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De novo engineering of protein interactions: Retrospective and current advances 蛋白质相互作用的从头工程：回顾和当前进展。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-26 DOI: 10.1016/j.sbi.2026.103240
Alisa Khramushin, Evgenia Elizarova, Bruno E. Correia
{"title":"De novo engineering of protein interactions: Retrospective and current advances","authors":"Alisa Khramushin,&nbsp;Evgenia Elizarova,&nbsp;Bruno E. Correia","doi":"10.1016/j.sbi.2026.103240","DOIUrl":"10.1016/j.sbi.2026.103240","url":null,"abstract":"<div><div>New deep learning–based methods for modeling and generation of protein structures have opened a new chapter in the field of protein design, transforming many previously unattainable challenges into routine tasks. Protein-binder design, an important and challenging task in protein engineering, has also experienced significant progress, promising to provide solutions to many therapeutic and bioengineering problems. Novel protein folds of tailored surface complementarity to their target can be generated and stabilized by amino acid sequences with unprecedentedly high experimental success rates. These advancements can be largely attributed to the power of the new structure prediction models, such as AlphaFold, as well as deep generative models that learn data distributions and allow sampling of new molecules conditioned on function-related features. In this review, we will discuss the development of binder design approaches, focusing on the state-of-the-art methods and their applications as well as new challenges.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103240"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147316777","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
Interpreting chemical crosslinks: Score-based approaches and deep neural networks 解释化学交联：基于分数的方法和深度神经网络。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-03-02 DOI: 10.1016/j.sbi.2026.103237
Xingyu Chen , Kai Steffen Stroh , Jan Erzberger , Florian Stengel , Riccardo Pellarin
{"title":"Interpreting chemical crosslinks: Score-based approaches and deep neural networks","authors":"Xingyu Chen ,&nbsp;Kai Steffen Stroh ,&nbsp;Jan Erzberger ,&nbsp;Florian Stengel ,&nbsp;Riccardo Pellarin","doi":"10.1016/j.sbi.2026.103237","DOIUrl":"10.1016/j.sbi.2026.103237","url":null,"abstract":"<div><div>Chemical cross-linking coupled with mass spectrometry (XL-MS) has become a powerful tool for probing residue-level proximities within macromolecular assemblies. By providing sparse but informative distance restraints, XL-MS can be integrated with electron microscopy and domain-level high-resolution structures to model the architecture of protein complexes. Unlike X-ray crystallography, electron microscopy, or solid-state Nuclear Magnetic Resonance (NMR), XL-MS can be applied under near-physiological conditions, scaled to large modular systems, and performed at higher throughput. In this review, we highlight recent advances in the field, with particular emphasis on the impact of AI-driven structure prediction. As an illustration, we describe a hybrid protocol that combines the Integrative Modeling Platform (IMP) with the deep neural network Chai-1 to dock and refine the helicase Dbp10 on a transient ribosome biogenesis intermediate using XL-MS restraints.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103237"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147347606","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
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
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