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}
Pub Date : 2025-12-01DOI: 10.1038/s41589-025-02078-w
{"title":"Configurational isomerization around the metal center underlies chemoselectivity of a radical halogenase","authors":"","doi":"10.1038/s41589-025-02078-w","DOIUrl":"https://doi.org/10.1038/s41589-025-02078-w","url":null,"abstract":"","PeriodicalId":18832,"journal":{"name":"Nature chemical biology","volume":"21 1","pages":""},"PeriodicalIF":14.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645239","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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