18th November 2021 - 4pm - Liang Huang: Seminar

TITLE: COVID-19 Vaccine Design using Mathematical Linguistics

ABSTRACT:

To defeat the current COVID-19 pandemic, messenger RNA (mRNA) vaccine has emerged as a promising approach thanks to its rapid and scalable production and non-infectious and non-integrating properties. However, designing an mRNA sequence to achieve high stability and immunogenicity remains a challenging problem due to the exponentially large search space (e.g., there are 2.4 x 10^632 possible mRNA sequence candidates for the spike protein of SARS-CoV-2). Surprisingly, we show that this problem can be reduced to a classical problem in both formal language theory (intersection between CFG and DFA) and computational linguistics (lattice parsing), which can be solved in O(n^3) time. As a result, we can design the optimal mRNA vaccine for SARS-CoV-2 spike protein in just about 10 minutes. Wet lab experiments on mice confirmed that our designs lead to 9-20x higher immunogenicity compared to standard baselines. We anticipate that future mRNA vaccines (COVID, flu, other infectious diseases, cancer vaccines, etc.) will be all based on our approach.

To conclude, classical results (dating back to 1961) from theoretical computer science and mathematical linguistics helped us solve the very challenging and extremely important problem in fighting the COVID-19 pandemic.

BIOGRAPHY:

Liang Huang (PhD, Penn, 2008) is an Associate Professor of Computer Science at Oregon State University and Distinguished Scientist at Baidu Research USA. He is a leading theoretical computational linguist, and was recognized at ACL 2008 (Best Paper Award) and ACL 2019 (Keynote Speech), but in recent years he has been more interested in applying his expertise in parsing, translation, and grammar formalisms to biology problems such as RNA folding and RNA design, a direction his late advisor Aravind Joshi pioneered 20 years ago. Since the outbreak of COVID-19, he has shifted his attention to the fight against the virus, which resulted in efficient algorithms for stable mRNA vaccine design, adapted from classical theory and algorithms from the 1960s.

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