Pub Date : 2025-10-30DOI: 10.1016/j.jsb.2025.108258
Alain Morales-Martínez , Edgar Garduño , José María Carazo , Carlos Oscar S. Sorzano , José Luis Vilas
Cryo-electron tomography (cryo-ET) is a microscopy technique that enables the acquisition of 3D images of biological samples. Research in cell biology has shown that cellular processes are carried out by groups of macromolecules that interact in a crowded environment. In such an environment, where multiple biological macromolecules coexist and intertwine, semantic segmentation becomes even more challenging but crucial to understanding the structure and function of macromolecular complexes. However, manual semantic segmentation can be time-consuming, highly subjective, and prone to variability, which poses significant obstacles in studies dealing with large volumes of data. In contrast, automated algorithms such as Convolutional Neural Networks (CNNs) can process large-scale datasets with minimal human resources, thereby reducing the subjectivity associated with manual segmentation. In this work, we propose a convolutional neural network architecture that combines the features of U-Net, DeepLab, SegNet, Gated-SCNN, LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and GAN (Generative Adversarial Network) architectures. This hybrid architecture effectively learns to identify different types of membranes and can replicate the behavior of a skilled human annotator. This system demonstrates a strong ability to segment various cellular membranes and vesicle structures.
{"title":"Membrane and vesicle structure detection in cryo-electron tomography based on deep learning","authors":"Alain Morales-Martínez , Edgar Garduño , José María Carazo , Carlos Oscar S. Sorzano , José Luis Vilas","doi":"10.1016/j.jsb.2025.108258","DOIUrl":"10.1016/j.jsb.2025.108258","url":null,"abstract":"<div><div>Cryo-electron tomography (cryo-ET) is a microscopy technique that enables the acquisition of 3D images of biological samples. Research in cell biology has shown that cellular processes are carried out by groups of macromolecules that interact in a crowded environment. In such an environment, where multiple biological macromolecules coexist and intertwine, semantic segmentation becomes even more challenging but crucial to understanding the structure and function of macromolecular complexes. However, manual semantic segmentation can be time-consuming, highly subjective, and prone to variability, which poses significant obstacles in studies dealing with large volumes of data. In contrast, automated algorithms such as Convolutional Neural Networks (CNNs) can process large-scale datasets with minimal human resources, thereby reducing the subjectivity associated with manual segmentation. In this work, we propose a convolutional neural network architecture that combines the features of U-Net, DeepLab, SegNet, Gated-SCNN, LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), and GAN (Generative Adversarial Network) architectures. This hybrid architecture effectively learns to identify different types of membranes and can replicate the behavior of a skilled human annotator. This system demonstrates a strong ability to segment various cellular membranes and vesicle structures.</div></div>","PeriodicalId":17074,"journal":{"name":"Journal of structural biology","volume":"217 4","pages":"Article 108258"},"PeriodicalIF":2.7,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In drug development, the efficacy of an antibody depends on how the antibody interacts with the target antigen. The strength of these interactions, measured through “binding affinity”, gives an indication of how successful an antibody is in neutralizing an antigen. Due to the high computational complexity of traditional techniques for binding affinity quantification, deep learning is recently employed for the task at hand. Despite the commendable improvements in deep learning-based binding affinity prediction, such approaches are highly dependent on the quality of the antibody–antigen structures and they tend to overlook the importance of capturing the evolutionary details of proteins upon mutation. Further, most of the existing datasets for the task only include antibody–antigen pairs related to one antigen variant and, thus, are not suitable for developing comprehensive data-driven approaches. To circumvent the said complexities, we first curate the largest and most generalized (i.e., including a wide array of antigen variants) datasets for antibody–antigen binding affinity prediction, consisting of more than sequence pairs, structure pairs and the corresponding continuous binding affinity values. Subsequently, we propose a novel deep geometric neural network comprising a structure-based model, which is to account atomistic-scale structural features, and a sequence-based model, which is to attribute sequential and evolutionary information, while sharing the learned information from each model through cross-attention blocks. Further, within each parallel model, we mimic the interaction space of antibodies and antigens through a set of multi-scale hierarchical attention blocks and the final latent vectors of each model are obtained by considering antibody and antigen representative vectors and the interaction vector. The proposed framework exhibited a 10% improvement in mean absolute error compared to the state-of-the-art models while showing a strong correlation () between the predictions and target values. Additionally, we extensively discuss the model optimization strategies, weight space analysis, and interpretability in a post-hoc fashion. We release our datasets and code publicly to support the development of antibody–antigen binding affinity prediction frameworks for the benefit of science and society.
{"title":"Deep geometric framework to predict antibody–antigen binding affinity","authors":"Nuwan Bandara , Dasun Premathilaka , Sachini Chandanayake , Sahan Hettiarachchi , Vithurshan Varenthirarajah , Aravinda Munasinghe , Kaushalya Madhawa , Subodha Charles","doi":"10.1016/j.jsb.2025.108257","DOIUrl":"10.1016/j.jsb.2025.108257","url":null,"abstract":"<div><div>In drug development, the efficacy of an antibody depends on how the antibody interacts with the target antigen. The strength of these interactions, measured through “binding affinity”, gives an indication of how successful an antibody is in neutralizing an antigen. Due to the high computational complexity of traditional techniques for binding affinity quantification, deep learning is recently employed for the task at hand. Despite the commendable improvements in deep learning-based binding affinity prediction, such approaches are highly dependent on the quality of the antibody–antigen structures and they tend to overlook the importance of capturing the evolutionary details of proteins upon mutation. Further, most of the existing datasets for the task only include antibody–antigen pairs related to one antigen variant and, thus, are not suitable for developing comprehensive data-driven approaches. To circumvent the said complexities, we first curate the largest and most generalized (i.e., including a wide array of antigen variants) datasets for antibody–antigen binding affinity prediction, consisting of more than <span><math><mrow><mn>100</mn><mi>K</mi></mrow></math></span> sequence pairs, <span><math><mrow><mn>8</mn><mi>K</mi></mrow></math></span> structure pairs and the corresponding continuous binding affinity values. Subsequently, we propose a novel deep geometric neural network comprising a structure-based model, which is to account atomistic-scale structural features, and a sequence-based model, which is to attribute sequential and evolutionary information, while sharing the learned information from each model through cross-attention blocks. Further, within each parallel model, we mimic the interaction space of antibodies and antigens through a set of multi-scale hierarchical attention blocks and the final latent vectors of each model are obtained by considering antibody and antigen representative vectors and the interaction vector. The proposed framework exhibited a 10% improvement in mean absolute error compared to the state-of-the-art models while showing a strong correlation (<span><math><mrow><mo>></mo><mn>0</mn><mo>.</mo><mn>87</mn></mrow></math></span>) between the predictions and target values. Additionally, we extensively discuss the model optimization strategies, weight space analysis, and interpretability in a post-hoc fashion. We release our datasets and code publicly to support the development of antibody–antigen binding affinity prediction frameworks for the benefit of science and society.</div></div>","PeriodicalId":17074,"journal":{"name":"Journal of structural biology","volume":"217 4","pages":"Article 108257"},"PeriodicalIF":2.7,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145370349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-21DOI: 10.1016/j.jsb.2025.108256
Daoyi Li , Xiaoqi Zhang , Wen Jiang
Helical symmetry is a common structural feature of many biological macromolecules. However, determination of the helical parameters and de novo 3D reconstruction remain challenging. We have developed a computational method, Helicon, which poses helical reconstruction as a linear regression problem with the projection matrix parameterized by the helical twist, rise, and axial symmetry. A sparse search of the twist and rise parameters would allow determination of helical parameters and 3D reconstruction directly from one 2D class average or a raw cryo-EM image. The Helicon method has been validated with simulation tests and experimental cryo-EM images of helical tubes, non-amyloid filaments, and amyloid fibrils. Imaging stitching and L1 regularization of linear regression were shown to improve the robustness for low-twist amyloids and noisy raw cryo-EM images. Using Helicon, we could successfully determine the helical parameters and perform de novo reconstruction of a previously unreported, low-abundance tau amyloid structure from a publicly available dataset.
{"title":"Helicon: Helical parameter determination and 3D reconstruction from one image","authors":"Daoyi Li , Xiaoqi Zhang , Wen Jiang","doi":"10.1016/j.jsb.2025.108256","DOIUrl":"10.1016/j.jsb.2025.108256","url":null,"abstract":"<div><div>Helical symmetry is a common structural feature of many biological macromolecules. However, determination of the helical parameters and <em>de novo</em> 3D reconstruction remain challenging. We have developed a computational method, Helicon, which poses helical reconstruction as a linear regression problem with the projection matrix parameterized by the helical twist, rise, and axial symmetry. A sparse search of the twist and rise parameters would allow determination of helical parameters and 3D reconstruction directly from one 2D class average or a raw cryo-EM image. The Helicon method has been validated with simulation tests and experimental cryo-EM images of helical tubes, non-amyloid filaments, and amyloid fibrils. Imaging stitching and L1 regularization of linear regression were shown to improve the robustness for low-twist amyloids and noisy raw cryo-EM images. Using Helicon, we could successfully determine the helical parameters and perform <em>de novo</em> reconstruction of a previously unreported, low-abundance tau amyloid structure from a publicly available dataset.</div></div>","PeriodicalId":17074,"journal":{"name":"Journal of structural biology","volume":"217 4","pages":"Article 108256"},"PeriodicalIF":2.7,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145355139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-20DOI: 10.1016/j.jsb.2025.108255
Wenhan Guo , Esther Alarcon , Jason E Sanchez , Chuan Xiao , Lin Li
PBCV-1, a giant virus classified among the Nucleocytoviricota virus (NCV) whose structure has been determined to near atomic resolution. The majority capsomers forming the capsid of PBCV-1 are Type I capsomers while five other type of variants have been found in recent high resolution structure. Interestingly, some variants, such as Type V capsomers, are found at particular capsid locations whose roles are unclear. To reveal the roles of a Type V capsomer, we replaced the Type V capsomer by a Type I capsomer to compare the interaction among the two types of capsomer variant, especially the interactions between each of the Type V/Type I capsomer and its local capsid microenvironment. Our results revealed significant differences between Type V and Type I capsomers. Notably, the Type V capsomer demonstrated a stronger binding force to the surrounding capsomers than the Type I capsomer. Moreover, the identified salt bridges between Type V/I capsomers and their surrounding capsomers corroborate the results of electrostatic calculations, further highlighting the important residues involved in these interactions. Understanding these local capsid microenvironments will be essential to elucidate the mechanisms governing viral capsid assembly.
{"title":"Local microenvironments of capsomer variants in the PBCV-1","authors":"Wenhan Guo , Esther Alarcon , Jason E Sanchez , Chuan Xiao , Lin Li","doi":"10.1016/j.jsb.2025.108255","DOIUrl":"10.1016/j.jsb.2025.108255","url":null,"abstract":"<div><div>PBCV-1, a giant virus classified among the Nucleocytoviricota virus (NCV) whose structure has been determined to near atomic resolution. The majority capsomers forming the capsid of PBCV<strong>-</strong>1 are Type I capsomers while five other type of variants have been found in recent high resolution structure. Interestingly, some variants, such as Type V capsomers, are found at particular capsid locations whose roles are unclear. To reveal the roles of a Type V capsomer, we replaced the Type V capsomer by a Type I capsomer to compare the interaction among the two types of capsomer variant, especially the interactions between each of the Type V/Type I capsomer and its local capsid microenvironment. Our results revealed significant differences between Type V and Type I capsomers. Notably, the Type V capsomer demonstrated a stronger binding force to the surrounding capsomers than the Type I capsomer. Moreover, the identified salt bridges between Type V/I capsomers and their surrounding capsomers corroborate the results of electrostatic calculations, further highlighting the important residues involved in these interactions. Understanding these local capsid microenvironments will be essential to elucidate the mechanisms governing viral capsid assembly.</div></div>","PeriodicalId":17074,"journal":{"name":"Journal of structural biology","volume":"217 4","pages":"Article 108255"},"PeriodicalIF":2.7,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145345910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-16DOI: 10.1016/j.jsb.2025.108254
Agata Leszczuk , Nataliia Kutyrieva-Nowak , Sebastian Rueda , Amit Basu
Arabinogalactan proteins (AGPs) are cell wall-plasma membrane proteins with a high level of glycosylation. The selective and high-affinity binding between AGP and the Yariv reagent has been widely used to carry out functional studies on AGPs by disrupting AGP functions using a non-genetic tool. The current work aimed to determine the molecular features of cell walls during Arabidopsis thaliana seed germination under conditions where AGP functions are blocked. To achieve this, we used molecular & imaging methods with molecular probes and for the first time − a new tool for AGP detection − a fluorescent analogue of the Yariv reagent. The most significant changes included a decrease in the content of AGPs, due to the addition of the Yariv reagent, and subsequent changes only in the content of AGPs upon transfer from the Yariv reagent to fresh Yariv-free medium. Additionally, as a result of the presence of the Yariv reagent, changes in the molecular masses of the analysed cell wall components were observed: lack of AGPs with small molecular mass and disappearance of homogalacturonan with high molecular mass. This work provided the first example of AGP labelling using antibodies and AzYariv-Cy5, and highlights the utility of AzYariv-Cy5 as a broad-spectrum tool for AGP studies.
{"title":"Changes in Arabidopsis thaliana seedling cell wall assembly induced by treatment with Yariv reagent – Molecular features & visualization with immunocytochemistry and a fluorescent Yariv reagent","authors":"Agata Leszczuk , Nataliia Kutyrieva-Nowak , Sebastian Rueda , Amit Basu","doi":"10.1016/j.jsb.2025.108254","DOIUrl":"10.1016/j.jsb.2025.108254","url":null,"abstract":"<div><div>Arabinogalactan proteins (AGPs) are cell wall-plasma membrane proteins with a high level of glycosylation. The selective and high-affinity binding between AGP and the Yariv reagent has been widely used to carry out functional studies on AGPs by disrupting AGP functions using a non-genetic tool. The current work aimed to determine the molecular features of cell walls during <em>Arabidopsis thaliana</em> seed germination under conditions where AGP functions are blocked. To achieve this, we used molecular & imaging methods with molecular probes and for the first time − a new tool for AGP detection − a fluorescent analogue of the Yariv reagent. The<!--> <!-->most significant changes included a decrease in the content of AGPs, due to the addition of the Yariv reagent, and subsequent changes only in the content of AGPs upon transfer from the Yariv reagent to fresh Yariv-free medium. Additionally, as a result of the presence of the Yariv reagent, changes in the molecular masses of the analysed cell wall components were observed: lack of AGPs with small molecular mass and disappearance of homogalacturonan with high molecular mass. This work provided the first example of AGP labelling using antibodies and AzYariv-Cy5, and highlights the utility of AzYariv-Cy5 as a broad-spectrum tool for AGP studies.</div></div>","PeriodicalId":17074,"journal":{"name":"Journal of structural biology","volume":"217 4","pages":"Article 108254"},"PeriodicalIF":2.7,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145318232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-11DOI: 10.1016/j.jsb.2025.108253
Aleksandra Skrajna , Clara Lenger , Emily Robinson , Kevin Cannon , Reta Sarsam , Richard G. Ouellette , Alberta M. Abotsi , Patrick Brennwald , Robert K. McGinty , Joshua D. Strauss , Richard W. Baker
Grid preparation is a rate-limiting step in determining high-resolution structures by single particle cryo-EM. Particle interaction with the air–water interface often leads to denaturation, aggregation, or a preferred orientation within the ice. Some samples yield insufficient quantities of particles when using traditional grid making techniques and require the use of solid supports that concentrate samples onto the grid. Recent advances in grid-preparation show that affinity grids are promising tools to selectively concentrate proteins while simultaneously protecting samples from the air–water interface. One such technique utilizes lipid monolayers containing a lipid species with an affinity handle. Some of the first affinity grids used a holey carbon layer coated with nickel nitrilotriacetic acid (Ni-NTA) lipid, which allowed for the binding of proteins bearing the commonly used poly-histidine affinity tag. These studies however used complicated protocols and were conducted before the “resolution revolution” of cryo-EM. Here, we provide a straightforward preparation method and systematic analysis of Ni-NTA lipid monolayers as a tool for high-resolution single particle cryo-EM. We found the lipid affinity grids concentrate particles away from the AWI in thin ice (∼30 nm). We determined three structures ranging from 2.4 to 3.0 Å resolution, showing this method is amenable to high-resolution. Furthermore, we determined a 3.1 Å structure of a sub-100 kDa protein without symmetry, demonstrating the utility for a range of biological macromolecules. Lipid monolayers are therefore an easily extendable tool for most systems and help alleviate common problems such as low yield, disruption by the air–water interface, and thicker ice.
{"title":"Nickel-NTA lipid-monolayer affinity grids allow for high-resolution structure determination by cryo-EM","authors":"Aleksandra Skrajna , Clara Lenger , Emily Robinson , Kevin Cannon , Reta Sarsam , Richard G. Ouellette , Alberta M. Abotsi , Patrick Brennwald , Robert K. McGinty , Joshua D. Strauss , Richard W. Baker","doi":"10.1016/j.jsb.2025.108253","DOIUrl":"10.1016/j.jsb.2025.108253","url":null,"abstract":"<div><div>Grid preparation is a rate-limiting step in determining high-resolution structures by single particle cryo-EM. Particle interaction with the air–water interface often leads to denaturation, aggregation, or a preferred orientation within the ice. Some samples yield insufficient quantities of particles when using traditional grid making techniques and require the use of solid supports that concentrate samples onto the grid. Recent advances in grid-preparation show that affinity grids are promising tools to selectively concentrate proteins while simultaneously protecting samples from the air–water interface. One such technique utilizes lipid monolayers containing a lipid species with an affinity handle. Some of the first affinity grids used a holey carbon layer coated with nickel nitrilotriacetic acid (Ni-NTA) lipid, which allowed for the binding of proteins bearing the commonly used poly-histidine affinity tag. These studies however used complicated protocols and were conducted before the “resolution revolution” of cryo-EM. Here, we provide a straightforward preparation method and systematic analysis of Ni-NTA lipid monolayers as a tool for high-resolution single particle cryo-EM. We found the lipid affinity grids concentrate particles away from the AWI in thin ice (∼30 nm). We determined three structures ranging from 2.4 to 3.0 Å resolution, showing this method is amenable to high-resolution. Furthermore, we determined a 3.1 Å structure of a sub-100 kDa protein without symmetry, demonstrating the utility for a range of biological macromolecules. Lipid monolayers are therefore an easily extendable tool for most systems and help alleviate common problems such as low yield, disruption by the air–water interface, and thicker ice.</div></div>","PeriodicalId":17074,"journal":{"name":"Journal of structural biology","volume":"217 4","pages":"Article 108253"},"PeriodicalIF":2.7,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.jsb.2025.108252
Kazuaki Hoshi
Toll-like receptor 9 (TLR9) recognizes pathogenic DNA molecules containing unmethylated cytosine-phosphate-guanine motifs (CpG DNA) and initiates signaling cascades essential for enhancing immune responses. TLR9 is a type I transmembrane receptor comprising an N-terminal leucine-rich repeat (LRR) domain, a transmembrane domain, and a C-terminal Toll/interleukin-1 receptor (TIR) domain. Most studies have focused on the interaction between the LRR domain and its DNA ligands. However, the TIR domain is crucial for interacting with adapter proteins such as myeloid differentiation factor 88 (MyD88). The aim of this study was to predict changes in the orientation of the TIR domain in human TLR9 (hTLR9) and its complexes with agonistic or antagonistic DNA molecules using the AlphaFold server. AlphaFold predicted the overall structure of hTLR9 with high confidence scores, including part of the TIR domain. Interestingly, binding of agonistic and antagonistic DNA molecules to the N-terminal LRR domain induced a structural change in the orientation of the TIR domain compared to the unbound TLR9 structure. The TIR domain in the predicted hTLR9 model displayed a secondary structure similar to that of the previously reported human TLR1 crystal structure. The predicted model suggests that ligand binding to the N-terminal LRR domain causes a change in the orientation of the TIR domain of hTLR9, likely due to bending of the transmembrane region.
{"title":"Prediction of a structural change in the orientation of the cytoplasmic signaling unit of human Toll-like receptor 9 upon binding of agonistic and antagonistic DNA molecules","authors":"Kazuaki Hoshi","doi":"10.1016/j.jsb.2025.108252","DOIUrl":"10.1016/j.jsb.2025.108252","url":null,"abstract":"<div><div>Toll-like receptor 9 (TLR9) recognizes pathogenic DNA molecules containing unmethylated cytosine-phosphate-guanine motifs (CpG DNA) and initiates signaling cascades essential for enhancing immune responses. TLR9 is a type I transmembrane receptor comprising an N-terminal leucine-rich repeat (LRR) domain, a transmembrane domain, and a C-terminal Toll/interleukin-1 receptor (TIR) domain. Most studies have focused on the interaction between the LRR domain and its DNA ligands. However, the TIR domain is crucial for interacting with adapter proteins such as myeloid differentiation factor 88 (MyD88). The aim of this study was to predict changes in the orientation of the TIR domain in human TLR9 (hTLR9) and its complexes with agonistic or antagonistic DNA molecules using the AlphaFold server. AlphaFold predicted the overall structure of hTLR9 with high confidence scores, including part of the TIR domain. Interestingly, binding of agonistic and antagonistic DNA molecules to the N-terminal LRR domain induced a structural change in the orientation of the TIR domain compared to the unbound TLR9 structure. The TIR domain in the predicted hTLR9 model displayed a secondary structure similar to that of the previously reported human TLR1 crystal structure. The predicted model suggests that ligand binding to the N-terminal LRR domain causes a change in the orientation of the TIR domain of hTLR9, likely due to bending of the transmembrane region.</div></div>","PeriodicalId":17074,"journal":{"name":"Journal of structural biology","volume":"217 4","pages":"Article 108252"},"PeriodicalIF":2.7,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145280609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-28DOI: 10.1016/j.jsb.2025.108251
Ruchika Bajaj
This short review article traces the evolution of membrane protein structural biology over time and describes various challenges faced and overcome by researchers in the field, highlighting some of the major breakthroughs and advancements in the field. It presents a thematic exploration of membrane protein structural biology emphasizing on persistent technical and conceptual challenges from protein expression to structural techniques shaping the field with landmark innovations advancing our ability to determine membrane protein structures. The review specifically focus on a few key areas: sourcing and expressing membrane proteins, developing purification strategies and membrane mimetics, and the emergence of powerful structural tools such as X-ray crystallography, cryo-electron microscopy (cryo-EM) and micro-electron diffraction (MicroED). Each section discusses major advancements addressing long standing bottlenecks and opening avenues to understand structure–function relationships in membrane proteins. Furthermore, it also briefly discusses the impact of important discoveries and future perspectives for the field. The review concludes by discussing current emerging frontiers in the field including in-situ structural methods, AI driven structure prediction and future directions for integrative and dynamic membrane protein research.
{"title":"Hurdles and advancements in experimental membrane protein structural biology","authors":"Ruchika Bajaj","doi":"10.1016/j.jsb.2025.108251","DOIUrl":"10.1016/j.jsb.2025.108251","url":null,"abstract":"<div><div>This short review article traces the evolution of membrane protein structural biology over time and describes various challenges faced and overcome by researchers in the field, highlighting some of the major breakthroughs and advancements in the field. It presents a thematic exploration of membrane protein structural biology emphasizing on persistent technical and conceptual challenges from protein expression to structural techniques shaping the field with landmark innovations advancing our ability to determine membrane protein structures. The review specifically focus on a few key areas: sourcing and expressing membrane proteins, developing purification strategies and membrane mimetics, and the emergence of powerful structural tools such as X-ray crystallography, cryo-electron microscopy (cryo-EM) and micro-electron diffraction (MicroED). Each section discusses major advancements addressing long standing bottlenecks and opening avenues to understand structure–function relationships in membrane proteins. Furthermore, it also briefly discusses the impact of important discoveries and future perspectives for the field. The review concludes by discussing current emerging frontiers in the field including <em>in-situ</em> structural methods, AI driven structure prediction and future directions for integrative and dynamic membrane protein research.</div></div>","PeriodicalId":17074,"journal":{"name":"Journal of structural biology","volume":"217 4","pages":"Article 108251"},"PeriodicalIF":2.7,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145199956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-20DOI: 10.1016/j.jsb.2025.108249
Sergey Gorelick , Sailakshmi Velamoor , Patrick Cleeve , Sylvain Trépout , Le Ying , Vivek Naranbhai , Georg Ramm
Cryo-FIB milling of biological specimens is a critical and limiting step in the cryo-electron tomography workflow. Preparing electron-transparent cryo-lamellae is a serial, low-throughput process. Even with automation, a skilled operator can typically only produce 15–25 lamellae in a single cryo-FIB session. During sample handling, milling and transfer, the cryo-fixed cells as well as the supporting film layer face various mechanical forces and thermal stresses due to temperature fluctuations. Moreover, after cells are cryo-FIB milled, the resulting thin lamellae continue to endure external forces from mechanical handling and thermal stress. We propose a simple, yet highly effective modification to the standard rectangular milling pattern by implementing “fillets” or corner smoothing providing better mechanical stability. This adjustment helps to avoid sharp corners at the lamella edges, thereby reducing stress concentration. As a result, this modification decreases the likelihood of lamella breakage and improves the overall yield of ready-for-TEM lamellae by over 40 % as verified experimentally.
{"title":"Mind the corner: Fillets in cryo-FIB lamella preparation to minimise sample loss caused by stress concentration and lamella breakage","authors":"Sergey Gorelick , Sailakshmi Velamoor , Patrick Cleeve , Sylvain Trépout , Le Ying , Vivek Naranbhai , Georg Ramm","doi":"10.1016/j.jsb.2025.108249","DOIUrl":"10.1016/j.jsb.2025.108249","url":null,"abstract":"<div><div>Cryo-FIB milling of biological specimens is a critical and limiting step in the cryo-electron tomography workflow. Preparing electron-transparent cryo-lamellae is a serial, low-throughput process. Even with automation, a skilled operator can typically only produce 15–25 lamellae in a single cryo-FIB session. During sample handling, milling and transfer, the cryo-fixed cells as well as the supporting film layer face various mechanical forces and thermal stresses due to temperature fluctuations. Moreover, after cells are cryo-FIB milled, the resulting thin lamellae continue to endure external forces from mechanical handling and thermal stress. We propose a simple, yet highly effective modification to the standard rectangular milling pattern by implementing “fillets” or corner smoothing providing better mechanical stability. This adjustment helps to avoid sharp corners at the lamella edges, thereby reducing stress concentration. As a result, this modification decreases the likelihood of lamella breakage and improves the overall yield of ready-for-TEM lamellae by over 40 % as verified experimentally.</div></div>","PeriodicalId":17074,"journal":{"name":"Journal of structural biology","volume":"217 4","pages":"Article 108249"},"PeriodicalIF":2.7,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The shell of Lingula anatina, a living representative of early brachiopods, exemplifies a unique organophosphatic biomineralization strategy that integrates mineral phases with organic components for structural enhancement. This study employs scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), inductively coupled plasma optical emission spectrometry (ICP-OES), X-ray diffraction (XRD), and Raman spectroscopy to comprehensively analyse the microstructure, composition, and mineralogy of the shell. SEM imaging reveals distinct regional microarchitectures, from compact fibrous laminae to porous, reticulate layers, indicating functional specialization in structural reinforcement and flexibility. Elemental analyses confirm a calcium-phosphate matrix dominated by fluorapatite and enriched with trace elements like Mg, Mn, and Fe. XRD and Raman data validate the coexistence of crystalline fluorapatite and calcite with significant amorphous phases. These findings highlight Lingula’s evolutionary retention of a hierarchical, organic–inorganic composite shell adapted for environmental interaction, structural resilience, and biomineral control.
{"title":"A Multi-Technique Investigation to Explore the Structural Integrity and Chemical Complexity of the Brachiopod Lingula anatina (Lamarck, 1801) Shells","authors":"Prabad Pratim Pal, Sourav Bar, Santosh Kumar Bera, Debkumar Sahoo, Sudipta Kumar Ghorai","doi":"10.1016/j.jsb.2025.108248","DOIUrl":"10.1016/j.jsb.2025.108248","url":null,"abstract":"<div><div>The shell of <em>Lingula anatina</em>, a living representative of early brachiopods, exemplifies a unique organophosphatic biomineralization strategy that integrates mineral phases with organic components for structural enhancement. This study employs scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), inductively coupled plasma optical emission spectrometry (ICP-OES), X-ray diffraction (XRD), and Raman spectroscopy to comprehensively analyse the microstructure, composition, and mineralogy of the shell. SEM imaging reveals distinct regional microarchitectures, from compact fibrous laminae to porous, reticulate layers, indicating functional specialization in structural reinforcement and flexibility. Elemental analyses confirm a calcium-phosphate matrix dominated by fluorapatite and enriched with trace elements like Mg, Mn, and Fe. XRD and Raman data validate the coexistence of crystalline fluorapatite and calcite with significant amorphous phases. These findings highlight <em>Lingula’s</em> evolutionary retention of a hierarchical, organic–inorganic composite shell adapted for environmental interaction, structural resilience, and biomineral control.</div></div>","PeriodicalId":17074,"journal":{"name":"Journal of structural biology","volume":"217 4","pages":"Article 108248"},"PeriodicalIF":2.7,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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