A2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes


  • Kui Xu Tsinghua University
  • Zhe Wang Sensetime Group Limited
  • Jianping Shi Sensetime Group Limited
  • Hongsheng Li Chinese University of Hong Kong
  • Qiangfeng Cliff Zhang Tsinghua University




Constructing of molecular structural models from CryoElectron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.




How to Cite

Xu, K., Wang, Z., Shi, J., Li, H., & Zhang, Q. C. (2019). A2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1230-1237. https://doi.org/10.1609/aaai.v33i01.33011230



AAAI Technical Track: Applications