Algorithm Discovers Ideal mRNA Sequences for Enhancing COVID-19 Vaccines via an Online Search Platform
In a groundbreaking development, a new algorithm called LinearDesign, developed by researchers David Mathews and Liang Huang, promises to revolutionize the field of mRNA-based vaccines and therapies. This innovative tool optimizes mRNA sequences by co-optimizing two key factors: the minimum free energy (MFE) of the RNA structure and the codon adaptation index (CAI), which influences protein translation efficiency.
The algorithm employs a graph-based dynamic programming approach enhanced with a beam search heuristic to efficiently generate optimized sequences that balance RNA structural stability and codon usage for better protein production. By formulating a sequence-structure score that weights both MFE and CAI, LinearDesign designs mRNA sequences predicted to fold into stable, low-energy structures while also encoding proteins with codons preferred by the host cell machinery.
This co-optimization is crucial as lower MFE corresponds to more stable RNA folding, which helps prevent degradation and unwanted structural changes, and higher CAI improves translation efficiency, increasing the amount of protein produced. LinearDesign significantly outperforms previous methods, achieving this co-optimization much faster, for example, in 19 minutes compared to hours for some alternatives, without significant loss in solution quality.
Experimental validations have shown that mRNA sequences designed with LinearDesign tend to yield improved molecular stability, higher protein production, and increased antibody levels in biological contexts, enhancing immune response efficacy in vaccines. The tool is designed to identify the best sequence out of a huge space of possibilities for a specific protein.
The results of this study were reported in the prestigious journal Nature. Dmitri Ermolenko, PhD, working at the University of Rochester Center for RNA Biology, believes that the algorithm developed by Mathews and Huang will be indispensable for making optimal mRNA sequences for vaccines and other treatments. The algorithm could be valuable to companies that make mRNA vaccines and to research teams developing mRNA-based therapies.
David Mathews' lab at the medical center develops software packages that scientists and companies can use to predict and analyze RNA secondary structure. Mathews collaborated with Moderna on the application of his research to mRNA design. The algorithm assesses both the structure and genetic code of mRNAs.
In the context of COVID-19 vaccines, tests revealed 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 mRNA used in COVID vaccines directs our bodies to make the COVID spike protein. Some mRNA sequences that encode the spike protein are more efficient than others, and the LinearDesign algorithm helps identify these optimal sequences.
The University of Rochester's research history includes the creation of the Turner Rules, a set of parameters that predict the folding stability of RNA, by Douglas Turner, PhD, a retired professor of Chemistry at UR, and David Mathews. Elizabeth Grayhack, PhD, associate professor of Biochemistry and Biophysics at the medical center, believes that the new design will have a huge impact on vaccine development due to its ability to address the main limitation of current mRNA vaccines—they don't make enough protein.
Liang Huang, a collaborator on the LinearDesign algorithm, founded a company, Coderna.AI, to conduct mRNA design research; Mathews is a co-founder of the company. The tool is hoped to help companies develop or improve their mRNA therapies, making a significant contribution to the field of personalized medicine.
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