Constructing of molecular structural models from Cryo-Electron 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.
@article{arxiv.1901.00785,
title = {A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes},
author = {Kui Xu and Zhe Wang and Jiangping Shi and Hongsheng Li and Qiangfeng Cliff Zhang},
journal= {arXiv preprint arXiv:1901.00785},
year = {2019}
}