Current opinion in structural biology最新文献

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Editorial overview: Cryo-electron microscopy (2025) 编辑概述：低温电子显微镜（2025）

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI: 10.1016/j.sbi.2025.103200

Axel T. Brunger, Gabriel A. Frank

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 : 2026-02-01 Epub 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

Advances in the determination of disordered protein ensemble 无序蛋白集合测定的研究进展

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.sbi.2025.103198

Hamidreza Ghafouri , Silvio C.E. Tosatto , Alexander Miguel Monzon

Intrinsically disordered proteins (IDPs) play essential roles in regulation, signaling, and phase separation, yet their structural complexity cannot be captured by a single conformation. Instead, they populate dynamic ensembles that encode a context-dependent function. Recent advances in experimental techniques coupled with physics-based simulations, coarse-grained models, and machine learning, have transformed our ability to generate and interpret IDP ensembles. Integrative frameworks now combine complementary data with computational approaches to refine ensembles at both local and global levels. Nevertheless, challenges remain in benchmarking, error estimation, and modeling assemblies involving protein–protein and protein–nucleic acid interactions. We highlight recent progress and outline the emerging directions that will shape the next generation of ensemble determination methods.

内在无序蛋白（IDPs）在调控、信号传导和相分离中发挥着重要作用，但其结构复杂性不能被单一构象捕获。相反，它们填充编码上下文相关函数的动态集成。实验技术的最新进展，加上基于物理的模拟、粗粒度模型和机器学习，已经改变了我们生成和解释IDP集合的能力。综合框架现在将互补数据与计算方法结合起来，在本地和全球层面上完善整体。然而，在涉及蛋白质-蛋白质和蛋白质-核酸相互作用的基准测试、误差估计和建模组装方面仍然存在挑战。我们强调了最近的进展，并概述了将塑造下一代系综确定方法的新兴方向。

引用次数: 0

Generative molecular dynamics 生成分子动力学

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2026-02-01 Epub Date: 2026-01-15 DOI: 10.1016/j.sbi.2025.103213

Simon Olsson

Understanding biomolecular function depends on bridging experimental observables with models that capture structural, stationary, and dynamical properties. Molecular dynamics (MD) simulations, in principle provide a bridge, but the sampling problem remains a fundamental roadblock toward this goal. In this mini-review, I outline recent progress in the area of Generative MD (GenMD)—an approach where generative AI (GenAI) is used to mimic the statistical distributions resulting from MD simulations, which are inaccessible using current numerical algorithms. Here, I highlight a few exemplars of GenMD and then outline open problems and current limitations.

理解生物分子功能依赖于将实验观察结果与捕获结构、静止和动态特性的模型连接起来。分子动力学（MD）模拟，原则上提供了一个桥梁，但采样问题仍然是实现这一目标的根本障碍。在这篇小型综述中，我概述了生成MD （GenMD）领域的最新进展-一种使用生成AI （GenAI）来模拟MD模拟产生的统计分布的方法，这是使用当前的数值算法无法实现的。在这里，我将重点介绍GenMD的几个例子，然后概述尚未解决的问题和当前的限制。

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 : 2026-02-01 Epub 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

Deep learning–based postprocessing and model building for cryo-electron microscopy maps 基于深度学习的低温电镜图后处理和模型构建。

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2026-02-01 Epub Date: 2025-12-15 DOI: 10.1016/j.sbi.2025.103196

Tao Li, Sheng-You Huang

Cryo-electron microscopy (cryo-EM) has emerged as one of the most powerful techniques for determining the structures of biological macromolecules. The ultimate goal of cryo-EM is to determine the atomic structure of target molecules, where map postprocessing and atomic-model building are two crucial final steps of the cryo-EM pipeline. With the fast development of artificial intelligence, deep learning has been implemented in various stages of cryo-EM. Here, we present a comprehensive overview of recent advances in map postprocessing and model building for cryo-EM maps with focuses on deep learning–based methods. We also discuss the advantages and limitations of current approaches as well as challenges that are left for future research.

低温电子显微镜（cryo-EM）已成为测定生物大分子结构最有力的技术之一。低温电镜的最终目标是确定目标分子的原子结构，其中地图后处理和原子模型建立是低温电镜管道的两个关键的最后步骤。随着人工智能的快速发展，深度学习已应用于低温电镜的各个阶段。在这里，我们全面概述了低温电镜地图后处理和模型构建的最新进展，重点是基于深度学习的方法。我们还讨论了当前方法的优点和局限性，以及未来研究的挑战。

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 : 2026-02-01 Epub 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

Computational design of intrinsically disordered proteins 内在无序蛋白质的计算设计

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2026-02-01 Epub Date: 2026-01-23 DOI: 10.1016/j.sbi.2025.103210

Giulio Tesei , Francesco Pesce , Kresten Lindorff-Larsen

Protein design has the potential to revolutionize biotechnology and medicine. While most efforts have focused on proteins with well-defined structures, increased recognition of the functional significance of intrinsically disordered regions, together with improvements in their modeling, has paved the way to their computational design. This review summarizes recent advances in designing intrinsically disordered regions with tailored conformational ensembles, molecular recognition, and phase behavior. We discuss challenges in combining models of predictive accuracy with scalable design workflows and outline emerging strategies that integrate knowledge-based, physics-based, and machine-learning approaches.

蛋白质设计具有革新生物技术和医学的潜力。虽然大多数努力都集中在具有明确结构的蛋白质上，但对内在无序区域功能意义的认识的增加，以及对其建模的改进，为其计算设计铺平了道路。本文综述了近年来在设计具有定制构象集成、分子识别和相行为的内在无序区域方面的进展。我们讨论了将预测精度模型与可扩展设计工作流相结合的挑战，并概述了整合基于知识、基于物理和机器学习方法的新兴策略。

引用次数: 0

Decrypting cryptic pockets with physics-based simulations and artificial intelligence 用基于物理的模拟和人工智能解密神秘的口袋

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2026-02-01 Epub Date: 2026-01-27 DOI: 10.1016/j.sbi.2025.103215

Si Zhang, Gregory R. Bowman

Cryptic pockets are promising targets for drug discovery that greatly expand the druggable proteome. In particular, they can provide opportunities to target proteins previously thought to be “undruggable” due to a lack of pockets in structures of the ground state. However, their transient and hidden nature renders them difficult to detect through conventional experimental screening methods. Recent advances in computational methodologies and resources have greatly enhanced our ability to identify and characterize such elusive pockets. This review highlights key developments in computational approaches, including physics-based molecular dynamics simulations, artificial intelligence–driven models, and hybrid strategies that integrate both to enhance cryptic pocket discovery and functional interpretation.

隐口袋是药物发现的有希望的目标，它极大地扩展了可药物蛋白质组。特别是，它们可以提供机会来靶向以前被认为是“不可药物”的蛋白质，因为基态结构中缺乏口袋。然而，它们的瞬态和隐蔽性使得它们难以通过传统的实验筛选方法检测到。计算方法和资源的最新进展大大提高了我们识别和描述这些难以捉摸的口袋的能力。这篇综述强调了计算方法的关键发展，包括基于物理的分子动力学模拟、人工智能驱动的模型和混合策略，这些策略结合了这两种方法来增强隐口袋的发现和功能解释。

引用次数: 0

The current understanding of KRAS oligomerization on membranes 目前对膜上KRAS寡聚的认识

IF 6.1 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current opinion in structural biology

Pub Date : 2026-02-01 Epub Date: 2025-12-12 DOI: 10.1016/j.sbi.2025.103190

Nastazia Lesgidou , Michail Papadourakis , Nishita Mandal , Sepehr Dehghani-Ghahnaviyeh , Camilo Velez-Vega , José S. Duca , Zoe Cournia

KRAS, a member of the RAS family of small GTPases, is frequently mutated in cancers and localizes to the inner leaflet of the plasma membrane, where it has been suggested to form dimers and higher-order oligomers (nanoclusters). These nanoclusters are dynamic, reversible, and may be critical for signal amplification and specificity. In this perspective, we review the current understanding of KRAS oligomerization on membranes and its relevance for downstream signaling. Moreover, we discuss potential KRAS–KRAS interfaces, the effectors contributing to nanoclustering, such as the influence of the membrane lipid composition on KRAS nanoclustering, and outline the effect of small molecules on the RAS signaling pathway and nanoclustering.

KRAS是小gtpase RAS家族的一员，在癌症中经常发生突变，并定位于质膜的内小叶，在那里它被认为形成二聚体和高阶低聚物（纳米簇）。这些纳米团簇是动态的、可逆的，可能对信号放大和特异性至关重要。从这个角度来看，我们回顾了目前对膜上KRAS寡聚化及其与下游信号传导的相关性的理解。此外，我们还讨论了潜在的KRAS - KRAS界面，促进纳米聚类的效应因子，如膜脂组成对KRAS纳米聚类的影响，并概述了小分子对RAS信号通路和纳米聚类的影响。

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