Fitting Atomic Structures into Cryo-EM Maps by Coupling Deep Learning-Enhanced Map Processing with Global-Local Optimization.

Journal: Journal of chemical information and modeling
PMID: 40152222

Abstract

With the breakthroughs in protein structure prediction technology, constructing atomic structures from cryo-electron microscopy (cryo-EM) density maps through structural fitting has become increasingly critical. However, the accuracy of the constructed models heavily relies on the precision of the structure-to-map fitting. In this study, we introduce DEMO-EMfit, a progressive method that integrates deep learning-based backbone map extraction with a global-local structural pose search to fit atomic structures into density maps. DEMO-EMfit was extensively evaluated on a benchmark data set comprising both cryo-electron tomography (cryo-ET) and cryo-EM maps of protein and nucleic acid complexes. The results demonstrate that DEMO-EMfit outperforms state-of-the-art approaches, offering an efficient and accurate tool for fitting atomic structures into density maps.

Authors

  • Yaxian Cai
    College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
  • Ziying Zhang
    College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
  • Xiangyu Xu
    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
  • Liang Xu
  • Yu Chen
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
  • Guijun Zhang
    College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China. Electronic address: zgj@zjut.edu.cn.
  • Xiaogen Zhou
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109.

Keywords

External Resources

Popular Topics
Recent Journals