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.

尽管低温电子显微镜(Cryo-EM)的发展已经被证明通过成像溶液中的生物分子来彻底改变结构生物学领域，但绝大多数蛋白质不能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超过一半处方药的主要靶点。这项拟议的工作开发了一种新的数学和统计方法，将提高冷冻-EM的分辨率，目标是膜蛋白的3D成像和重建。 我们要解决的主要挑战，以及这种方法的新颖性，都来自统计学和微分几何。从统计学的角度来看，该建议重新审视了低温EM形状重建的范式，用称为“变分推理”的更快、可扩展的学习过程取代了传统的“期望最大化”学习过程。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“变分自动编码器”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，同时对其精度进行基准测试，我们利用了CryoEM产生的大量原始数据。反过来，处理更多的数据会提高重建的生物分子形状的分辨率。拟议项目的原创性是利用尚未渗透到机器学习和生物成像社区的统计和数学。我们的建议也广泛适用于低温电磁成像和生物成像。重建生物分子形状是几种成像方式的重点，例如用于单粒子成像的相干衍射，这些成像方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。