mRNA2vec: mRNA Embedding with Language Model in the 5'UTR-CDS for mRNA Design
This work addresses the costly selection of mRNA sequences for drug design, offering a novel embedding approach with specific gains in prediction tasks, though it is incremental as it builds on existing language model frameworks.
The paper tackled the problem of selecting optimal mRNA sequences for vaccines and therapeutics by proposing mRNA2vec, a contextual language model-based embedding method that uses the 5'UTR and CDS regions, which demonstrated significant improvements in translation efficiency and expression level prediction tasks compared to state-of-the-art methods.
Messenger RNA (mRNA)-based vaccines are accelerating the discovery of new drugs and revolutionizing the pharmaceutical industry. However, selecting particular mRNA sequences for vaccines and therapeutics from extensive mRNA libraries is costly. Effective mRNA therapeutics require carefully designed sequences with optimized expression levels and stability. This paper proposes a novel contextual language model (LM)-based embedding method: mRNA2vec. In contrast to existing mRNA embedding approaches, our method is based on the self-supervised teacher-student learning framework of data2vec. We jointly use the 5' untranslated region (UTR) and coding sequence (CDS) region as the input sequences. We adapt our LM-based approach specifically to mRNA by 1) considering the importance of location on the mRNA sequence with probabilistic masking, 2) using Minimum Free Energy (MFE) prediction and Secondary Structure (SS) classification as additional pretext tasks. mRNA2vec demonstrates significant improvements in translation efficiency (TE) and expression level (EL) prediction tasks in UTR compared to SOTA methods such as UTR-LM. It also gives a competitive performance in mRNA stability and protein production level tasks in CDS such as CodonBERT.