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<p>Cryogenic electron microscopy (cryo-EM) single-particle analysis (SPA) has become a powerful technique for macromolecular structure determination. However, its effectiveness is often constrained by limited particle numbers or missing views. To address these challenges, we present CoCoFold, a fine-tuned framework that integrates raw cryo-EM particle images into AlphaFold to directly guide atomic model prediction. CoCoFold adopts a memory-efficient tuning strategy by introducing a fused attention mechanism into AlphaFold’s structure module. Moreover, a differentiable network links predicted structures with cryo-EM observations, enabling end-to-end refinement against experimental data. Benchmark experiments with the escalating quantity insufficiency and view-missing of cryo-EM observations, demonstrate that CoCoFold consistently outperforms state-of-the-art methods across multiple evaluation metrics.</p>

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Fine-tuning AlphaFold with limited cryo-EM observations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月19日

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  • Junwen Liao,
  • Dihan Zheng,
  • Hui Zhang,
  • Linfeng Zhang,
  • Mingxu Hu,
  • Chenglong Bao

摘要

Cryogenic electron microscopy (cryo-EM) single-particle analysis (SPA) has become a powerful technique for macromolecular structure determination. However, its effectiveness is often constrained by limited particle numbers or missing views. To address these challenges, we present CoCoFold, a fine-tuned framework that integrates raw cryo-EM particle images into AlphaFold to directly guide atomic model prediction. CoCoFold adopts a memory-efficient tuning strategy by introducing a fused attention mechanism into AlphaFold’s structure module. Moreover, a differentiable network links predicted structures with cryo-EM observations, enabling end-to-end refinement against experimental data. Benchmark experiments with the escalating quantity insufficiency and view-missing of cryo-EM observations, demonstrate that CoCoFold consistently outperforms state-of-the-art methods across multiple evaluation metrics.