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<p>Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind and perform chemistry. Cryo-electron microscopy and cryo-electron tomography can access the intrinsic heterogeneity of these complexes and are therefore key tools for understanding their function. However, three-dimensional reconstruction of the collected imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here we introduce cryoDRGN-AI, a method leveraging an expressive neural representation and combining an exhaustive search strategy with gradient-based optimization to process challenging heterogeneous datasets. Using cryoDRGN-AI, we reveal new conformational states in large datasets, reconstruct previously unresolved motions from unfiltered datasets and demonstrate ab initio reconstruction of biomolecular complexes from in situ data. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-electron microscopy and cryo-electron tomography as a high-throughput tool for structural biology and discovery.</p>

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CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets

  • 期刊论文

  • 发表日期：

    2025年6月26日

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  • Axel Levy,
  • Rishwanth Raghu,
  • J. Ryan Feathers,
  • Michal Grzadkowski,
  • Frédéric Poitevin,
  • Jake D. Johnston,
  • Francesca Vallese,
  • Oliver Biggs Clarke,
  • Gordon Wetzstein,
  • Ellen D. Zhong

摘要

Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind and perform chemistry. Cryo-electron microscopy and cryo-electron tomography can access the intrinsic heterogeneity of these complexes and are therefore key tools for understanding their function. However, three-dimensional reconstruction of the collected imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here we introduce cryoDRGN-AI, a method leveraging an expressive neural representation and combining an exhaustive search strategy with gradient-based optimization to process challenging heterogeneous datasets. Using cryoDRGN-AI, we reveal new conformational states in large datasets, reconstruct previously unresolved motions from unfiltered datasets and demonstrate ab initio reconstruction of biomolecular complexes from in situ data. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-electron microscopy and cryo-electron tomography as a high-throughput tool for structural biology and discovery.