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
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
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
{"title":"Recent advances in machine learning predictions of protein-ligand binding affinities","authors":"Jian Jiang ,&nbsp;Daixin Li ,&nbsp;Guilin Wang ,&nbsp;Guo-Wei Wei","doi":"10.1016/j.sbi.2025.103193","DOIUrl":"10.1016/j.sbi.2025.103193","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103193"},"PeriodicalIF":6.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610609","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
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
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
{"title":"Integrative modelling of biomolecular dynamics","authors":"Daria Gusew ,&nbsp;Carl G. Henning Hansen ,&nbsp;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":"2026-02-01","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}
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
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
{"title":"Computational design of intrinsically disordered proteins","authors":"Giulio Tesei ,&nbsp;Francesco Pesce ,&nbsp;Kresten Lindorff-Larsen","doi":"10.1016/j.sbi.2025.103210","DOIUrl":"10.1016/j.sbi.2025.103210","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103210"},"PeriodicalIF":6.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023065","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
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
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
{"title":"Decrypting cryptic pockets with physics-based simulations and artificial intelligence","authors":"Si Zhang,&nbsp;Gregory R. Bowman","doi":"10.1016/j.sbi.2025.103215","DOIUrl":"10.1016/j.sbi.2025.103215","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103215"},"PeriodicalIF":6.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073712","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
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
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
{"title":"The current understanding of KRAS oligomerization on membranes","authors":"Nastazia Lesgidou ,&nbsp;Michail Papadourakis ,&nbsp;Nishita Mandal ,&nbsp;Sepehr Dehghani-Ghahnaviyeh ,&nbsp;Camilo Velez-Vega ,&nbsp;José S. Duca ,&nbsp;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":"2026-02-01","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}
引用次数: 0
Editorial overview: Exploring protein conformational landscapes for catalysis in the beginning of the artificial intelligence era 编辑概述：探索人工智能时代初期催化的蛋白质构象景观。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-02-01 Epub Date: 2025-12-22 DOI: 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,&nbsp;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":"2026-02-01","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}
引用次数: 0
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
Current opinion in structural biology Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.sbi.2025.103197
Nicholas C. Pinette , Mailyn Terrado , Jennifer M. Bui , Nada Lallous , Jörg Gsponer
{"title":"Next-generation predictors of protein phase behavior","authors":"Nicholas C. Pinette ,&nbsp;Mailyn Terrado ,&nbsp;Jennifer M. Bui ,&nbsp;Nada Lallous ,&nbsp;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":"2026-02-01","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}
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
Current opinion in structural biology Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.sbi.2025.103194
Priscila Z. Ramos , Carolina M.C. Catta-Preta , Rafael M. Couñago
{"title":"Target engagement in bacterial and protozoan pathogens: in vitro and cellular assays for drug discovery","authors":"Priscila Z. Ramos ,&nbsp;Carolina M.C. Catta-Preta ,&nbsp;Rafael M. Couñago","doi":"10.1016/j.sbi.2025.103194","DOIUrl":"10.1016/j.sbi.2025.103194","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"96 ","pages":"Article 103194"},"PeriodicalIF":6.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617596","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 breakthroughs in understanding the allosteric features of Ras GTPases and their effector and regulatory protein interactions, enabling drug design 在了解Ras GTPases的变构特征及其效应蛋白和调节蛋白相互作用方面的最新突破，使药物设计成为可能。
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.2025.103183
Nisha Bhattarai , Matthias Buck
{"title":"Recent breakthroughs in understanding the allosteric features of Ras GTPases and their effector and regulatory protein interactions, enabling drug design","authors":"Nisha Bhattarai ,&nbsp;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":"2026-02-01","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}
引用次数: 0
Computational modeling of protein–ligand interactions: From binding site identification to pose prediction and beyond 蛋白质-配体相互作用的计算模型：从结合位点鉴定到姿态预测及其他
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2025-12-01 Epub Date: 2025-09-10 DOI: 10.1016/j.sbi.2025.103152
Viet-Khoa Tran-Nguyen, Anne-Claude Camproux
{"title":"Computational modeling of protein–ligand interactions: From binding site identification to pose prediction and beyond","authors":"Viet-Khoa Tran-Nguyen,&nbsp;Anne-Claude Camproux","doi":"10.1016/j.sbi.2025.103152","DOIUrl":"10.1016/j.sbi.2025.103152","url":null,"abstract":"<div><div>Protein-ligand modeling is a cornerstone of modern drug discovery, facilitating the identification and optimization of bioactive compounds that modulate protein function. Computational approaches provide cost-effective and scalable strategies for exploring the growing chemical and biological spaces, accelerating early-stage drug development. Advances in both physics-based methods and data-driven machine learning techniques have expanded the range and accuracy of tools available for modeling protein-ligand interactions. This review provides a current and concise view of key methodologies in protein-ligand modeling, including binding site prediction and the generation and evaluation of target-bound ligand conformations. It also discusses state-of-the-art machine learning approaches that are reshaping how these tasks are performed and enhancing the accuracy of binding site, binding pose, and binding affinity predictions.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"95 ","pages":"Article 103152"},"PeriodicalIF":6.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027162","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