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A Computational Platform for In-Situ Structure Determination at Near-Atomic Resolution using Cryo-Electron Tomography

A Computational Platform for In-Situ Structure Determination at Near-Atomic Resolution using Cryo-Electron Tomography
使用冷冻电子断层扫描以近原子分辨率原位结构测定的计算平台
批准号:
10183362
负责人:
Alberto Bartesaghi
金额:
$31.89万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-09-01 至 2025-05-31

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中文摘要
翻译
项目总结 了解蛋白质如何在细胞内相互作用以执行特定功能是现代 生物学,对于了解这些分子在生物医学中扮演的不同角色至关重要。低温电子 断层扫描(Cryo-ET)结合亚体积平均(SVA)是目前唯一能 允许在其未受干扰的自然环境中以纳米分辨率成像大分子。多数 然而,成功的研究一直是大的络合物或超分子组装,并在分辨率上 太低了,不能揭示分子水平的相互作用。这项技术发展的总体目标 该项目的目的是设计计算工具,以提高冷冻-ET/SVA的分辨率,并将其适用范围扩大到 更广泛的生物医学相关靶点。具体目标是:(1)我们将制定策略,改善 从低剂量断层摄影投影确定倾斜对比传递函数的准确性,(2)我们 将设计的算法用于提高子体对齐、重建和分类的精度 在减少与数据处理相关的计算B因素方面,以及(3)我们将优化成像和 数据处理参数,以实现对更广泛类别的目标的高分辨率研究,包括小目标 复合体。作为原则的证明,我们实现了我们平台的第一代原型,并在 用冷凝器成像的单分散样品。初步结果表明,我们的平台:(1)改进了 在可实现的分辨率方面的最先进技术,以及(2)可以用于确定300 kDa的结构 酶的分辨率为3.9,这是该领域的一项突破性成就。我们的研究是创新的 因为它寻求克服低温所需的基本技术挑战,以实现 这种新兴的成像技术。这项提议意义重大,因为这将是第一次证明- 分子量靶可以用低温电子显微镜在近原子分辨率下成像,这表明这种技术 是目前最有前景的重要生物分子原位成像方法。最终,通过缩小“分辨率差距” 研究高分辨率单分散样品的策略(X射线、核磁共振和单粒子冷冻)之间的关系 EM)和在其自然环境中研究蛋白质的技术,我们的方法将允许可视化 蛋白质复合体以前所未有的详细程度处于其功能状态。
英文摘要
PROJECT SUMMARY Understanding how proteins interact within the cell to perform specific functions is a major goal of modern biology, and vital for understanding the diverse roles these molecules play in biomedicine. Cryo-electron tomography (cryo-ET) combined with sub-volume averaging (SVA) is currently the only imaging technology that allows imaging macromolecules within their unperturbed native environment at nanometer resolutions. Most successful studies, however, have been of large complexes or supramolecular assemblies, and at resolutions that are too low to reveal molecular level interactions. The overall objective of this Technology Development project is to design computational tools to improve the resolution of cryo-ET/SVA and extend its applicability to a wider class of biomedically relevant targets. The specific aims are: (1) we will develop strategies to improve the accuracy of the tilted contrast transfer function determination from low-dose tomographic projections, (2) we will design algorithms to improve the accuracy of sub-volume alignment, reconstruction and classification aimed at reducing the computational B-factors associated with data processing, and (3) we will optimize imaging and data processing parameters to enable high-resolution studies of a wider class of targets including small complexes. As proof of principle, we implemented a first-generation prototype of our platform and tested it on monodisperse samples imaged by cryo-ET. The preliminary results demonstrate that our platform: (1) improves the state-of-the-art in terms of achievable resolution, and (2) can be used to determine the structure of a 300kDa enzyme at 3.9 Å resolution, representing a ground-breaking achievement for the field. Our research is innovative because it seeks to overcome fundamental technical challenges in cryo-ET needed to realize the full potential of this emerging imaging technology. The proposal is significant because it will be the first demonstration that low- molecular weight targets can be imaged at near-atomic resolution using cryo-ET, indicating that this technique is the most promising route for imaging important biomolecules in-situ. Ultimately, by closing the “resolution gap” between strategies for studying monodisperse samples at high-resolution (X-ray, NMR and single-particle cryo- EM) and techniques to study proteins in their native environments, our methods will allow the visualization of protein complexes in their functional state at unprecedented levels of detail.
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A Computational Platform for In-Situ Structure Determination at Near-Atomic Resolution using Cryo-Electron Tomography
  • 批准号:
    10466802
  • 项目类别:
  • 资助金额:
    $31.82万
  • 财政年份:
    2021
  • 负责人:
    Alberto Bartesaghi
  • 依托单位:
A Computational Platform for In-Situ Structure Determination at Near-Atomic Resolution using Cryo-Electron Tomography
  • 批准号:
    10624852
  • 项目类别:
  • 资助金额:
    $31.74万
  • 财政年份:
    2021
  • 负责人:
    Alberto Bartesaghi
  • 依托单位:
A Computational Platform for In-Situ Structure Determination at Near-Atomic Resolution using Cryo-Electron Tomography
  • 批准号:
    10581369
  • 项目类别:
  • 资助金额:
    $21.0万
  • 财政年份:
    2021
  • 负责人:
    Alberto Bartesaghi
  • 依托单位:
海外基金