SBIR/STTR Award attributes
Project Summary/Abstract: Computational techniques for gene engineering, such as codon optimization, use synonymous codon changes to increase protein production. Applications for these computational gene optimizations include recombinant protein drugs, nucleic acid therapies, and mRNA vaccines. Although codon optimization increases protein production in certain systems, synonymous changes to a gene sequence can cause unexpected detrimental results to the protein. Further, researchers have been critical of codon optimization for human therapeutics as the optimization process can affect protein conformation and function, and reduce efficacy. Therefore, codon optimization may not provide an optimal strategy for increasing protein production or designing safe and effective therapeutics. CFDRC has utilized state-of-the-art natural language processing techniques to learn how synonymous codons are used by a target organism and apply this learning to gene engineering. We demonstrated our model could predict the E. Coli synonymous codon usage with 73% accuracy, significantly above prior reports. We believe that using this AI-based approach to gene engineering will provide an optimal strategy for increasing protein production and may increase the efficacy of therapeutics.
