanted to use computational techniques to optimize current antibodies and be able to design new antibodies in silico, in light of the shutdown of many labs due to the COVID-19 pandemic. As such, we immediately met with UIUC. We had an amazing conversation with them regarding both of our projects. We sent each other some of our preliminary sequences, and talked about how our projects could be integrated.
How have they helped us?
After discussing with UIUC, we found that their model to optimize antibodies was much more extensive than ours. They also had access to Rosetta, which was able to model binding affinities. We asked a lot of questions about their model design and implementation. They focused quite a bit on the phylogeny of the mutation of SARS-CoV-2, which is not a factor we were looking at. Overall, they helped us quite a bit in further fleshing out our model design and implementation by giving us support, encouragement, and several avenues to conduct further research.
How have we helped them?
During our conversations, we really helped UIUC get a better idea on how they software could translate into real life. We asked them a lot about how they see their software being used. We proposed that their software could be used in conjunction with our model and DNA origami nanostructure, since the in silico nature of their project would allow for rapid prototyping and implementation of antibodies for different viral strains. Additionally, we are implementing UIUC’s generated antibody sequences with our own, where we will be able to experimentally validate them in the coming weeks for Phase 2. This is because we were able to get preliminary indirect lab access, while they are not sure of when they’ll have access to the lab in the foreseeable future.
After talking with UIUC, we saw a lot of potential for an effective therapeutic that could be rapidly and cheaply available to everyone. Since UIUC is focusing on the mutations of SARS-CoV-2 over time, their model is extremely dynamic. As such, this means that their model is generalizable to other diseases that mutate quickly, such as influenza or HIV. While talking, both our team and UIUC discussed the potential of doctor’s have an ensemble of DNA origami therapeutics on site, each with a different mRNA sequence for a different strain of a virus. Then, the doctor would be easily able to treat the patient depending on what strain of the virus they have, rather than having to get new treatment for the strain. Since mRNA sequences are cheap to produce, this would be a cheap and fast alternative to current methods. Additionally, UIUC’s algorithm would allow us to create many possible designs, and allow doctors to use ones they think will work the best. If a certain one is not effective, then we can reiterate and further improve the sequences and DNA origami nanostructure. As we transition into Phase 2, we are working to experimentally validate antibodies both of our teams produced. We are also looking to validate each other’s sequences with our models. This will allow us to better assess and understand the strengths and weaknesses of our model. We are extremely excited, and working with UIUC thus far has been an amazing experience. Hopefully we’ll be able to get lunch in person next summer!