Current opinion in structural biology最新文献

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Toward understanding whole enzymatic reaction cycles using multi-scale molecular simulations 利用多尺度分子模拟来理解整个酶促反应循环

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-09-11 DOI: 10.1016/j.sbi.2025.103153

Shingo Ito , Chigusa Kobayashi , Kiyoshi Yagi , Yuji Sugita

Enzymes effectively catalyze chemical reactions at their active sites. The reactions involve three microscopic events at the active sites: substrate binding, multi-step chemical reactions, and product release. These events are often coupled with enzyme conformational changes, making theoretical and computational analyses more challenging. Advanced molecular simulations, involving molecular dynamics (MD) and hybrid quantum mechanics/molecular mechanics (QM/MM), are now utilized to investigate the functions of enzymes such as tryptophan synthase and P-type ATPases. Here, we summarize recent multiscale molecular simulations that incorporate multiple microscopic events in enzyme functions. The coupling of enzyme conformational changes and chemical reactions can predict a proper direction in enzymatic reaction cycles, which requires accurate predictions of the free energy changes between different physiological states. Using machine learning (ML) methods, all the microscopic events in enzyme catalysis could be described with the same accuracy as quantum chemistry. We also discuss recent developments in ML/MM simulations for enzyme catalysis.

酶在其活性位点上有效地催化化学反应。该反应涉及活性位点的三个微观事件：底物结合、多步化学反应和产物释放。这些事件通常伴随着酶的构象变化，使得理论和计算分析更具挑战性。先进的分子模拟，包括分子动力学（MD）和混合量子力学/分子力学（QM/MM），现在被用来研究酶的功能，如色氨酸合成酶和p型atp酶。在这里，我们总结了最近的多尺度分子模拟，包括酶功能中的多个微观事件。酶的构象变化与化学反应的耦合可以预测酶的反应周期的正确方向，这需要准确预测不同生理状态之间的自由能变化。利用机器学习（ML）方法，酶催化中的所有微观事件都可以像量子化学一样精确地描述。我们还讨论了酶催化的ML/MM模拟的最新进展。

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Computational modeling of protein–ligand interactions: From binding site identification to pose prediction and beyond 蛋白质-配体相互作用的计算模型：从结合位点鉴定到姿态预测及其他

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-09-10 DOI: 10.1016/j.sbi.2025.103152

Viet-Khoa Tran-Nguyen, Anne-Claude Camproux

Protein-ligand modeling is a cornerstone of modern drug discovery, facilitating the identification and optimization of bioactive compounds that modulate protein function. Computational approaches provide cost-effective and scalable strategies for exploring the growing chemical and biological spaces, accelerating early-stage drug development. Advances in both physics-based methods and data-driven machine learning techniques have expanded the range and accuracy of tools available for modeling protein-ligand interactions. This review provides a current and concise view of key methodologies in protein-ligand modeling, including binding site prediction and the generation and evaluation of target-bound ligand conformations. It also discusses state-of-the-art machine learning approaches that are reshaping how these tasks are performed and enhancing the accuracy of binding site, binding pose, and binding affinity predictions.

蛋白质配体建模是现代药物发现的基石，促进了调节蛋白质功能的生物活性化合物的鉴定和优化。计算方法为探索不断增长的化学和生物空间，加速早期药物开发提供了具有成本效益和可扩展的策略。基于物理的方法和数据驱动的机器学习技术的进步扩大了用于建模蛋白质-配体相互作用的工具的范围和准确性。本文综述了蛋白质配体建模的关键方法，包括结合位点预测和靶结合配体构象的生成和评估。它还讨论了最先进的机器学习方法，这些方法正在重塑这些任务的执行方式，并提高结合位点、结合姿态和结合亲和力预测的准确性。

引用次数: 0

Dynamic characteristics of proteolysis-targeting chimera systems revealed by in silico computations 用计算机计算揭示蛋白水解靶向嵌合体系统的动态特性。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-09-10 DOI: 10.1016/j.sbi.2025.103151

Kexin Xu , Jingxuan Ge , Rongfan Tang , Tingjun Hou , Huiyong Sun

Proteolysis-targeting chimeras (PROTACs) achieve irreversible clearance of target proteins by hijacking the ubiquitin–proteasome system, breaking the design paradigm of traditional inhibitory drugs. The development of computational approaches has effectively promoted the rational design of PROTACs, yet existing methods mainly focus on predicting the static structure of PROTAC systems, with methodological gaps in analyzing their dynamic characteristics. Knowing that the dynamic behaviors can dramatically influence the stability and degradation efficacy of a PROTAC system, we systematically summarize the recent progresses of using structure-based and structure–artificial intelligence–hybrid methodologies for characterizing the dynamic behaviors of PROTAC systems, with a focus on elucidating the dynamic characteristics of target protein–PROTAC–E3 ligase ternary structures and prediction of their key properties.

蛋白水解靶向嵌合体（Proteolysis-targeting chimeras, PROTACs）通过劫持泛素-蛋白酶体系统实现对靶蛋白的不可逆清除，打破了传统抑制药物的设计范式。计算方法的发展有效地促进了PROTAC系统的合理设计，但现有方法主要集中在预测PROTAC系统的静态结构，在分析其动态特性方面存在方法上的空白。鉴于动态行为对PROTAC体系的稳定性和降解效果有重要影响，本文系统总结了近年来基于结构和结构-人工智能-混合方法表征PROTAC体系动态行为的研究进展，重点阐述了目标蛋白-PROTAC- e3连接酶三元结构的动态特征及其关键性质的预测。

引用次数: 0

Ras/Raf dimerization model for activation of Raf kinase 激活Raf激酶的Ras/Raf二聚化模型

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-09-09 DOI: 10.1016/j.sbi.2025.103150

Marcela de Barros, Gregory Labrie, Carla Mattos

Our previously proposed Ras dimerization model is consistent with recent details observed by NMR in that Raf activation is centered on the Ras/Raf dimer, distinct from one in which Ras activates Raf as a monomer with the Raf cysteine rich domain inserted in the membrane. We review mechanistic understanding of Raf activation within nanoclusters of Ras on the membrane, with a shift to dimers upon binding Raf. This sets the stage for a signaling platform composed of Ras/Raf and Galectin dimers that facilitates the release of Raf autoinhibition and folding of the Raf intrinsically disordered region between the Ras-binding domains and the kinase bound to 14-3-3 and MEK. This platform could provide synchronized units for signal amplification and is consistent with a Ras stationary phase observed in cells.

我们之前提出的Ras二聚化模型与最近通过核磁共振观察到的细节一致，因为Raf的激活集中在Ras/Raf二聚体上，与Ras激活Raf作为一个单体并在膜中插入Raf半胱氨酸富结构域的模型不同。我们回顾了膜上Ras纳米簇内Raf激活的机制，并在结合Raf时转向二聚体。这为一个由Ras/Raf和半乳糖凝集素二聚体组成的信号传导平台奠定了基础，该平台促进了Raf自抑制的释放和Ras结合域与14-3-3和MEK结合的激酶之间Raf内在无序区域的折叠。该平台可以为信号放大提供同步单元，并且与细胞中观察到的Ras固定相一致。

引用次数: 0

How residence time works in allosteric drugs 在变构药中停留时间是如何起作用的

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-08-29 DOI: 10.1016/j.sbi.2025.103149

Ruth Nussinov , Hyunbum Jang

Drug residence time defines the duration the drug is bound to its protein target. It is a crucial determinant of drug action. Yet, a priori estimating it in the design could be the most challenging. The mechanisms of allosteric and orthosteric drugs differ in how they affect it. Binding at the active site, the residence time of orthosteric drugs is primarily affected by binding kinetics, which is not the case for allosteric drugs. Allosteric drugs determine the orthosteric drug residence time by the nature and extent of the population shift that they promote, which modulate the active site conformation. However, cooperative binding is bidirectional; orthosteric drug binding at the active site can increase (decrease) residence time at the allosteric site.

药物停留时间定义了药物与蛋白质靶标结合的时间。它是药物作用的关键决定因素。然而，在设计中先验地估计它可能是最具挑战性的。变构药和正构药的作用机制不同。在活性位点结合时，正构药物的停留时间主要受结合动力学的影响，而变构药物则不同。变构药物通过其所促进的种群迁移的性质和程度来调节活性位点构象，从而决定了正构药物的停留时间。然而，合作绑定是双向的；在活性位点的正位药物结合可以增加（减少）在变构位点的停留时间。

引用次数: 0

Recent advances in quantifying protein conformational ensembles with dipolar EPR spectroscopy 偶极EPR光谱定量蛋白质构象群的最新进展

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-08-23 DOI: 10.1016/j.sbi.2025.103139

Reza Dastvan , Stefan Stoll

This perspective highlights recent applications and technological progress in dipolar electron paramagnetic resonance (EPR) spectroscopy, including double electron–electron resonance (DEER) spectroscopy. These methods provide nanoscale distance distributions between site-specific spin labels in biomacromolecules. The resulting data are particularly well suited for quantifying the structure and energetics of conformational ensembles of multi-state and flexible proteins. Recent applications span a wide range of systems and are accompanied by innovations in spin labeling, deuteration, in-cell measurements, integrative multi-technique approaches, and novel computational modeling methods combined with structure prediction tools.

本文重点介绍了偶极电子顺磁共振（EPR）光谱学，包括双电子-电子共振（DEER）光谱学的最新应用和技术进展。这些方法提供了生物大分子中位点特异性自旋标记之间的纳米级距离分布。所得数据特别适合于量化多态和柔性蛋白质的构象集合体的结构和能量学。最近的应用跨越了广泛的系统，并伴随着自旋标记、氘化、细胞内测量、综合多技术方法和结合结构预测工具的新型计算建模方法的创新。

引用次数: 0

Cryo-focused ion beam milling for cryo-electron tomography: Shaping the future of in situ structural biology 低温聚焦离子束铣削用于低温电子断层扫描：塑造原位结构生物学的未来

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-08-22 DOI: 10.1016/j.sbi.2025.103138

Sven Klumpe , Jürgen M. Plitzko

Cryo-focused ion beam instruments to produce cellular thin sections for subsequent imaging by cryo-electron tomography have become an integral part of the methodologies for in situ structural biology, enabling high-resolution imaging of biological structures in their native environment. The application of these instruments has opened windows into cells that allowed unprecedented insights into the ultrastructure of cells and more recently, small multicellular organisms and tissues. While great strides have been made in the characterization of cryo-FIB milling and the streamlining of workflows with these tools, many limitations and technical challenges remain to be resolved. Here, we discuss the technical and technological challenges ahead to continue the steep rise of biological discoveries by in-cell cryo-electron tomography to enable cellular structural biology in the multicellular context.

低温聚焦离子束仪器为随后的低温电子断层成像生产细胞薄切片，已经成为原位结构生物学方法的一个组成部分，使生物结构在其原生环境中的高分辨率成像成为可能。这些仪器的应用为研究细胞打开了一扇窗，使我们能够前所未有地深入了解细胞的超微结构，以及最近的小型多细胞生物和组织。虽然在低温fib铣削表征和使用这些工具简化工作流程方面取得了很大进展，但仍有许多限制和技术挑战有待解决。在这里，我们讨论了未来的技术和技术挑战，以继续通过细胞内低温电子断层扫描来实现多细胞环境下的细胞结构生物学的生物学发现。

引用次数: 0

Role of RNA in genome folding: It's all about affinity RNA在基因组折叠中的作用：这与亲和力有关

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-08-20 DOI: 10.1016/j.sbi.2025.103136

Rafal Czapiewski, Nick Gilbert

In mammalian cells, RNA species make up ∼10% of chromatin by mass and play a structural role in the nucleus by acting as scaffolds and influencing genome organisation. Although many proteins bind nuclear RNAs, these interactions are often non-specific, making it challenging to define RNA's role in genome folding. Nonetheless, a clearer picture is emerging. Some RNAs, like NEAT1 and MALAT1, have high affinity for specific RNA-binding proteins and form the basis for nuclear bodies. In contrast, many nuclear proteins bind RNA weakly, resulting in numerous low-affinity interactions. We propose that these interactions generate a complex RNA-protein network with dynamic, gel-like properties that modulate chromatin folding and transcription factor mobility. This suggests an exciting feedback mechanism in which newly transcribed RNA contributes directly to shaping chromatin architecture.

在哺乳动物细胞中，RNA种类占染色质质量的约10%，并通过充当支架和影响基因组组织在细胞核中发挥结构作用。尽管许多蛋白质结合核RNA，但这些相互作用通常是非特异性的，这使得定义RNA在基因组折叠中的作用具有挑战性。尽管如此，一幅更清晰的图景正在浮现。一些rna，如NEAT1和MALAT1，对特定的rna结合蛋白具有高亲和力，是核体的基础。相比之下，许多核蛋白与RNA结合较弱，导致许多低亲和力相互作用。我们提出，这些相互作用产生了一个复杂的rna -蛋白网络，具有动态的凝胶样特性，可以调节染色质折叠和转录因子的迁移。这表明了一种令人兴奋的反馈机制，其中新转录的RNA直接有助于形成染色质结构。

引用次数: 0

Functional sub-states link conformational landscapes and protein evolution 功能亚态连接构象景观和蛋白质进化

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-08-16 DOI: 10.1016/j.sbi.2025.103134

Morito Sakuma , Karol Buda , H. Adrian Bunzel , Christopher Frøhlich , Nobuhiko Tokuriki

The intrinsic conformational flexibility of proteins creates structural heterogeneity, giving rise to conformational ensembles within the energy landscape. When conformational ensembles harbor distinct functional sub-states, mutations can reshape the conformational landscape, thereby altering the distribution of functional sub-states and driving the evolution of novel functions. In this review, we provide a conceptual framework that elucidates the importance of functional sub-states and how evolution can select them. We highlight key studies that have uncovered functional sub-states and discuss recent insights into the transitions of functional sub-states during evolutionary trajectories. Finally, we outline critical techniques for studying functional sub-states, address the challenges faced in analyzing these sub-states, and explore future advancements in the field of protein evolution.

蛋白质固有的构象灵活性创造了结构的异质性，在能量景观中产生了构象集成。当构象集合体包含不同的功能亚态时，突变可以重塑构象景观，从而改变功能亚态的分布并驱动新功能的进化。在这篇综述中，我们提供了一个概念框架，阐明了功能子状态的重要性以及进化如何选择它们。我们重点介绍了发现功能亚状态的关键研究，并讨论了进化轨迹中功能亚状态转变的最新见解。最后，我们概述了研究功能亚状态的关键技术，解决了分析这些亚状态所面临的挑战，并探讨了蛋白质进化领域的未来进展。

引用次数: 0

Modeling flexible RNA 3D structures and RNA-protein complexes 建模柔性RNA三维结构和RNA-蛋白复合物

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-08-16 DOI: 10.1016/j.sbi.2025.103137

Rui João Loureiro, Satyabrata Maiti, Kuntal Mondal, Sunandan Mukherjee, Janusz M. Bujnicki

RNA and RNA–protein (RNP) complexes are central to many cellular processes, but the determination of their structures remains challenging due to RNA flexibility and interaction diversity. This review highlights recent computational advances, particularly from the past two years, in predicting and analyzing RNA and RNP structures. We discuss template-based modeling, docking, molecular simulations, and deep learning approaches, with an emphasis on emerging hybrid methods that integrate these strategies. Special attention is given to tools for modeling conformational heterogeneity, folding pathways, and dynamic binding. We also outline machine learning and simulation techniques for ensemble prediction and explore future directions including quantum-enhanced modeling. Together, these developments are enabling more accurate and scalable modeling of both the static and dynamic aspects of RNA and RNP complexes.

RNA和RNA -蛋白（RNP）复合物是许多细胞过程的核心，但由于RNA的灵活性和相互作用的多样性，确定其结构仍然具有挑战性。这篇综述强调了最近的计算进展，特别是过去两年，在预测和分析RNA和RNP结构方面。我们讨论了基于模板的建模、对接、分子模拟和深度学习方法，重点介绍了整合这些策略的新兴混合方法。特别注意的是建模的工具构象异质性，折叠途径，和动态结合。我们还概述了集成预测的机器学习和模拟技术，并探索了未来的方向，包括量子增强建模。总之，这些发展使RNA和RNP复合物的静态和动态方面的建模更加准确和可扩展。

引用次数: 0

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