A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes
This addresses the compute-intensive bottleneck in molecular structure determination for structural biologists, offering a significant speed and accuracy improvement.
The paper tackles the problem of constructing molecular structural models from Cryo-EM density volumes, proposing a learning-based method that achieves 91% coverage on a new dataset and is hundreds of times faster and several times more accurate than existing automated solutions.
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.