Pub Date : 2025-12-29DOI: 10.1038/s41589-025-02112-x
Marcus D Hartmann
{"title":"Writing the CRBN degron.","authors":"Marcus D Hartmann","doi":"10.1038/s41589-025-02112-x","DOIUrl":"https://doi.org/10.1038/s41589-025-02112-x","url":null,"abstract":"","PeriodicalId":18832,"journal":{"name":"Nature chemical biology","volume":" ","pages":""},"PeriodicalIF":13.7,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145857154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Proximity labeling techniques such as TurboID and APEX2 have become pivotal tools for studying protein interactions. However, the spatial patterns of labeling methods within the submicrometer range remain poorly understood. Here we used DNA nanostructure platforms to precisely measure the labeling radii of TurboID and APEX2 through in vitro assays. Our DNA nanoruler design enables the deployment of oligonucleotide-barcoded labeling targets with nanometer precision near the enzymes. By quantifying labeling yields using qPCR and mapping them against target distances, we uncovered surprising insights into the labeling mechanisms. Contrary to the prevailing diffusive labeling model, our results demonstrate that TurboID primarily operates through contact-dependent labeling. Similarly, APEX2 shows high labeling efficiency within its direct contact range. In parallel, it exhibits low-level diffusive labeling toward more distant phenols. These findings reframe our understanding in the mechanism of proximity labeling enzymes while highlighting the potential of DNA nanotechnology in spatially profiling reactive species.
{"title":"Spatial barcoding reveals reaction radii and contact-dependent mechanism of proximity labeling","authors":"Zhe Yang, Yu Zhang, Yuxin Fang, Yuan Zhang, Jiasheng Du, Xiaowen Shen, Kecheng Zhang, Peng Zou, Zhixing Chen","doi":"10.1038/s41589-025-02086-w","DOIUrl":"https://doi.org/10.1038/s41589-025-02086-w","url":null,"abstract":"Proximity labeling techniques such as TurboID and APEX2 have become pivotal tools for studying protein interactions. However, the spatial patterns of labeling methods within the submicrometer range remain poorly understood. Here we used DNA nanostructure platforms to precisely measure the labeling radii of TurboID and APEX2 through in vitro assays. Our DNA nanoruler design enables the deployment of oligonucleotide-barcoded labeling targets with nanometer precision near the enzymes. By quantifying labeling yields using qPCR and mapping them against target distances, we uncovered surprising insights into the labeling mechanisms. Contrary to the prevailing diffusive labeling model, our results demonstrate that TurboID primarily operates through contact-dependent labeling. Similarly, APEX2 shows high labeling efficiency within its direct contact range. In parallel, it exhibits low-level diffusive labeling toward more distant phenols. These findings reframe our understanding in the mechanism of proximity labeling enzymes while highlighting the potential of DNA nanotechnology in spatially profiling reactive species.","PeriodicalId":18832,"journal":{"name":"Nature chemical biology","volume":"363 1","pages":""},"PeriodicalIF":14.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145814068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1038/s41589-025-02118-5
Majda Bratovič
{"title":"Whale’s secret to long life","authors":"Majda Bratovič","doi":"10.1038/s41589-025-02118-5","DOIUrl":"10.1038/s41589-025-02118-5","url":null,"abstract":"","PeriodicalId":18832,"journal":{"name":"Nature chemical biology","volume":"22 1","pages":"6-6"},"PeriodicalIF":13.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1038/s41589-025-02119-4
Benjamin McIlwain
{"title":"Helping sperm keep the beat","authors":"Benjamin McIlwain","doi":"10.1038/s41589-025-02119-4","DOIUrl":"10.1038/s41589-025-02119-4","url":null,"abstract":"","PeriodicalId":18832,"journal":{"name":"Nature chemical biology","volume":"22 1","pages":"6-6"},"PeriodicalIF":13.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1038/s41589-025-02120-x
Gene Chong
{"title":"Clocking in for DNA repair","authors":"Gene Chong","doi":"10.1038/s41589-025-02120-x","DOIUrl":"10.1038/s41589-025-02120-x","url":null,"abstract":"","PeriodicalId":18832,"journal":{"name":"Nature chemical biology","volume":"22 1","pages":"6-6"},"PeriodicalIF":13.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1038/s41589-025-02110-z
Russell Johnson
Machine learning-based tools have revolutionized how scientists study protein structure. Here, Nature Chemical Biology speaks to Cecilia Clementi, Bruno Correia and Peilong Lu about progress in developing computational tools for predicting protein structure and properties, how these programs can be used for protein design, and the developments they would like to see in the field.
{"title":"Harnessing advances in artificial intelligence for protein design","authors":"Russell Johnson","doi":"10.1038/s41589-025-02110-z","DOIUrl":"10.1038/s41589-025-02110-z","url":null,"abstract":"Machine learning-based tools have revolutionized how scientists study protein structure. Here, Nature Chemical Biology speaks to Cecilia Clementi, Bruno Correia and Peilong Lu about progress in developing computational tools for predicting protein structure and properties, how these programs can be used for protein design, and the developments they would like to see in the field.","PeriodicalId":18832,"journal":{"name":"Nature chemical biology","volume":"22 1","pages":"1-4"},"PeriodicalIF":13.7,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1038/s41589-025-02083-z
Karla M. Castro, Joseph L. Watson, Jue Wang, Joshua Southern, Reyhaneh Ayardulabi, Sandrine Georgeon, Stéphane Rosset, David Baker, Bruno E. Correia
De novo protein design has seen major success in scaffolding single functional motifs; however, in nature, most proteins present multiple functional sites. Here, we describe an approach to simultaneously scaffold multiple functional sites in a single-domain protein using deep learning. We designed small single-domain immunogens, under 130 residues, that present three distinct and irregular motifs from respiratory syncytial virus. These motifs together comprise nearly half of the designed proteins; hence, the overall folds are quite unusual with little global similarity to proteins in the Protein Data Bank. Despite this, X-ray crystal structures confirmed the accuracy of presentation of each of the motifs and the multiepitope design yields improved cross-reactive titers and neutralizing response compared to a single-epitope immunogen. The successful presentation of three distinct binding surfaces in a small single-domain protein highlights the power of generative deep learning methods to solve complex protein design problems.
{"title":"Accurate single-domain scaffolding of three nonoverlapping protein epitopes using deep learning","authors":"Karla M. Castro, Joseph L. Watson, Jue Wang, Joshua Southern, Reyhaneh Ayardulabi, Sandrine Georgeon, Stéphane Rosset, David Baker, Bruno E. Correia","doi":"10.1038/s41589-025-02083-z","DOIUrl":"https://doi.org/10.1038/s41589-025-02083-z","url":null,"abstract":"De novo protein design has seen major success in scaffolding single functional motifs; however, in nature, most proteins present multiple functional sites. Here, we describe an approach to simultaneously scaffold multiple functional sites in a single-domain protein using deep learning. We designed small single-domain immunogens, under 130 residues, that present three distinct and irregular motifs from respiratory syncytial virus. These motifs together comprise nearly half of the designed proteins; hence, the overall folds are quite unusual with little global similarity to proteins in the Protein Data Bank. Despite this, X-ray crystal structures confirmed the accuracy of presentation of each of the motifs and the multiepitope design yields improved cross-reactive titers and neutralizing response compared to a single-epitope immunogen. The successful presentation of three distinct binding surfaces in a small single-domain protein highlights the power of generative deep learning methods to solve complex protein design problems.","PeriodicalId":18832,"journal":{"name":"Nature chemical biology","volume":"15 1","pages":""},"PeriodicalIF":14.8,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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