Pub Date : 2026-01-15DOI: 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.
{"title":"Generative molecular dynamics","authors":"Simon Olsson","doi":"10.1016/j.sbi.2025.103213","DOIUrl":"10.1016/j.sbi.2025.103213","url":null,"abstract":"<div><div>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 <em>the sampling problem</em> 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.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103213"},"PeriodicalIF":6.1,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.sbi.2025.103199
Matthias Buck, Monika Fuxreiter
{"title":"Editorial overview: Exploring protein conformational landscapes for catalysis in the beginning of the artificial intelligence era","authors":"Matthias Buck, Monika Fuxreiter","doi":"10.1016/j.sbi.2025.103199","DOIUrl":"10.1016/j.sbi.2025.103199","url":null,"abstract":"","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103199"},"PeriodicalIF":6.1,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145818466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 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.
{"title":"Deep learning–based postprocessing and model building for cryo-electron microscopy maps","authors":"Tao Li, Sheng-You Huang","doi":"10.1016/j.sbi.2025.103196","DOIUrl":"10.1016/j.sbi.2025.103196","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103196"},"PeriodicalIF":6.1,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145767354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1016/j.sbi.2025.103183
Nisha Bhattarai , Matthias Buck
Ras guaosine triphosphate hydrolase (GTPase) are central to key cell signaling pathways and, when mutated, drive many cancers. Thought to be undruggable, dramatic progress has been made in the last decade in the design and screening of drugs, in large part thanks to an emerging detailed understanding of Ras conformational changes, excited/sparsely populated states, and allosteric interactions with ligands and protein-binding partners. This perspective reviews this recent progress and how it has been enabled by deep mutational scanning, solution nuclear magnetic resonance (NMR) spectroscopic studies, as well as computational modeling and simulations. We critically discuss these developments over the last 5 years, also for the GTPase-activating proteins (GAP) NF1 and plexin, effector proteins, plexin and Raf, and make suggestions on the gaps in our understanding that still exist.
{"title":"Recent breakthroughs in understanding the allosteric features of Ras GTPases and their effector and regulatory protein interactions, enabling drug design","authors":"Nisha Bhattarai , Matthias Buck","doi":"10.1016/j.sbi.2025.103183","DOIUrl":"10.1016/j.sbi.2025.103183","url":null,"abstract":"<div><div>Ras guaosine triphosphate hydrolase (GTPase) are central to key cell signaling pathways and, when mutated, drive many cancers. Thought to be undruggable, dramatic progress has been made in the last decade in the design and screening of drugs, in large part thanks to an emerging detailed understanding of Ras conformational changes, excited/sparsely populated states, and allosteric interactions with ligands and protein-binding partners. This perspective reviews this recent progress and how it has been enabled by deep mutational scanning, solution nuclear magnetic resonance (NMR) spectroscopic studies, as well as computational modeling and simulations. We critically discuss these developments over the last 5 years, also for the GTPase-activating proteins (GAP) NF1 and plexin, effector proteins, plexin and Raf, and make suggestions on the gaps in our understanding that still exist.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103183"},"PeriodicalIF":6.1,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145767374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"The current understanding of KRAS oligomerization on membranes","authors":"Nastazia Lesgidou , Michail Papadourakis , Nishita Mandal , Sepehr Dehghani-Ghahnaviyeh , Camilo Velez-Vega , José S. Duca , Zoe Cournia","doi":"10.1016/j.sbi.2025.103190","DOIUrl":"10.1016/j.sbi.2025.103190","url":null,"abstract":"<div><div>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)<strong>.</strong> These nanoclusters are dynamic<strong>,</strong> reversible<strong>,</strong> 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.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103190"},"PeriodicalIF":6.1,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 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.
{"title":"Advances in the determination of disordered protein ensemble","authors":"Hamidreza Ghafouri , Silvio C.E. Tosatto , Alexander Miguel Monzon","doi":"10.1016/j.sbi.2025.103198","DOIUrl":"10.1016/j.sbi.2025.103198","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103198"},"PeriodicalIF":6.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 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.
{"title":"Next-generation predictors of protein phase behavior","authors":"Nicholas C. Pinette , Mailyn Terrado , Jennifer M. Bui , Nada Lallous , Jörg Gsponer","doi":"10.1016/j.sbi.2025.103197","DOIUrl":"10.1016/j.sbi.2025.103197","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103197"},"PeriodicalIF":6.1,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145733582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 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。
{"title":"Resolving structural heterogeneity in situ through cryogenic electron tomography","authors":"Jackson Carrion , Joseph H. Davis","doi":"10.1016/j.sbi.2025.103188","DOIUrl":"10.1016/j.sbi.2025.103188","url":null,"abstract":"<div><div>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 <em>in vitro</em> and <em>in situ</em> to enable a more objective comparison of existent and emerging methods for particle classification and heterogeneous 3D reconstruction.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103188"},"PeriodicalIF":6.1,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145721578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 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.
{"title":"Integrative modelling of biomolecular dynamics","authors":"Daria Gusew , Carl G. Henning Hansen , Kresten Lindorff-Larsen","doi":"10.1016/j.sbi.2025.103195","DOIUrl":"10.1016/j.sbi.2025.103195","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103195"},"PeriodicalIF":6.1,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145721546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号