Project Description

Prior to the onset of the COVID-19 pandemic, our original project topic was related to engineering bacteria to either treat biofilms or detect fungal infections in forests. However, with the beginning of the COVID-19 pandemic, all Harvard students were forced to leave campus and continue all classes and research activities virtually until further notice. With this development, we decided to rethink our project to choose a topic more conducive to virtual research, considering it was highly unlikely that any Harvard iGEM members would be allowed back on campus to do wetlab research. After reading a wide variety of news items and scholarly papers about COVID-19, we decided to pivot our project towards COVID-19. We believe in the power of synthetic biology to solve a wide variety of biological problems, and considering the magnitude of the COVID-19 health crisis, we felt it was our duty as synthetic biologists and iGEMers to do something to help address this crisis.

Due to our own college’s restrictions and our lack of lab access, we were inspired to create a project that allowed other researchers to address the pandemic with only minimal access to labs. In particular, we wanted to create a technology that could help treat the COVID-19 pandemic but could also be generalized to other health crises with minimal wetlab work. Our goal was (and still is) to design our project to be as computationally driven as possible, so minimal wetlab experimentation would be needed to produce a finished treatment. This was the catalyst for our project idea.

After conducting literature reviews of treatments being developed for COVID-19, the concept of antibody therapy caught our eye because of its simplicity and potential. In short, antibody therapy involves the transfusion of antibodies against SARS-CoV-2 into infected patients. The antibody transfusions not only neutralize the virus and slow its rate of spread but also label virions for the immune system to destroy. Reviews by Abraham and Marovich et al. helped convince us of the potential that antibody treatments had in improving health outcomes in infected patients. However, there were a few key challenges to antibody treatment that our project needed to address in order to be viable: identification of therapeutic antibodies, the time-intensive process of antibody expression and purification, and the overall cost of antibody production.

We use DNA origami because it is modular for each antibody, has high specificity, and has shown to be extremely safe for people. DNA origami is a burgeoning technology that has a great potential, and we thought this suited well for our desire to creatively innovate solutions to real world issues. For much more detail about why we chose DNA origami over other more well-known alternatives, please see the Design page.

Our project addresses all of these challenges by identifying a therapeutic antibody in silico and then delivering the mRNA sequence for this antibody to a patient’s own B cells, where the antibody can be translated and produced as a treatment. We chose to use machine learning (specifically ensemble learning) to identify a novel therapeutic antibody because of the power that machine learning has to improve on existing antibodies, as described in a review by Graves et al. Our goal was to create a machine learning algorithm that could iterate on existing antibodies and biochemically determine an antibody sequence that would be likely to neutralize the SARS-CoV-2 virus. We also needed to identify a delivery method that could deliver the mRNA sequence for our novel antibody to B cells in vivo. We specifically wanted to deliver the mRNA sequence instead of the fully translated antibody because nucleic acids (such as mRNA) are often significantly cheaper and less labor-intensive to produce and purify compared to full proteins. We eventually decided to use DNA origami as the delivery method for our antibody sequence for a variety of reasons, which are given in more detail on the “Motivations” page. DNA origami is a method for constructing interactive nanostructures out of DNA. In short, DNA origami involves folding a long single-stranded piece of DNA known as the “scaffold” into a particular shape, with shorter DNA strands known as “staples” being used to fix the scaffold in the desired shape. There are a wide variety of computational tools that can be used to design and simulate DNA origami nanostructures, and our goal was to employ these tools to design and validate a DNA origami structure that could deliver our antibody sequence to its intended target.

We need this type of therapeutic because we want treatments that are easily accessible to people, no matter their socioeconomic background. We need treatment that will be readily available on a moment's notice. All of our lives and families have been effected by COVID-19, and so we want to make a difference.

Ultimately, our project uses machine learning to design a novel therapeutic antibody against the SARS-CoV-2 virus and uses DNA origami to deliver the sequence for this antibody to B cells in vivo. Our project is entirely virtual this year, but we hope to experimentally validate it in the future. It is important to note that although our project is intended to address COVID-19 this year, it can be generalized to a wide variety of other viral and bacterial diseases. The machine learning algorithm can be easily tweaked to identify a therapeutic antibody against almost any target pathogen protein of choice, and the DNA origami delivery vehicle can be adapted to deliver any mRNA sequence by changing the sequences of some of the staples in the nanostructure. Both of these changes can be done computationally with no lab access. In this way, our project could serve as a broader therapeutic pipeline to rapidly address emerging diseases without the requirement of extensive lab experiments. The figure below summarizes our pipeline that we have developed in this project, and the video below summarizes our work as a whole.

References to literature for all sections of our project can be found on the References page of our wiki.


We hope you enjoy the video and that you enjoy exploring our wiki!