Predicting mRNA degradation to improve vaccine stability TAMUEngineering
], and it is our hope the RNAdegformer will aid design of more stable mRNA vaccines that can withstand harsher conditions than current ones. It is important to note, however, that there is still significant gap between errors on the 107 bp mRNA OpenVaccine public set sequences and the 130 bp mRNA OpenVaccine private set sequences, both due to difference in sequence length and diversity. Actual COVID-19 candidates are even longer and modeling those remains a challenge in the future.
In summary, we have developed a convolution and transformer-based deep learning platform toward prediction of mRNA degradation and half-lives. Our work has demonstrated success in RNA stability and half-life predictions. We believe that by further development and optimization, we will solve many challenges including understanding RNA degradation and structure relationships, aiding next-generation mRNA therapy development, and more.
Using unsupervised , supervised and semi-supervised learning in conjunction with each other, we demonstrate that RNAdegformer outperforms the top solution in OpenVaccine at predicting RNA degradation rates at each position of a given RNA sequence, a task of great importance to predict and produce stable mRNA vaccines and therapeutics.
RNAdegformer generalizes better to predict half-lives of sequences much longer than those in the training dataset compared with other machine learning and dynamic programming algorithms. RNAdegformer also reveals feature importance in predicting mRNA degradation through the usage of leave-one-feature-out test, advancing our understanding of RNA degradationS.H. and B.G conceived the project. R.S.provided critical feedback on the analysis of RNA sequences and biological context. S.H. implemented the deep learning algorithms and participated in the OpenVaccine challenge. Q.S. supervised the project and provided guidance. S.H., B.G. and R.S.
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