发布求助
文献互助 智能选刊 最新文献

Current opinion in structural biology最新文献

筛选
英文 中文
Structural biology of γδ T cell receptors γδ T细胞受体的结构生物学
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.2026.103221
Liam Rashleigh , Mengqi Pan , Jamie Rossjohn , Michael T. Rice
{"title":"Structural biology of γδ T cell receptors","authors":"Liam Rashleigh ,&nbsp;Mengqi Pan ,&nbsp;Jamie Rossjohn ,&nbsp;Michael T. Rice","doi":"10.1016/j.sbi.2026.103221","DOIUrl":"10.1016/j.sbi.2026.103221","url":null,"abstract":"<div><div>T cell receptor (TCR) diversity underpins cellular immunity. While αβ TCRs have been extensively studied in the context of major histocompatibility complex (MHC)-restricted antigen recognition, the γδ TCR system remains underexplored. Unlike their αβ counterparts, γδ TCRs display versatile, often MHC-independent recognition modes, engaging diverse ligands ranging from butyrophilins (BTNs) and other disparate molecules. Recent advances in cryo-electron microscopy (cryo-EM) paired with crystallographic data have illuminated critical aspects of γδ TCR -ligand interactions, the CD3 complex architecture, and the inherent flexibility underpinning their varied recognition modes. In this review, we compare the classical αβ TCR-MHC paradigm against the backdrop of emerging γδ TCR structures, highlighting the latest cryo-EM findings and their implications for immunobiology.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103221"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171554","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
Recent advances in AI-driven pKa prediction for proteins and small molecules 人工智能驱动蛋白和小分子pKa预测的最新进展。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-15 DOI: 10.1016/j.sbi.2026.103220
Yandong Huang
{"title":"Recent advances in AI-driven pKa prediction for proteins and small molecules","authors":"Yandong Huang","doi":"10.1016/j.sbi.2026.103220","DOIUrl":"10.1016/j.sbi.2026.103220","url":null,"abstract":"<div><div>Advances in machine-learning techniques and the availability of high-quality p<em>K</em><sub>a</sub> databases have promoted the development of AI-driven p<em>K</em><sub>a</sub> predictors. This review surveys recent advances in AI-driven p<em>K</em><sub>a</sub> prediction for both proteins and small molecules, and reveals that methodology has evolved along divergent trajectories for the two molecular classes, giving rise to largely independent lineages. Finally, open challenges in p<em>K</em><sub>a</sub> prediction, including data scarcity, thermodynamic consistency, and a general-purpose model, are highlighted for future development.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103220"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146206791","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
Graph neural networks for molecular dynamics simulations 分子动力学模拟的图形神经网络。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-25 DOI: 10.1016/j.sbi.2026.103238
Mohd Ahsan , Chinmai Pindi , Souvik Sinha , Amun C. Patel , Giulia Palermo
{"title":"Graph neural networks for molecular dynamics simulations","authors":"Mohd Ahsan ,&nbsp;Chinmai Pindi ,&nbsp;Souvik Sinha ,&nbsp;Amun C. Patel ,&nbsp;Giulia Palermo","doi":"10.1016/j.sbi.2026.103238","DOIUrl":"10.1016/j.sbi.2026.103238","url":null,"abstract":"<div><div>Graph neural networks (GNNs) are emerging as powerful tools for advancing molecular dynamics (MD) simulations, providing data-driven frameworks to complement traditional physics-based approaches. By representing atoms and their interactions as graphs, GNNs naturally encode chemical and structural information, enabling accurate neural network force fields trained on quantum data, automated discovery of collective variables for enhanced sampling, and efficient prediction of atomic forces to extend simulation timescales. Beyond driving MD, GNNs facilitate the analysis of high-dimensional trajectories, offering interpretable insights through attention mechanisms or transferable embeddings. Applications such as protein–DNA assembly, pretrained featurizers, and cryptic pocket discovery illustrate the breadth of GNNs, underscoring their potential to transform biomolecular simulations and accelerate mechanistic and translational discoveries.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103238"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147303576","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
Protein dynamics prediction by integrating biophysics and artificial intelligence 结合生物物理学和人工智能的蛋白质动力学预测。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-17 DOI: 10.1016/j.sbi.2026.103223
Hengyan Huang , Xingyue Guan , Wenfei Li , Jian Zhang , Wei Wang
{"title":"Protein dynamics prediction by integrating biophysics and artificial intelligence","authors":"Hengyan Huang ,&nbsp;Xingyue Guan ,&nbsp;Wenfei Li ,&nbsp;Jian Zhang ,&nbsp;Wei Wang","doi":"10.1016/j.sbi.2026.103223","DOIUrl":"10.1016/j.sbi.2026.103223","url":null,"abstract":"<div><div>Proteins often rely on conformational dynamics to perform their biological functions. A detailed understanding of protein dynamics is fundamental to revealing the biophysical principles of life and to accelerating therapeutic discovery. However, purely data-driven artificial intelligence (AI) methods face significant challenges in capturing the full spectrum of protein conformational dynamics. This review highlights recent advances in overcoming these challenges through the integration of biophysical constraints with AI-driven approaches. By combining fundamental biophysical principles, experimentally measured biophysical data, and physics-based methodologies into AI models, the integrated approaches show promise in enhancing both the performance and interpretability of protein dynamics predictions. Several key perspectives and future directions in the field are also discussed.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103223"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146218900","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
Lipid scrambling: New players, new questions, new opportunities 脂质争夺：新玩家，新问题，新机会。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-24 DOI: 10.1016/j.sbi.2026.103227
Cristian Rocha-Roa , Stefano Vanni
{"title":"Lipid scrambling: New players, new questions, new opportunities","authors":"Cristian Rocha-Roa ,&nbsp;Stefano Vanni","doi":"10.1016/j.sbi.2026.103227","DOIUrl":"10.1016/j.sbi.2026.103227","url":null,"abstract":"<div><div>Nearly a quarter of the proteins encoded in most organisms are transmembrane proteins. Contrary to textbook description, many feature a hydrophilic groove which is laterally exposed to the hydrophobic region of the lipid membrane. This cavity is stabilized by neighboring lipid headgroups that sink deep into the membrane and consequently move bidirectionally from one leaflet to the other, in a process nicknamed lipid ‘scrambling.’ These proteins, called scramblases, have been reported to serve in many cellular functions, ranging from lipid redistribution during organelle growth to cellular apoptosis. Despite their importance, the identity of most scramblases has remained a mystery for many years. In the last few years, <em>in silico</em> techniques have accelerated the discovery of dozens of new scramblases. Nonetheless, together with these discoveries, key questions have emerged. In this review, we highlight some open questions in this emerging field and showcase how modern computational techniques can help addressing them.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103227"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147303481","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
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
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
{"title":"Editorial overview: Cryo-electron microscopy (2025)","authors":"Axel T. Brunger,&nbsp;Gabriel A. Frank","doi":"10.1016/j.sbi.2025.103200","DOIUrl":"10.1016/j.sbi.2025.103200","url":null,"abstract":"","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103200"},"PeriodicalIF":6.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145920789","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
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
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
{"title":"Resolving structural heterogeneity in situ through cryogenic electron tomography","authors":"Jackson Carrion ,&nbsp;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":"2026-02-01","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}
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
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
{"title":"Advances in the determination of disordered protein ensemble","authors":"Hamidreza Ghafouri ,&nbsp;Silvio C.E. Tosatto ,&nbsp;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":"2026-02-01","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}
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
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
{"title":"Deep learning–based postprocessing and model building for cryo-electron microscopy maps","authors":"Tao Li,&nbsp;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":"2026-02-01","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}
引用次数: 0
Generative molecular dynamics 生成分子动力学
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-02-01 Epub Date: 2026-01-15 DOI: 10.1016/j.sbi.2025.103213
Simon Olsson
{"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":"2026-02-01","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}
引用次数: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书