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CryoAtlas: A Large Curated Dataset and Unified Benchmark for Cryo-EM Atomic Model Building — arXiv:2610.41411

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41411
License (as archived): CC0-1.0
Archived: 2026-11-03T07:41:33.197Z
SHA-256 of the archived text: 6eae45b216a48b81d3567cc57a6111b07d7eac26a1671efadfe3b98e4488f717
Stable URL of this page: https://metraipolis.com/documento/dc_e116a04f1e95

How to cite

CryoAtlas: A Large Curated Dataset and Unified Benchmark for Cryo-EM Atomic Model Building — arXiv:2610.41411 — arXiv (en), https://arxiv.org/abs/2610.41411 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e116a04f1e95 (Metraipolis archive dc_e116a04f1e95, sha256 6eae45b216a48b81d3567cc57a6111b07d7eac26a1671efadfe3b98e4488f717)

BibTeX

@misc{dce116a04f1e95,
  title = {CryoAtlas: A Large Curated Dataset and Unified Benchmark for Cryo-EM Atomic Model Building — arXiv:2610.41411},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41411},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e116a04f1e95; sha256 6eae45b216a48b81d3567cc57a6111b07d7eac26a1671efadfe3b98e4488f717},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41411. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41411 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:39:37.292Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).