SJ 是 DAE-TIFR 博士生奖学金的获得者，并且资金得到认可。KRV 感谢 DBT B-Life 赠款 DBT/PR12422/MED/31/287/2014 以及印度政府原子能部在项目识别号下的支持。RTI4006.

在大分子的 CryoEM 图中模拟小分子配体

作者没有什么可透露的。

近年来，显微镜硬件和软件的改进导致冷冻电子显微镜结构数量的增加。尽管目前在单颗粒冷冻EM中实现的最高分辨率为1.2 Å 57,58,59，但大多数结构的确定分辨率约为3-4 Å。在中低分辨率图谱中对配体进行建模可能很棘手，而且往往充满歧义。鉴于冷冻电镜在学术界和制药行业广泛用于转化研究和药物发现，确保配体建模正确?...

细胞通过同时独立地进行无数的化学反应来实现其功能，每个化学反应都经过精心调节，以确保它们的生存和对环境线索的适应性。这是通过分子识别实现的，它使生物分子（尤其是蛋白质）能够与其他大分子以及小分子或配体形成瞬时或稳定的复合物1。因此，蛋白质-配体相互作用是生物学中所有过程的基础，包括蛋白质表达和活性的调节、酶对底物和辅因子的识别，以及细胞如何感知和传递信号 1,2。更好地了解蛋白质-配体复合物的动力学、热力学和结构特性揭示了配体相互作用的分子基础，并通过优化药物相互作用和特异性促进了合理的药物设计。研究蛋白质-配体相互作用的一种经济且更快的方法是使用分子对接，这是一种计算方法，可以虚拟筛选各种小分子并预测这些配体与靶蛋白的结合模式和亲和力3。然而，通过 X 射线衍射 （XRD）、核磁共振 （NMR） 或电子冷冻显微镜 （cryoEM） 确定的高分辨率结构的实验证据为此类预测提供了必要的证据，并有助于开发针对给定靶点的更新、更有效的激活剂或抑制剂。本文使用缩写“cryoEM”，因为该技术通常被称为“cryoEM”。然而，关于选择正确的命名法一直存在争议，最近，有人提出了术语冷冻基因样本 Electron Microscopy （cryoEM） 来表示样本处于低温并用电子成像4。同样，从冷冻电镜得出的图被称为电子电位、静电势或库仑电位，为了简单起见，这里我们使用冷冻电镜图5,6,7,8,9,10。
尽管 XRD 一直是蛋白质-配体复合物高分辨率结构测定的金标准技术，但分辨率革命后的11 cryoEM 已经获得了动力，正如过去几年中沉积在电子显微镜数据库 （EMDB） 12 中的库仑电位图或冷冻 EM 图的激增所表明的那样 14。由于样品制备、成像和数据处理方法的进步，2010 年至 2020 年间，使用冷冻电镜的蛋白质数据库 （PDB）14 沉积数量从 0.7% 增加到 17%，2020 年报告的结构中约有 50% 是以 3.5 Å 或更高的分辨率确定的 15,16。CryoEM 已迅速被包括制药行业在内的结构生物学界采用，因为它允许以近原子分辨率研究柔性和非结晶性生物大分子，尤其是膜蛋白和多蛋白复合物，克服了结晶过程并获得通过 XRD 进行高分辨率结构测定所需的衍射良好的晶体。
在cryoEM图谱中准确建模配体至关重要，因为它在分子水平上是蛋白质-配体复合物的蓝图。X 射线晶体学中使用了几种自动配体构建工具，这些工具依赖于配体密度的形状和拓扑结构，以便将配体拟合或构建到电子密度中 17,18,19,20。然而，如果分辨率低于 3 Å，这些方法往往会产生不太理想的结果，因为它们识别和构建所依赖的拓扑特征变得不那么明确。在许多情况下，这些方法已被证明在将配体准确建模到 cryoEM 图谱中是无效的，因为这些图谱是在中低分辨率范围内确定的，通常在 3.5 Å-5 Å17 之间。
使用 cryoEM 测定蛋白质-配体复合物 3D 结构的第一步包括将配体与蛋白质共纯化（当配体与蛋白质具有高结合亲和力时）或在网格制备之前将蛋白质溶液与配体孵育特定持续时间。随后，将少量样品放置在等离子体清洁的多孔透射电镜网格上，然后在液态乙烷中快速冷冻，最后使用冷冻透射电镜成像。对数十万到数百万个单个粒子的 2D 投影图像进行平均，以重建大分子的 3D （3D） 库仑势图。在许多情况下，由于图谱上的各向异性分辨率（即大分子之间的分辨率不均匀）、配体结合区域的灵活性以及数据中的噪声，这些图谱中的配体和溶剂分子的识别和建模带来了重大挑战。许多为 XRD 开发的建模、改进和可视化工具现在正被用于冷冻电子市场，用于相同的目的 18,19,20,21。在本文中，概述了目前用于识别配体、构建模型和优化从 cryoEM 得出的坐标的各种方法和软件。已经提供了一个分步方案来说明使用具有不同分辨率和复杂性的特定蛋白质-配体复合物对配体进行建模所涉及的过程。
在冷冻EM图谱中对配体进行建模的第一步包括识别图谱中的配体密度（非蛋白质）。如果配体结合不会引起蛋白质的任何构象变化，那么计算蛋白质-配体复合物和载脂蛋白之间的简单差异图基本上突出了额外密度的区域，表明配体的存在。这种差异可以立即观察到，因为它只需要两张图谱，甚至在3D细化过程中的中间图也可以用来检查配体是否存在。此外，如果分辨率足够高 （<3.0 Å），那么差异图还可以深入了解水分子的位置以及与配体和蛋白质残基相互作用的离子。
在没有 apo-protein maps 的情况下，现在可以使用 Servalcat22，它可作为独立工具使用，并且作为 Refmac 改进的一部分也已集成到 CCP-EM 软件套件 23,24 和 CCP4 8.0 版本25,26 中。Servalcat 允许使用未锐化的半图和载脂蛋白模型作为输入来计算 FSC 加权差 （Fo-Fc） 图。Fo-Fc 省略图表示实验图（Fo）和从模型派生的图（Fc）之间的差异。在模型中没有配体的情况下，Fo-Fc图谱中的正密度与实验EM图谱重叠通常表明配体的存在。这里的假设是蛋白质链在图谱中拟合良好，剩余的正密度表明配体的位置。然而，重要的是要仔细检查正密度是否源于建模的不准确性，例如蛋白质侧链的错误转子。
第二步涉及从可用的化学信息中获取或创建配体的笛卡尔坐标文件，该配体具有明确定义的几何形状。CCP4 单体库中已有的标准配体（例如，ATP 和 NADP+）可以通过其单体登录代码检索坐标和几何文件来用于精细化。但是，对于未知或非标准配体，可以使用各种工具来创建几何文件。其中一些示例包括 Phenix28 中的 eLBOW27 - （电子配体构建器和优化工作台）、Lidia - Coot29 中的内置工具、JLigand/ACEDRG30,31、CCP-EM23,24、Ligprep32-薛定谔套件中的 Glide 模块。然后，在实验冷冻电镜图和Coot中的差异图的指导下，将配体坐标文件拟合到密度中。接下来是 Phenix28 中的实时空间细化或 Refmac33 中的倒数细化。需要 Linux 工作站或配备优质显卡和上述软件的笔记本电脑。这些程序中的大多数都包含在各种套件中。CCP-EM24 和 Phenix28 可供学术用户免费使用，并包含本文中使用的各种工具，包括 Coot、Refmac533、34、35、36、Servalcat、phenix.real_space_refine 等。同样，Chimera37 和 ChimeraX38 为学术用户提供免费许可证。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CCP4-8.0</td>
			<td>Consortium of several institutes</td>
			<td>https://www.ccp4.ac.uk</td>
			<td>Free for academic users and includes Coot and list of tools developed for X-ray crystallography</td>
		</tr>
		<tr>
			<td>CCP-EM</td>
			<td>Consortium of several institutes</td>
			<td>https://www.ccpem.ac.uk/download.php</td>
			<td>Free for academic users and includes Coot, Relion and many others</td>
		</tr>
		<tr>
			<td>Coot&#160;</td>
			<td>Paul Emsley, LMB, Cambridge</td>
			<td>https://www2.mrc-lmb.cam.ac.uk/personal/pemsley/coot/</td>
			<td>General software for model building but also available with other suites described above</td>
		</tr>
		<tr>
			<td>DockinMap (Phenix)</td>
			<td>Consortium of several institutes</td>
			<td>https://phenix-online.org/documentation/reference/dock_in_map.html</td>
			<td>Software inside the Phenix suite for docking model into cryoEM maps</td>
		</tr>
		<tr>
			<td>Electron Microscopy Data Bank&#160;</td>
			<td>Consortium of several institutes</td>
			<td>https://www.ebi.ac.uk/emdb/</td>
			<td>Public Repository for Electron Microscopy maps</td>
		</tr>
		<tr>
			<td>Falcon</td>
			<td>Thermo Fisher Scientific&#160;</td>
			<td>https://assets.thermofisher.com/TFS-Assets/MSD/Technical-Notes/Falcon-3EC-Datasheet.pdf</td>
			<td>Commercial, camera from Thermo Fisher&#160;</td>
		</tr>
		<tr>
			<td>Phenix&#160;</td>
			<td>Consortium of several institutes</td>
			<td>https://phenix-online.org/download</td>
			<td>Free for academic users and includes Coot</td>
		</tr>
		<tr>
			<td>Protein Data Bank</td>
			<td>Consortium of several institutes</td>
			<td>https://rcsb.org</td>
			<td>Public database of macromolecular structures</td>
		</tr>
		<tr>
			<td>Pymol</td>
			<td>Schrodinger</td>
			<td>https://pymol.org/2/</td>
			<td>Molecular viusalization tool. Educational version is free but comes with limitation. The full version can be obtained with a small fee.</td>
		</tr>
		<tr>
			<td>Relion</td>
			<td>MRC-LMB, Cambridge</td>
			<td>https://relion.readthedocs.io/en/release-4.0/Installation.html</td>
			<td>Software for cryoEM image processing, also available with CCP-EM</td>
		</tr>
		<tr>
			<td>Titan Krios</td>
			<td>Thermo Fisher Scientific&#160;</td>
			<td>https://www.thermofisher.com/in/en/home/electron-microscopy/products/transmission-electron-microscopes/krios-g4-cryo-tem.html?cid=msd_ls_xbu_xmkt_tem-krios_285811_gl_pso_gaw_tpne1c&#38; 
			gad_source=1&#38;gclid=CjwKCAiA-P-rBhBEEiwAQEXhHyw5c8MKThmdA 
			AkZesWC4FYQSwIQRk 
			ZApkj08MfYG040DtiiuL8 
			RihoCebEQAvD_BwE</td>
			<td>Commercial, cryoTEM from Thermo Fisher</td>
		</tr>
		<tr>
			<td>UCSF Chimera</td>
			<td>UCSF, USA</td>
			<td>https://www.cgl.ucsf.edu/chimera/download.html</td>
			<td>General purpose software for display, analysis and more</td>
		</tr>
		<tr>
			<td>UCSF Chimera X</td>
			<td>UCSF, USA</td>
			<td>https://www.cgl.ucsf.edu/chimerax/</td>
			<td>General purpose software for display, analysis and more</td>
		</tr>
	</tbody>
</table>

modeling ligands into maps derived from electron cryomicroscopy

破译大分子复合物中的蛋白质-配体相互作用对于理解分子机制、潜在的生物学过程和药物开发至关重要。近年来，低温样品电子显微镜（cryoEM）已成为确定大分子结构和研究近原子分辨率配体结合模式的有力技术。由于目标分子的各向异性分辨率和数据中固有的噪声，在冷冻电磁图谱中识别和建模非蛋白质分子通常具有挑战性。在本文中，向读者介绍了目前用于配体鉴定、模型构建和使用选定大分子细化原子坐标的各种软件和方法。如烯醇化酶所示，识别配体存在的最简单方法之一是减去有和没有配体获得的两张图谱。即使在更高的阈值下，配体的额外密度也可能在差异图中脱颖而出。在某些情况下，如代谢型谷氨酸受体 mGlu5 的情况所示，当无法生成这种简单的差异图时。最近引入的推导Fo-Fc省略图的方法可以作为验证和证明配体存在的工具。最后，以经过充分研究的β-半乳糖苷酶为例，分析了分辨率对冷冻电子显微镜图谱中配体和溶剂分子建模的影响，并对冷冻电子镜在药物发现中的应用进行了展望。

Cells accomplish their functions by carrying out innumerable chemical reactions simultaneously and independently, each meticulously regulated to ensure their survival and adaptability in response to environmental cues. This is achieved by molecular recognition, which enables biomolecules, especially proteins, to form transient or stable complexes with other macromolecules as well as small molecules or ligands1. Thus, protein-ligand interactions are fundamental to all processes in biology, which include the regulation of protein expression and activity, the recognition of substrates and cofactors by enzymes, as well as how cells perceive and relay signals1,2. A better understanding of the kinetic, thermodynamic, and structural properties of the protein-ligand complex reveals the molecular basis of ligand interaction and also facilitates rational drug design by optimizing drug interaction and specificity. An economical and faster approach to studying protein-ligand interaction is to use molecular docking, which is a computational method that virtually screens a diverse range of small molecules and predicts the binding mode and affinity of these ligands to target proteins3. However, experimental evidence from high-resolution structures determined by X-ray Diffraction (XRD), Nuclear Magnetic Resonance (NMR), or electron cryomicroscopy (cryoEM) provides the essential proof for such predictions and aids in the development of newer and more effective activators or inhibitors for a given target. This article uses the abbreviation &#39;cryoEM&#39; as the technique is commonly referred to. However, there is an ongoing debate on choosing the correct nomenclature, and recently, the term cryogenic-sample Electron Microscopy (cryoEM) has been proposed to indicate that the sample is at cryogenic temperature and imaged with electrons4. Similarly, the maps derived from cryoEM have been called electron potential, electrostatic potential, or Coulomb potential, and for simplicity, here we use cryoEM maps5,6,7,8,9,10.
Although XRD has been the gold standard technique in high-resolution structure determination of protein-ligand complexes, post resolution-revolution11 cryoEM has gained momentum, as indicated&#160;by the surge of Coulomb potential maps or cryoEM maps deposited in the Electron Microscopy Database (EMDB)12,13 over the last few years14. Owing to advancements in sample preparation, imaging, and data processing methods, the number of Protein Data Bank (PDB)14 depositions employing cryoEM increased from 0.7% to 17% between 2010 and 2020, with approximately 50% of reported structures in 2020 being determined at 3.5&#8197;&#197; resolution or better15,16. CryoEM has been rapidly adopted by the structural biology community, including the pharmaceutical industry, as it allows the study of flexible and non-crystalline biological macromolecules, especially membrane proteins and multi-protein complexes, at near-atomic resolution, overcoming the process of crystallization and obtaining well-diffracting crystals required for high-resolution structure determination by XRD.
Accurate modeling of the ligand in the cryoEM map is paramount, as it serves as a blueprint of the protein-ligand complex at a molecular level. There are several automated ligand-building tools used in X-ray crystallography&#160;that depend on the shape and topology of the ligand density in order to fit or build the ligand into the electron density17,18,19,20. Nevertheless, if the resolution is lower than 3 &#197;, these approaches tend to produce less desirable outcomes because the topological features on which they depend for recognition and building become less defined. In many instances, these methods have proven ineffective in accurately modeling ligands into cryoEM maps, as these maps have been determined in the low-to-medium resolution range, typically between 3.5 &#197;-5 &#197;17.
The first step in 3D structure determination of a protein-ligand complex by cryoEM involves either co-purifying the ligand with the protein (when the ligand has a high binding affinity to the protein) or incubating the protein solution with the ligand for a specific duration before grid preparation. Subsequently, a small sample volume is placed onto a plasma-cleaned holey TEM grid, followed by flash freezing in liquid ethane and ultimately imaging with a cryo-TEM. The 2D projection images from hundreds of thousands to millions of individual particles are averaged to reconstruct a 3-dimensional (3D) Coulomb potential map of the macromolecule. Identifying and modeling ligands and solvent molecules in these maps pose significant challenges in many cases due to the anisotropic resolution across the map (i.e., the resolution is not uniform across the macromolecule), flexibility in the region where the ligand is bound, and the noise in the data. Many of the modeling, refinement, and visualization tools that were developed for XRD are now being adapted for use in cryoEM for the same purposes18, 19, 20, 21. In this article, an overview of various methods and software currently used to identify ligands, build models, and refine the coordinates derived from cryoEM is presented. A step-by-step protocol has been provided to illustrate the processes involved in modeling ligands using specific protein-ligand complexes with varying resolution and complexity.
The first step in modeling ligands in cryoEM maps includes the identification of the ligand density (non-protein) in the map. If ligand binding does not induce any conformational change in the protein, then calculating a simple difference map between the protein-ligand complex and the apo-protein essentially highlights the regions of extra density, suggesting the presence of the ligand. Such differences can be observed immediately, as it just requires two maps, and even intermediate maps during the process of 3D refinement can be used to check if the ligand is present. Additionally, if the resolution is high enough (&#60;3.0 &#197;), then the difference map can also provide insights into the location of water molecules as well as ions interacting with the ligand and the protein residues.
In the absence of the apo-protein map, it is now possible to use Servalcat22, which is available as a standalone tool and has also been integrated into the CCP-EM software suite23,24 as part of the Refmac refinement and in CCP4 8.0 release25,26. Servalcat allows the calculation of an FSC weighted difference (Fo-Fc) map using the unsharpened half-maps and the apoprotein model as input. The Fo-Fc omit map represents the disparity between the experimental map (Fo) and the map derived from the model (Fc). In the absence of a ligand in the model, a positive density in a Fo-Fc map that overlaps with the experimental EM map typically suggests the presence of the ligand. The assumption here is that the protein chain is well-fitted in the map, and the remaining positive density indicates the location of the ligand. However, it is important to meticulously examine whether the positive density stems from modeling inaccuracies, such as the wrong rotamer of a protein side-chain.
The second step involves obtaining or creating a cartesian coordinate file of the ligand with well-defined geometry from the available chemical information. Standard ligands (for example, ATP and NADP+) that are already available in the CCP4 monomer library can be used for refinement by retrieving the coordinate and geometry files via their monomer accession code. However, for unknown or non-standard ligands, various tools are available to create the geometry files. Some of the examples include the eLBOW27 - (electronic ligand builder and optimization workbench) in Phenix28, Lidia - an in-built tool in Coot29, JLigand/ACEDRG30,31, CCP-EM23,24, Ligprep32-a module of Glide within the Schrodinger suite. The ligand coordinate file is then fitted in the density, guided by both the experimental cryoEM map and the difference map in Coot. This is followed by real-space refinement in Phenix28 or reciprocal refinement in Refmac33. A Linux workstation or a laptop equipped with a good graphics card and the above-mentioned software is required. Most of these programs are included in various suites. CCP-EM24&#160;and Phenix28 are freely available to academic users and include a variety of tools that are used in this article, including Coot, Refmac533,34,35,36, Servalcat, phenix.real_space_refine, etc. Similarly,&#160;Chimera37, and ChimeraX38 provide free licenses to academic users.

作者聚焦：通过在冷冻电镜图中对配体进行建模来探索细胞过程

将配体建模为源自电子冷冻显微镜的图谱

示例 1 来自结核分枝杆菌的烯醇化酶催化糖酵解的倒数第二步，并将 2-磷酸甘油酸转化为磷酸烯醇丙酮酸 （PEP），这是几种代谢途径的重要中间体44,45。在相同像素大小 1.07 Å 下收集 apo-烯醇化酶和 PEP 结合烯醇化酶样品的 CryoEM 数据，并使用 Relion 3.146,47 进行图像处理。载脂蒲-烯醇化酶?...

该协议介绍了可用于在大分子的冷冻EM图谱中建模小分子配体的工具。

Deciphering the protein-ligand interactions in a macromolecular complex is crucial for understanding the molecular mechanism, underlying biological ...

我们感兴趣的是了解整个细胞中发生的生理过程，特别是在膜界面处。我们使用冷冻电镜作为主要技术，并研究各种有趣和具有挑战性的生物学问题，研究多种大分子复合物。几十年前，确定膜蛋白和其他不稳定复合物的结构具有挑战性。然而，冷冻电镜（一种新型洗涤剂和脂质模拟物）的技术进步为这些复合物的快速结构测定铺平了道路。这些结构现在可以以高分辨率获得，并有可能用于药物发现。在单粒子冷冻电子显微镜中，挑战包括获取稳定和均匀的样本、在薄冰中以随机方向嵌入样本、处理大量低信噪比的图像，以及在 3D 重建过程中准确确定角度取向和分类。由于数据中固有的噪声以及各向同性且图谱分辨率低、样品异质性和配体灵活性，因此在配体蛋白复合物的冷冻电镜图谱中识别和建模小分子具有挑战性。该协议是在低至中分辨率冷冻电镜中识别和建模配体和溶剂分子的分步指南。

modeling-ligands-into-maps-derived-from-electron-cryomicroscopy

Research

JoVE Journal

Biochemistry

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

855 次观看.  Tata Institute of Fundamental Research. 我们感兴趣的是了解整个细胞中发生的生理过程，特别是在膜界面处。我们使用冷冻电镜作为主要技术，并研究各种有趣和具有挑战性的生物学问题，研究多种大分子复合物。几十年前，确定膜蛋白和其他不稳定复合物的结构具有挑战性。然而，冷冻电镜（一种新型洗涤剂和脂质模拟物）的技术进步为这些复合物的快速结构测定铺平了道路。这些结构现在可以以高分辨?...

视频: 作者聚焦：通过在冷冻电镜图中对配体进行建模来探索细胞过程

Deciphering the protein-ligand interactions in a macromolecular complex is crucial for understanding the molecular mechanism, underlying biological processes, and drug development. In recent years, cryogenic sample electron microscopy (cryoEM) has emerged as a powerful technique to determine the structures of macromolecules and to investigate the mode of ligand binding at near-atomic resolution. Identifying and modeling non-protein molecules in cryoEM maps is often challenging due to anisotropic resolution across the molecule of interest and inherent noise in the data. In this article, the readers are introduced to various software and methods currently used for ligand identification, model building, and refinement of atomic coordinates using selected macromolecules. One of the simplest ways to identify the presence of a ligand, as illustrated with the enolase enzyme, is to subtract the two maps obtained with and without the ligand. The extra density of the ligand is likely to stand out in the difference map even at a higher threshold. There are instances, as shown in the case of metabotropic Glutamate receptor mGlu5, when such simple difference maps cannot be generated. The recently introduced method of deriving the Fo-Fc omit map can serve as a tool for validating and demonstrating the presence of the ligand. Finally, using the well-studied &#946;-galactosidase as an example, the effect of resolution on modeling the ligands and solvent molecules in cryoEM maps is analyzed, and an outlook on how cryoEM can be used in drug discovery is presented.

This protocol introduces the tools available for modeling small-molecule ligands in cryoEM maps of macromolecules.