Building protein structure models for intermediate resolution cryo-electron microscopy maps

项目摘要 低温电子显微镜(Cryo-EM)是结构生物学中的一项新兴技术，它能够 测定生物大分子的三维(3D)结构。与传统的 结构生物学技术，如X射线结晶学和核磁共振，冷冻-EM的一大优势是它 具有解决大分子组装的能力。此外，低温EM最近的技术突破已经 能够以接近原子级别的分辨率确定3D结构。Cryo-EM无疑将成为 这是未来十年结构生物学的核心研究方法。随着低温电磁的快速积累 对于结构化数据，开发能够有效地构建和提取数据的计算方法变得至关重要 来自EM图的生物大分子的三维结构。这个项目的目标是开发计算机 用于建模全局和局部结构以及用于解释嵌入EM中的3D结构的方法 地图的分辨率约为4？至中等分辨率。最近，我们开发了一种新的从头蛋白质结构。 建模方法，MainMast，其可以从EM密度图中对蛋白质结构进行建模，而无需使用 地图上的现有模板或片段结构。在主杆研制成功的基础上，我们 进一步扩展MainMast的能力，以实现更精确的建模和多链建模。在……里面 此外，我们还将开发新的中分辨率EM地图建模方法，该方法结合了 粗粒度蛋白质结构建模技术、蛋白质结构预测方法和一种低粒度蛋白质结构预测方法。 具有深度学习的分辨率图像处理方法，一种最先进的强大的机器学习方法。 拟议的项目充分利用了在结构确定方面所做的巨大努力和取得的进展 通过开发计算工具，允许研究人员执行高效和可靠的结构 三维电磁密度图分析。该项目将极大地促进对分子的研究 通过提供一种有效的三维结构建模手段来研究大分子的作用机理。

Project Summary Cryo-electron microscopy (cryo-EM) is an emerging technique in structural biology, which is capable of determining three-dimensional (3D) structures of biological macromolecules. Compared to conventional structural biology techniques, such as X-ray crystallography and NMR, a major advantage of cryo-EM is its ability to solve large macromolecular assemblies. Moreover, recent technical breakthroughs in cryo-EM have enabled determination of 3D structures at nearly atomic-level resolutions. Cryo-EM will undoubtedly become a method of central importance in structural biology in the next decade. With the rapid accumulation of cryo-EM structure data, it has become crucial to develop computational methods that can effectively build and extract 3D structures of biological macromolecules from EM maps. The goal of this project is to develop computational methods for modeling both global and local structures and for interpreting 3D structures embedded in EM maps of around 4 Å to medium-resolution. Recently, we have developed a new de novo protein structure modeling method, MAINMAST, which can model protein structures from an EM density map without using existing template or fragment structures on the map. Based on the successful development of MAINMAST, we further extend the capability of MAINMAST toward more accurate modeling and for multiple-chain modeling. In addition, we will also develop novel modeling methods for medium-resolution EM maps, which combine a coarse-grained protein structure modeling technique, methods in protein structure prediction, and a low- resolution image processing approach with deep learning, a state-of-the-art powerful machine learning method. The proposed project capitalizes on the tremendous efforts and progress made in structural determination with cryo-EM by developing computational tools that allow researchers to perform efficient and reliable structure analyses for 3D EM density maps. The project will greatly facilitate investigation into the molecular mechanisms of macromolecule function by providing an efficient means of 3D structure modeling.