Pub Date : 2026-01-21DOI: 10.1016/j.jmb.2026.169655
Bo Yuan , Subhra Kanti Das , Weilin Wang , Gillian Camacho , Ashok Kumar , Jeffrey J. Hayes , Tae-Hee Lee
Linker histone H1 plays crucial roles in nucleosome compaction and chromatin condensation. The highly basic C-terminal domain (CTD) of H1 interacts strongly with DNA, a critical aspect of its contribution to H1 function. Despite its critical roles in gene packaging in chromatin where nucleosomes are linked as an array, how H1 CTD interacts with nucleosome arrays remain poorly understood. Here we report a single-molecule FRET study of the conformation and conformational dynamics of the CTD of H1 bound to a 12-mer nucleosome array. According to our results, H1 CTDs within a nucleosome array show signs of highly heterogeneous conformations that are overall more extended and dynamic than that bound to a mono-nucleosome. This observation suggests that H1 CTD interacts randomly with two or more DNA linkers across nucleosomes in an array. This suggestion is further supported by our observation that these domains become more condensed and less dynamic as the arrays become less condensed at a lower NaCl concentration. Our results also suggest that histone H3 and H4 acetylation mimetics and tailless H3 result in H1 CTD interacting less with multiple linkers as they induce a less condensed structure of nucleosome arrays, thereby driving H1 CTD back to its own nucleosome. Our data support that H1 CTD interacts non-specifically with DNA linkers that are either local or distal and that modifications of the H3 and H4 tail domains can regulate H1-mediated chromatin condensation at both the nucleosome and nucleosome array levels.
{"title":"Conformations of Linker Histone H1 Bound to Nucleosome Arrays","authors":"Bo Yuan , Subhra Kanti Das , Weilin Wang , Gillian Camacho , Ashok Kumar , Jeffrey J. Hayes , Tae-Hee Lee","doi":"10.1016/j.jmb.2026.169655","DOIUrl":"10.1016/j.jmb.2026.169655","url":null,"abstract":"<div><div>Linker histone H1 plays crucial roles in nucleosome compaction and chromatin condensation. The highly basic C-terminal domain (CTD) of H1 interacts strongly with DNA, a critical aspect of its contribution to H1 function. Despite its critical roles in gene packaging in chromatin where nucleosomes are linked as an array, how H1 CTD interacts with nucleosome arrays remain poorly understood. Here we report a single-molecule FRET study of the conformation and conformational dynamics of the CTD of H1 bound to a 12-mer nucleosome array. According to our results, H1 CTDs within a nucleosome array show signs of highly heterogeneous conformations that are overall more extended and dynamic than that bound to a mono-nucleosome. This observation suggests that H1 CTD interacts randomly with two or more DNA linkers across nucleosomes in an array. This suggestion is further supported by our observation that these domains become more condensed and less dynamic as the arrays become less condensed at a lower NaCl concentration. Our results also suggest that histone H3 and H4 acetylation mimetics and tailless H3 result in H1 CTD interacting less with multiple linkers as they induce a less condensed structure of nucleosome arrays, thereby driving H1 CTD back to its own nucleosome. Our data support that H1 CTD interacts non-specifically with DNA linkers that are either local or distal and that modifications of the H3 and H4 tail domains can regulate H1-mediated chromatin condensation at both the nucleosome and nucleosome array levels.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 6","pages":"Article 169655"},"PeriodicalIF":4.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040102","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}
Protein-protein interactions are central to numerous cellular processes, including transport, signaling, and immune response. Structural modeling of protein assemblies typically relies on AlphaFold or docking methods, which produce structural models evaluated against a single experimental reference. While AlphaFold2 and its extension, AlphaFold-Multimer, have advanced complex prediction, they, and conventional docking tools, offer only static representations. However, flexibility at protein-protein interfaces is increasingly recognized as critical for function. To address this limitation, DynaBench provides a benchmark of interface dynamics in biologically relevant protein assemblies. We performed MD simulations for over 200 protein-protein complexes listed in the Docking Benchmark 5.5 (https://zlab.umassmed.edu/benchmark/), generating three 100 ns long replicas per complex. All trajectories are now publicly available online (http://www-lbt.ibpc.fr/DynaBench) via the MDposit platform (INRIA node), which is part of the EU-funded Molecular Dynamics Data Bank (MDDB). These simulations offer a unique resource for exploring interfacial flexibility, training machine learning models, redefining accuracy metrics for model evaluation, and informing the design of protein interfaces.
{"title":"DynaBench: Dynamic data for the docking benchmark.","authors":"Aye Berçin Barlas, Benoist Laurent, Ezgi Karaca, Chantal Prévost, Sophie Sacquin-Mora","doi":"10.1016/j.jmb.2026.169650","DOIUrl":"10.1016/j.jmb.2026.169650","url":null,"abstract":"<p><p>Protein-protein interactions are central to numerous cellular processes, including transport, signaling, and immune response. Structural modeling of protein assemblies typically relies on AlphaFold or docking methods, which produce structural models evaluated against a single experimental reference. While AlphaFold2 and its extension, AlphaFold-Multimer, have advanced complex prediction, they, and conventional docking tools, offer only static representations. However, flexibility at protein-protein interfaces is increasingly recognized as critical for function. To address this limitation, DynaBench provides a benchmark of interface dynamics in biologically relevant protein assemblies. We performed MD simulations for over 200 protein-protein complexes listed in the Docking Benchmark 5.5 (https://zlab.umassmed.edu/benchmark/), generating three 100 ns long replicas per complex. All trajectories are now publicly available online (http://www-lbt.ibpc.fr/DynaBench) via the MDposit platform (INRIA node), which is part of the EU-funded Molecular Dynamics Data Bank (MDDB). These simulations offer a unique resource for exploring interfacial flexibility, training machine learning models, redefining accuracy metrics for model evaluation, and informing the design of protein interfaces.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169650"},"PeriodicalIF":4.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146027836","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 : 2026-01-20DOI: 10.1016/j.jmb.2026.169648
Yunyu Shi
I am a Professor of Biophysics at the University of Science and Technology of China (USTC). I graduated from the Department of Physics, USTC, majoring in Biophysics, in 1965. From 1965 to 1970, I worked as an assistant researcher at the Institute of Chinese Medicine. I joined USTC as an Assistant Professor in 1970 and became a visiting scholar in the Department of Physical Chemistry at the University of Roma from 1979 to1981. My studies on computational biology were mentored by Professor De Santis and supported by the Ministry of Education of China. My studies on bio-NMR were mentored by Professor F. Conti at University of Roma while I simultaneously studied at the CNRS Structural Chemistry Laboratory in Italy. I returned to USTC in 1981 and established a multidisciplinary research team.
In 1985 and 1990, I visited the Department of Physical Chemistry, University of Groningen in the Netherlands for six months, under the guidance of Professor H.J.C. Berendsen and Dr. W.F. Van Gunsteren to learn molecular dynamics (MD) simulation of protein and under the guidance of Professor R. Kaptein and Dr. R. Boelens to learn heteronuclear multidimensional NMR experiments for structure determination of proteins in solution. Our laboratory is the first research group to do MD simulation on protein in China and is also one of the pioneer laboratories using NMR to study protein structure in solution in China.
我是中国科学技术大学（USTC）生物物理学教授。我于1965年毕业于中国科学技术大学物理系生物物理学专业。从1965年到1970年，我在中国中医研究所担任助理研究员。1970年加入中国科大任助理教授，1979年至1981年在罗马大学物理化学系做访问学者。我的计算生物学研究是在De Santis教授的指导下进行的，并得到了中国教育部的支持。我的生物核磁共振研究是在罗马大学F. Conti教授的指导下进行的，同时我在意大利CNRS结构化学实验室学习。1981年回到中国科大，组建了多学科研究团队。1985年和1990年赴荷兰格罗宁根大学物理化学系学习6个月，师从H. J.C. Berendsen教授和W. F. Van Gunsteren博士学习蛋白质的分子动力学（MD）模拟，师从R. Kaptein教授和R. Boelens博士学习溶液中蛋白质结构测定的多核多维核磁共振实验。我们的实验室是国内第一个对蛋白质进行MD模拟的研究小组，也是国内最早使用NMR研究溶液中蛋白质结构的实验室之一。
{"title":"Pioneers: Growing Together With Molecular Dynamics Simulation and NMR for Studying Biomacromolecules in China","authors":"Yunyu Shi","doi":"10.1016/j.jmb.2026.169648","DOIUrl":"10.1016/j.jmb.2026.169648","url":null,"abstract":"<div><div>I am a Professor of Biophysics at the University of Science and Technology of China (USTC). I graduated from the Department of Physics, USTC, majoring in Biophysics, in 1965. From 1965 to 1970, I worked as an assistant researcher at the Institute of Chinese Medicine. I joined USTC as an Assistant Professor in 1970 and became a visiting scholar in the Department of Physical Chemistry at the University of Roma from 1979 to1981. My studies on computational biology were mentored by Professor De Santis and supported by the Ministry of Education of China. My studies on bio-NMR were mentored by Professor F. Conti at University of Roma while I simultaneously studied at the CNRS Structural Chemistry Laboratory in Italy. I returned to USTC in 1981 and established a multidisciplinary research team.</div><div>In 1985 and 1990, I visited the Department of Physical Chemistry, University of Groningen in the Netherlands for six months, under the guidance of Professor H.J.C. Berendsen and Dr. W.F. Van Gunsteren to learn molecular dynamics (MD) simulation of protein and under the guidance of Professor R. Kaptein and Dr. R. Boelens to learn heteronuclear multidimensional NMR experiments for structure determination of proteins in solution. Our laboratory is the first research group to do MD simulation on protein in China and is also one of the pioneer laboratories using NMR to study protein structure in solution in China.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 6","pages":"Article 169648"},"PeriodicalIF":4.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146027854","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 : 2026-01-20DOI: 10.1016/j.jmb.2026.169649
Sen-Fang Sui
I established my independent laboratory in 1989 at the Department of Biological Sciences and Biotechnology (which was renamed the School of Life Sciences in 2009) at Tsinghua University. Since 1991, I have held a professorship at Tsinghua University for more than three decades. My laboratory was one of the earliest in China to apply three-dimensional electron microscopy to the study of the structures of biological macromolecules. In the 1990s, my laboratory primarily focused on the two-dimensional crystallization of proteins on lipid monolayer and their structural studies using electron microscopy. By the late 1990s, our laboratory gradually shifted towards structural research using the single-particle electron microscopy technique. A representative achievement of this period was the elucidation of the transition of the protease DegP between a cage-like oligomer in solution and a bowl-shaped oligomer on membrane surfaces. Following the resolution revolution in cryo-electron microscopy, we determined the high-resolution structure of an 18-megadalton phycobilisome in 2017—marking the first near-atomic model of this giant complex since its discovery half a century ago. In recent years, we have focused on in situ high-resolution structure determination and method development. In 2023, we reported a near-atomic resolution in situ structure of the phycobilisome–photosystem membrane protein megacomplex,determined from cellular lamellae.
{"title":"Pioneers: Academic Career in Cryo-EM Structural Biology","authors":"Sen-Fang Sui","doi":"10.1016/j.jmb.2026.169649","DOIUrl":"10.1016/j.jmb.2026.169649","url":null,"abstract":"<div><div>I established my independent laboratory in 1989 at the Department of Biological Sciences and Biotechnology (which was renamed the School of Life Sciences in 2009) at Tsinghua University. Since 1991, I have held a professorship at Tsinghua University for more than three decades. My laboratory was one of the earliest in China to apply three-dimensional electron microscopy to the study of the structures of biological macromolecules. In the 1990s, my laboratory primarily focused on the two-dimensional crystallization of proteins on lipid monolayer and their structural studies using electron microscopy. By the late 1990s, our laboratory gradually shifted towards structural research using the single-particle electron microscopy technique. A representative achievement of this period was the elucidation of the transition of the protease DegP between a cage-like oligomer in solution and a bowl-shaped oligomer on membrane surfaces. Following the resolution revolution in cryo-electron microscopy, we determined the high-resolution structure of an 18-megadalton phycobilisome in 2017—marking the first near-atomic model of this giant complex since its discovery half a century ago. In recent years, we have focused on <em>in situ</em> high-resolution structure determination and method development. In 2023, we reported a near-atomic resolution <em>in situ</em> structure of the phycobilisome–photosystem membrane protein megacomplex,determined from cellular lamellae.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 6","pages":"Article 169649"},"PeriodicalIF":4.5,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146027828","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 : 2026-01-19DOI: 10.1016/j.jmb.2026.169642
Yeongjin Baek , Hyojeong Lee , Eun-Su Park , Minho Park , Yurim Wee , Hyunmin Kim , Soung-Hun Roh , Nam-Chul Ha
Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neurodegenerative disease characterized by Cu, Zn-superoxide dismutase (SOD1) misfolding and aggregation, particularly in familial cases. The G93A mutation in SOD1, strongly associated with familial ALS, is widely studied in transgenic mouse models of the disease. In this study, we investigated the filament structure of the G93A mutant SOD1 using cryo-electron microscopy. The resulting fibrils consisted of a single protofilament with a left-handed helical twist, closely resembling those formed by wild-type (WT) SOD1 under identical conditions. Self- and cross-seeding experiments promoted filament formation in both WT and G93A mutant SOD1, compared to the no-seed condition. Notably, the G93A mutant exhibited significantly higher susceptibility to proteolysis in its native state than WT SOD1. Mass spectrometry analysis suggested that the structurally disordered electrostatic loop acts as a key common intermediate structure in filament formation for both WT and G93A mutant SOD1. These findings suggest that shared filament formation pathways underlie the aggregation of both WT and G93A mutant SOD1, providing new insights into the molecular mechanisms contributing to ALS pathogenesis.
{"title":"Structural Comparison of the Human G93A Mutant SOD1 to the Wild-type SOD1 Filaments","authors":"Yeongjin Baek , Hyojeong Lee , Eun-Su Park , Minho Park , Yurim Wee , Hyunmin Kim , Soung-Hun Roh , Nam-Chul Ha","doi":"10.1016/j.jmb.2026.169642","DOIUrl":"10.1016/j.jmb.2026.169642","url":null,"abstract":"<div><div>Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neurodegenerative disease characterized by Cu, Zn-superoxide dismutase (SOD1) misfolding and aggregation, particularly in familial cases. The G93A mutation in SOD1, strongly associated with familial ALS, is widely studied in transgenic mouse models of the disease. In this study, we investigated the filament structure of the G93A mutant SOD1 using cryo-electron microscopy. The resulting fibrils consisted of a single protofilament with a left-handed helical twist, closely resembling those formed by wild-type (WT) SOD1 under identical conditions. Self- and cross-seeding experiments promoted filament formation in both WT and G93A mutant SOD1, compared to the no-seed condition. Notably, the G93A mutant exhibited significantly higher susceptibility to proteolysis in its native state than WT SOD1. Mass spectrometry analysis suggested that the structurally disordered electrostatic loop acts as a key common intermediate structure in filament formation for both WT and G93A mutant SOD1. These findings suggest that shared filament formation pathways underlie the aggregation of both WT and G93A mutant SOD1, providing new insights into the molecular mechanisms contributing to ALS pathogenesis.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 6","pages":"Article 169642"},"PeriodicalIF":4.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146016914","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 : 2026-01-19DOI: 10.1016/j.jmb.2026.169645
Colin Diesh, Garrett Stevens, Caroline Bridge, Gregory Hogue, Robert Buels, Scott Cain, Lincoln Stein, Ian Holmes
Recent advances in protein structure prediction have created high-confidence candidate structures for nearly every known protein-coding gene. At the same time, many software packages have been created to visualize protein structures, protein multiple sequence alignments (MSAs), and protein annotations. However, few software tools can highlight the direct relationship between nucleotide variation of protein-coding genes in genome space and the evolutionary and structural context of that variation in protein space. To help address these needs, we created a suite of robust and reusable JavaScript components to show protein structures, MSAs, phylogenies, and their relationship to protein-coding gene regions using the JBrowse 2 genome browser. This software allows users to interface with web services such as AlphaFoldDB and Foldseek to access pre-computed structures, or to upload protein structures from sources such as ColabFold or PDB. Our resources are available at https://github.com/GMOD/proteinbrowser.
{"title":"Proteins in the Genome Browser: Integration of Phylogenies, Alignments, and Structures With Nucleotide-level Evidence in JBrowse 2.","authors":"Colin Diesh, Garrett Stevens, Caroline Bridge, Gregory Hogue, Robert Buels, Scott Cain, Lincoln Stein, Ian Holmes","doi":"10.1016/j.jmb.2026.169645","DOIUrl":"10.1016/j.jmb.2026.169645","url":null,"abstract":"<p><p>Recent advances in protein structure prediction have created high-confidence candidate structures for nearly every known protein-coding gene. At the same time, many software packages have been created to visualize protein structures, protein multiple sequence alignments (MSAs), and protein annotations. However, few software tools can highlight the direct relationship between nucleotide variation of protein-coding genes in genome space and the evolutionary and structural context of that variation in protein space. To help address these needs, we created a suite of robust and reusable JavaScript components to show protein structures, MSAs, phylogenies, and their relationship to protein-coding gene regions using the JBrowse 2 genome browser. This software allows users to interface with web services such as AlphaFoldDB and Foldseek to access pre-computed structures, or to upload protein structures from sources such as ColabFold or PDB. Our resources are available at https://github.com/GMOD/proteinbrowser.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169645"},"PeriodicalIF":4.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146016955","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 : 2026-01-19DOI: 10.1016/j.jmb.2026.169647
Chunyan Wang , Huaxin Yu , Taehyun Park , Ian J. Molineux , Jun Liu
Salmonella phage P22 deploys a highly coordinated tail machine to recognize hosts and initiate infection. Here, we present a cryo-EM structure of wild-type P22 that defines how the tail apparatus assembles onto the capsid and how they interface. Flexible loop residues on both the portal protein gp1 and the capsid protein gp5 undergo pronounced positional shifts and engage multiple partners to accommodate the C12–C5 symmetry mismatch at the portal-capsid interface. The portal protein gp1 forms a distinctive ∼15-nm barrel that projects deep into the capsid interior. Comparison with a mutant lacking the three internal E (ejection) proteins indicates that these proteins reside within the portal-tail lumen in a poorly ordered state, yet are essential for stabilizing the extended portal barrel. We further show how the hub protein gp10 orchestrates the assembly of four distinct particle isomers through its coordinated interactions with portal gp1, adaptor gp4, tailspike gp9, and needle gp26. Finally, cryo-electron tomography reveals that the gp10 hub acts as a structural foundation for the assembly of one E protein into an extracellular channel that breaches the cell surface, with other E proteins forming a genome-translocating trans-envelope conduit.
{"title":"Structural Basis for Bacteriophage P22 Assembly and Infection Initiation","authors":"Chunyan Wang , Huaxin Yu , Taehyun Park , Ian J. Molineux , Jun Liu","doi":"10.1016/j.jmb.2026.169647","DOIUrl":"10.1016/j.jmb.2026.169647","url":null,"abstract":"<div><div><em>Salmonella</em> phage P22 deploys a highly coordinated tail machine to recognize hosts and initiate infection. Here, we present a cryo-EM structure of wild-type P22 that defines how the tail apparatus assembles onto the capsid and how they interface. Flexible loop residues on both the portal protein gp1 and the capsid protein gp5 undergo pronounced positional shifts and engage multiple partners to accommodate the C12–C5 symmetry mismatch at the portal-capsid interface. The portal protein gp1 forms a distinctive ∼15-nm barrel that projects deep into the capsid interior. Comparison with a mutant lacking the three internal E (ejection) proteins indicates that these proteins reside within the portal-tail lumen in a poorly ordered state, yet are essential for stabilizing the extended portal barrel. We further show how the hub protein gp10 orchestrates the assembly of four distinct particle isomers through its coordinated interactions with portal gp1, adaptor gp4, tailspike gp9, and needle gp26. Finally, cryo-electron tomography reveals that the gp10 hub acts as a structural foundation for the assembly of one E protein into an extracellular channel that breaches the cell surface, with other E proteins forming a genome-translocating <em>trans</em>-envelope conduit.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 6","pages":"Article 169647"},"PeriodicalIF":4.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146016985","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 : 2026-01-19DOI: 10.1016/j.jmb.2026.169646
Reidun Twarock, Peter G Stockley
The importance of the genetic code in virology is universally acknowledged. However, it is less known that viral genomes can harbour a second code, embedded within the genetic code, that orchestrates the efficient assembly and genome packaging in many viral systems. Since its discovery in a bacterial virus, the molecular details and function of this mechanism have been characterised in a broad range of viral families, including major human pathogens. This Perspective article reports on the hallmarks of this "assembly/packaging code", the journey of its discovery, and the enticing opportunities it brings both in antiviral therapy and in virus nanotechnology.
{"title":"Packaging Signal-mediated Assembly - How Viruses Outsmart Their Hosts.","authors":"Reidun Twarock, Peter G Stockley","doi":"10.1016/j.jmb.2026.169646","DOIUrl":"10.1016/j.jmb.2026.169646","url":null,"abstract":"<p><p>The importance of the genetic code in virology is universally acknowledged. However, it is less known that viral genomes can harbour a second code, embedded within the genetic code, that orchestrates the efficient assembly and genome packaging in many viral systems. Since its discovery in a bacterial virus, the molecular details and function of this mechanism have been characterised in a broad range of viral families, including major human pathogens. This Perspective article reports on the hallmarks of this \"assembly/packaging code\", the journey of its discovery, and the enticing opportunities it brings both in antiviral therapy and in virus nanotechnology.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169646"},"PeriodicalIF":4.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146016919","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 : 2026-01-16DOI: 10.1016/j.jmb.2026.169643
Alvaro M Navarro, Santiago Palacios, Thierry Galmarini, Oriol Bárcenas, Salvador Ventura, Cristina Marino-Buslje
Protein aggregation plays a central role in the pathogenesis of many neurodegenerative diseases and poses major challenges in protein engineering. A key driver of this process is the presence of aggregation-prone regions (APRs) within protein sequences. We present AggrescanAI, a deep learning-based tool that predicts residue-level aggregation propensity directly from sequence. It leverages contextual embeddings from the ProtT5 protein language model, which captures rich information implicitly encoded in the sequence, without requiring structural data. The model was trained on a set of experimentally annotated APRs, expanded via homology transfering, evaluated by cross-validation, and validated with an external benchmark. AggrescanAI outperforms state of the art predictors and captures aggregation shifts induced by pathogenic mutations. To facilitate accessibility, we provide a user-friendly and fully open Google Colab notebook: https://gitlab.com/bioinformatics-fil/aggrescanai. AggrescanAI represents a new generation of sequence-based aggregation predictors, powered by deep learning and protein language models.
{"title":"AggrescanAI: Prediction of Aggregation-Prone Regions Using Contextualized Embeddings.","authors":"Alvaro M Navarro, Santiago Palacios, Thierry Galmarini, Oriol Bárcenas, Salvador Ventura, Cristina Marino-Buslje","doi":"10.1016/j.jmb.2026.169643","DOIUrl":"10.1016/j.jmb.2026.169643","url":null,"abstract":"<p><p>Protein aggregation plays a central role in the pathogenesis of many neurodegenerative diseases and poses major challenges in protein engineering. A key driver of this process is the presence of aggregation-prone regions (APRs) within protein sequences. We present AggrescanAI, a deep learning-based tool that predicts residue-level aggregation propensity directly from sequence. It leverages contextual embeddings from the ProtT5 protein language model, which captures rich information implicitly encoded in the sequence, without requiring structural data. The model was trained on a set of experimentally annotated APRs, expanded via homology transfering, evaluated by cross-validation, and validated with an external benchmark. AggrescanAI outperforms state of the art predictors and captures aggregation shifts induced by pathogenic mutations. To facilitate accessibility, we provide a user-friendly and fully open Google Colab notebook: https://gitlab.com/bioinformatics-fil/aggrescanai. AggrescanAI represents a new generation of sequence-based aggregation predictors, powered by deep learning and protein language models.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169643"},"PeriodicalIF":4.5,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145996881","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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