Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome dwell times, and in turn, predict the speed of translation. The proposed method, RiboGL, combines graph and recurrent neural networks to account for both graph and sequence-based features. The model takes a mixed graph representing the secondary structure of the mRNA sequence as input, which incorporates both sequence and structure codon neighbors. In our experiments, RiboGL greatly outperforms the state-of-the-art RiboMIMO model for ribosome density prediction. We also conduct multiple ablation studies to justify the design choices made in building the pipeline. Additionally, we use gradient-based interpretability to understand how the codon context and the structural neighbors affect the ribosome dwell time at the A site. By individually analyzing the genes in the dataset, we elucidate how structure neighbors could also potentially play a role in defining the ribosome dwell times. Importantly, since structure neighbors can be far away in the sequence, a recurrent model alone could not easily extract this information. This study lays the foundation for understanding how the mRNA secondary structure can be exploited for dwell time prediction, and how in the future other graph modalities such as features from the nascent polypeptide can be used to further our understanding.