Bridging the gap between omics and structural data: A framework for interpreting protein-RNA interaction specificity
- 1Institute for Integrative Biology of the Cell
Abstract
Protein-RNA interactions play a central role in many cellular processes, such as gene regulation, protein synthesis and viral infections. Although a few thousand protein-RNA complexes have been structurally characterized, they represent only a small fraction of all interactions. This lack of experimental data limits the ability of deep-learning based approaches such as AlphaFold3 to predict structures of protein-RNA interactions. In this study, we investigate protein-RNA binding specificity by enriching experimental structures with omics data. To this end, we present a scoring approach and associated web resource quantifying the agreement between experimental structures of protein-RNA interfaces and their omics-derived binding preferences, allowing us to identify interaction motif cores. Through key structural and evolutionary features, we further highlight that these motif cores correspond to important interface regions. We then leverage the dataset of protein-RNA complexes for which structural information can be combined with binding preferences in order to benchmark the ability of AlphaFold3 to predict protein-RNA interaction specificity. To this aim, we run AlphaFold3 predictions using different RNA inputs, from the exact sequence present in the experimental structure to a non-specific sequence, including sequences embedding the consensus binding motif from in vitro experiments. We show that despite good prediction quality, the sensitivity of AlphaFold3 to the exact RNA sequence used as input indicates signs of memorization. We examine some particular complexes and uncover challenges encountered by our scoring workflow and AlphaFold3, especially in the case of alternative binding modes. Altogether, this work evidences both the promise and current limitations of deep learning approaches for protein-RNA structure prediction, and provides a resource to guide their further development.
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BibTeX
@misc{fauconnet2026bridging,
title = {{Bridging the gap between omics and structural data: A framework for interpreting protein-RNA interaction specificity}},
author = {Y. Fauconnet and S. Deng and R. Koundri and M. Pagani and C. Quignot and D. Antonio and J. Andreani},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.24.754079},
url = {https://www.biorxiv.org/content/10.64898/2026.09.24.754079v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Bridging the gap between omics and structural data: A framework for interpreting protein-RNA interaction specificity
AU - Y. Fauconnet
AU - S. Deng
AU - R. Koundri
AU - M. Pagani
AU - C. Quignot
AU - D. Antonio
AU - J. Andreani
PY - 2026
DA - 2026/09/24
PB - bioRxiv
DO - 10.64898/2026.09.24.754079
UR - https://www.biorxiv.org/content/10.64898/2026.09.24.754079v1
AB - Protein-RNA interactions play a central role in many cellular processes, such as gene regulation, protein synthesis and viral infections. Although a few thousand protein-RNA complexes have been structurally characterized, they represent only a small fraction of all interactions. This lack of experimental data limits the ability of deep-learning based approaches such as AlphaFold3 to predict structures of protein-RNA interactions. In this study, we investigate protein-RNA binding specificity by enriching experimental structures with omics data. To this end, we present a scoring approach and associated web resource quantifying the agreement between experimental structures of protein-RNA interfaces and their omics-derived binding preferences, allowing us to identify interaction motif cores. Through key structural and evolutionary features, we further highlight that these motif cores correspond to important interface regions. We then leverage the dataset of protein-RNA complexes for which structural information can be combined with binding preferences in order to benchmark the ability of AlphaFold3 to predict protein-RNA interaction specificity. To this aim, we run AlphaFold3 predictions using different RNA inputs, from the exact sequence present in the experimental structure to a non-specific sequence, including sequences embedding the consensus binding motif from in vitro experiments. We show that despite good prediction quality, the sensitivity of AlphaFold3 to the exact RNA sequence used as input indicates signs of memorization. We examine some particular complexes and uncover challenges encountered by our scoring workflow and AlphaFold3, especially in the case of alternative binding modes. Altogether, this work evidences both the promise and current limitations of deep learning approaches for protein-RNA structure prediction, and provides a resource to guide their further development.
ER -