ProtRNA

Protein language models (PLM), such as the highly successful ESM-2, have proven to be particularly effective. However, language models designed for RNA continue to face challenges. A key question is: can the information derived from PLMs be harnessed and transferred to RNA? To investigate this, a model termed ProtRNA has been developed by cross-modality transfer learning strategy for addressing the challenges posed by RNA’s limited and less conserved sequences. By leveraging the evolutionary and physicochemical information encoded in protein sequences, the ESM-2 model is adapted to processing "low-resource" RNA sequence data. The results show comparable or even superior performance in various RNA downstream tasks, with only 1/8 the trainable parameters and 1/6 the training data employed by other baseline RNA language models. This approach highlights the potential of cross-modality transfer learning in biological language models.