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

ABSTRACT: Ground-breaking 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 and structural information related to protein-RNA interactions that are used as inputs to the existing approaches. Here, we introduce ProRNA3D-single, a new deep-learning framework for protein-RNA complex structure prediction with only single-sequence input. Using a novel geometric attention-enabled pairing of biological language models of protein and RNA, a previously unexplored avenue, ProRNA3D-single enables the prediction of interatomic p

SUBMITTER: Roche R 

PROVIDER: S-EPMC11291176 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature