Pub Date : 2025-10-04DOI: 10.1016/j.sbi.2025.103160
Jeremy A. Owen , Leonid A. Mirny
Epigenetic memory—the stable inheritance of a cellular state over cell generations—has long been associated with chromatin modifications. But individual modifications are very dynamic. How can they carry information across cell generations? Recent theoretical work suggests the answer might lie, in part, in the three-dimensional organization of the genome. Cooperation between marks brought together by genome folding can correct epigenetic errors, making stable memory units out of unstable marks. If marks direct the phase separation of chromatin, the resulting bidirectional coupling between marks and structure provides a mechanism for many of these units to operate independently along the genome. Models of bidirectional coupling have helped identify elements, such as formation of a dense compartment, 3D mark spreading, and limited enzyme, which may be key to stable epigenetic memory. An analogy between these 3D models and a classic model of associative memory hints at a way chromatin could perform sophisticated information processing.
{"title":"Chromatin as a three-dimensional memory machine","authors":"Jeremy A. Owen , Leonid A. Mirny","doi":"10.1016/j.sbi.2025.103160","DOIUrl":"10.1016/j.sbi.2025.103160","url":null,"abstract":"<div><div>Epigenetic memory—the stable inheritance of a cellular state over cell generations—has long been associated with chromatin modifications. But individual modifications are very dynamic. How can they carry information across cell generations? Recent theoretical work suggests the answer might lie, in part, in the three-dimensional organization of the genome. Cooperation between marks brought together by genome folding can correct epigenetic errors, making stable memory units out of unstable marks. If marks direct the phase separation of chromatin, the resulting bidirectional coupling between marks and structure provides a mechanism for many of these units to operate independently along the genome. Models of bidirectional coupling have helped identify elements, such as formation of a dense compartment, 3D mark spreading, and limited enzyme, which may be key to stable epigenetic memory. An analogy between these 3D models and a classic model of associative memory hints at a way chromatin could perform sophisticated information processing.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"95 ","pages":"Article 103160"},"PeriodicalIF":6.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145217985","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}
Pub Date : 2025-10-04DOI: 10.1016/j.sbi.2025.103166
Manisha Kalsan, Sadiya Mirza, Divyangana Bathla, Shandar Ahmad
Sequence-dependent intrinsic conformational dynamics confer specificity to transcriptional factor recognition of genomic DNA. Their genome-scale investigation using all-atom simulations is challenging, and alternative approaches by coarse-graining DNA into beads-and-sticks or polymer models have their own limitations. One parallel approach is what we describe here as a dictionary-based approach. This has been shown to explain several transcriptional events in biological systems but has been inadequately reviewed. These approaches represent studies based on a finite number of DNA fragments and their corresponding conformational properties, scaled up to genomes by pooling nearby fragments and machine learning models. This article aims to organize efforts made in generating these models and their recent successful applications to stimulate further development of this approach.
{"title":"Dictionary based approaches for studying intrinsic DNA shape in transcription factor recognition","authors":"Manisha Kalsan, Sadiya Mirza, Divyangana Bathla, Shandar Ahmad","doi":"10.1016/j.sbi.2025.103166","DOIUrl":"10.1016/j.sbi.2025.103166","url":null,"abstract":"<div><div>Sequence-dependent <em>intrinsic</em> conformational dynamics confer specificity to transcriptional factor recognition of genomic DNA. Their genome-scale investigation using all-atom simulations is challenging, and alternative approaches by coarse-graining DNA into beads-and-sticks or polymer models have their own limitations. One parallel approach is what we describe here as a dictionary-based approach. This has been shown to explain several transcriptional events in biological systems but has been inadequately reviewed. These approaches represent studies based on a finite number of DNA fragments and their corresponding conformational properties, scaled up to genomes by pooling nearby fragments and machine learning models. This article aims to organize efforts made in generating these models and their recent successful applications to stimulate further development of this approach.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"95 ","pages":"Article 103166"},"PeriodicalIF":6.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145217986","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}
Pub Date : 2025-10-01Epub Date: 2025-08-08DOI: 10.1016/j.sbi.2025.103129
Francesca M Marassi, Guido Pintacuda
Membrane proteins have evolved to function as part of specialized biological membranes, and their structures and activities are highly susceptible to their local environment. Detergents and lipid mimetics replicate certain aspects of biological membranes, and have been used to produce an exceptional body of structural data, but do not fully capture the complex, asymmetric properties of the native environment and can alter structure and function. Here, we review recent advances in nuclear magnetic resonance (NMR) that enable the examination of membrane protein structure and activity in situ, within native membranes. The development of optimized protein expression strategies, isotopic labeling schemes, powerful instrumentation and specialized pulse sequences offer new opportunities for exploring the new frontier of in situ structural biology. By outlining the framework for in situ NMR of membrane proteins from conceptualization to experiments we hope to inspire new research in this growing and important area.
{"title":"Solid-state NMR of membrane proteins in situ.","authors":"Francesca M Marassi, Guido Pintacuda","doi":"10.1016/j.sbi.2025.103129","DOIUrl":"10.1016/j.sbi.2025.103129","url":null,"abstract":"<p><p>Membrane proteins have evolved to function as part of specialized biological membranes, and their structures and activities are highly susceptible to their local environment. Detergents and lipid mimetics replicate certain aspects of biological membranes, and have been used to produce an exceptional body of structural data, but do not fully capture the complex, asymmetric properties of the native environment and can alter structure and function. Here, we review recent advances in nuclear magnetic resonance (NMR) that enable the examination of membrane protein structure and activity in situ, within native membranes. The development of optimized protein expression strategies, isotopic labeling schemes, powerful instrumentation and specialized pulse sequences offer new opportunities for exploring the new frontier of in situ structural biology. By outlining the framework for in situ NMR of membrane proteins from conceptualization to experiments we hope to inspire new research in this growing and important area.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"94 ","pages":"103129"},"PeriodicalIF":6.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01Epub Date: 2025-08-09DOI: 10.1016/j.sbi.2025.103132
Shani Tcherner Elad, Noa Ben-Asher, Leeya Engel
Cryo-electron microscopy (cryo-EM) has emerged as a transformative tool in structural biology, enabling high-resolution visualization of macromolecules in their native states. Cryo-focused ion beam milling (cryo-FIB) and other advances in sample preparation have expanded the range of biological samples that can be studied with cryo-EM to include cells and tissues. While the dream of high-resolution structural analysis of proteins within their native, cellular context is now being realized, sample preparation, especially from tissues, is still labor-intensive and technically challenging. Here we review the latest innovations in cryo-EM sample preparation, including support fabrication and functionalization, cell micropatterning, and techniques for thinning frozen biological samples. Beyond streamlining and improving the repeatability of sample preparation, these advances are expanding the impact of cryo-EM by enabling unprecedented visualization of structures within cells and tissues in healthy and diseased states, as well as structural analysis of biological processes at well-controlled time points.
{"title":"Cool and collected: Advances in sample preparation for cryo-electron microscopy.","authors":"Shani Tcherner Elad, Noa Ben-Asher, Leeya Engel","doi":"10.1016/j.sbi.2025.103132","DOIUrl":"10.1016/j.sbi.2025.103132","url":null,"abstract":"<p><p>Cryo-electron microscopy (cryo-EM) has emerged as a transformative tool in structural biology, enabling high-resolution visualization of macromolecules in their native states. Cryo-focused ion beam milling (cryo-FIB) and other advances in sample preparation have expanded the range of biological samples that can be studied with cryo-EM to include cells and tissues. While the dream of high-resolution structural analysis of proteins within their native, cellular context is now being realized, sample preparation, especially from tissues, is still labor-intensive and technically challenging. Here we review the latest innovations in cryo-EM sample preparation, including support fabrication and functionalization, cell micropatterning, and techniques for thinning frozen biological samples. Beyond streamlining and improving the repeatability of sample preparation, these advances are expanding the impact of cryo-EM by enabling unprecedented visualization of structures within cells and tissues in healthy and diseased states, as well as structural analysis of biological processes at well-controlled time points.</p>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"94 ","pages":"103132"},"PeriodicalIF":6.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144815961","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}
Pub Date : 2025-09-30DOI: 10.1016/j.sbi.2025.103163
Rui Jin , Xiaojie Lu
DNA-encoded library (DEL) technology has enabled efficient discovery of both non-covalent and covalent inhibitors, with covalent binders typically identified via covalent DELs (CoDELs) containing diverse electrophilic warheads. Recent developments have expanded CoDEL applications beyond cysteine to residues like lysine, tyrosine, arginine, and glutamic acid. The integration of CoDEL with activity-based protein profiling (ABPP) has further enabled the identification of potential protein targets for CoDEL screening using residue-selective warheads. Additionally, proteome profiling with fully-functionalized tags has guided target identification for focused DELs with privileged structures. This review highlights recent advances in CoDEL technologies for targeting both cysteine and non-cysteine residues, and discusses how proteomics facilitates hit discovery through CoDELs and focused DELs.
{"title":"Recent advances in DNA-encoded libraries: From covalent targeting to protein profiling","authors":"Rui Jin , Xiaojie Lu","doi":"10.1016/j.sbi.2025.103163","DOIUrl":"10.1016/j.sbi.2025.103163","url":null,"abstract":"<div><div>DNA-encoded library (DEL) technology has enabled efficient discovery of both non-covalent and covalent inhibitors, with covalent binders typically identified via covalent DELs (CoDELs) containing diverse electrophilic warheads. Recent developments have expanded CoDEL applications beyond cysteine to residues like lysine, tyrosine, arginine, and glutamic acid. The integration of CoDEL with activity-based protein profiling (ABPP) has further enabled the identification of potential protein targets for CoDEL screening using residue-selective warheads. Additionally, proteome profiling with fully-functionalized tags has guided target identification for focused DELs with privileged structures. This review highlights recent advances in CoDEL technologies for targeting both cysteine and non-cysteine residues, and discusses how proteomics facilitates hit discovery through CoDELs and focused DELs.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"95 ","pages":"Article 103163"},"PeriodicalIF":6.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205730","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}
Pub Date : 2025-09-27DOI: 10.1016/j.sbi.2025.103164
Francesco Rinaldi , Stefania Girotto
Cells have evolved multiple pathways to preserve genome integrity, with homologous recombination (HR) playing a central role in the accurate repair of DNA-double strand breaks (DSBs) by using homologous templates. Several proteins are involved in HR, and their mutations have been associated with cancer initiation and progression. In this review, we present an overview of recent structural insights into the HR pathway, highlighting the pivotal role of structural approaches in elucidating this complex and finely regulated DNA repair machinery, with the aim of advancing understanding and informing future research in the field.
{"title":"Understanding how structure shapes the architecture of homologous recombination","authors":"Francesco Rinaldi , Stefania Girotto","doi":"10.1016/j.sbi.2025.103164","DOIUrl":"10.1016/j.sbi.2025.103164","url":null,"abstract":"<div><div>Cells have evolved multiple pathways to preserve genome integrity, with homologous recombination (HR) playing a central role in the accurate repair of DNA-double strand breaks (DSBs) by using homologous templates. Several proteins are involved in HR, and their mutations have been associated with cancer initiation and progression. In this review, we present an overview of recent structural insights into the HR pathway, highlighting the pivotal role of structural approaches in elucidating this complex and finely regulated DNA repair machinery, with the aim of advancing understanding and informing future research in the field.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"95 ","pages":"Article 103164"},"PeriodicalIF":6.1,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184958","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}
Pub Date : 2025-09-25DOI: 10.1016/j.sbi.2025.103159
Vahap Gazi Fidan , Konuralp Ilim , Attila Gursoy , S. Banu Ozkan , Ozlem Keskin
Allosteric modulation offers an increasingly attractive route for precise intervention in enzymatic pathways. This review outlines emerging strategies for the identification and exploitation of allosteric sites, emphasizing computational frameworks that integrate evolutionary, structural, and dynamic features with machine learning models. We discuss how perturbation-based simulations, network analyses, and deep mutational data are reshaping our understanding of allosteric regulation. In parallel, advances in experimental techniques have enabled validation of cryptic and functionally relevant pockets across diverse enzyme families. We further catalog FDA-approved allosteric modulators of enzymes, highlighting therapeutic designs that leverage distal regulation to enhance specificity and overcome resistance. Taken together, these developments reveal the growing utility of allostery in drug design and underscore its potential to expand the therapeutic target space beyond conventional binding sites.
{"title":"Rewiring enzyme regulation: Allosteric drugs and predictive tools","authors":"Vahap Gazi Fidan , Konuralp Ilim , Attila Gursoy , S. Banu Ozkan , Ozlem Keskin","doi":"10.1016/j.sbi.2025.103159","DOIUrl":"10.1016/j.sbi.2025.103159","url":null,"abstract":"<div><div>Allosteric modulation offers an increasingly attractive route for precise intervention in enzymatic pathways. This review outlines emerging strategies for the identification and exploitation of allosteric sites, emphasizing computational frameworks that integrate evolutionary, structural, and dynamic features with machine learning models. We discuss how perturbation-based simulations, network analyses, and deep mutational data are reshaping our understanding of allosteric regulation. In parallel, advances in experimental techniques have enabled validation of cryptic and functionally relevant pockets across diverse enzyme families. We further catalog FDA-approved allosteric modulators of enzymes, highlighting therapeutic designs that leverage distal regulation to enhance specificity and overcome resistance. Taken together, these developments reveal the growing utility of allostery in drug design and underscore its potential to expand the therapeutic target space beyond conventional binding sites.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"95 ","pages":"Article 103159"},"PeriodicalIF":6.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155579","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}
Pub Date : 2025-09-22DOI: 10.1016/j.sbi.2025.103162
Elodie Laine , Sergei Grudinin , Roman Klypa , Isaure Chauvot de Beauchêne
A few years after AlphaFold revolutionised the field of protein structure prediction, the new frontiers and limitations in structural biology have become clearer. Predicting protein–nucleic acid interactions currently stands as one of the major unresolved challenges in the field. This knowledge gap stems from the scarcity and limited diversity of experimental data, as well as the unique geometric, physicochemical, and evolutionary properties of nucleic acids. Despite these challenges, innovative ideas and promising methodological developments have emerged for both predicting protein–nucleic acid complex structures and designing nucleic acids capable of binding to specific protein conformations. This review presents these recent advances and discusses promising avenues, including the integration of high-throughput profiling data, the development of more rigourous and richer evaluation benchmarks, and the discovery of biologically meaningful regulatory and structural signals using self-supervised learning.
{"title":"Navigating protein–nucleic acid sequence-structure landscapes with deep learning","authors":"Elodie Laine , Sergei Grudinin , Roman Klypa , Isaure Chauvot de Beauchêne","doi":"10.1016/j.sbi.2025.103162","DOIUrl":"10.1016/j.sbi.2025.103162","url":null,"abstract":"<div><div>A few years after AlphaFold revolutionised the field of protein structure prediction, the new frontiers and limitations in structural biology have become clearer. Predicting protein–nucleic acid interactions currently stands as one of the major unresolved challenges in the field. This knowledge gap stems from the scarcity and limited diversity of experimental data, as well as the unique geometric, physicochemical, and evolutionary properties of nucleic acids. Despite these challenges, innovative ideas and promising methodological developments have emerged for both predicting protein–nucleic acid complex structures and designing nucleic acids capable of binding to specific protein conformations. This review presents these recent advances and discusses promising avenues, including the integration of high-throughput profiling data, the development of more rigourous and richer evaluation benchmarks, and the discovery of biologically meaningful regulatory and structural signals using self-supervised learning.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"95 ","pages":"Article 103162"},"PeriodicalIF":6.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118931","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}
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