Point-Cloud Enhancement and Structural Interpretation Framework for Cryo-EM Maps

Abstract

Cryo-Electron Microscopy (cryo-EM) has become a cornerstone of modern structural biochemistry, enabling the reconstruction of 3D protein maps at near-atomic resolution. Despite its transformative impact, interpreting maps remains challenging. Structural heterogeneity and molecular flexibility often produce low-resolution densities, which post-processing can only sharpen in well-ordered regions. This leaves flexible areas either poorly resolved or lost. Recent deep-learning approaches have demonstrated strong potential for enhancing cryo-EM maps, enabling an extended interpretation of cryo-EM densities. Most of these methods operate on small volumetric blocks, which restricts the receptive field of the model and prevents it from leveraging the broader structural context of the protein. To address this limitation, we introduce CryoPC, a model that enriches local block-based processing with a compact point-cloud representation of the entire map. By conditioning the network on this global representation, CryoPC is able to capture both local and distant structural features, allowing it to make more globally consistent predictions. We demonstrate that incorporating this global context consistently improves enhancement quality across a variety of samples. Furthermore, CryoPC achieves great performance compared to existing methods of similar speed, offering a practical and scalable solution for cryo-EM map enhancement. Finally, we present a framework for assesing a quality of enhanced maps using metrics from the Phenix package.