Protein Complex Structure Modeling by Cross-Modal Alignment between Cryo-EM Maps and Protein Sequences

Cryo-electron microscopy (cryo-EM) technique is widely used for protein structure determina- tion. Current automatic cryo-EM protein complex modeling methods mostly rely on prior chain separation. However, chain separation without sequence guidance often suffers from errors caused by cross-chain interaction or noise densities, which would accumulate and mislead the subsequent steps. Here, we present EModelX, a fully automated cryo-EM protein complex structure modeling method, which achieves sequence-guiding modeling through cross-modal alignments between cryo-EM maps and protein sequences. EModelX first employs multi-task deep learning to predict Cα atoms, backbone atoms, and amino acid types from cryo-EM maps, which is subsequently used to sample Cα traces with amino acid profiles. The profiles are then aligned with protein sequences to obtain initial structural models, which yielded an average RMSD of 1.17 ̊A in our test set, approaching atomic-level precision in recovering PDB- deposited structures. After filling unmodelled gaps through sequence-guiding Cα threading, the final models achieved an average TM-score of 0.81, significantly higher than 0.56 by the best of the compared methods. For 18 out of 99 maps in the test set, EModelX built models with higher map-model correlation coefficients (CC box) than PDB structures, highlighting its potential to improve PDB structures. The further combination with AlphaFold can improve the average TM-score to 0.91. EModelX is accessible at https://bio-web1.nscc-gz.cn/app/EModelX.