CryoDomain: Sequence-free Protein Domain Identification from Low-resolution Cryo-EM Density Maps
Muzhi Dai, Zhuoer Dong, Weining Fu, Kui Xu, Qiangfeng Cliff Zhang
Abstract
Cryo-electron microscopy (cryo-EM) has revolutionized the field of structural biology, determining structures of large protein machines and sharpening the understanding of fundamental biological processes. Despite cryo-EM’s unique capacity to discover novel proteins from unpurified samples and reveal the intricate structures of protein complexes within native cellular environments, the advancement of protein identification methods for cryo-EM lags behind. Without prior knowledge, such as sequence, protein identification from low-resolution density maps remains challenging. Here we introduce CryoDomain, an innovative method for identifying protein domains — conserved constituent units of proteins — from low-resolution cryo-EM density maps without requiring prior knowledge of protein sequences. CryoDomain leverages cross-modal alignment to correlate cryo-EM density maps with atomic structures, transferring the knowledge learned on a large atomic structure dataset to a sparse density map dataset. On two protein domain benchmarks constructed from CATH and SCOPe, CryoDomain significantly outperforms the state-of-the-art methods for domain identification from low-resolution density maps. CryoDomain liberates structural biologists from the tedious tasks of density inspection and database searching during protein identification. It has the potential to extend the border of unbiased structure discovery and cellular landscape investigation using cryo-EM.
BibTeX
@article{Dai_Dong_Fu_Xu_Zhang_2025, title={CryoDomain: Sequence-free Protein Domain Identification from Low-resolution Cryo-EM Density Maps}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/31987}, DOI={10.1609/aaai.v39i1.31987}, abstractNote={Cryo-electron microscopy (cryo-EM) has revolutionized the field of structural biology, determining structures of large protein machines and sharpening the understanding of fundamental biological processes. Despite cryo-EM’s unique capacity to discover novel proteins from unpurified samples and reveal the intricate structures of protein complexes within native cellular environments, the advancement of protein identification methods for cryo-EM lags behind. Without prior knowledge, such as sequence, protein identification from low-resolution density maps remains challenging. Here we introduce CryoDomain, an innovative method for identifying protein domains — conserved constituent units of proteins — from low-resolution cryo-EM density maps without requiring prior knowledge of protein sequences. CryoDomain leverages cross-modal alignment to correlate cryo-EM density maps with atomic structures, transferring the knowledge learned on a large atomic structure dataset to a sparse density map dataset. On two protein domain benchmarks constructed from CATH and SCOPe, CryoDomain significantly outperforms the state-of-the-art methods for domain identification from low-resolution density maps. CryoDomain liberates structural biologists from the tedious tasks of density inspection and database searching during protein identification. It has the potential to extend the border of unbiased structure discovery and cellular landscape investigation using cryo-EM.}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Dai, Muzhi and Dong, Zhuoer and Fu, Weining and Xu, Kui and Zhang, Qiangfeng Cliff}, year={2025}, month={Apr.}, pages={119-127} }