Deep Learning for 3-D reconstruction of heterogeneous molecular structures from Cryo-EM data

利用冷冻电镜数据进行异质分子结构 3D 重建的深度学习

• 批准号: BB/Y513878/1
• 负责人: Pier Luigi Dragotti
• 金额: $ 30.12万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY513878%2F1
• 关键词: Deep; Learning; reconstruction; heterogeneous; molecular

Determining the structure of biomolecules is the goal of structuralbiology and is essential to understand biological mechanismsresponsible for life and in drug discovery. Biological macromoleculescan be thought of as complex machines that perform complexoperations in living cells. These dynamic machines pass throughvarious conformations in the course of their actions and a completeunderstanding of their working requires the determination of multipleconformations.Single particle Electron Cryo-Microscopy (Cryo-EM) has emerged as a unique method to determine molecular structures at near-atomic resolution. Achieving high-resolution estimation of structures of dynamic protein complexes requires large numbers of images and computationally intensive algorithms. Such reconstruction problems have been often approached by devising methods that use information about the imaging procedure and the properties of the object that needs to be reconstructed and many remarkable breakthroughs have been achieved over the years. These computational approaches are called "model-based" methods andhave the advantage to be predictable and stable. However, they can be computationally expensive and do not always derive maximum value from complex data. In particular, they are often unable to resolve complex heterogeneous structures. In contrast "data-driven" methods like deep neural networks have demonstrated, in other contexts, a remarkable ability to improve the quality of biomedical images. The problem with many deep learning approaches is that they are not predictable in the sense that often even small deviations in the inputdata can result in a huge deviation of the output, which can have devastating effects in bio-imaging applications. Moreover, it is often very difficult to interpret what a deep network machine is really optimizing.This project will advance a new family of deep neural networks for the3-D reconstruction of dynamic protein complexes from cryo-electrondata. By working closely with structural biologists, we will put forwardapproaches that systematically embed prior knowledge and constraintsabout the signal and the physics of the data formation process into thedeep neural network architectures. We will also collaborate with Prof.M. Unser and his team from EPFL Switzerland. They will provideexpertise in the area of analysis of stability of deep neural networksand will share their experience in developing AI-based methods formolecule reconstruction from Cryo-EM data. We expect that thisapproach and these collaborations will allow us to introduce stable andinterpretable neural networks able to resolve heterogeneous biologicalstructures at a resolution that current methods cannot. The methodsproduced will be in open-source format, integrated in existingcomputational suites like CCP-EM and made available to the broadestpossible community.

确定生物分子的结构是结构生物学的目标，对于理解生命和药物发现的生物学机制至关重要。可以将生物大分子视为在活细胞中进行复杂操作的复杂机器。这些动态机器在其作用过程中通过各种构象，并且对其工作的完全理解需要确定多重构造。单个粒子电子冷冻微镜（Cryo-EM）已成为确定近原子分子分子结构的独特方法。实现动态蛋白质复合物结构的高分辨率估计需要大量图像和计算密集型算法。经常通过设计有关成像过程的信息以及需要重建的对象的特性的方法来解决此类重建问题，并且多年来已经实现了许多显着的突破。这些计算方法被称为“基于模型”的方法，并且没有可预测和稳定的优势。但是，它们在计算上可能很昂贵，并且并不总是从复杂数据中得出最大值。特别是，它们通常无法解析复杂的异质结构。相比之下，在其他情况下，像深神经网络这样的“数据驱动”方法表明，提高生物医学图像质量的能力很有能力。许多深度学习方法的问题是，它们是无法预测的，因为即使输入数据中的小偏差通常也会导致输出的巨大偏差，这在生物成像应用程序中可能会产生毁灭性的影响。此外，解释深网络机器真正优化的是非常困难的。该项目将推进一个新的深神经网络家族，用于从冷冻电子中的动态蛋白质复合物重建3-D。通过与结构生物学家紧密合作，我们将进行前瞻性操作，以系统地嵌入先验知识和约束数据形成过程的信号和物理学，并将其物理化为THEDEEP神经网络体系结构。我们还将与教授合作。 Unser和他的团队来自EPFL瑞士。他们将在深度神经网络的稳定性分析领域提供专长，将分享他们从Cryo-Em数据开发基于AI的方法Formoletecules重建方面的经验。我们预计，这种操作和这些合作将使我们能够以当前方法无法解决的解决方案来引入能够解决异质生物结构的稳定且可解释的神经网络。该方法生产的将以开源形式集成在CCP-EM等现有计算套件中，并提供给广泛的社区。