Developing universal vaccines for coronaviruses will serve the long-term purpose of averting dire pandemics in future as such vaccines are effective against different types of coronaviruses that can cause widespread infections including COVID-19, SARS, MERS, etc. Computational molecular design and bioinformatics can accelerate the discovery of such vaccines several folds. Historically, both these powerful tools have been used separately for design. However, to leverage both the accuracy of molecular modeling techniques and the rapid predictive capability of bioinformatics tools, an adaptive feedback strategy between the two is necessary. We envision such a strategy for the discovery of universal vaccines that will involve identifying common motifs of proteins among the viruses and selecting the ones that will be effective in triggering the immune response against all coronaviruses; designing effective carrier to deliver such peptides. The integrated and automated workflow with feedback between different stages should also be scalable across volunteer computing platforms. With our expertise in molecular modeling (molecular simulations and computational chemistry), continuum modeling, and machine learning, we seek to partner with experts in experimental techniques, bioinformatics, and software development to realize the design framework. The added advantage is that the framework is not specific to coronaviruses and can be used for computational discovery of any types of vaccines.

What it does

High throughput epitope-antibody binding predictions with minimal user input

How I built it

Python and other open-source and free-to-use frameworks

Challenges I ran into

Accomplishments that I'm proud of

Minimal input, high throughput design

What I learned

Maximum pipeline automation is absolutely essential to make it easy for non-experts to use

What's next for Computational-Vaccine

Extend the framework by including other crucial components of vaccine design such as adjuvant and delivery system design

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