Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models.

Groundbreaking progress has been made in structure prediction of biomolecular assemblies, including the recent breakthrough of AlphaFold 3. However, it remains challenging for AlphaFold 3 and other state-of-the-art deep-learning-based methods to accurately predict protein-RNA complex structures, in part due to the limited availability of evolutionary information related to protein-RNA interactions that are used as inputs to the existing approaches. Here, we introduce ProRNA3D-single, a deep-learning framework for protein-RNA complex structure prediction. Using a geometric attention-enabled pairing of biological language models of protein and RNA, a previously unexplored avenue, ProRNA3D-single predicts interatomic protein-RNA interaction maps, which are then transformed into multi-scale geometric restraints for modeling 3D structures of protein-RNA complexes via geometry optimization. Benchmark tests show that ProRNA3D-single outperforms state-of-the-art methods, including AlphaFold 3, particularly when evolutionary information is limited, and exhibits robustness and performance resilience by attaining state-of-the-art accuracy with only single-sequence input.