A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes

Understand

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.