Phase separation into membrane-less organelles regulates protein activity in eukaryotic cells. miRNA-repressed mRNAs and Ago proteins localize to RNA-processing bodies (P-bodies), which are subcellular structures formed by several RNA-binding and regulatory proteins. Ago2, the essential miRNA-binding protein, forms a complex with miRNAs to repress protein synthesis by binding to mRNAs and targeting them to P-bodies. However, factors controlling Ago2 and miRNA-repressed mRNA compartmentalization into P-bodies are not fully understood. We developed a detergent-permeabilized cell-based assay system to observe the phase separation of exogenously added Ago2 into P-bodies in vitro. We observed that miRNA binding to Ago2 is essential for its localization to P-bodies, which is also ATP dependent. Osmolarity and salt concentration also affect Ago2 compartmentalization to P-bodies. Amyloid beta oligomers enhance Ago2 targeting to P-bodies by slowing down cellular Ago2 dynamics and inhibiting mTORC1 activity. However, the RNA-binder HuR disrupts P-body targeting by “sponging” out Ago2-associated miRNAs.
{"title":"HuR prevents amyloid beta-induced phase separation of miRNA-bound Ago2 to RNA-processing bodies","authors":"Sritama Ray, Sumangal Roychowdhury, Yogaditya Chakrabarty, Saikat Banerjee, Alisiara Hobbs, Krishnananda Chattopadhyay, Kamalika Mukherjee, Suvendra N. Bhattacharyya","doi":"10.1016/j.str.2025.02.003","DOIUrl":"https://doi.org/10.1016/j.str.2025.02.003","url":null,"abstract":"Phase separation into membrane-less organelles regulates protein activity in eukaryotic cells. miRNA-repressed mRNAs and Ago proteins localize to RNA-processing bodies (P-bodies), which are subcellular structures formed by several RNA-binding and regulatory proteins. Ago2, the essential miRNA-binding protein, forms a complex with miRNAs to repress protein synthesis by binding to mRNAs and targeting them to P-bodies. However, factors controlling Ago2 and miRNA-repressed mRNA compartmentalization into P-bodies are not fully understood. We developed a detergent-permeabilized cell-based assay system to observe the phase separation of exogenously added Ago2 into P-bodies <em>in vitro</em>. We observed that miRNA binding to Ago2 is essential for its localization to P-bodies, which is also ATP dependent. Osmolarity and salt concentration also affect Ago2 compartmentalization to P-bodies. Amyloid beta oligomers enhance Ago2 targeting to P-bodies by slowing down cellular Ago2 dynamics and inhibiting mTORC1 activity. However, the RNA-binder HuR disrupts P-body targeting by “sponging” out Ago2-associated miRNAs.","PeriodicalId":22168,"journal":{"name":"Structure","volume":"85 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569639","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 : 2025-03-06DOI: 10.1016/j.str.2025.02.001
Marcos Ostolga-Chavarría, Héctor Miranda-Astudillo, Diego González-Halphen
In this issue of Structure, Krah et al.1 present a comprehensive study combining molecular dynamics (MD) simulations, free-energy calculations, and in vivo mutagenesis experiments to investigate how water molecules interact with the F1FO-ATP synthase c-ring domain. Their findings highlight the potential of this bacterial enzyme as a drug target.
{"title":"Fine-tuned structural modifications enable specific drug design against multidrug-resistant pathogens","authors":"Marcos Ostolga-Chavarría, Héctor Miranda-Astudillo, Diego González-Halphen","doi":"10.1016/j.str.2025.02.001","DOIUrl":"https://doi.org/10.1016/j.str.2025.02.001","url":null,"abstract":"In this issue of <em>Structure</em>, Krah et al.<span><span><sup>1</sup></span></span> present a comprehensive study combining molecular dynamics (MD) simulations, free-energy calculations, and <em>in vivo</em> mutagenesis experiments to investigate how water molecules interact with the F<sub>1</sub>F<sub>O</sub>-ATP synthase <em>c</em>-ring domain. Their findings highlight the potential of this bacterial enzyme as a drug target.","PeriodicalId":22168,"journal":{"name":"Structure","volume":"67 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561190","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 : 2025-02-26DOI: 10.1016/j.str.2025.01.023
Alberto Palacios-Abella, Andrés López-Perrote, Jasminka Boskovic, Sandra Fonseca, Cristina Úrbez, Vicente Rubio, Oscar Llorca, David Alabadí
The R2TP complex is a specialized HSP90 cochaperone essential for the maturation of macromolecular complexes such as RNAPII and TORC1. R2TP is formed by a hetero-hexameric ring of AAA-ATPases RuvBL1 and RuvBL2, which interact with RPAP3 and PIH1D1. Several R2TP-like complexes have been described, but these are less well characterized. Here, we identified, characterized and determined the cryo-electron microscopy (cryo-EM) structure of R2T from Arabidopsis thaliana, which lacks PIH1D1 and is probably the only form of the complex in seed plants. In contrast to R2TP, R2T is organized as two rings of AtRuvBL1-AtRuvBL2a interacting back-to-back, with one AtRPAP3 anchored per ring. AtRPAP3 has no effect on the ATPase activity of AtRuvBL1-AtRuvBL2a and binds with a different stoichiometry than in human R2TP. We show that the interaction of AtRPAP3 with AtRuvBL2a and AtHSP90 occurs via a conserved mechanism. However, the distinct architectures of R2T and R2TP suggest differences in their functions and mechanisms.
{"title":"The structure of the R2T complex reveals a different architecture from the related HSP90 cochaperone R2TP","authors":"Alberto Palacios-Abella, Andrés López-Perrote, Jasminka Boskovic, Sandra Fonseca, Cristina Úrbez, Vicente Rubio, Oscar Llorca, David Alabadí","doi":"10.1016/j.str.2025.01.023","DOIUrl":"https://doi.org/10.1016/j.str.2025.01.023","url":null,"abstract":"The R2TP complex is a specialized HSP90 cochaperone essential for the maturation of macromolecular complexes such as RNAPII and TORC1. R2TP is formed by a hetero-hexameric ring of AAA-ATPases RuvBL1 and RuvBL2, which interact with RPAP3 and PIH1D1. Several R2TP-like complexes have been described, but these are less well characterized. Here, we identified, characterized and determined the cryo-electron microscopy (cryo-EM) structure of R2T from <em>Arabidopsis thaliana</em>, which lacks PIH1D1 and is probably the only form of the complex in seed plants. In contrast to R2TP, R2T is organized as two rings of AtRuvBL1-AtRuvBL2a interacting back-to-back, with one AtRPAP3 anchored per ring. AtRPAP3 has no effect on the ATPase activity of AtRuvBL1-AtRuvBL2a and binds with a different stoichiometry than in human R2TP. We show that the interaction of AtRPAP3 with AtRuvBL2a and AtHSP90 occurs via a conserved mechanism. However, the distinct architectures of R2T and R2TP suggest differences in their functions and mechanisms.","PeriodicalId":22168,"journal":{"name":"Structure","volume":"90 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495775","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}
IGF2BP family proteins (IGF2BPs) contain six tandem RNA-binding domains (RBDs), resulting in highly complex RNA binding properties. Dissecting how IGF2BPs recognize their RNA targets is essential for understanding their regulatory roles in gene expression. Here, we have determined the crystal structures of mouse IGF2BP3 constructs complexed with different RNA substrates. Our structures reveal that the IGF2BP3-RRM12 domains can recognize CA-rich elements up to 5-nt in length, mainly through RRM1. We also captured the antiparallel RNA-binding mode of the IGF2BP3-KH12 domains, with five nucleotides bound by KH1 and two nucleotides bound by KH2. Furthermore, our structural and biochemical studies suggest that the IGF2BP3-KH12 domains could recognize the “zipcode” RNA element within the β-actin mRNA. Finally, we analyzed the similarities and differences of the RNA-binding properties between the KH12 and KH34. Our studies provide structural insights into RNA target recognition by mouse IGF2BP3.
{"title":"Structural basis for the RNA binding properties of mouse IGF2BP3","authors":"Xiaojia Li, Wenting Guo, Yan Wen, Chunyan Meng, Qingrong Zhang, Haitao Chen, Xiaomiao Zhao, Baixing Wu","doi":"10.1016/j.str.2025.01.022","DOIUrl":"https://doi.org/10.1016/j.str.2025.01.022","url":null,"abstract":"IGF2BP family proteins (IGF2BPs) contain six tandem RNA-binding domains (RBDs), resulting in highly complex RNA binding properties. Dissecting how IGF2BPs recognize their RNA targets is essential for understanding their regulatory roles in gene expression. Here, we have determined the crystal structures of mouse IGF2BP3 constructs complexed with different RNA substrates. Our structures reveal that the IGF2BP3-RRM12 domains can recognize CA-rich elements up to 5-nt in length, mainly through RRM1. We also captured the antiparallel RNA-binding mode of the IGF2BP3-KH12 domains, with five nucleotides bound by KH1 and two nucleotides bound by KH2. Furthermore, our structural and biochemical studies suggest that the IGF2BP3-KH12 domains could recognize the “zipcode” RNA element within the β-actin mRNA. Finally, we analyzed the similarities and differences of the RNA-binding properties between the KH12 and KH34. Our studies provide structural insights into RNA target recognition by mouse IGF2BP3.","PeriodicalId":22168,"journal":{"name":"Structure","volume":"15 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463086","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 : 2025-02-20DOI: 10.1016/j.str.2025.01.021
Rafael Miranda, Francesca Anson, Shannon T. Smith, Mark Ultsch, Connie A. Tenorio, Lionel Rougé, Brennan Farrell, Emel Adaligil, Jeffrey K. Holden, Seth F. Harris, Erin C. Dueber
The ubiquitin-specific protease (USP) family of deubiquitinases (DUBs) are regulators of Ub signaling that share a common catalytic-domain fold. The dynamic nature of this domain is important for controlling the function of USPs, with inter- and intramolecular interactions often influencing the structure and enzymatic activity of these DUBs. This conformational flexibility, in combination with the high sequence conservation of the USP active site, has made it challenging to readily identify potent and selective inhibitors for individual USPs. Here, we demonstrate how a naive, macrocycle-mRNA display selection rapidly yielded high-affinity binders to USP7 that specifically inhibit the DUB with nanomolar half-maximal inhibitory concentration (IC50) values. Structural analysis of the macrocycles bound to USP7 revealed a variety of binding modes and identified inhibition hotspots on the enzyme that mirror those used by small-molecule inhibitors. Together, these data suggest that initial macrocyclic hits could serve as pivotal tools in developing USP-specific inhibitors and probing USP biology.
{"title":"Discovery and characterization of potent macrocycle inhibitors of ubiquitin-specific protease-7","authors":"Rafael Miranda, Francesca Anson, Shannon T. Smith, Mark Ultsch, Connie A. Tenorio, Lionel Rougé, Brennan Farrell, Emel Adaligil, Jeffrey K. Holden, Seth F. Harris, Erin C. Dueber","doi":"10.1016/j.str.2025.01.021","DOIUrl":"https://doi.org/10.1016/j.str.2025.01.021","url":null,"abstract":"The ubiquitin-specific protease (USP) family of deubiquitinases (DUBs) are regulators of Ub signaling that share a common catalytic-domain fold. The dynamic nature of this domain is important for controlling the function of USPs, with inter- and intramolecular interactions often influencing the structure and enzymatic activity of these DUBs. This conformational flexibility, in combination with the high sequence conservation of the USP active site, has made it challenging to readily identify potent and selective inhibitors for individual USPs. Here, we demonstrate how a naive, macrocycle-mRNA display selection rapidly yielded high-affinity binders to USP7 that specifically inhibit the DUB with nanomolar half-maximal inhibitory concentration (IC<sub>50</sub>) values. Structural analysis of the macrocycles bound to USP7 revealed a variety of binding modes and identified inhibition hotspots on the enzyme that mirror those used by small-molecule inhibitors. Together, these data suggest that initial macrocyclic hits could serve as pivotal tools in developing USP-specific inhibitors and probing USP biology.","PeriodicalId":22168,"journal":{"name":"Structure","volume":"38 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451615","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 : 2025-02-12DOI: 10.1016/j.str.2025.01.020
Pavol Harar, Lukas Herrmann, Philipp Grohs, David Haselbach
In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training datasets. The protracted generation time of physics-based models, often employed to produce these datasets, limits their broad applicability. We introduce FakET, a method based on neural style transfer, capable of simulating the forward operator of any cryo transmission electron microscope. It can be used to adapt a synthetic training dataset according to reference data producing high-quality simulated micrographs or tilt-series. To assess the quality of our generated data, we used it to train a state-of-the-art localization and classification architecture and compared its performance with a counterpart trained on benchmark data. Remarkably, our technique matches the performance, boosts data generation speed <span><span style=""></span><span data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><mrow is="true"><mn is="true">750</mn><mo linebreak="goodbreak" linebreakstyle="after" is="true">×</mo></mrow></math>' role="presentation" style="font-size: 90%; display: inline-block; position: relative;" tabindex="0"><svg aria-hidden="true" focusable="false" height="1.971ex" role="img" style="vertical-align: -0.235ex;" viewbox="0 -747.2 2280 848.5" width="5.296ex" xmlns:xlink="http://www.w3.org/1999/xlink"><g fill="currentColor" stroke="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><g is="true"><g is="true"><use xlink:href="#MJMAIN-37"></use><use x="500" xlink:href="#MJMAIN-35" y="0"></use><use x="1001" xlink:href="#MJMAIN-30" y="0"></use></g><g is="true" transform="translate(1501,0)"><use xlink:href="#MJMAIN-D7"></use></g></g></g></svg><span role="presentation"><math xmlns="http://www.w3.org/1998/Math/MathML"><mrow is="true"><mn is="true">750</mn><mo is="true" linebreak="goodbreak" linebreakstyle="after">×</mo></mrow></math></span></span><script type="math/mml"><math><mrow is="true"><mn is="true">750</mn><mo linebreak="goodbreak" linebreakstyle="after" is="true">×</mo></mrow></math></script></span>, uses <span><span style=""></span><span data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><mrow is="true"><mn is="true">33</mn><mo linebreak="goodbreak" linebreakstyle="after" is="true">×</mo></mrow></math>' role="presentation" style="font-size: 90%; display: inline-block; position: relative;" tabindex="0"><svg aria-hidden="true" focusable="false" height="1.971ex" role="img" style="vertical-align: -0.235ex;" viewbox="0 -747.2 1779.5 848.5" width="4.133ex" xmlns:xlink="http://www.w3.org/1999/xlink"><g fill="currentColor" stroke="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><g is="true"><g is="true"><use xlink:href="#MJMAIN-33"></use><use x="500" xlink:href="#MJMAIN-33" y="0"></use></g><g is="true" transform="translate(1001
{"title":"FakET: Simulating cryo-electron tomograms with neural style transfer","authors":"Pavol Harar, Lukas Herrmann, Philipp Grohs, David Haselbach","doi":"10.1016/j.str.2025.01.020","DOIUrl":"https://doi.org/10.1016/j.str.2025.01.020","url":null,"abstract":"In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training datasets. The protracted generation time of physics-based models, often employed to produce these datasets, limits their broad applicability. We introduce FakET, a method based on neural style transfer, capable of simulating the forward operator of any cryo transmission electron microscope. It can be used to adapt a synthetic training dataset according to reference data producing high-quality simulated micrographs or tilt-series. To assess the quality of our generated data, we used it to train a state-of-the-art localization and classification architecture and compared its performance with a counterpart trained on benchmark data. Remarkably, our technique matches the performance, boosts data generation speed <span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mrow is=\"true\"><mn is=\"true\">750</mn><mo linebreak=\"goodbreak\" linebreakstyle=\"after\" is=\"true\">&#xD7;</mo></mrow></math>' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"1.971ex\" role=\"img\" style=\"vertical-align: -0.235ex;\" viewbox=\"0 -747.2 2280 848.5\" width=\"5.296ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><g is=\"true\"><use xlink:href=\"#MJMAIN-37\"></use><use x=\"500\" xlink:href=\"#MJMAIN-35\" y=\"0\"></use><use x=\"1001\" xlink:href=\"#MJMAIN-30\" y=\"0\"></use></g><g is=\"true\" transform=\"translate(1501,0)\"><use xlink:href=\"#MJMAIN-D7\"></use></g></g></g></svg><span role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mrow is=\"true\"><mn is=\"true\">750</mn><mo is=\"true\" linebreak=\"goodbreak\" linebreakstyle=\"after\">×</mo></mrow></math></span></span><script type=\"math/mml\"><math><mrow is=\"true\"><mn is=\"true\">750</mn><mo linebreak=\"goodbreak\" linebreakstyle=\"after\" is=\"true\">×</mo></mrow></math></script></span>, uses <span><span style=\"\"></span><span data-mathml='<math xmlns=\"http://www.w3.org/1998/Math/MathML\"><mrow is=\"true\"><mn is=\"true\">33</mn><mo linebreak=\"goodbreak\" linebreakstyle=\"after\" is=\"true\">&#xD7;</mo></mrow></math>' role=\"presentation\" style=\"font-size: 90%; display: inline-block; position: relative;\" tabindex=\"0\"><svg aria-hidden=\"true\" focusable=\"false\" height=\"1.971ex\" role=\"img\" style=\"vertical-align: -0.235ex;\" viewbox=\"0 -747.2 1779.5 848.5\" width=\"4.133ex\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g fill=\"currentColor\" stroke=\"currentColor\" stroke-width=\"0\" transform=\"matrix(1 0 0 -1 0 0)\"><g is=\"true\"><g is=\"true\"><use xlink:href=\"#MJMAIN-33\"></use><use x=\"500\" xlink:href=\"#MJMAIN-33\" y=\"0\"></use></g><g is=\"true\" transform=\"translate(1001","PeriodicalId":22168,"journal":{"name":"Structure","volume":"129 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393302","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 : 2025-02-11DOI: 10.1016/j.str.2025.01.019
Natalia E. Ketaren, Peter C. Fridy, Vladimir Malashkevich, Tanmoy Sanyal, Marc Brillantes, Mary K. Thompson, Deena A. Oren, Jeffrey B. Bonanno, Andrej Šali, Steven C. Almo, Brian T. Chait, Michael P. Rout
Nanobodies are single domain antibody variants proving themselves to be compelling tools for research, disease diagnostics, and as therapeutics targeting a myriad of disease agents. However, despite this potential, their mechanisms of paratope presentation and structural stabilization have not been fully explored. Here, we show that unlike monoclonal antibodies, a nanobody repertoire maximizes sampling of an antigen surface by binding a single antigen in at least three different orientations, which are correlated with their paratope composition. Structure-guided reengineering of several nanobodies reveals that a single point mutation within the paratope or a highly conserved region of a nanobody’s framework 3 (FR3) can markedly improve antigen affinity, nanobody stability, or both. Conversely, we show the negative impact on antigen affinity when “over-stabilizing” nanobodies. Collectively our results provide a universal strategy to tune a nanobody’s affinity by modifying specific residues that can readily be applied to guide nanobody optimization and functionalization.
{"title":"Unique mechanisms to increase structural stability and enhance antigen binding in nanobodies","authors":"Natalia E. Ketaren, Peter C. Fridy, Vladimir Malashkevich, Tanmoy Sanyal, Marc Brillantes, Mary K. Thompson, Deena A. Oren, Jeffrey B. Bonanno, Andrej Šali, Steven C. Almo, Brian T. Chait, Michael P. Rout","doi":"10.1016/j.str.2025.01.019","DOIUrl":"https://doi.org/10.1016/j.str.2025.01.019","url":null,"abstract":"Nanobodies are single domain antibody variants proving themselves to be compelling tools for research, disease diagnostics, and as therapeutics targeting a myriad of disease agents. However, despite this potential, their mechanisms of paratope presentation and structural stabilization have not been fully explored. Here, we show that unlike monoclonal antibodies, a nanobody repertoire maximizes sampling of an antigen surface by binding a single antigen in at least three different orientations, which are correlated with their paratope composition. Structure-guided reengineering of several nanobodies reveals that a single point mutation within the paratope or a highly conserved region of a nanobody’s framework 3 (FR3) can markedly improve antigen affinity, nanobody stability, or both. Conversely, we show the negative impact on antigen affinity when “over-stabilizing” nanobodies. Collectively our results provide a universal strategy to tune a nanobody’s affinity by modifying specific residues that can readily be applied to guide nanobody optimization and functionalization.","PeriodicalId":22168,"journal":{"name":"Structure","volume":"128 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385080","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 : 2025-02-11DOI: 10.1016/j.str.2025.01.018
Yueyue Shen, Zheng Jiang, Rong Liu
The accurate prediction of conformational epitopes promotes our understanding of antigen-antibody interactions. All existing algorithms depend on a feature-based strategy, which limits their performance. A template-based strategy can provide complementary information, and the interplay between these two strategies could improve the prediction of epitopes. Here, we present DynaBCE, a dynamic ensemble algorithm to effectively identify conformational B cell epitopes (BCEs). Using novel handcrafted structural descriptors and embeddings from protein language models, we developed machine learning and deep learning modules based on boosting algorithms and geometric graph neural networks, respectively. Furthermore, we built a template module by leveraging known structural template information and transformer-based algorithms to capture binding signatures. Finally, we integrated the three modules using a dynamic weighting approach to maximize the strength of each module for different samples. DynaBCE achieved promising results for both native and predicted structures and outperformed previous methods as demonstrated in various evaluation scenarios.
{"title":"Dynamic integration of feature- and template-based methods improves the prediction of conformational B cell epitopes","authors":"Yueyue Shen, Zheng Jiang, Rong Liu","doi":"10.1016/j.str.2025.01.018","DOIUrl":"https://doi.org/10.1016/j.str.2025.01.018","url":null,"abstract":"The accurate prediction of conformational epitopes promotes our understanding of antigen-antibody interactions. All existing algorithms depend on a feature-based strategy, which limits their performance. A template-based strategy can provide complementary information, and the interplay between these two strategies could improve the prediction of epitopes. Here, we present DynaBCE, a dynamic ensemble algorithm to effectively identify conformational B cell epitopes (BCEs). Using novel handcrafted structural descriptors and embeddings from protein language models, we developed machine learning and deep learning modules based on boosting algorithms and geometric graph neural networks, respectively. Furthermore, we built a template module by leveraging known structural template information and transformer-based algorithms to capture binding signatures. Finally, we integrated the three modules using a dynamic weighting approach to maximize the strength of each module for different samples. DynaBCE achieved promising results for both native and predicted structures and outperformed previous methods as demonstrated in various evaluation scenarios.","PeriodicalId":22168,"journal":{"name":"Structure","volume":"28 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385077","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 : 2025-02-10DOI: 10.1016/j.str.2025.01.017
Aldrex Munsayac, Wellington C. Leite, Jesse B. Hopkins, Ian Hall, Hugh M. O’Neill, Sarah C. Keane
The structures of RNA:RNA complexes regulate many biological processes. Despite their importance, protein-free RNA:RNA complexes represent a tiny fraction of experimentally determined structures. Here, we describe a joint small-angle X-ray and neutron scattering (SAXS/SANS) approach to structurally interrogate conformational changes in a model RNA:RNA complex. Using SAXS, we measured the solution structures of the individual RNAs and of the overall RNA:RNA complex. With SANS, we demonstrate, as a proof of principle, that isotope labeling and contrast matching (CM) can be combined to probe the bound state structure of an RNA within a selectively deuterated RNA:RNA complex. Furthermore, we show that experimental scattering data can validate and improve predicted AlphaFold 3 RNA:RNA complex structures to reflect its solution structure. Our work demonstrates that in silico modeling, SAXS, and CM-SANS can be used in concert to directly analyze conformational changes within RNAs when in complex, enhancing our understanding of RNA structure in functional assemblies.
{"title":"Selective deuteration of an RNA:RNA complex for structural analysis using small-angle scattering","authors":"Aldrex Munsayac, Wellington C. Leite, Jesse B. Hopkins, Ian Hall, Hugh M. O’Neill, Sarah C. Keane","doi":"10.1016/j.str.2025.01.017","DOIUrl":"https://doi.org/10.1016/j.str.2025.01.017","url":null,"abstract":"The structures of RNA:RNA complexes regulate many biological processes. Despite their importance, protein-free RNA:RNA complexes represent a tiny fraction of experimentally determined structures. Here, we describe a joint small-angle X-ray and neutron scattering (SAXS/SANS) approach to structurally interrogate conformational changes in a model RNA:RNA complex. Using SAXS, we measured the solution structures of the individual RNAs and of the overall RNA:RNA complex. With SANS, we demonstrate, as a proof of principle, that isotope labeling and contrast matching (CM) can be combined to probe the bound state structure of an RNA within a selectively deuterated RNA:RNA complex. Furthermore, we show that experimental scattering data can validate and improve predicted AlphaFold 3 RNA:RNA complex structures to reflect its solution structure. Our work demonstrates that <em>in silico</em> modeling, SAXS, and CM-SANS can be used in concert to directly analyze conformational changes within RNAs when in complex, enhancing our understanding of RNA structure in functional assemblies.","PeriodicalId":22168,"journal":{"name":"Structure","volume":"13 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143375426","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 : 2025-02-06DOI: 10.1016/j.str.2025.01.016
Kristine Bourke Arnvig, Finn Werner
In this issue of Structure, Dikunova et al.1 report the structure of the trimeric torpedo complex, the key factor responsible for transcription termination by RNA polymerase II R(NAPII) at the end of protein-encoding genes. The comparison between meso- and thermophilic torpedoes provides intriguing insights into thermal adaptions and mechanisms of termination.
在本期《结构》杂志上，Dikunova 等人1 报告了三聚鱼雷复合体的结构，该复合体是 RNA 聚合酶 II R(NAPII)在编码蛋白质基因末端终止转录的关键因子。通过比较中温鱼雷和嗜热鱼雷，人们对热适应性和终止机制有了更深入的了解。
{"title":"Transcription termination—Some like it hot","authors":"Kristine Bourke Arnvig, Finn Werner","doi":"10.1016/j.str.2025.01.016","DOIUrl":"https://doi.org/10.1016/j.str.2025.01.016","url":null,"abstract":"In this issue of <em>Structure</em>, Dikunova et al.<span><span><sup>1</sup></span></span> report the structure of the trimeric torpedo complex, the key factor responsible for transcription termination by RNA polymerase II R(NAPII) at the end of protein-encoding genes. The comparison between meso- and thermophilic torpedoes provides intriguing insights into thermal adaptions and mechanisms of termination.","PeriodicalId":22168,"journal":{"name":"Structure","volume":"12 1","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143192636","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：