Intermediate-to-low resolution feature detection in cryoEM maps using cascaded neural networks

了解生物分子的功能对于理解生命是如何维持的以及为与其功能相关的疾病设计特定的治疗方法至关重要。蛋白质构成细胞成分的最大部分，并且经常组装在一起，也与其他生物分子一起组装成在许多细胞过程中发挥重要作用的大分子机器。分子机器的三维（3D）结构形成了其功能的平台，确定3D结构对于了解其活动的细节至关重要。低温电子显微镜（cryo-EM）对近自然状态下的大分子组装体的结构测定产生了巨大的影响。这些组件可以单独研究（单粒子分析）或在天然细胞环境（电子断层扫描）。冷冻EM技术和软件的进步有助于提高可以识别的细节水平。尽管如此，生物样品的固有性质往往使它们不太适合高分辨率结构测定。存放在公共存储库EMDB中的88%的冷冻电镜结构的分辨率低于3.5 μ m，因此不包含原子细节。大分子组装体的3D结构细节作为密度图获得。解释地图的细节需要检测组件的组件的结构特征。使用标准的自动化工具解释中分辨率（3.5至6厘米）和低分辨率（> 6厘米）的地图是非常困难的。现有的方法通常通过六维搜索过程来检测结构特征，这在计算上是昂贵的，并且与大量的假阳性相关联。此外，对于这些方法中的大多数，要求已知组件的每个部件的结构细节。相关的问题，在手的验证功能来自低分辨率的地图数据和表示这些低分辨率的模型本身。蛋白质结构的基本结构组织和从1D氨基酸序列进行3D折叠的过程已经研究了几十年。蛋白质使用一组有限的模块化特征，如二级结构和折叠，功能形式由这些特征的独特排列形成。还观察到中间水平的特征，其中一些二级结构组织成稳定的基序或子折叠。我们计划利用蛋白质结构的分层特征组织，并使用为模式识别建立的强大的深度学习方法，我们的目标是解决中低分辨率地图中的特征识别问题。我们将使用不同大小的结构特征库，从二级结构和较小的基序（例如蛋白质链的转折）到子折叠和折叠。覆盖中间尺寸特征的一组专门的图案或子折叠将基于紧凑性（接触）生成。深度神经网络架构将被设计为检测地图中的这些3D结构特征，并通过分层来反映结构层次。我们还计划使用开发的网络验证现有的结构模型来自低分辨率数据。在未来，我们希望扩展这项工作，通过使用额外的基于序列的信息组装功能来构建结构模型。所开发的方法将有助于扩展结构的可解释性在中间和低分辨率，并更好地利用这些数据，以深入了解生物功能的机制。拟议的发展将作为一个方便用户的工具实施，并分发给科学界。我们预计，其他科学领域可能会从为噪声数据的多标签3D分割设计的机器学习架构中受益。

Understanding the function of biomolecules is fundamental to comprehend how life is sustained and design specific therapeutics for diseases associated with their function. Proteins form the largest fraction of cell constituents and often assemble together, and also with other biomolecules, into large molecular machines that perform vital roles in many cellular processes. The three dimensional (3D) structure of a molecular machine forms the platform for its function and determining the 3D structure is crucial to understand the details of its activity. Cryogenic electron microscopy (cryo-EM) has had an immense impact on the structure determination of such large molecular assemblies in a near native state. These assemblies can either be studied in isolation (single particle analysis) or in the native cellular environment (electron tomography). Advances in technology and software for cryo-EM have helped to push the level of detail that can be discerned. Nonetheless, intrinsic properties of biological samples often make them less amenable to high resolution structure determination. 88% of cryo-EM structures deposited in the public repository EMDB are worse than 3.5Å resolution, and therefore don't contain atomic detail. The 3D structural details of a macromolecular assembly are obtained as a density map. Interpreting details of the map requires detection of structural features of the components of the assembly. Intermediate (between 3.5 Å to 6Å) and low (>6Å) resolution maps are extremely difficult to interpret using standard automated tools. Available methods usually detect structural features by six-dimensional search procedures that are computationally expensive and are associated with a large number of false-positives. Moreover, for most of these methods, it is required that the structural details of each component of the assembly is known. Related problems that go in hand are validation of features derived from low resolution map data and representation of these low resolution models themselves. The basic structural organization of protein structures and the process of 3D folding from a 1D sequence of amino acids have been studied over several decades. Proteins use a finite set of modular features like secondary structures and folds, and the functional form is formed of a unique arrangement of these features. An intermediate level of features is also observed where a few secondary structures organize into stable motifs or sub-folds. We plan to exploit the hierarchical feature organization of protein structures and using powerful deep learning approaches established for pattern recognition we aim to address the problem of feature recognition in intermediate-to-low resolution maps. We will use structural feature libraries of different sizes ranging from secondary structures and smaller motifs (e.g. turns of the protein chain) to sub-folds and folds. A specialized set of motifs or sub-folds covering the intermediate size features will be generated based on compactness (contacts). Deep neural network architectures will be designed to detect these 3D structural features in the map, with layers arranged to reflect the structural hierarchy. We also plan to use the developed networks for validation of existing structure models derived from low resolution data. In the future we would like to extend this work to potentially build structural models by assembling the features using additional sequence based information. The developed approach would help to extend structure interpretability at intermediate and low-resolutions and make better use of such data to get insights into the mechanisms of biological function. The proposed development will be implemented as a user-friendly tool and distributed to the scientific community. We anticipate that other scientific fields could potentially benefit from the machine learning architecture designed for such multi-label 3D segmentation from noisy data.