Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps

• Published in: Briefings in bioinformatics Vol. 24; no. 2
• Main Authors: Nakamura, Andrew, Meng, Hanze, Zhao, Minglei, Wang, Fengbin, Hou, Jie, Cao, Renzhi, Si, Dong
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19.03.2023; Oxford Publishing Limited (England)
• Subjects: Amino acids; Coulomb potential; Cryoelectron Microscopy - methods; Deoxyribonucleic acid; DNA; DNA structure; Electron microscopy; Human diseases; Humans; Macromolecular Substances - chemistry; Macromolecules; Modelling; Models, Molecular; Molecular modelling; Molecular structure; Nucleic acids; Problem Solving Protocol; Protein Conformation; Protein structure; Proteins; Ribonucleic acid; RNA; protein-DNA/RNA; macromolecular modeling; machine learning; cryo-EM; artificial intelligence
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbac632

Abstract Cryo-electron microscopy (cryo-EM) allows a macromolecular structure such as protein-DNA/RNA complexes to be reconstructed in a three-dimensional coulomb potential map. The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial-intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fitting residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https://deeptracer.uw.edu/.