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A Search Engine for mRNA: Algorithm Identifies Optimal Sequences to Improve COVID Vaccines

Friday, May 19, 2023

Messenger RNA vaccines proved their worth in the COVID pandemic, and new software stands to make the already transformative technology even more powerful. 

Scientists developed an algorithm to identify the most stable, efficient mRNA sequences for vaccines. Tests show that the algorithm-derived mRNAs resist deterioration longer, produce more COVID spike protein, and dramatically increase antibody levels in mice compared to currently used mRNA vaccines. The results were reported in the journal Nature.

Study authors believe their tool will be valuable to companies that make mRNA vaccines and to research teams developing mRNA-based therapies for genetic disorders, cancer and a plethora of other diseases that can be treated by using mRNA to express a needed protein.

Searching for the Strongest mRNA

The COVID shots given throughout the pandemic have many advantages—scalable production, safety, efficacy—but suffer from some big drawbacks, including the need for ultra-cold storage and the resultant distribution challenges, and waning immunity. These limitations are due to the fact that mRNAs are inherently unstable and prone to degradation (they are constantly being “eaten” by enzymes present in cells).

The “secret sauce” for creating stronger mRNA sequences requires the right balance of two factors: structure and genetic code. Past research shows that mRNAs with a tight, rigid structure, as opposed to a floppy, unconfined structure, degrade more slowly (structure consolidates mRNAs and provides protection from hungry enzymes). Consequently, they stay in cells for a longer period and have more time to make the desired protein.

The mRNA used in COVID vaccines directs our bodies to make the COVID spike protein. The number of mRNA sequences that encode the spike protein is enormous—larger than the number of atoms in the universe. But, some of these genetic instructions are more efficient than others: one set may allow cells to churn out protein more quickly, while a different set might have redundancies that lead to sluggish protein production.

So, how do you find the right combination of structure and code? RNA expert David Mathews, MD, PhD and computer scientist Liang Huang, PhD, collaborated to create an algorithm that assesses both factors. Like a Google search for mRNA sequences, their algorithm spits out the top result for a specific protein amongst the almost infinite number of possibilities.

“Our tool is designed to identify the best sequence out of a huge space that you could never explore experimentally,” said Mathews, co-corresponding author of the Nature study and the Lynne E. Maquat professor of Biochemistry and Biophysics at the University of Rochester Medical Center. “Prior approaches did a poor job of searching this space. We hope this breakthrough will help companies to develop or improve their mRNA therapies.”