Pub Date : 2025-11-12DOI: 10.1016/j.jmb.2025.169535
Mengqiu Wang , Zhiwei Zhang , Xinxin Zhang , Ruoyan Dai , Zhenghui Wang , Zeyao Chen , Lixin Lei , Zhenxing Li , Qianjin Guo
Recent advances in spatial transcriptomics (ST) have significantly enhanced our understanding of tissue structure and intercellular interactions. However, existing methods for spatial domain identification and cell type deconvolution still face challenges related to accuracy, robustness, and computational efficiency. To address these issues, we introduce SpatialFusion, an innovative deep learning model designed to improve both spatial domain identification and cell type deconvolution by integrating gene expression and spatial coordinates. The core innovation of SpatialFusion lies in its use of graph neural networks (GNN) and attention mechanisms to capture complex spatial relationships through multi-dimensional embeddings of spatial data. By employing a dual-encoding strategy (co-learning of spatial graphs and feature maps) and self-supervised contrastive learning, the model significantly enhances accuracy and robustness across datasets. Experimental results demonstrate that SpatialFusion outperforms existing methods in accuracy and resolution when applied to the human DLPFC dataset, particularly in capturing complex, layer-specific expression patterns. The model also shows strong robustness in cell type deconvolution, accurately mapping spatial cell type distributions despite noise and low cell density. In breast cancer tumor microenvironment analysis, SpatialFusion revealed spatial heterogeneity and identified potential therapeutic targets, COX6C and CCND1, providing valuable insights for precision medicine.
{"title":"SpatialFusion: A Unified Model for Integrating Spatial Transcriptomics to Unveil Cell-type Distribution, Interaction, and Functional Heterogeneity in Tissue Microenvironments","authors":"Mengqiu Wang , Zhiwei Zhang , Xinxin Zhang , Ruoyan Dai , Zhenghui Wang , Zeyao Chen , Lixin Lei , Zhenxing Li , Qianjin Guo","doi":"10.1016/j.jmb.2025.169535","DOIUrl":"10.1016/j.jmb.2025.169535","url":null,"abstract":"<div><div>Recent advances in spatial transcriptomics (ST) have significantly enhanced our understanding of tissue structure and intercellular interactions. However, existing methods for spatial domain identification and cell type deconvolution still face challenges related to accuracy, robustness, and computational efficiency. To address these issues, we introduce SpatialFusion, an innovative deep learning model designed to improve both spatial domain identification and cell type deconvolution by integrating gene expression and spatial coordinates. The core innovation of SpatialFusion lies in its use of graph neural networks (GNN) and attention mechanisms to capture complex spatial relationships through multi-dimensional embeddings of spatial data. By employing a dual-encoding strategy (co-learning of spatial graphs and feature maps) and self-supervised contrastive learning, the model significantly enhances accuracy and robustness across datasets. Experimental results demonstrate that SpatialFusion outperforms existing methods in accuracy and resolution when applied to the human DLPFC dataset, particularly in capturing complex, layer-specific expression patterns. The model also shows strong robustness in cell type deconvolution, accurately mapping spatial cell type distributions despite noise and low cell density. In breast cancer tumor microenvironment analysis, SpatialFusion revealed spatial heterogeneity and identified potential therapeutic targets, COX6C and CCND1, providing valuable insights for precision medicine.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 2","pages":"Article 169535"},"PeriodicalIF":4.5,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145522560","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 : 2025-11-10DOI: 10.1016/j.jmb.2025.169537
Raechell, Wei-Ven Tee, Bingxue Dong, Enrico Guarnera, Igor N Berezovsky
The thermodynamic stability of proteins and regulation of their functional activity can be described within the energy landscape framework, where the former is provided by a unique native conformational ensemble separated by an energy gap from misfolded structures, and the latter is based on conformational transitions between structural states in the native ensemble. This work investigates the relationship between fundamentals of structural stability and dynamics-driven allosteric regulation. We describe here general proteomic trends and fold/function-specific determinants of protein stability. The intricate relationship between stability and allostery has been observed, showing how requirements on stability and thermal adaptation drive and shape the protein's "structural platform", while complementary sequence-structure determinants control the allosteric signaling and regulation. We illustrate our findings using four groups of proteins - inorganic pyrophosphatase and β-glucosidase representing hydrolases, the CheY signaling protein, and adenylate kinase - obtained from host organisms spanning from psychrophiles to hyperthermophiles. We also show that allosteric effects of mutations in adenylate kinase account for experimentally observed changes in organismal fitness expressed in bacterial growth rates. Epistasis arising from the effects of these mutations is another important phenomenon, resulting in unexpected non-additive changes in fitness that could not be explained by the stability changes alone. The findings in this work and options for further investigations of the stability-signaling relationship are provided by the sequence-dependent model of allostery employed here and implemented in AlloSigMA 3 - the latest update of our AlloSigMA web-server (https://allosigma.bii.a-star.edu.sg).
{"title":"On the Relationship Between Protein Stability, Thermostability, and Allosteric Signaling.","authors":"Raechell, Wei-Ven Tee, Bingxue Dong, Enrico Guarnera, Igor N Berezovsky","doi":"10.1016/j.jmb.2025.169537","DOIUrl":"10.1016/j.jmb.2025.169537","url":null,"abstract":"<p><p>The thermodynamic stability of proteins and regulation of their functional activity can be described within the energy landscape framework, where the former is provided by a unique native conformational ensemble separated by an energy gap from misfolded structures, and the latter is based on conformational transitions between structural states in the native ensemble. This work investigates the relationship between fundamentals of structural stability and dynamics-driven allosteric regulation. We describe here general proteomic trends and fold/function-specific determinants of protein stability. The intricate relationship between stability and allostery has been observed, showing how requirements on stability and thermal adaptation drive and shape the protein's \"structural platform\", while complementary sequence-structure determinants control the allosteric signaling and regulation. We illustrate our findings using four groups of proteins - inorganic pyrophosphatase and β-glucosidase representing hydrolases, the CheY signaling protein, and adenylate kinase - obtained from host organisms spanning from psychrophiles to hyperthermophiles. We also show that allosteric effects of mutations in adenylate kinase account for experimentally observed changes in organismal fitness expressed in bacterial growth rates. Epistasis arising from the effects of these mutations is another important phenomenon, resulting in unexpected non-additive changes in fitness that could not be explained by the stability changes alone. The findings in this work and options for further investigations of the stability-signaling relationship are provided by the sequence-dependent model of allostery employed here and implemented in AlloSigMA 3 - the latest update of our AlloSigMA web-server (https://allosigma.bii.a-star.edu.sg).</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169537"},"PeriodicalIF":4.5,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145501318","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 : 2025-11-10DOI: 10.1016/j.jmb.2025.169534
Evan Pacheco , Aron A Shoara , Logan W Donaldson
Slf1 and Sro9 are paralogous RNA-binding proteins in Saccharomyces cerevisiae that belong to the LARP1 (La-related protein 1) subgroup of the greater La family. These proteins function as translational regulators during cellular stress, acting through either direct mRNA binding or interactions with ribosomal factors. In this study, we characterized the structural and RNA-binding properties of the La-motif (LaM) domains of Slf1 and Sro9 using a combination of nuclear magnetic resonance (NMR) spectroscopy, calorimetry, and molecular dynamics (MD) simulations. Both LaM domains exhibited micromolar affinity for RNA ligands, including poly(A). Notably, the Sro9 LaM domain displayed a thermal denaturation midpoint of 36 °C suggesting a potential regulatory mechanism for this protein during hyperthermic stress. An NMR analysis of the Slf1 LaM domain revealed that its RNA binding platform undergoes widespread conformational sampling on the micro- to millisecond timescale, even in the presence of RNA. Molecular dynamics simulations corroborated these experimental NMR observations and highlighted the role of transient aromatic stacking during RNA binding. Furthermore, a glutamine substitution mutant (Q278A in Slf1) known to impair RNA binding also destabilized the protein-RNA interaction in molecular simulations. Collectively, our findings confirm that RNA binding by LaM domains is an evolutionarily conserved feature among eukaryotes and provide critical insights into the structural and dynamic mechanisms underlying Slf1 and Sro9 function in yeast.
{"title":"RNA Binding by the Yeast Slf1 and Sro9 La-motif Domains","authors":"Evan Pacheco , Aron A Shoara , Logan W Donaldson","doi":"10.1016/j.jmb.2025.169534","DOIUrl":"10.1016/j.jmb.2025.169534","url":null,"abstract":"<div><div>Slf1 and Sro9 are paralogous RNA-binding proteins in <em>Saccharomyces cerevisiae</em> that belong to the LARP1 (La-related protein 1) subgroup of the greater La family. These proteins function as translational regulators during cellular stress, acting through either direct mRNA binding or interactions with ribosomal factors. In this study, we characterized the structural and RNA-binding properties of the La-motif (LaM) domains of Slf1 and Sro9 using a combination of nuclear magnetic resonance (NMR) spectroscopy, calorimetry, and molecular dynamics (MD) simulations. Both LaM domains exhibited micromolar affinity for RNA ligands, including poly(A). Notably, the Sro9 LaM domain displayed a thermal denaturation midpoint of 36 °C suggesting a potential regulatory mechanism for this protein during hyperthermic stress. An NMR analysis of the Slf1 LaM domain revealed that its RNA binding platform undergoes widespread conformational sampling on the micro- to millisecond timescale, even in the presence of RNA. Molecular dynamics simulations corroborated these experimental NMR observations and highlighted the role of transient aromatic stacking during RNA binding. Furthermore, a glutamine substitution mutant (Q278A in Slf1) known to impair RNA binding also destabilized the protein-RNA interaction in molecular simulations. Collectively, our findings confirm that RNA binding by LaM domains is an evolutionarily conserved feature among eukaryotes and provide critical insights into the structural and dynamic mechanisms underlying Slf1 and Sro9 function in yeast.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"437 24","pages":"Article 169534"},"PeriodicalIF":4.5,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145501332","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 : 2025-11-09DOI: 10.1016/j.jmb.2025.169538
Sita Sirisha Madugula , Vindi M. Jayasinghe-Arachchige , Charlene R. Norgan Radler , Shouyi Wang , Jin Liu
The CRISPR/Cas9 system is a powerful gene-editing tool. Its specificity and stability rely on complex allosteric regulation. Understanding these allosteric regulations is essential for developing high-fidelity Cas9 variants with reduced off-target effects. Here, we used a novel structure-based machine learning (ML) approach to systematically identify long-range allosteric networks in Cas9. Our ML model was trained using all available Cas9 structures, ensuring a comprehensive representation of Cas9’s structural landscape. We then applied this model to Streptococcus pyogenes Cas9 (SpCas9) to demonstrate the feature selection process. Using Cα–Cα inter-residue distances, we mapped key allosteric networks and refined them through a two-stage SHAP feature selection (FS) strategy, reducing a vast feature space to 28 critical Lysine–Arginine (Lys–Arg) residue pairs that mediate SpCas9 interdomain communication, stability, and specificity. These Lys–Arg pairs initially shared a 46.5 Å inter-residue distance, but molecular dynamics simulations revealed distinct stabilization behaviors, indicating a hierarchical allosteric network. Further mutational analysis of R78A-K855A (M1) and R765A–K1246A (M2) identified an “electrostatic valley,” a stabilizing network where positively charged residues interact with negatively charged DNA to maintain SpCas9’s structural integrity. Disrupting this valley through direct (M2) or allosteric (M1) mutations destabilized SpCas9’s DNA-bound conformation, leading to distinct pathways for improving SpCas9 specificity. This study provides a new framework for understanding allostery in Cas9, integrating ML-driven structural analysis with MD simulations. By identifying key allosteric residues and introducing the electrostatic valley as a central concept, we offer a rational strategy for engineering high-fidelity Cas9 variants. Beyond Cas9, our approach can be applied to uncover allosteric hotspots in other enzyme regulations and rational protein design.
{"title":"Structure-Based Classification of CRISPR/Cas9 Proteins: A Machine Learning Approach to Elucidating Cas9 Allostery","authors":"Sita Sirisha Madugula , Vindi M. Jayasinghe-Arachchige , Charlene R. Norgan Radler , Shouyi Wang , Jin Liu","doi":"10.1016/j.jmb.2025.169538","DOIUrl":"10.1016/j.jmb.2025.169538","url":null,"abstract":"<div><div>The CRISPR/Cas9 system is a powerful gene-editing tool. Its specificity and stability rely on complex allosteric regulation. Understanding these allosteric regulations is essential for developing high-fidelity Cas9 variants with reduced off-target effects. Here, we used a novel structure-based machine learning (ML) approach to systematically identify long-range allosteric networks in Cas9. Our ML model was trained using all available Cas9 structures, ensuring a comprehensive representation of Cas9’s structural landscape. We then applied this model to <em>Streptococcus pyogenes</em> Cas9 (SpCas9) to demonstrate the feature selection process. Using Cα–Cα inter-residue distances, we mapped key allosteric networks and refined them through a two-stage SHAP feature selection (FS) strategy, reducing a vast feature space to 28 critical Lysine–Arginine (Lys–Arg) residue pairs that mediate SpCas9 interdomain communication, stability, and specificity. These Lys–Arg pairs initially shared a 46.5 Å inter-residue distance, but molecular dynamics simulations revealed distinct stabilization behaviors, indicating a hierarchical allosteric network. Further mutational analysis of R78A-K855A (M1) and R765A–K1246A (M2) identified an “electrostatic valley,” a stabilizing network where positively charged residues interact with negatively charged DNA to maintain SpCas9’s structural integrity. Disrupting this valley through direct (M2) or allosteric (M1) mutations destabilized SpCas9’s DNA-bound conformation, leading to distinct pathways for improving SpCas9 specificity. This study provides a new framework for understanding allostery in Cas9, integrating ML-driven structural analysis with MD simulations. By identifying key allosteric residues and introducing the electrostatic valley as a central concept, we offer a rational strategy for engineering high-fidelity Cas9 variants. Beyond Cas9, our approach can be applied to uncover allosteric hotspots in other enzyme regulations and rational protein design.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 2","pages":"Article 169538"},"PeriodicalIF":4.5,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145494018","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 : 2025-11-07DOI: 10.1016/j.jmb.2025.169536
Josue Pacheco , Maria Yousefi , Hanjing Yang , Shuxing Li , Linda Chelico , Xiaojiang S. Chen
The anti-HIV-1 activity of the double-domain cytidine deaminases APOBEC3G (A3G) and APOBEC3F (A3F) depends on their encapsidation into progeny virions. While A3G requires AA-dinucleotide recognition by its N-terminal deaminase domain (CD1) for packaging, the mechanism for A3F encapsidation has remained unclear. Here, we present the structure of an A3F CD1 variant, revealing AA-binding pocket residues nearly identical to those of A3G CD1. Modeling further shows that A3F’s C-terminal deaminase domain (CD2) harbors a similarly conserved AA-binding pocket. Both A3F CD1 and CD2 preferentially bind AA/GA-containing RNA, and mutations in the AA-binding pocket of either domain in full-length A3F do not impair virion packaging or antiviral activity, indicating functional redundancy. Consistently, double-domain chimeras with A3F CD1 or CD2 at either terminus are efficiently packaged and restrict HIV-1 through both deaminase-dependent and -independent mechanisms. In contrast, A3G exhibits strict domain-position dependence: only constructs with A3G CD1 at the N-terminus support packaging, and HIV-restriction activity varies with the particular domain at the C-terminus. A3G CD1 at the C-terminus is inactive, but the A3G CD2 at the C-terminus is active with either the A3F CD1 or A3F CD2 at the N-terminus. These findings highlight the mechanistic flexibility of A3F, in which either domain can mediate RNA recognition, virion encapsidation, and antiviral activity.
{"title":"Both Domains of APOBEC3F Recognize AA RNA Motifs to Support HIV-1 Virion Encapsidation and Antiviral Function","authors":"Josue Pacheco , Maria Yousefi , Hanjing Yang , Shuxing Li , Linda Chelico , Xiaojiang S. Chen","doi":"10.1016/j.jmb.2025.169536","DOIUrl":"10.1016/j.jmb.2025.169536","url":null,"abstract":"<div><div>The anti-HIV-1 activity of the double-domain cytidine deaminases APOBEC3G (A3G) and APOBEC3F (A3F) depends on their encapsidation into progeny virions. While A3G requires AA-dinucleotide recognition by its N-terminal deaminase domain (CD1) for packaging, the mechanism for A3F encapsidation has remained unclear. Here, we present the structure of an A3F CD1 variant, revealing AA-binding pocket residues nearly identical to those of A3G CD1. Modeling further shows that A3F’s C-terminal deaminase domain (CD2) harbors a similarly conserved AA-binding pocket. Both A3F CD1 and CD2 preferentially bind AA/GA-containing RNA, and mutations in the AA-binding pocket of either domain in full-length A3F do not impair virion packaging or antiviral activity, indicating functional redundancy. Consistently, double-domain chimeras with A3F CD1 or CD2 at either terminus are efficiently packaged and restrict HIV-1 through both deaminase-dependent and -independent mechanisms. In contrast, A3G exhibits strict domain-position dependence: only constructs with A3G CD1 at the N-terminus support packaging, and HIV-restriction activity varies with the particular domain at the C-terminus. A3G CD1 at the C-terminus is inactive, but the A3G CD2 at the C-terminus is active with either the A3F CD1 or A3F CD2 at the N-terminus. These findings highlight the mechanistic flexibility of A3F, in which either domain can mediate RNA recognition, virion encapsidation, and antiviral activity.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"438 2","pages":"Article 169536"},"PeriodicalIF":4.5,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145480274","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 : 2025-11-05DOI: 10.1016/j.jmb.2025.169532
Gongwei Chen , Chang Cai , Xiaoyu Liu , Lei Wang , Xianyou Zhu
Circular RNAs (circRNAs) play a key role in regulating cellular drug sensitivity, and they hold significant promise as potential biomarkers in disease treatment and precision medicine. However, existing circRNA-drug sensitivity association (CDSA) prediction methods generally face the following challenges: high dependence on wet experiments, sparse association data leading to a high percentage of false negatives, insufficient expression of embedded features, and inadequate modeling of higher-order heterogeneous relationships. To solve the above problems, this paper proposes a prediction model, HGCJAMH, based on the higher-order moment-guided model and hypergraph jump learning mechanism, which introduces the KNN and K-means algorithms to construct a multiview hypergraph and models the higher-order complex relationships between circRNAs and drugs. Moreover, it effectively enhances the discriminative power and stability of feature representations through a high moment-guided convolution mechanism and skip-graph contrastive learning. After that, the feature attention mechanism and hierarchical multi-view fusion module are further introduced to realize the adaptive integration and enhanced expression of information from different views. Finally, the association prediction is realized by neural network matrix complementation. In the 5-fold cross-validation, the HGCJAMH model achieves 98.19% AUC and 98.18% AUPR, which is significantly better than several existing mainstream models. The ablation experiments and case validation show that the proposed method not only has superior performance but also has good biological interpretability, demonstrating great potential in the CDSA prediction task. The source code and dataset are available at https://github.com/Forthedark-web/HGCJAMH.
{"title":"HGCJAMH: A Method for circRNA-Drug Sensitivity Prediction Based on Higher-Order Moment-Guided Model and Hypergraph Jumping Learning Mechanism","authors":"Gongwei Chen , Chang Cai , Xiaoyu Liu , Lei Wang , Xianyou Zhu","doi":"10.1016/j.jmb.2025.169532","DOIUrl":"10.1016/j.jmb.2025.169532","url":null,"abstract":"<div><div>Circular RNAs (circRNAs) play a key role in regulating cellular drug sensitivity, and they hold significant promise as potential biomarkers in disease treatment and precision medicine. However, existing circRNA-drug sensitivity association (CDSA) prediction methods generally face the following challenges: high dependence on wet experiments, sparse association data leading to a high percentage of false negatives, insufficient expression of embedded features, and inadequate modeling of higher-order heterogeneous relationships. To solve the above problems, this paper proposes a prediction model, HGCJAMH, based on the higher-order moment-guided model and hypergraph jump learning mechanism, which introduces the KNN and K-means algorithms to construct a multiview hypergraph and models the higher-order complex relationships between circRNAs and drugs. Moreover, it effectively enhances the discriminative power and stability of feature representations through a high moment-guided convolution mechanism and skip-graph contrastive learning. After that, the feature attention mechanism and hierarchical multi-view fusion module are further introduced to realize the adaptive integration and enhanced expression of information from different views. Finally, the association prediction is realized by neural network matrix complementation. In the 5-fold cross-validation, the HGCJAMH model achieves 98.19% AUC and 98.18% AUPR, which is significantly better than several existing mainstream models. The ablation experiments and case validation show that the proposed method not only has superior performance but also has good biological interpretability, demonstrating great potential in the CDSA prediction task. The source code and dataset are available at <span><span>https://github.com/Forthedark-web/HGCJAMH</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"437 24","pages":"Article 169532"},"PeriodicalIF":4.5,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145470549","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 : 2025-11-05DOI: 10.1016/j.jmb.2025.169533
Ilias Zarguit , Marc K.M. Cajili , Bert van Erp , Samuel Schwab , Nico van der Vis , Marianne Julienne Bakker , John van Noort , Remus T. Dame
DNA-wrapping histone proteins play a central role in chromatin organization, gene expression and regulation in most eukaryotes and archaea. While the structure and function of eukaryotic histones are well-characterized, archaeal histones and their complexes with DNA require further scrutiny. Distinct from their eukaryotic counterparts, previously characterized canonical archaeal histones assemble on DNA into an ‘endless’ superhelical nucleoprotein complex called a hypernucleosome. In this study, we explored whether hypernucleosome formation is a conserved feature of canonical archaeal histones. Moreover, to further elucidate how hypernucleosomes are regulated, we also explored how changes in the physico-chemical conditions, particularly the presence of Mg2+, influence the hypernucleosome. Using a combination of Tethered Particle Motion (TPM) and single-molecule force spectroscopy, we established that T. kodakarensis histones assemble into hypernucleosomes on DNA, similar to the M. fervidus histones HMfA and HMfB, the only canonical histones structurally characterized in previous studies. However, the effects of Mg2+ ions are distinct despite the histones’ high sequence- and structural similarity. We propose a model in which Mg2+ ions exert a generic effect on hypernucleosome compactness and stability due to electrostatic DNA shielding, with additional differential effects depending on histone identity.
{"title":"Modulation of Archaeal Hypernucleosome Structure and Stability by Mg2+","authors":"Ilias Zarguit , Marc K.M. Cajili , Bert van Erp , Samuel Schwab , Nico van der Vis , Marianne Julienne Bakker , John van Noort , Remus T. Dame","doi":"10.1016/j.jmb.2025.169533","DOIUrl":"10.1016/j.jmb.2025.169533","url":null,"abstract":"<div><div>DNA-wrapping histone proteins play a central role in chromatin organization, gene expression and regulation in most eukaryotes and archaea. While the structure and function of eukaryotic histones are well-characterized, archaeal histones and their complexes with DNA require further scrutiny. Distinct from their eukaryotic counterparts, previously characterized canonical archaeal histones assemble on DNA into an ‘endless’ superhelical nucleoprotein complex called a hypernucleosome. In this study, we explored whether hypernucleosome formation is a conserved feature of canonical archaeal histones. Moreover, to further elucidate how hypernucleosomes are regulated, we also explored how changes in the physico-chemical conditions, particularly the presence of Mg<sup>2+</sup>, influence the hypernucleosome. Using a combination of Tethered Particle Motion (TPM) and single-molecule force spectroscopy, we established that <em>T. kodakarensis</em> histones assemble into hypernucleosomes on DNA, similar to the <em>M. fervidus</em> histones HMfA and HMfB, the only canonical histones structurally characterized in previous studies. However, the effects of Mg<sup>2+</sup> ions are distinct despite the histones’ high sequence- and structural similarity. We propose a model in which Mg<sup>2+</sup> ions exert a generic effect on hypernucleosome compactness and stability due to electrostatic DNA shielding, with additional differential effects depending on histone identity.</div></div>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"437 24","pages":"Article 169533"},"PeriodicalIF":4.5,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145470483","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 : 2025-11-04DOI: 10.1016/j.jmb.2025.169510
Julia Kowal , Dongchun Ni , Scott M. Jackson , Ioannis Manolaridis , Henning Stahlberg , Kaspar P. Locher
{"title":"Corrigendum to “Structural Basis of Drug Recognition by the Multidrug Transporter ABCG2”. [J. Mol. Biol. 433 (2021) 166980]","authors":"Julia Kowal , Dongchun Ni , Scott M. Jackson , Ioannis Manolaridis , Henning Stahlberg , Kaspar P. Locher","doi":"10.1016/j.jmb.2025.169510","DOIUrl":"10.1016/j.jmb.2025.169510","url":null,"abstract":"","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":"437 24","pages":"Article 169510"},"PeriodicalIF":4.5,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145450465","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 : 2025-11-01Epub Date: 2025-07-25DOI: 10.1016/j.jmb.2025.169357
Yunpeng He, Dongyue Hou, Yuzong Chen, Xian Zeng
Biomolecular interaction kinetics underpin essential cellular mechanisms, yet quantitative databases remain scarce for RNA-protein interactions (RPIs)-a critical regulatory axis in post-transcriptional control, synthetic biology, and therapeutic development. We previously established KDBI (Kinetic Data of Bio-molecular Interactions database) to catalog quantitative kinetics data across diverse biomolecular interaction types. Here, we present KDBI-RP, a dedicated extension focused on RPI kinetics, addressing the growing demand for RNA-centric kinetic research. KDBI-RP systematically integrates binding data for RNA-protein interactions, including kinetic rate constants-association (kon, 3657 entries) and dissociation (koff, 3761 entries)-supplemented by equilibrium dissociation constants (Kd, 175,932 entries). The database offers well-curated information on kinetic constants, assay conditions, literature sources, and comprehensive sequence, structural, and functional annotations for proteins, RNAs, and their complexes. KDBI-RP is freely accessible at http://www.kdbirp.aiddlab.com. We anticipate that KDBI-RP will serve as a valuable resource for the RNA biology and RNA-based medicine research communities.
{"title":"KDBI-RP: Kinetic Data of RNA-Protein Interactions Database.","authors":"Yunpeng He, Dongyue Hou, Yuzong Chen, Xian Zeng","doi":"10.1016/j.jmb.2025.169357","DOIUrl":"10.1016/j.jmb.2025.169357","url":null,"abstract":"<p><p>Biomolecular interaction kinetics underpin essential cellular mechanisms, yet quantitative databases remain scarce for RNA-protein interactions (RPIs)-a critical regulatory axis in post-transcriptional control, synthetic biology, and therapeutic development. We previously established KDBI (Kinetic Data of Bio-molecular Interactions database) to catalog quantitative kinetics data across diverse biomolecular interaction types. Here, we present KDBI-RP, a dedicated extension focused on RPI kinetics, addressing the growing demand for RNA-centric kinetic research. KDBI-RP systematically integrates binding data for RNA-protein interactions, including kinetic rate constants-association (k<sub>on</sub>, 3657 entries) and dissociation (k<sub>off</sub>, 3761 entries)-supplemented by equilibrium dissociation constants (K<sub>d</sub>, 175,932 entries). The database offers well-curated information on kinetic constants, assay conditions, literature sources, and comprehensive sequence, structural, and functional annotations for proteins, RNAs, and their complexes. KDBI-RP is freely accessible at http://www.kdbirp.aiddlab.com. We anticipate that KDBI-RP will serve as a valuable resource for the RNA biology and RNA-based medicine research communities.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169357"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144726322","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}
Ureaplasma is one of the smallest pathogenic bacteria, generating approximately 95% of its adenosine triphosphate (ATP) solely through urease. Studies on Ureaplasma parvum, a species of Ureaplasma, have confirmed that adding urease inhibitors inhibits bacterial growth. The Km and Vmax of the urease-mediated reaction were estimated to be 4.3 ± 0.2 mM and 3,333.3 ± 38.0 μmol NH3/min/mg protein, respectively. The cryo-electron microscopy (cryo-EM) structure of Ureaplasma parvum urease (UPU) at a resolution of 2.03 Å reveals a trimer of heterotrimers comprising three proteins: UreA, UreB, and UreC. The active site is well conserved among the known ureases. However, the Vmax of UPU was higher than that of most known ureases, including those ureases derived from Sporosarcina pasteurii (SPU) and Klebsiella aerogenes (KAU) with identical oligomeric state. All-atom molecular dynamics simulations showed that the flap and UreB are more open in UPU than SPU and KAU. His-tagged wild-type recombinant UPU (WT-rUPU) revealed estimated Km and Vmax values of 4.1 ± 0.3 mM and 769.2 ± 7.4 µmol NH3/min/mg protein, respectively. Amino acid substitutions of recombinant UPUs within the flap region to SPU. Amongst the flap region variants, the Vmax of K331N variant was 48-fold lower than that of WT-rUPU. ICP-MS analysis reveals that one molecule of UPU, WT-rUPU, and K331N-rUPU contains 3.7, 0.8, and 0.1 Ni2+ atoms, respectively, suggesting that a wide-open flap of urease may contribute to delivering nickel into the enzyme, resulting in a high Vmax. Ureaplasma evolved highly efficient UPU through a few amino acid substitutions in the disorganized loop of the mobile flap region.
{"title":"Structural Analysis and Molecular Dynamics Simulations of Urease From Ureaplasma parvum.","authors":"Heng Ning Wu, Junso Fujita, Yukiko Nakura, Masao Inoue, Koichiro Suzuki, Toru Ekimoto, Bingjie Yin, Yohta Fukuda, Kazuo Harada, Tsuyoshi Inoue, Mitsunori Ikeguchi, Keiichi Namba, Itaru Yanagihara","doi":"10.1016/j.jmb.2025.169368","DOIUrl":"10.1016/j.jmb.2025.169368","url":null,"abstract":"<p><p>Ureaplasma is one of the smallest pathogenic bacteria, generating approximately 95% of its adenosine triphosphate (ATP) solely through urease. Studies on Ureaplasma parvum, a species of Ureaplasma, have confirmed that adding urease inhibitors inhibits bacterial growth. The K<sub>m</sub> and V<sub>max</sub> of the urease-mediated reaction were estimated to be 4.3 ± 0.2 mM and 3,333.3 ± 38.0 μmol NH<sub>3</sub>/min/mg protein, respectively. The cryo-electron microscopy (cryo-EM) structure of Ureaplasma parvum urease (UPU) at a resolution of 2.03 Å reveals a trimer of heterotrimers comprising three proteins: UreA, UreB, and UreC. The active site is well conserved among the known ureases. However, the V<sub>max</sub> of UPU was higher than that of most known ureases, including those ureases derived from Sporosarcina pasteurii (SPU) and Klebsiella aerogenes (KAU) with identical oligomeric state. All-atom molecular dynamics simulations showed that the flap and UreB are more open in UPU than SPU and KAU. His-tagged wild-type recombinant UPU (WT-rUPU) revealed estimated K<sub>m</sub> and V<sub>max</sub> values of 4.1 ± 0.3 mM and 769.2 ± 7.4 µmol NH<sub>3</sub>/min/mg protein, respectively. Amino acid substitutions of recombinant UPUs within the flap region to SPU. Amongst the flap region variants, the V<sub>max</sub> of K331N variant was 48-fold lower than that of WT-rUPU. ICP-MS analysis reveals that one molecule of UPU, WT-rUPU, and K331N-rUPU contains 3.7, 0.8, and 0.1 Ni<sup>2+</sup> atoms, respectively, suggesting that a wide-open flap of urease may contribute to delivering nickel into the enzyme, resulting in a high V<sub>max</sub>. Ureaplasma evolved highly efficient UPU through a few amino acid substitutions in the disorganized loop of the mobile flap region.</p>","PeriodicalId":369,"journal":{"name":"Journal of Molecular Biology","volume":" ","pages":"169368"},"PeriodicalIF":4.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768237","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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