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用于基于结构的药物发现的冷冻电子显微镜(cryoEM)进展。

Advances in cryo-electron microscopy (cryoEM) for structure-based drug discovery.

作者信息

Rubach Pawel, Majorek Karolina A, Gucwa Michal, Murzyn Krzysztof, Wlodawer Alexander, Minor Wladek

机构信息

Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.

Institute of Information Systems and Digital Economy, Warsaw School of Economics, Warsaw, Poland.

出版信息

Expert Opin Drug Discov. 2025 Feb;20(2):163-176. doi: 10.1080/17460441.2025.2450636. Epub 2025 Jan 16.

DOI:10.1080/17460441.2025.2450636
PMID:39789967
Abstract

INTRODUCTION

Macromolecular X-ray crystallography (XRC), nuclear magnetic resonance (NMR), and cryo-electron microscopy (cryoEM) are the primary techniques for determining atomic-level, three-dimensional structures of macromolecules essential for drug discovery. With advancements in artificial intelligence (AI) and cryoEM, the Protein Data Bank (PDB) is solidifying its role as a key resource for 3D macromolecular structures. These developments underscore the growing need for enhanced quality metrics and robust validation standards for experimental structures.

AREAS COVERED

This review examines recent advancements in cryoEM for drug discovery, analyzing structure quality metrics, resolution improvements, metal-ligand and water molecule identification, and refinement software. It compares cryoEM with other techniques like XRC and NMR, emphasizing the global expansion of cryoEM facilities and its increasing significance in drug discovery.

EXPERT OPINION

CryoEM is revolutionizing structural biology and drug discovery, particularly for large, complex structures in induced proximity and antibody-antigen interactions. It supports vaccine design, CAR T-cell optimization, gene editing, and gene therapy. Combined with AI, cryoEM enhances particle identification and 3D structure determination. With recent breakthroughs, cryoEM is emerging as a crucial tool in drug discovery, driving the development of new, effective therapies.

摘要

引言

大分子X射线晶体学(XRC)、核磁共振(NMR)和冷冻电子显微镜(cryoEM)是确定药物发现中至关重要的大分子原子水平三维结构的主要技术。随着人工智能(AI)和冷冻电子显微镜技术的进步,蛋白质数据库(PDB)正巩固其作为三维大分子结构关键资源的作用。这些进展凸显了对提高实验结构质量指标和强大验证标准的需求日益增长。

涵盖领域

本综述考察了冷冻电子显微镜在药物发现方面的最新进展,分析了结构质量指标、分辨率提升、金属配体和水分子识别以及精修软件。它将冷冻电子显微镜与X射线晶体学和核磁共振等其他技术进行了比较,强调了冷冻电子显微镜设施的全球扩展及其在药物发现中日益重要的地位。

专家观点

冷冻电子显微镜正在彻底改变结构生物学和药物发现,特别是对于诱导接近和抗体-抗原相互作用中的大型复杂结构。它支持疫苗设计、嵌合抗原受体(CAR)T细胞优化、基因编辑和基因治疗。与人工智能相结合,冷冻电子显微镜增强了颗粒识别和三维结构确定。随着最近的突破,冷冻电子显微镜正在成为药物发现中的关键工具,推动新的有效疗法的开发。

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本文引用的文献

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Ice thickness control and measurement in the VitroJet for time-efficient single particle structure determination.用于高效单粒子结构测定的VitroJet中的冰厚度控制与测量
J Struct Biol. 2024 Dec;216(4):108139. doi: 10.1016/j.jsb.2024.108139. Epub 2024 Oct 20.
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Low-dose cryo-electron ptychography of proteins at sub-nanometer resolution.在亚纳米分辨率下用低剂量冷冻电子断层摄影术对蛋白质进行成像。
Nat Commun. 2024 Sep 14;15(1):8062. doi: 10.1038/s41467-024-52403-5.
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Small LEA proteins mitigate air-water interface damage to fragile cryo-EM samples during plunge freezing.
小分子 LEA 蛋白可减轻在 plunge 冷冻过程中脆弱的 cryo-EM 样品在气-液界面处的损伤。
Nat Commun. 2024 Sep 4;15(1):7705. doi: 10.1038/s41467-024-52091-1.
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Self-assembled superstructure alleviates air-water interface effect in cryo-EM.自组装超结构减轻了 cryo-EM 中的气-液界面效应。
Nat Commun. 2024 Aug 24;15(1):7300. doi: 10.1038/s41467-024-51696-w.
5
CheckMyMetal (CMM): validating metal-binding sites in X-ray and cryo-EM data.CheckMyMetal(CMM):验证 X 射线和冷冻电镜数据中的金属结合位点。
IUCrJ. 2024 Sep 1;11(Pt 5):871-877. doi: 10.1107/S2052252524007073.
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Cryo-electron microscopy reveals hydrogen positions and water networks in photosystem II.低温电子显微镜揭示了光合作用系统 II 中的氢原子位置和水分子网络。
Science. 2024 Jun 21;384(6702):1349-1355. doi: 10.1126/science.adn6541. Epub 2024 Jun 20.
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CryoEM grid preparation: a closer look at advancements and impact of preparation mode and new approaches.冷冻电镜网格制备:深入探讨制备模式和新方法的进展和影响。
Biochem Soc Trans. 2024 Jun 26;52(3):1529-1537. doi: 10.1042/BST20231553.
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Data-driven regularization lowers the size barrier of cryo-EM structure determination.数据驱动正则化降低低温电子显微镜结构测定的尺寸障碍。
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Nature. 2024 Jun;630(8016):493-500. doi: 10.1038/s41586-024-07487-w. Epub 2024 May 8.
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Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality.用于冷冻电镜密度修正的机器学习方法对生物大分子和配体密度质量有不同影响。
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