Current opinion in structural biology最新文献

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Chromatin higher-order folding as influenced by preferred values of linker DNA 染色质高阶折叠受连接体DNA偏好值的影响。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-09-19 DOI: 10.1016/j.sbi.2025.103154

Zilong Li , Stephanie Portillo-Ledesma , Tamar Schlick

Specific values of nucleosome spacing have long been associated with distinct chromatin organization, but recent studies reveal surprising structural and functional consequences of small changes in regular linker DNA length. This opinion article revisits experimental and modeling studies addressing the classic 10n versus 10n + 5 spacing, highlighting how this 5 bp difference can alter nucleosome orientation, fiber topology, and higher-order chromatin behavior. We underscore how differences in model parameters and system design yield different trends for the effect of linker DNA lengths on chromatin architecture. However, chromatin structure in vivo reflects the heterogeneous nucleosome spacing in combination with other cellular variables like salt conditions, epigenetic marks, and protein and RNA binding, which work together to shape gene folding and direct gene regulation.

核小体间隔的特定值长期以来与不同的染色质组织有关，但最近的研究揭示了常规连接体DNA长度的微小变化带来的令人惊讶的结构和功能后果。这篇观点文章回顾了经典的10n和10n + 5间距的实验和建模研究，强调了这5bp的差异如何改变核小体的取向、纤维拓扑结构和高阶染色质行为。我们强调模型参数和系统设计的差异如何产生连接体DNA长度对染色质结构影响的不同趋势。然而，体内染色质结构与其他细胞变量如盐条件、表观遗传标记、蛋白质和RNA结合等一起反映了核小体间距的异质性，它们共同塑造基因折叠并指导基因调控。

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Resistance mutations, drug binding and drug residence times 耐药突变、药物结合和药物停留时间。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-09-17 DOI: 10.1016/j.sbi.2025.103158

Ran Friedman

The rapid evolution of microorganisms and cancer cells makes it difficult to treat tumours and infectious diseases, because resistance to drugs is the rule rather than the exception. Structures or models of protein–drug complexes help to understand how mutations lead to resistance and to design better drugs. However, it is difficult to reason how small changes in the structure lead to drug resistance. Thus, protein and drug dynamics need to be considered. Strategies to increase drug residence are sought after to increase the efficacy of drugs. Computational methods to calculate the effect of mutations on drug binding and residence times are being developed and improved, but are challenging. A priori prediction of a mutation's effect on drug binding is an even greater challenge. On the other hand, knowledge about protein–drug complexes has led to the development of multiple design strategies that aim to reduce mutation-driven drug resistance.

微生物和癌细胞的快速进化使肿瘤和传染病的治疗变得困难，因为对药物的耐药性是普遍现象，而不是例外。蛋白质-药物复合物的结构或模型有助于了解突变如何导致耐药性和设计更好的药物。然而，很难解释结构上的微小变化是如何导致耐药性的。因此，需要考虑蛋白质和药物动力学。为了提高药物的疗效，人们一直在寻求增加药物驻留的策略。计算突变对药物结合和停留时间影响的计算方法正在发展和改进，但具有挑战性。先验地预测突变对药物结合的影响是一个更大的挑战。另一方面，关于蛋白质-药物复合物的知识导致了多种设计策略的发展，旨在减少突变驱动的耐药性。

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From snapshots to ensembles: Integrating experimental data and dynamics 从快照到集成：整合实验数据和动力学

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-09-15 DOI: 10.1016/j.sbi.2025.103155

Vanessa Leone , Fabrizio Marinelli

Protein function arises from the interplay of structure, dynamics, and biomolecular interactions. Despite advances in cryo-EM and AI-based structure prediction, capturing dynamic and energetic features remains a challenge. Biophysical methods like NMR, EPR, HDX-MS, SAXS, and cryo-EM provide valuable but often indirect signals. Connecting these to molecular mechanisms requires integrative approaches that combine experiments with physics-based simulations, revealing both stable structures and transient, functionally important intermediates. This review highlights recent advances in integrative modeling using the maximum entropy principle to build dynamic ensembles from diverse data while addressing uncertainty and bias. These methods help resolve heterogeneity and interpret low-resolution data. We conclude by exploring how integrative modeling, enhanced sampling, and AI-driven tools enable new insights into slow, large-scale conformational changes.

蛋白质的功能源于结构、动力学和生物分子相互作用的相互作用。尽管低温电镜和基于人工智能的结构预测取得了进展，但捕捉动态和能量特征仍然是一个挑战。生物物理方法如NMR， EPR, HDX-MS， SAXS和cro - em提供有价值但通常是间接的信号。将这些与分子机制联系起来需要综合的方法，将实验与基于物理的模拟相结合，揭示稳定的结构和瞬态的，功能重要的中间体。这篇综述强调了综合建模的最新进展，利用最大熵原理从不同的数据中构建动态集成，同时解决不确定性和偏差。这些方法有助于解决异质性和解释低分辨率数据。最后，我们探讨了综合建模、增强采样和人工智能驱动的工具如何为缓慢、大规模的构象变化提供新的见解。

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Editorial overview: Artificial intelligence methodologies in structural biology 编辑概述：结构生物学中的人工智能方法

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-09-13 DOI: 10.1016/j.sbi.2025.103156

Chaok Seok, Pratyush Tiwary

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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。

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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。

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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。

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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。

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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。

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