Using only sequence information for RNA structure prediction presents several limitations. Firstly, RNA sequences can exhibit significant variability, and similar sequences may fold into different structures due to factors such as environmental conditions or the presence of specific binding partners. This variability can lead to challenges in accurately predicting distances and, consequently, the 3D structure based solely on sequence data.
Secondly, sequence information alone may not capture the intricate interactions and spatial relationships between nucleotides that are critical for accurate structure prediction. For example, the influence of tertiary interactions, which are not directly encoded in the primary sequence, can be crucial for determining the final folded conformation of RNA.
To address these limitations, the framework could be adapted to incorporate additional data sources, such as experimental structural data (e.g., from NMR or X-ray crystallography), evolutionary information (e.g., from multiple sequence alignments), and biochemical data (e.g., chemical probing results). By integrating these diverse data types, the model could leverage complementary information that enhances its predictive power.
For instance, evolutionary conservation data can provide insights into functionally important regions of RNA, guiding the model to focus on critical nucleotides that are likely to be involved in maintaining structural integrity. Similarly, incorporating chemical probing data can inform the model about nucleotide accessibility and interactions, further refining distance predictions. By creating a multi-modal framework that synthesizes various data sources, the accuracy and robustness of RNA structure predictions can be significantly improved.