Improving membrane proteins' 3D reconstructions with cryo-electron microscopy

利用冷冻电子显微镜改善膜蛋白的 3D 重建

• 批准号: 10378342
• 负责人: Nina Miolane
• 金额: $ 20万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10378342
• 关键词: 3-Dimensional; Cryoelectron Microscopy; Instruction; Membrane Proteins; improved; reconstruction

While the development of cryo-electron microscopy (cryo-EM) has already proven to revolutionize the field of structural biology by imaging biomolecules in solution, the vast majority of proteins cannot be reconstructed at a satisfying resolution. Among them, membrane proteins still remain an imaging challenge for biologists - despite being prominent targets for over half of prescription drugs on the pharmaceutical market. The proposed work develops a novel mathematical and statistical method that will enhance the resolution of cryo-EM, targeting the 3D imaging and reconstruction of membrane proteins. The main challenges that we solve, and the novelty of this approach, come from statistics and differential geometry. From a statistical perspective, the proposal revisits the paradigm of cryo-EM shape reconstruction, by replacing the traditional "expectation-maximization" learning procedure by its faster and scalable counterpart, called "variational inference". In order to apply "variational inference" in this context, our solution implements the differential geometry of 3D shape spaces within the recent and popular dimension reduction method of "variational autoencoders". By improving the efficiency of the image reconstruction algorithm, while benchmarking its accuracy, we leverage the extraordinary amount of raw data produced by cryoEM. In turn, processing more data improves the resolution of the reconstructed biomolecular shapes. The originality of the proposed project is to leverage statistics and mathematics that have not yet penetrated the communities of machine learning and biological imaging. Our proposal is also broadly applicable beyond cryo-EM and biological imaging. Reconstructing biomolecular shapes is the focus of several imaging modalities, such as coherent diffraction for single particle imaging, that produce images whose analysis is an on-going research of members of our team. The proposal translates to the representation of shapes for these technologies.

虽然冷冻电子显微镜 (cryo-EM) 的发展已被证明可以通过对溶液中的生物分子进行成像而彻底改变结构生物学领域，但绝大多数蛋白质无法以令人满意的分辨率重建。其中，膜蛋白仍然是生物学家面临的成像挑战——尽管它是制药市场上一半以上处方药的突出目标。拟议的工作开发了一种新颖的数学和统计方法，该方法将提高冷冻电镜的分辨率，针对膜蛋白的 3D 成像和重建。 我们解决的主要挑战以及这种方法的新颖性来自统计学和微分几何。从统计角度来看，该提案重新审视了冷冻电镜形状重建的范式，用更快且可扩展的对应程序（称为“变分推理”）取代传统的“期望最大化”学习程序。为了在这种情况下应用“变分推理”，我们的解决方案在最近流行的“变分自编码器”降维方法中实现了 3D 形状空间的微分几何。通过提高图像重建算法的效率，同时对其准确性进行基准测试，我们利用了冷冻电镜产生的大量原始数据。反过来，处理更多数据可以提高重建生物分子形状的分辨率。该项目的独创性在于利用尚未渗透到机器学习和生物成像领域的统计学和数学。我们的建议还广泛适用于冷冻电镜和生物成像之外。重建生物分子形状是多种成像模式的重点，例如单粒子成像的相干衍射，这些模式产生的图像的分析是我们团队成员正在进行的研究。该提案转化为这些技术的形状表示。