The proposed framework, which utilizes a Distance Transformer (DiT) to predict RNA distance matrices solely from primary sequence information, can be enhanced by integrating additional structural information, particularly secondary structure data. One approach to achieve this is by incorporating secondary structure predictions as auxiliary inputs to the DiT model. This could involve concatenating secondary structure features, such as base pairing probabilities or predicted secondary structure motifs, with the primary sequence embeddings generated by the RNA bidirectional language model.
By enriching the input representation with secondary structure information, the model can better capture the spatial constraints and interactions between nucleotides that are not solely evident from the sequence. For instance, secondary structure elements like stems, loops, and bulges can provide critical context that influences the distances between nucleotide bases. Additionally, the model could be modified to include attention mechanisms that specifically focus on secondary structure features, allowing it to learn more complex relationships between sequence and structure.
Furthermore, the training process could be adapted to include multi-task learning, where the model simultaneously predicts both distance matrices and secondary structures. This would encourage the model to learn shared representations that benefit both tasks, potentially leading to improved accuracy in distance predictions. Overall, integrating secondary structure information into the DiT framework could significantly enhance its predictive capabilities and provide a more comprehensive understanding of RNA structural dynamics.