CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints

TL;DR

CryoNet.Refine is presented, an end-to-end deep learning framework that automates and accelerates molecular structure refinement and consistently achieves substantial improvements in both model-map correlation and overall geometric quality metrics.

Abstract

High-resolution structure determination by cryo-electron microscopy (cryo-EM) requires the accurate fitting of an atomic model into an experimental density map. Traditional refinement pipelines such as Phenix.real_space_refine and Rosetta are computationally expensive, demand extensive manual tuning, and present a significant bottleneck for researchers. We present CryoNet.Refine, an end-to-end deep learning framework that automates and accelerates molecular structure refinement. Our approach utilizes a one-step diffusion model that integrates a density-aware loss function with robust stereochemical restraints, enabling rapid optimization of a structure against experimental data. CryoNet.Refine provides a unified and versatile solution capable of refining protein complexes as well as DNA/RNA-protein complexes. In benchmarks against Phenix.real_space_refine, CryoNet.Refine consistently achieves substantial improvements in both model-map correlation and overall geometric quality metrics. By offering a scalable, automated, and powerful alternative, CryoNet.Refine aims to serve as an essential tool for next-generation cryo-EM structure refinement. Web server: https://cryonet.ai/refine; Source code: https://github.com/kuixu/cryonet.refine.

Figures (13)

• Figure 1: (a) Workflow of CryoNet. Refine. Atomic models are colored by model–map correlation coefficients (CC), with blue indicating high CC and red indicating low CC. (b) The input atomic model, refined atomic model within cryo-EM density map.
• Figure 2: (a) Overview of the CryoNet. Refine framework. The CryoNet. Refine Network consists of four modules: Atom encoder, Sequence embedder, Diffusion module, and Density generator. The input atomic model is first processed by the encoders, and the resulting features are fed into a one-step diffusion module to generate an initial refined atomic model. Subsequently, the Density generator creates a synthetic density map, which is used to compute density loss against the input density map and geometry loss based on geometry restraints. These losses are then backpropagated to optimize the diffusion module, while the atomic model is further refined through multiple recycle steps until convergence. (b) Model-map correlation coefficient trajectory over 234 recycling CryoNet. Refine iterations on the structure of the human concentrative nucleoside transporter CNT3(rcsb_6KSW). The input density map is EMD-0775, the input atomic model is predicted by AlphaFold3.
• Figure 3: Model–map correlation coefficients on protein complex benchmark.
• Figure 4: Model geometric metrics. (Color gradient: blue for better, red for worse)
• Figure 5: The input atomic models from AlphaFold3, the refined atomic model from Phenix. real_space_refine and CryoNet. Refine on the Medicago truncatula HISN5 protein (PDB-7oj5; EMD-22692) and the human concentrative nucleoside transporter CNT3 (PDB-6ksw, EMD-0775) complex. Inserts in the right panel show that the main-chains and side-chains generated from CryoNet. Refine model align well with the density map.