CryoAtlas: A Large Curated Dataset and Unified Benchmark for Cryo-EM Atomic Model Building

Authors: Mingrui Li , Minzhang Li , Weichen Qin , Yufan Xie , Zhangzhi Xiong , Sixian Shen , Jiakai Zhang , Yuan Pei , Jingyi Yu

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Abstract: Deep learning has accelerated automated atomic model building from cryo-EM maps, yet progress remains limited by the lack of large, high-quality datasets and standardized evaluation benchmarks. We address this gap by introducing CryoAtlas, a rigorously curated dataset and unified benchmark for density-guided atomic model building of protein chains. By integrating and filtering EMDB and PDB entries (up to March 2026), CryoAtlas provides 17,809 map-model-sequence entries, including a curated 16,657-entry training split. From this resource, we construct a 618-target benchmark with a shared evaluation protocol that jointly assesses structural accuracy, model-to-map fit, and stereochemical validity. Using this framework, we benchmark state-of-the-art learning-based methods against Phenix and AlphaFold3, comprehensively analyzing the impact of map resolution, sequence length, protein category and sequence identity on modeling accuracy. As a case study, retraining the ModelAngelo CNN component on CryoAtlas improves performance on the benchmark. CryoAtlas provides an open, reproducible foundation for future cryo-EM atomic model building research. Project details and resource release updates are available at this https URL .

Comments:

NeurIPS 2026 E&D Track

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.41411 [cs.AI]

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arXiv:2610.41411v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41411

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arXiv-issued DOI via DataCite (pending registration)

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From: Minzhang Li [ view email ]

[v1]

Fri, 30 Oct 2026 08:22:11 UTC (24,716 KB)

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