Protein structure determination from low-resolution experimental data

摘要 确定蛋白质的三维结构在生物学中至关重要， 为药物设计提供生物学机制和重要靶点的见解。虽然高- 分辨率X射线衍射数据提供了细胞成分的原子视图，对于许多 有趣的和生物相关的复合物，它可能只可能获得低分辨率 结构信息。近年来，低温电子显微镜（cryo-EM）已成为一种 这是一种强大的方法，可以深入了解这些大型分子机器的结构。但对于 许多复杂的利益，数据往往是：a）非常有限的分辨率，缺乏原子 细节，和B）在数据质量方面高度异构。这两种影响都是由于 许多这些复合物的固有灵活性。从中提取详细的原子信息 数据，在理解功能，突变的影响，或在设计药物的关键是 由于观察次数少和蛋白质构象空间大， 可以采用。我们提出了一套计算方法来推断高精度的原子 从这些低分辨率的数据模型，揭示了这些复杂的详细结构的见解。 在上一个赠款期间，我们：a）开发了一套工具，用于准确地建立和 将蛋白质模型细化为低分辨率的实验cryoEM图谱，B）发布了这些工具 作为免费软件，并提供教程，使低温电子显微镜使用 这些方法，c）与数十名显微镜学家密切合作，开发定制的 他们感兴趣的特定系统的方法。在本建议中，我们进一步这些方法， 在几个不同的方向。我们开发了准确识别配体的方法， 在低分辨率数据集中精确建模配体构象。我们用机器制造- 学习引导的蛋白质结构预测，并开发方法，以更快速，更强大 解释低分辨率的冷冻电镜数据集我们认为这些方法应该足够快， 适合在在线数据处理管道中使用。最后，我们开发了一种方法， 通过建立一组异质的蛋白质模型来解开结构异质性， 解释了单粒子图像的变化，提供了对构象的结构见解 单粒子数据的不均匀性。 拟议的研究的总体目标是强大的和可访问的方法来解释-到 原子精度水平-低分辨率和异构冷冻EM数据集。结合起来， 这项建议的三个目标将大大提高我们推断原子能的能力。 这些数据集的交互。随着显微镜学家继续研究更复杂的蛋白质 复合物，这些方法将需要揭示原子水平的见解如何生物医学 重要的蛋白质复合物发挥其功能，以及在人类疾病中出现的问题。

Abstract Determination of a protein’s three-dimensional structure is of critical importance in biology, providing insights to biological mechanisms and important targets for drug design. While high- resolution X-ray diffraction data provides an atomic view of cellular components, for many interesting and biologically relevant complexes, it may only be possible to obtain low-resolution structural information. In recent years, cryo-electron microscopy (cryo-EM) has emerged as a powerful method to gain structural insights into these large molecular machines. However, for many complexes of interest, data is often: a) extremely limited in resolution, lacking atomic details, and b) highly heterogeneous in terms of data quality. Both effects are due to the inherent flexibility of many of these complexes. Extracting detailed atomic information from this data, critical in understanding function, the effects of mutation, or in designing drugs is impossible due to the low number of observations and the large conformational space proteins may adopt. We propose a suite of computational methods for inferring high-accuracy atomic models from this low-resolution data, revealing detailed structural insights into these complexes. In the previous granting period, we: a) developed a set of tools for accurately building and refining protein models into low-resolution experimental cryoEM maps, b) released these tools as freely available software and gave tutorials to enable cryo-electron microscopists to use these methods, and c) closely collaborated with dozens of microscopists to develop custom methodology for their particular systems of interest. In this proposal, we further these methods in several distinct directions. We develop methodology for accurately identifying ligands and accurately modelling ligand conformations in low-resolution datasets. We build off of machine- learning-guided protein structure prediction, and develop methods to more rapidly and robustly interpret low-resolution cryo-EM datasets. We feel these methods should be fast enough to be suitable for use in an on-line data processing pipeline. Finally, we develop methodology to untangle structural heterogeneity by building a heterogeneous set of protein models that best explains variations in single-particle images, providing structural insights into the conformational heterogeneity of single-particle data. The overall goal of the proposed research is robust and accessible methods to interpret – to the level of atomic accuracy – low-resolution and heterogeneous cryo-EM datasets. Combined, the three aims in this proposal will lead to dramatic improvements in our ability to infer atomic interactions from such datasets. As microscopists continue tackling more complicated protein complexes, these methods will be needed to reveal atom-level insights into how biomedically important protein complexes perform their function and what goes wrong in human disease.