Artificial Intelligence Increases COVID-19 Vaccine Immune Response by 128X
An Artificial intelligence algorithm named LinearDesign can rapidly design highly stable COVID-19 mRNA vaccine sequences. This helps to achieve a 128-fold increase in the COVID-19 vaccine's antibody response.
This major leap in both stability and efficacy for COVID-19 vaccine sequences is developed by a team of researchers from Oregon State University, StemiRNA Therapeutics, and the University of Rochester Medical Center. The findings of this research appeared in the journal Nature.
‘COVID-19 vaccines designed through artificial intelligence tool LinearDesign may offer better protection with the same dosage, and have fewer side effects.’
mRNA, or Messager RNA, has emerged as a revolutionary technology for vaccine development and potential treatments against cancer and other diseases. Serving as a vital messenger that carries genetic instructions from DNA, it enables the creation of specific proteins.
With numerous advantages in safety, efficacy, and production, mRNA has been swiftly adopted in the process of COVID-19 vaccine development. However, the natural instability of mRNA results in insufficient protein expression that weakens a vaccine's capacity to stimulate strong immune responses.
This instability also poses challenges for storing and transporting mRNA vaccines, especially in developing countries where resources are often limited. Previous research has shown that optimizing the secondary structure stability of mRNA, when combined with optimal codons, leads to improved protein expression.
The challenge lies in the mRNA design space, which is incredibly vast due to synonymous codons. For instance, there are approximately 10^632 mRNAs that can be translated into the same SARS-CoV-2 Spike protein, presenting insurmountable challenges for prior methods.
Artificial Intelligence Algorithm for Optimized COVID-19 Design Improves Stability and Immunogenicity
Researchers used a technique in language processing called lattice parsing, which represents potential word connections in a lattice graph and selects the most plausible option based on grammar.Similarly, they created a graph that compactly represents all mRNA candidates, using deterministic finite-state automaton (DFA). Applying lattice parsing to mRNA, finding the optimal mRNA is akin to identifying the most likely sentence among a range of similar-sounding alternatives.
Using this approach, LinearDesign takes a mere 11 minutes to generate the most stable mRNA sequence that encodes Spike protein. In a head-to-head comparison, the sequences designed by LinearDesign exhibited significantly improved results compared to existing vaccine sequences.
For COVID-19 mRNA vaccine sequences, the algorithm achieved up to a 5-fold increase in stability (mRNA half-life), a 3-fold increase in protein expression levels (within 48 hours), and an incredible 128-fold increase in antibody response.
For VZV mRNA vaccine sequences, the study reported up to a 6-fold increase in stability (mRNA molecule half-life), a 5.3-fold increase in protein expression levels (48 hours), and an 8-fold increase in antibody response.
This research can apply mRNA medicine encoding to a wider range of therapeutic proteins, such as monoclonal antibodies and anti-cancer drugs, promising broad applications and far-reaching impact.
Moving forward, researchers will continue to explore AI applications in life sciences, broadening the scope and depth of inclusive technology, and championing the health and well-being of all humanity.
Source: Eurekalert