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From filtering to denoising: Increasing visual interpretability of cryo-electron tomograms 从滤波到去噪：提高低温电子断层图的视觉可解释性。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-06-01 Epub Date: 2026-05-26 DOI: 10.1016/j.sbi.2026.103293
Théophile Stoll , Florian Fäßler
{"title":"From filtering to denoising: Increasing visual interpretability of cryo-electron tomograms","authors":"Théophile Stoll ,&nbsp;Florian Fäßler","doi":"10.1016/j.sbi.2026.103293","DOIUrl":"10.1016/j.sbi.2026.103293","url":null,"abstract":"<div><div>Cryo-electron tomography has emerged as the premier technique for ultrastructural analysis of natively preserved biological specimens and <em>in situ</em> structure determination. Each tomogram of a cell contains valuable information on the imaged molecular assemblies, leading to potential discoveries, but it also contains enormous amounts of noise. This noise, in combination with the typical low contrast in raw cryo-electron tomograms, hampers the discovery process. To overcome this impairment on the levels of tomogram reconstruction and the reconstructed tomogram, the field has employed a variety of image processing techniques, ranging from binning and low-pass filters removing the typically noisier high-frequency Fourier components to neural network-based denoisers. Here, we provide an overview of the approaches that are used in current research studies and an outlook on a set of newly developed strategies leveraging neural networks to raise the visual interpretability of tomograms and thereby, hopefully, increase the rate of new discoveries.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"98 ","pages":"Article 103293"},"PeriodicalIF":6.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148027909","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}
引用次数: 0
Physical genomics: Why gene regulation is tug of war between polymer physics and biochemistry 物理基因组学：为什么基因调控是聚合物物理学和生物化学之间的拉锯战。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-06-01 Epub Date: 2026-05-25 DOI: 10.1016/j.sbi.2026.103285
Sarah Harris , Agnes Noy , Wilma K. Olson
{"title":"Physical genomics: Why gene regulation is tug of war between polymer physics and biochemistry","authors":"Sarah Harris ,&nbsp;Agnes Noy ,&nbsp;Wilma K. Olson","doi":"10.1016/j.sbi.2026.103285","DOIUrl":"10.1016/j.sbi.2026.103285","url":null,"abstract":"<div><div>DNA–protein interactions underlie genome activity, governing gene expression as well as the physical organisation of DNA. Until recently, DNA–protein complexes were predominantly described at atomic resolution using short DNA fragments, concealing how proteins recognise and manipulate the long, supercoiled DNA present in cells. Now single-molecule imaging and cryo-electron microscopy (cryo-EM) are showing how longer DNA sequences are recognised by proteins and computations are predicting how these elements influence larger-scale genomic structures. Here we discuss how the polymeric nature of DNA influences its atomic-level structure and dynamics, as well as the implications for DNA recognition and ultimately biological function. We emphasise how theory and simulation help interpret these effects, which are difficult to replicate using conventional experimental settings.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"98 ","pages":"Article 103285"},"PeriodicalIF":6.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148013973","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}
引用次数: 0
Context matters: A view on metadata analysis in cryo-electron tomography 背景问题：低温电子断层扫描中元数据分析的观点。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-06-01 Epub Date: 2026-05-27 DOI: 10.1016/j.sbi.2026.103291
Markus Schreiber , Beata Turoňová
{"title":"Context matters: A view on metadata analysis in cryo-electron tomography","authors":"Markus Schreiber ,&nbsp;Beata Turoňová","doi":"10.1016/j.sbi.2026.103291","DOIUrl":"10.1016/j.sbi.2026.103291","url":null,"abstract":"<div><div>Cryo-electron tomography (cryo-ET) enables the analysis of biological samples <em>in situ</em>, revealing the complex interplay that shapes (sub)cellular architecture. Across the cryo-ET workflow, diverse forms of contextual information—from experimental metadata to spatial organization and particle-specific annotations—can inform the experimental design, guide computational approaches, and deepen the interpretation of tomograms.</div><div>In this review, we highlight recent advances in contextual analysis that complement and enhance commonly used cryo-ET workflows, while also expanding their scope. Examples range from sample preparation to data analysis aided by molecular dynamics simulations. Together, they illustrate how different notions of context enrich <em>in situ</em> investigations and allow cryo-ET to extend beyond high-resolution structure determination toward a more comprehensive understanding of cellular environments.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"98 ","pages":"Article 103291"},"PeriodicalIF":6.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148041438","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}
引用次数: 0
Labelling in cryogenic electron tomography - Bridging the gap between correlative light and electron microscopy and protein identification 低温电子断层扫描中的标记。弥合相关光学和电子显微镜与蛋白质鉴定之间的差距。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-06-01 Epub Date: 2026-05-27 DOI: 10.1016/j.sbi.2026.103292
Emily A. Machala, Lindsay A. Baker
{"title":"Labelling in cryogenic electron tomography - Bridging the gap between correlative light and electron microscopy and protein identification","authors":"Emily A. Machala,&nbsp;Lindsay A. Baker","doi":"10.1016/j.sbi.2026.103292","DOIUrl":"10.1016/j.sbi.2026.103292","url":null,"abstract":"<div><div>Cryogenic electron tomography (cryoET) offers unparalleled views into the molecular architecture of cells. As no stains or fixation are used, electrons scatter off the native atoms, and all molecules contribute to the final tomogram. As a result, it can be challenging to identify proteins of interest, especially inside a crowded cellular environment. Recent developments in molecular tags for cryoET provide several options for identifying proteins in reconstructed tomograms, but these are often not appropriate for finding an area of interest when collecting data. To increase the utility and throughput of cryoET, future approaches should combine correlative light and electron microscopy (CLEM) with tagging, so that a single modification can be used at small and large spatial scales. Automation of the detection of tags in tomograms and correlation between imaging modalities using machine learning methods will help increase the throughput of these methods, making them more suitable for rare events or structure determination by sub-tomogram averaging.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"98 ","pages":"Article 103292"},"PeriodicalIF":6.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148041440","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}
引用次数: 0
Transformers as a substrate for structural biology 变压器作为结构生物学的基质。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-06 DOI: 10.1016/j.sbi.2025.103218
Ashar J. Malik , Stephanie Portelli , David B. Ascher
{"title":"Transformers as a substrate for structural biology","authors":"Ashar J. Malik ,&nbsp;Stephanie Portelli ,&nbsp;David B. Ascher","doi":"10.1016/j.sbi.2025.103218","DOIUrl":"10.1016/j.sbi.2025.103218","url":null,"abstract":"<div><div>Transformers are rapidly reshaping structural biology. We argue the reason is “Emergent Latent Biology” (ELB): transformers place proteins into high-dimensional representations where hidden biophysical patterns become easier to see. We explore this concept across four key areas: protein folding, variant effects, protein–protein and protein–drug interactions. Highlighting recent gains, we note that traditional, physics-based calculations are still required for the hardest quantitative jobs, like predicting precise binding strength. Furthermore, we draw attention to major pitfalls, arguing progress depends on solving the critical “chemistry gap,” modelling chemical modifications, and the “dynamics gap”, predicting protein movement, which requires better validation methods and new large-scale experiments.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103218"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137351","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}
引用次数: 0
Why are there no clinically-approved drugs targeting disordered proteins? 为什么没有临床批准的针对无序蛋白质的药物？
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-03-02 DOI: 10.1016/j.sbi.2026.103236
Thomas Löhr , Gogulan Karunanithy , Gabriella T. Heller
{"title":"Why are there no clinically-approved drugs targeting disordered proteins?","authors":"Thomas Löhr ,&nbsp;Gogulan Karunanithy ,&nbsp;Gabriella T. Heller","doi":"10.1016/j.sbi.2026.103236","DOIUrl":"10.1016/j.sbi.2026.103236","url":null,"abstract":"<div><div>Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are critical regulators in health and disease but remain underexploited as drug targets. Unlike folded proteins, they populate dynamic ensembles where interactions can be transient or multivalent, and both enthalpic and entropic contributions shape binding, complicating ligand discovery. Here, we analyze three key barriers hindering progress: (1) nontraditional binding mechanisms that challenge classical drug design, (2) experimental and computational limitations for studying disorder, and (3) a lack of systematic datasets. Our analysis of the Biological Magnetic Resonance Data Bank (BMRB) and BindingDB highlights the extreme underrepresentation of IDPs and IDRs, underscoring the need for community-driven data resources. By integrating new binding paradigms, tailored methodologies, and standardized datasets, drug discovery can begin to harness IDPs as a new therapeutic frontier.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103236"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147347623","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}
引用次数: 0
In situ structural studies of membrane protein megacomplexes 膜蛋白巨复合体的原位结构研究
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.sbi.2026.103222
Shan Sun, Sen-Fang Sui
{"title":"In situ structural studies of membrane protein megacomplexes","authors":"Shan Sun,&nbsp;Sen-Fang Sui","doi":"10.1016/j.sbi.2026.103222","DOIUrl":"10.1016/j.sbi.2026.103222","url":null,"abstract":"<div><div>Membrane protein complexes are essential for cellular functions, which rely on both constituent protein structures and their interactions within native membranes. While <em>in vitro</em> methods have successfully yielded high-resolution structures of individual proteins and subcomplexes, these approaches typically require detergent extraction and extensive purification, which can disrupt the native membrane environment and potentially alter the supramolecular organization. <em>In situ</em> structural biology has therefore emerged as an effective strategy to overcome these limitations by directly visualizing macromolecular machines within their physiological context. With continuous technological advancements, several recent studies have resolved <em>in situ</em> structures of large protein complexes at high or even near-atomic resolution. This review focuses on recent <em>in situ</em> high-resolution studies of membrane protein megacomplexes, highlighting key technical innovations, structural insights, and the remaining challenges and opportunities in the field.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103222"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171553","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}
引用次数: 0
Rational protein design 合理的蛋白质设计
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-12 DOI: 10.1016/j.sbi.2026.103224
Joel J. Chubb , Aimee L. Boyle , Katherine I. Albanese
{"title":"Rational protein design","authors":"Joel J. Chubb ,&nbsp;Aimee L. Boyle ,&nbsp;Katherine I. Albanese","doi":"10.1016/j.sbi.2026.103224","DOIUrl":"10.1016/j.sbi.2026.103224","url":null,"abstract":"<div><div>Protein design enables the creation of novel structures and functions beyond those found in nature, with recent progress accelerated by computational modeling and machine learning. However, many automated methods act as black boxes, limiting mechanistic insight. Here we highlight the continuing importance of rational protein design, defined as an approach rooted in physical principles, chemical intuition, and sequence–structure–function relationships. We outline three complementary strategies: backbone-first, sequence-first, and function-first, which provide interpretable design frameworks and enable robust scaffold generation, motif incorporation, and functional engineering. Looking forward, we argue that hybrid workflows combining rational principles with machine learning offer the most promising route to dynamic, explainable, and generalizable protein design.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103224"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171552","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}
引用次数: 0
Moving the antibody: Molecular dynamics for molecular mechanisms and developability 移动抗体：分子动力学的分子机制和可展性。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-18 DOI: 10.1016/j.sbi.2026.103225
Matteo Cagiada , Charlotte M. Deane
{"title":"Moving the antibody: Molecular dynamics for molecular mechanisms and developability","authors":"Matteo Cagiada ,&nbsp;Charlotte M. Deane","doi":"10.1016/j.sbi.2026.103225","DOIUrl":"10.1016/j.sbi.2026.103225","url":null,"abstract":"","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103225"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146225816","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}
引用次数: 0
From sequence to structure: A comprehensive review of deep learning models for RNA structure prediction 从序列到结构：RNA结构预测的深度学习模型的全面回顾。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-05 DOI: 10.1016/j.sbi.2025.103216
Utkarsh Upadhyay, Anton Dorn, Christian Faber, Alexander Schug
{"title":"From sequence to structure: A comprehensive review of deep learning models for RNA structure prediction","authors":"Utkarsh Upadhyay,&nbsp;Anton Dorn,&nbsp;Christian Faber,&nbsp;Alexander Schug","doi":"10.1016/j.sbi.2025.103216","DOIUrl":"10.1016/j.sbi.2025.103216","url":null,"abstract":"<div><div>RNA structure prediction remains one of the most challenging problems in computational biology, with significant implications for understanding gene regulation, drug design, and synthetic biology. While deep learning has revolutionized protein structure prediction, RNA presents unique challenges including limited training data, complex noncanonical interactions, and conformational flexibility. This review examines the evolution from traditional physics-based methods to current deep learning approaches for RNA secondary and tertiary structure prediction. After briefly exploring traditional methods, like Direct Coupling Analysis and physics-based simulations, we systematically review three deep learning paradigms: language model–based methods, end-to-end structure predictors, and geometry-distance prediction approaches. Furthermore, we identify critical future research directions focusing on advanced tokenization strategies to address data scarcity and explainable artificial intelligence techniques to improve model interpretability. Despite significant progress, achieving transformative performance requires continued methodological innovation, specifically designed for RNA’s unique characteristics, and a substantial expansion of high-quality structural datasets.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103216"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131433","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}
引用次数: 0
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