  N6-methyladenosine (m6A) is the most abundant mRNA modification within
mammalian cells, holding pivotal significance in the regulation of mRNA
stability, translation, and splicing. Furthermore, it plays a critical role in
the regulation of RNA degradation by primarily recruiting the YTHDF2 reader
protein. However, the selective regulation of mRNA decay of the m6A-methylated
mRNA through YTHDF2 binding is poorly understood. To improve our understanding,
we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated
degradation of m6A-methylated mRNAs. We meticulously assembled a high-quality
training dataset by integrating multiple data sources for the HeLa cell line.
To overcome the limitation of small training samples, we employed a
pre-training-fine-tuning strategy by first performing a self-supervised
pre-training of the model on 427,760 unlabeled m6A site sequences. The test
results demonstrated the importance of this pre-training strategy in enabling
m6A-BERT-Deg to outperform other benchmark models. We further conducted a
comprehensive model interpretation and revealed a surprising finding that the
presence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated
mRNA degradation, subsequently enhancing mRNA stability. We also extended our
analyses to the HEK293 cell line, shedding light on the context-dependent
YTHDF2-mediated mRNA degradation.
