Zhenwei Luo1,2,3 , Fengyun Ni1 , Qinghua Wang 4 & Jianpeng Ma 1,2,3
1 Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, China. 2 Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China. 3 Shanghai AI Laboratory, Shanghai, China. 4 Center for Biomolecular Innovation, Harcam Biomedicines, Shanghai, China.
Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analysis often yields static structures. Here we describe OPUS-DSD, an algorithm capable of efficiently reconstructing the structural landscape embedded in cryo-EM data. OPUS-DSD uses a three-dimensional convolutional encoder–decoder architecture trained with cryo-EM images, thereby encoding structural variations into a smooth and easily analyzable low-dimension space. This space can be traversed to reconstruct continuous dynamics or clustered to identify distinct conformations. OPUS-DSD can offer meaningful insights into the structural variations of macromolecules, filling in the gaps left by traditional cryo-EM structural determination, and potentially improves the reconstruction resolution by reliably clustering similar particles within the dataset. These functionalities are especially relevant to the study of highly dynamic biological systems. OPUS-DSD is available at https://github.com/alncat/opusDSD.