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Ligand-like lipid interactions with membrane proteins: Simulations and machine learning 配体样脂质与膜蛋白的相互作用：模拟和机器学习。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-19 DOI: 10.1016/j.sbi.2026.103226
George Hedger , Edward Lyman , Sarah L. Rouse
{"title":"Ligand-like lipid interactions with membrane proteins: Simulations and machine learning","authors":"George Hedger ,&nbsp;Edward Lyman ,&nbsp;Sarah L. Rouse","doi":"10.1016/j.sbi.2026.103226","DOIUrl":"10.1016/j.sbi.2026.103226","url":null,"abstract":"<div><div>Membrane lipids can bind to specific sites on membrane proteins in a ligand-like manner and modulate protein structure and function. Molecular dynamics simulations encompass a suite of approaches to identify, characterise, and explain the atomic-level mechanisms that underlie the functional effects of ligand-like lipids on membrane proteins. Simulations have shown good agreement with available structural data on lipid-protein interactions. Building on successes, simulations are now used to identify new interactions and mechanisms <em>de novo</em> for a given membrane protein. In this age of abundance, it is increasingly possible to analyse patterns across large groups of proteins and in ever more complex membrane environments. The dawn of machine learning approaches in lipid-protein cofolding holds considerable promise to synergistically capitalise on this availability of simulation data and uncover new facets of ligand-like lipid biology.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103226"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257423","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
Machine learning, docking, or physics for structure prediction of ligand-induced ternary complexes 用于配体诱导三元配合物结构预测的机器学习、对接或物理
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-01-30 DOI: 10.1016/j.sbi.2025.103217
Riccardo Solazzo , Shu-Yu Chen , Sereina Riniker
{"title":"Machine learning, docking, or physics for structure prediction of ligand-induced ternary complexes","authors":"Riccardo Solazzo ,&nbsp;Shu-Yu Chen ,&nbsp;Sereina Riniker","doi":"10.1016/j.sbi.2025.103217","DOIUrl":"10.1016/j.sbi.2025.103217","url":null,"abstract":"<div><div>Proteolysis-targeting chimeras (PROTACs) and molecular glues promote targeted protein degradation by recruiting an E3 ligase to proteins of interest (POIs). An accurate 3D structure of the ternary complex formed by E3 ligase, ligand, and POI is central to the rational design of degraders. Elucidating this structure with crystallography or cryo-EM can be challenging due to conformational flexibility, dynamic protein-protein interactions, and high-dimensional binding landscapes. To facilitate structure-based design in the absence of an experimental structure, computational approaches have been proposed: (i) multistep methods involving traditional docking pipelines, and (ii) single-step methods with deep learning models to directly predict the complex structure. Multistep methods are limited by sampling complexity, accurate input structures, scoring accuracy, and computational cost, while single-step methods are faster but are constrained by training-data scarcity. Here, we examine recent advances and emerging tools in modeling ternary complexes, critically discuss their predictive power and limitations, and highlight remaining challenges.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103217"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077215","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
Drug–target residence time: Analyzing cooperativity effects in G protein-coupled receptors by mathematical modeling and molecular dynamics simulations 药物靶停留时间：用数学模型和分子动力学模拟分析G蛋白偶联受体的协同效应。
IF 6.1 2区 生物学
Current opinion in structural biology Pub Date : 2026-04-01 Epub Date: 2026-02-07 DOI: 10.1016/j.sbi.2025.103214
Antonio J. Ortiz , Antoniel A.S. Gomes , Pedro Renault , David Romero , Antoni Guillamon , Jesús Giraldo
{"title":"Drug–target residence time: Analyzing cooperativity effects in G protein-coupled receptors by mathematical modeling and molecular dynamics simulations","authors":"Antonio J. Ortiz ,&nbsp;Antoniel A.S. Gomes ,&nbsp;Pedro Renault ,&nbsp;David Romero ,&nbsp;Antoni Guillamon ,&nbsp;Jesús Giraldo","doi":"10.1016/j.sbi.2025.103214","DOIUrl":"10.1016/j.sbi.2025.103214","url":null,"abstract":"<div><div>Drug–target residence time (τ) is reviewed from two perspectives: mathematics and molecular dynamics. The first focuses on the quantification of τ using a mathematical formalism applicable to different pharmacological mechanistic conditions. This formalism is based on the concept of the smallest-modulus eigenvalue of a subsystem of interest, in which the global formation process has been eliminated. The second includes relevant studies of recent years to provide a structural explanation of τ predictions. Special attention is paid to physically supported artificial intelligence methods. The main objective of this minireview is to promote a combined approach in which mathematics and physics work synergistically to describe the complexity associated with τ in G protein-coupled receptors.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103214"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146141361","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
Altered residence time as a cause of drug resistance 滞留时间的改变是抗药性的原因
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.2026.103219
Brian M. Farrell , Markus A. Seeliger
{"title":"Altered residence time as a cause of drug resistance","authors":"Brian M. Farrell ,&nbsp;Markus A. Seeliger","doi":"10.1016/j.sbi.2026.103219","DOIUrl":"10.1016/j.sbi.2026.103219","url":null,"abstract":"<div><div>Drug-target residence time is a crucial determinant of pharmacological efficacy, complementing traditional equilibrium affinity measures. Variations in residence time influence drug selectivity, therapeutic windows, and resistance development, yet its molecular underpinnings remain incompletely understood. Here we review factors governing residence time, including kinetic parameters and structural influences, and examine how mutations can alter dissociation rates to confer drug resistance. We highlight recent advances in experimental and computational methods, such as molecular dynamics simulations, that enable prediction and rational design of compounds with optimized residence times. These insights underscore the importance of incorporating kinetic considerations into drug discovery to improve efficacy and overcome resistance. Our findings suggest that optimizing residence time offers a promising strategy to enhance therapeutic outcomes for diverse diseases.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103219"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171551","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
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
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