Deep learning models of RNA base-pairing structures generalize to unseen folds and make accurate zero-shot predictions of base-base interactions of RNA complexes

lang, m.; Litfin, T.; Chen, K.; Zhan, J.; Zhou, Y.

doi:10.1101/2023.09.26.559463

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bioRxiv · 10.1101/2023.09.26.559463

Deep learning models of RNA base-pairing structures generalize to unseen folds and make accurate zero-shot predictions of base-base interactions of RNA complexes

lang, m.·Litfin, T.·Chen, K.·Zhan, J.·Zhou, Y.

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Abstract

The intricate network of RNA-RNA interactions, crucial for orchestrating essential cellular processes like transcriptional and translational regulation, has been unveiling through high-throughput techniques and computational predictions. With the emergence of deep learning methodologies, the question arises: how do these cutting-edge techniques for base-pairing prediction compare to traditional free-energy-based approaches, particularly when applied to the challenging domain of interaction prediction via chain concatenation? In this study, we employ base pairs derived from three-dimensional RNA complex structures as the gold standard benchmark to assess the performance of 23 different methods, including recently developed deep learning models. Our results demonstrate that the deep-learning-based methods, SPOT-RNA can be generalized to make accurate zero-shot predictions of RNA-RNA interactions not only between previously unseen RNA structures but also between RNAs without monomeric structures. The finding underscores the potential of deep learning as a robust tool for advancing our understanding of these complex molecular interactions.

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lang, m., Litfin, T., Chen, K., Zhan, J., Zhou, Y.. 2023-09-28. Deep learning models of RNA base-pairing structures generalize to unseen folds and make accurate zero-shot predictions of base-base interactions of RNA complexes. https://doi.org/10.1101/2023.09.26.559463

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