IEEE, Santa Clara Valley Chapter presents "Improving the stability of RNA therapeutics through biophysics, machine learning, and crowdsourcing"

Improving the stability of RNA therapeutics through biophysics, machine learning, and crowdsourcing
Wednesday, March 24, 2021 - 6:00pm
Registration required
Hannah Wayment-Steele (Stanford)
Abstract / Description: 

The COVID-19 pandemic is unlikely to end until a majority of the world's population is vaccinated against the SARS-CoV-2 virus. The first authorized vaccine candidates have been based on messenger RNA molecules that promote translation of SARS-CoV-2 antigen proteins. However, these vaccines currently need to be kept frozen during shipping and storage, and have been logistically problematic for delivery to developing countries and to rural populations in developed countries. The inherent chemical instability of RNA molecules sets a fundamental limit on the stability of mRNA vaccines, but a largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form protected double-stranded regions while coding for the same proteins. We developed a series of principled biophysical models for RNA degradation, and used these models to launch crowdsourced design initiatives on the massive open online laboratory Eterna, as well as to develop fully-automated design algorithms. We anticipate this work will be formative for guiding future therapeutic and vaccine development in potency and stability.

Bio: Hannah Wayment-Steele is a PhD candidate in Chemistry at Stanford University. After studying chemistry and applied mathematics at Pomona College, she attended Cambridge University as a Churchill scholar, receiving a Masters for work with Daan Frenkel on the statistical mechanics of DNA nanomaterial assembly. Her current doctoral research with Rhiju Das brings together statistical mechanics, machine learning, and high-throughput experimentation to create improved models for RNA structure. This work has been applied to design of stabilized mRNA COVID vaccines.