Current opinion in structural biology最新文献

英文中文

Next-generation predictors of protein phase behavior 下一代蛋白质相行为预测因子

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-12-11 DOI: 10.1016/j.sbi.2025.103197

Nicholas C. Pinette , Mailyn Terrado , Jennifer M. Bui , Nada Lallous , Jörg Gsponer

Biomolecular condensates formed through protein phase separation are critical for cellular organization and regulation. Recent years have seen rapid growth in computational methods predicting proteins’ phase separation propensity and condensate localization, fueled by expanding datasets and advances in machine learning. Here, we review recent progress and limitations of state-of-the-art tools. Despite improvements, current models often fail to capture the complexity of phase separation, which depends on molecular interactions and contextual factors such as temperature, ionic strength, and macromolecular crowding. Encouragingly, new approaches are beginning to incorporate these biological variables, moving toward more physiologically relevant predictions. To accelerate progress, we advocate for stricter metadata standards and a coordinated, community-wide benchmarking of predictive tools to ensure robust and reproducible models for inference of protein phase behavior.

通过蛋白质相分离形成的生物分子凝聚物对细胞组织和调控至关重要。近年来，随着数据集的扩大和机器学习的进步，预测蛋白质相分离倾向和凝析物定位的计算方法迅速发展。在这里，我们回顾了最新的进展和最先进的工具的局限性。尽管有所改进，但目前的模型往往无法捕捉相分离的复杂性，这取决于分子相互作用和环境因素，如温度、离子强度和大分子拥挤。令人鼓舞的是，新的方法开始纳入这些生物变量，朝着更加生理学相关的预测迈进。为了加快进展，我们提倡更严格的元数据标准和协调的，社区范围的预测工具基准，以确保稳健和可复制的模型来推断蛋白质相行为。

引用次数: 0

Resolving structural heterogeneity in situ through cryogenic electron tomography 通过低温电子断层扫描原位分析结构不均匀性。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-12-09 DOI: 10.1016/j.sbi.2025.103188

Jackson Carrion , Joseph H. Davis

Cryogenic electron tomography (cryoET) has emerged as a powerful tool for studying the structural heterogeneity of proteins and their complexes, offering insights into macromolecular dynamics directly within cells. Driven by recent computational advances, including powerful machine learning frameworks, researchers can now resolve both discrete structural states and continuous conformational changes from 3D subtomograms and stacks of 2D particle-images acquired across tilt-series. In this review, we survey recent innovations in particle classification and heterogeneous 3D reconstruction methods, focusing specifically on the relative merits of workflows that operate on reconstructed 3D subtomogram volumes compared to those using extracted 2D particle-images. We additionally highlight how these methods have provided specific biological insights into the organization, dynamics, and structural variability of cellular components. Finally, we advocate for the development of benchmarking datasets collected in vitro and in situ to enable a more objective comparison of existent and emerging methods for particle classification and heterogeneous 3D reconstruction.

低温电子断层扫描（Cryogenic electron tomography, cryoET）已成为研究蛋白质及其复合物结构异质性的有力工具，可直接深入研究细胞内的大分子动力学。在最近的计算进步（包括强大的机器学习框架）的推动下，研究人员现在可以通过倾斜序列获得的3D子层析图和2D粒子图像堆栈来解决离散结构状态和连续构象变化。在这篇综述中，我们调查了最近在粒子分类和异构三维重建方法方面的创新，特别关注了与使用提取的二维粒子图像的工作流程相比，在重建的三维子层析图体积上操作的工作流程的相对优点。我们还强调了这些方法如何为细胞成分的组织、动力学和结构变异性提供特定的生物学见解。最后，我们提倡开发在体外和原位收集的基准数据集，以便对现有和新兴的颗粒分类和异构三维重建方法进行更客观的比较。

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引用次数: 0

Integrative modelling of biomolecular dynamics 生物分子动力学的综合建模。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-12-09 DOI: 10.1016/j.sbi.2025.103195

Daria Gusew , Carl G. Henning Hansen , Kresten Lindorff-Larsen

Much of our mechanistic understanding of the functions of biological macromolecules is based on static structural experiments, which can be modelled either as single structures or conformational ensembles. While these provide us with invaluable insights, they do not directly reveal that molecules are inherently dynamic. Advances in time-dependent and time-resolved experimental methods have made it possible to capture the dynamics of biomolecules at increasingly higher spatial and temporal resolutions. To complement these, computational models can represent the structural and dynamical behaviour of biomolecules at atomistic resolution and femtosecond timescale, and are therefore useful to interpret these experiments. Here, we review the progress in integrating simulations with dynamical experiments, focusing on the combination of simulations with time-resolved and time-dependent experimental data.

我们对生物大分子功能的机理理解大多是基于静态结构实验，它可以被建模为单一结构或构象集合。虽然这些为我们提供了宝贵的见解，但它们并不能直接揭示分子本质上是动态的。时间依赖和时间分辨实验方法的进步使得在越来越高的空间和时间分辨率下捕捉生物分子的动力学成为可能。为了补充这些，计算模型可以在原子分辨率和飞秒时间尺度上表示生物分子的结构和动力学行为，因此对解释这些实验很有用。本文综述了模拟与动态实验相结合的研究进展，重点介绍了模拟与时间分辨和时间依赖实验数据的结合。

引用次数: 0

Advances in automation for cryo-electron tomography data collection 低温电子断层成像数据采集自动化研究进展。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-12-01 DOI: 10.1016/j.sbi.2025.103192

Kedar Sharma, Mario J. Borgnia

Cryo-electron microscopy has become the preferred method for determining structures of macromolecular complexes both in isolation, using single particle analysis, and in their cellular contexts, using cryo-electron tomography (Cryo-ET) combined with subvolume averaging (SVA). Collection of tilt series for Cryo-ET introduces challenges such as low signal-to-noise ratios, sample radiation sensitivity, and mechanical imprecision of the microscope stage – particularly at high magnifications. Strategies to improve throughput and resolution include continuous tilt and beam-image-shift parallel acquisition, real-time predictive adjustments, and machine learning-driven targeting. Additionally, montage tomography has increased the observable cellular area, while innovations like rectangular condenser apertures promise improved dose efficiency. Web-based and machine learning-enhanced solutions for automated and remote microscope operation are improving the user experience. Collectively, these advancements represent a critical step towards robust, high-resolution, and user-friendly Cryo-ET, facilitating the visualization of macromolecular assemblies within their authentic biological environments.

低温电子显微镜已经成为确定大分子复合物结构的首选方法，无论是在分离中，使用单颗粒分析，还是在细胞环境中，使用低温电子断层扫描（Cryo-ET）结合亚体积平均（SVA）。Cryo-ET的倾斜系列收集带来了诸如低信噪比，样品辐射灵敏度和显微镜阶段的机械不精度等挑战-特别是在高放大倍率下。提高吞吐量和分辨率的策略包括连续倾斜和波束图像移位并行采集、实时预测调整和机器学习驱动的目标定位。此外，蒙太奇断层扫描增加了可观察到的细胞面积，而像矩形聚光器孔径这样的创新有望提高剂量效率。自动化和远程显微镜操作的基于网络和机器学习增强的解决方案正在改善用户体验。总的来说，这些进步代表了向强大，高分辨率和用户友好的Cryo-ET迈出的关键一步，促进了大分子组装在其真实生物环境中的可视化。

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引用次数: 0

Labeling systems for cryo-electron tomography 低温电子断层扫描标记系统

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-12-01 DOI: 10.1016/j.sbi.2025.103189

Richard G. Held

The unrealized goal of cryo-electron tomography (cryo-ET) is to visualize every protein within its cellular context. Such capability would enable molecular resolution mapping of three-dimensional protein topography and structure determination within a native context. Current technology limits the proteins identifiable within an individual tomogram to high-molecular-weight complexes. Localization of smaller target proteins requires the use of labeling systems that act as fiducial markers of target protein localization. Several labeling systems have been developed and recently employed, all of which involve trade-offs. The choice of which system to use depends on the biological question of interest. This review outlines considerations for the design and choice of labeling systems for cryo-ET, highlights recent applications, and outlines areas for future development.

低温电子断层扫描（cryo-ET）尚未实现的目标是在其细胞背景下可视化每一种蛋白质。这种能力将使三维蛋白质地形的分子分辨率制图和结构测定成为可能。目前的技术将单个断层扫描中可识别的蛋白质限制在高分子量复合物中。定位较小的靶蛋白需要使用标记系统作为靶蛋白定位的基础标记。几个标签系统已经开发和最近采用，所有这些都涉及权衡。选择使用哪种系统取决于感兴趣的生物学问题。这篇综述概述了设计和选择冷冻et标签系统的考虑因素，重点介绍了最近的应用，并概述了未来发展的领域。

引用次数: 0

Recent advances in machine learning predictions of protein-ligand binding affinities 机器学习预测蛋白质配体结合亲和力的最新进展

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-11-28 DOI: 10.1016/j.sbi.2025.103193

Jian Jiang , Daixin Li , Guilin Wang , Guo-Wei Wei

Accurately predicting protein–ligand binding affinities is a central task in rational drug design, as it directly influences hit discovery, lead optimization, and compound prioritization. Traditional approaches often suffer from limited accuracy, high computational cost, or dependence on heuristic scoring functions. Recent advances in machine learning (ML) have introduced new paradigms for the binding affinity prediction. In this review, we survey the latest developments in ML-based predictions of protein–ligand binding affinities across various directions, including structure-based approaches that leverage three-dimensional conformational data, ligand-based models that utilize mathematical approaches that employ topological invariants, and hybrid or alternative frameworks addressing diverse prediction scenarios. We highlight representative algorithms ranging from traditional supervised learning to deep learning architectures. Additionally, we discuss the current challenges faced in this domain. Finally, we outline emerging trends and potential future directions, which are poised to further enhance the accuracy and applicability of binding affinity prediction in drug discovery pipelines.

准确预测蛋白质-配体结合亲和力是合理药物设计的核心任务，因为它直接影响到靶向发现、先导优化和化合物优先级。传统的方法往往存在精度有限、计算成本高或依赖启发式评分函数的问题。机器学习（ML）的最新进展为结合亲和预测引入了新的范式。在这篇综述中，我们调查了基于机器学习的蛋白质-配体结合亲和预测的最新进展，包括利用三维构象数据的基于结构的方法，利用拓扑不变量的数学方法的基于配体的模型，以及解决各种预测场景的混合或替代框架。我们重点介绍了从传统监督学习到深度学习架构的代表性算法。此外，我们还讨论了该领域当前面临的挑战。最后，我们概述了新兴趋势和潜在的未来方向，这些趋势将进一步提高药物发现管道中结合亲和力预测的准确性和适用性。

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引用次数: 0

Target engagement in bacterial and protozoan pathogens: in vitro and cellular assays for drug discovery 细菌和原生动物病原体的靶标参与：药物发现的体外和细胞分析

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-11-28 DOI: 10.1016/j.sbi.2025.103194

Priscila Z. Ramos , Carolina M.C. Catta-Preta , Rafael M. Couñago

Target engagement (TE) assays are essential for confirming on-target activity, guiding medicinal chemistry, and linking molecular interactions to phenotypic outcomes. Despite their success in human drug discovery, their application to bacterial and protozoan pathogens remains limited due to biological complexity, technical barriers, and lack of high-quality chemical tools and protein reagents. This review surveys current TE strategies and highlights emerging tools such as live-cell bioluminescence resonance energy transfer, cellular thermal shif assay, and chemoproteomics. Expanding TE in pathogen research will deepen mechanistic insights, reduce development risk, and improve the chances of delivering safer, more effective anti-infective therapies.

靶接合（TE）分析对于确认靶上活性、指导药物化学以及将分子相互作用与表型结果联系起来至关重要。尽管它们在人类药物发现方面取得了成功，但由于生物复杂性、技术障碍以及缺乏高质量的化学工具和蛋白质试剂，它们在细菌和原生动物病原体上的应用仍然有限。本文综述了当前的TE策略，并重点介绍了新兴的工具，如活细胞生物发光共振能量转移、细胞热转移测定和化学蛋白质组学。在病原体研究中扩大TE将加深对机制的认识，降低发展风险，并提高提供更安全、更有效的抗感染治疗的机会。

引用次数: 0

Recent advances in artificial intelligence–driven biomolecular dynamics simulations based on machine learning force fields 基于机器学习力场的人工智能驱动生物分子动力学模拟研究进展

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-11-21 DOI: 10.1016/j.sbi.2025.103191

Taoyong Cui , Yutao Zhou , Tong Wang

Molecular dynamics simulations are crucial for investigating biomolecular mechanisms. The success of these simulations hinges on the accuracy, efficiency, and generalizability of the underlying force field. While classical molecular force fields are efficient yet approximate and quantum mechanics is accurate but computationally prohibitive for large systems, machine learning force fields (MLFFs) have emerged to bridge this gap. We review various MLFFs—from classically parametrized to end-to-end models—evaluating their performance in accuracy and efficiency. However, a significant challenge for MLFFs is generalizability as models trained on specific data often fail to extrapolate to unseen molecules or conformations. To address this, universal MLFFs, such as fragment-based methods like AI²BMD designed by Wang et al. and GEMS designed by Unke et al., are being developed. Beyond recent progress, we also discuss the inherent limitations and trade-offs of MLFFs. Looking forward, the integration of MLFFs with virtual cell models and coarse-grained representations is poised to enable whole-cell multiscale simulations.

分子动力学模拟对于研究生物分子机制至关重要。这些模拟的成功取决于潜在力场的准确性、效率和通用性。虽然经典的分子力场是有效的，但近似的，量子力学是准确的，但对于大型系统来说，计算上是禁止的，机器学习力场（MLFFs）已经出现，以弥补这一差距。我们回顾了从经典参数化到端到端模型的各种mlff，评估了它们在准确性和效率方面的性能。然而，MLFFs面临的一个重大挑战是可泛化性，因为在特定数据上训练的模型通常无法推断出看不见的分子或构象。为了解决这个问题，正在开发通用的mlff，例如Wang等人设计的基于片段的方法AI2BMD和Unke等人设计的GEMS。除了最近的进展，我们还讨论了MLFFs的固有局限性和权衡。展望未来，MLFFs与虚拟细胞模型和粗粒度表示的集成有望实现全细胞多尺度模拟。

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引用次数: 0

Strategies for studying discrete heterogeneity in situ using cryo-electron tomography 利用低温电子断层成像技术研究原位离散非均质性的策略。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-11-12 DOI: 10.1016/j.sbi.2025.103186

Alberto Bartesaghi

Structural variability plays a crucial role in enabling biological function, as the ability of proteins to adopt multiple conformations allows them to perform diverse cellular tasks. Cryo-electron tomography combined with subtomogram averaging and classification has emerged as a powerful technique for elucidating the conformational dynamics of proteins in their near-native environment. Increased data availability has provided a driving force for improvements in image classification algorithms which have enabled conformational heterogeneity studies of proteins in situ at higher resolutions than previously possible. In particular, the use of 2D particle projections extracted from raw tilt-series paired with constrained classification strategies of projection sets has emerged as a promising strategy for classifying particles in 3D. Despite these efforts, further method development will be needed to extend the applicability of current strategies for 3D classification to more challenging biological targets, including low-molecular weight complexes and membrane proteins.

结构变异性在实现生物功能方面起着至关重要的作用，因为蛋白质采用多种构象的能力使它们能够执行不同的细胞任务。低温电子断层扫描结合亚断层平均和分类已经成为一种强大的技术，用于阐明蛋白质在其接近天然环境中的构象动力学。数据可用性的增加为图像分类算法的改进提供了动力，这使得原位蛋白质的构象异质性研究能够以比以前更高的分辨率进行。特别是，利用从原始倾斜序列中提取的2D粒子投影与投影集的约束分类策略相结合，已经成为一种很有前途的3D粒子分类策略。尽管有这些努力，需要进一步的方法开发来扩展当前3D分类策略对更具挑战性的生物靶标的适用性，包括低分子量复合物和膜蛋白。

引用次数: 0

Regulation of receptor tyrosine kinase hetero-interactions 受体酪氨酸激酶异质相互作用的调控。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2025-11-12 DOI: 10.1016/j.sbi.2025.103187

Adam W. Smith , Francisco N. Barrera

Receptor tyrosine kinases (RTKs) control myriads of cellular functions. RTKs are paradigmatic examples of receptors where activity is directly dependent on quaternary structure. In most cases, the monomeric RTK is inactive, and function arises only after a ligand binding event leads the RTK to bind to another copy of itself, activating trans-autophosphorylation of tyrosine residues. Such RTK homodimerization can be accompanied by the formation of homomers of higher stoichiometry. However, RTK monomers can also bind to a second type of RTK, forming heterodimers. RTK heteromerization is believed to result in different signaling than homomerization. Despite its importance, we have a poor understanding of the factors that define if an RTK will form homomers or heteromers. This short review covers recent discoveries on the heteromerization of RTK, in what is called the RTK interactome. We discuss its translational potential, and how ligands and membrane lipids affect heteromer formation.

受体酪氨酸激酶（rtk）控制着无数的细胞功能。rtk是活性直接依赖于四级结构的受体的典型例子。在大多数情况下，单体RTK是无活性的，只有在配体结合事件导致RTK与自身的另一个拷贝结合，激活酪氨酸残基的反式自磷酸化后，才会产生功能。这种RTK同二聚化可以伴随着更高化学计量的同聚体的形成。然而，RTK单体也可以与第二种类型的RTK结合，形成异源二聚体。RTK异质化被认为与同源化产生不同的信号。尽管它很重要，但我们对定义RTK是否会形成同质体或异质体的因素了解甚少。这篇简短的综述涵盖了RTK异聚的最新发现，即所谓的RTK相互作用组。我们讨论了它的翻译潜力，以及配体和膜脂如何影响异构体的形成。

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