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.”
“The success of COVID vaccines confirmed that mRNA-based technologies can be absolutely transformative,” said Dmitri Ermolenko, PhD, who works with Mathews in the University of Rochester Center for RNA Biology. “I think Dave’s algorithm will be indispensable for making optimal mRNA sequences for vaccines and other treatments.”
The unique partnership between Mathews, a computational biologist whose lab develops tools to predict RNA structure, and Huang, a computational linguist with expertise in natural language processing at Oregon State University, led to the creation of LinearDesign. The algorithm produces results in around 10 minutes on a desktop computer. The COVID vaccines with the designed mRNA sequences produced three times more spike protein and led to a 128-fold increase in antibodies in mice compared to vaccines used today. Tests also revealed the designed mRNA extended shelf stability by fivefold.
“This work addresses the main limitation of current mRNA vaccines—they don’t make enough protein,” noted Elizabeth Grayhack, PhD, associate professor of Biochemistry and Biophysics at URMC. “What’s critical is that they took this problem from beginning to end; they came up with a clever design and they show that it actually works. I think this will have a huge impact on vaccine development.”
RNA Runs Deep at Rochester
The new study is the culmination of a long history of research at the University of Rochester. Twenty five years ago, Mathews was a student in the lab of Douglas Turner, PhD, a now retired professor of Chemistry at UR. Working together, they devised the Turner Rules—a set of parameters that predict the folding stability of RNA (how stable a structure a given RNA forms). According to Mathews, “In the 90’s, we had no idea that the Turner Rules would be so important for vaccine design. Doug’s work has had a huge impact on the field of RNA biology.”
“Back then, very little was known about RNA and there was only a small community of scientists working on it. There were many, many more papers about DNA and proteins,” said Turner, who still conducts research in Mathews’ lab at URMC. “Dave started working in my lab when he was a sophomore getting his undergraduate degree in physics. He has been key to this research all along the way.”
Mathews’ lab at URMC, which grew out of his work with Turner, develops software packages that scientists and companies can use to predict and analyze RNA secondary structure (genetic code of RNA sequences). Mathews collaborated with Moderna on the application of his research to mRNA design.
Mathews’ contribution to the research was funded by the National Institutes of Health. In addition to Mathews and Huang, researchers from Oregon State University, China Pharmaceutical University, National University of Singapore, Baidu Research and StemiRNA Therapeutics Inc. contributed to the study. Huang founded a company, Coderna.AI, to conduct mRNA design research; Mathews is a co-founder of the company.