Pub Date : 2024-12-06DOI: 10.1038/s41592-024-02547-5
Lin Tang
New experimental and computational methods illuminate the history of cell differentiation and its molecular underpinnings.
新的实验和计算方法阐明了细胞分化的历史及其分子基础。
{"title":"Mapping cell history and fates","authors":"Lin Tang","doi":"10.1038/s41592-024-02547-5","DOIUrl":"10.1038/s41592-024-02547-5","url":null,"abstract":"New experimental and computational methods illuminate the history of cell differentiation and its molecular underpinnings.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2227-2228"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789381","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 : 2024-12-06DOI: 10.1038/s41592-024-02533-x
Rong Fan
Spatial proteomics holds the potential to transform the study of proteins in situ in complex tissues, but it needs to be integrated with other layers of omics data to gain a holistic view of cellular function, heterogeneity and interactions, and the underlying mechanisms of these processes. I highlight current challenges and emerging opportunities for multi-omic spatial protein profiling to advance basic research and translational applications.
{"title":"Integrative spatial protein profiling with multi-omics","authors":"Rong Fan","doi":"10.1038/s41592-024-02533-x","DOIUrl":"10.1038/s41592-024-02533-x","url":null,"abstract":"Spatial proteomics holds the potential to transform the study of proteins in situ in complex tissues, but it needs to be integrated with other layers of omics data to gain a holistic view of cellular function, heterogeneity and interactions, and the underlying mechanisms of these processes. I highlight current challenges and emerging opportunities for multi-omic spatial protein profiling to advance basic research and translational applications.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2223-2225"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789378","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 : 2024-12-06DOI: 10.1038/s41592-024-02538-6
Bernd Bodenmiller
Spatial proteomics is advancing rapidly, transforming physiological and biomedical research by enabling the study of how multicellular structures and intercellular communication shape tissue function in health and disease. Through the analysis of large human tissue collections, spatial proteomics will reveal the complexities of human tissues and uncover multicellular modules that can serve as drug targets and diagnostics, paving the way for precision medicine and revolutionizing histopathology.
{"title":"Highly multiplexed imaging in the omics era: understanding tissue structures in health and disease","authors":"Bernd Bodenmiller","doi":"10.1038/s41592-024-02538-6","DOIUrl":"10.1038/s41592-024-02538-6","url":null,"abstract":"Spatial proteomics is advancing rapidly, transforming physiological and biomedical research by enabling the study of how multicellular structures and intercellular communication shape tissue function in health and disease. Through the analysis of large human tissue collections, spatial proteomics will reveal the complexities of human tissues and uncover multicellular modules that can serve as drug targets and diagnostics, paving the way for precision medicine and revolutionizing histopathology.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2209-2211"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789401","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 : 2024-12-06DOI: 10.1038/s41592-024-02541-x
Thierry M. Nordmann, Andreas Mund, Matthias Mann
Spatial mass spectrometry (MS)-proteomics is a rapidly evolving technology, particularly in the form of Deep Visual Proteomics (DVP), which allows the study of single cells directly in their native environment. We believe that this approach will reshape our understanding of tissue biology and redefine fundamental concepts in cell biology, tissue physiology and ultimately human health and disease.
{"title":"A new understanding of tissue biology from MS-based proteomics at single-cell resolution","authors":"Thierry M. Nordmann, Andreas Mund, Matthias Mann","doi":"10.1038/s41592-024-02541-x","DOIUrl":"10.1038/s41592-024-02541-x","url":null,"abstract":"Spatial mass spectrometry (MS)-proteomics is a rapidly evolving technology, particularly in the form of Deep Visual Proteomics (DVP), which allows the study of single cells directly in their native environment. We believe that this approach will reshape our understanding of tissue biology and redefine fundamental concepts in cell biology, tissue physiology and ultimately human health and disease.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2220-2222"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789382","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 : 2024-12-06DOI: 10.1038/s41592-024-02549-3
Madhura Mukhopadhyay
Immune–organoid systems will be the next generation of in vitro models.
免疫类器官系统将是下一代体外模型。
{"title":"Organoids get an upgrade","authors":"Madhura Mukhopadhyay","doi":"10.1038/s41592-024-02549-3","DOIUrl":"10.1038/s41592-024-02549-3","url":null,"abstract":"Immune–organoid systems will be the next generation of in vitro models.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2228-2229"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789390","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 : 2024-12-06DOI: 10.1038/s41592-024-02546-6
Rita Strack
Advances in multiplexed super-resolution microscopy will usher in the next era of spatial proteomics.
多路超分辨率显微技术的进步将引领空间蛋白质组学的下一个时代。
{"title":"Subcellular spatial proteomics","authors":"Rita Strack","doi":"10.1038/s41592-024-02546-6","DOIUrl":"10.1038/s41592-024-02546-6","url":null,"abstract":"Advances in multiplexed super-resolution microscopy will usher in the next era of spatial proteomics.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"21 12","pages":"2227-2227"},"PeriodicalIF":36.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789404","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 : 2024-12-05DOI: 10.1038/s41592-024-02495-0
Hojun Li, Parker Côté, Michael Kuoch, Jideofor Ezike, Katie Frenis, Anton Afanassiev, Laura Greenstreet, Mayuri Tanaka-Yano, Giuseppe Tarantino, Stephen Zhang, Jennifer Whangbo, Vincent L. Butty, Enrico Moiso, Marcelo Falchetti, Kate Lu, Guinevere G. Connelly, Vivian Morris, Dahai Wang, Antonia F. Chen, Giada Bianchi, George Q. Daley, Salil Garg, David Liu, Stella T. Chou, Aviv Regev, Edroaldo Lummertz da Rocha, Geoffrey Schiebinger, R. Grant Rowe
Over a lifetime, hematopoietic stem cells (HSCs) adjust their lineage output to support age-aligned physiology. In model organisms, stereotypic waves of hematopoiesis have been observed corresponding to defined age-biased HSC hallmarks. However, how the properties of hematopoietic stem and progenitor cells change over the human lifespan remains unclear. To address this gap, we profiled individual transcriptome states of human hematopoietic stem and progenitor cells spanning gestation, maturation and aging. Here we define the gene expression networks dictating age-specific differentiation of HSCs and the dynamics of fate decisions and lineage priming throughout life. We additionally identifiy and functionally validate a fetal-specific HSC state with robust engraftment and multilineage capacity. Furthermore, we observe that classification of acute myeloid leukemia against defined transcriptional age states demonstrates that utilization of early life transcriptional programs associates with poor prognosis. Overall, we provide a disease-relevant framework for heterochronic orientation of stem cell ontogeny along the real time axis of the human lifespan. This Resource presents an atlas classifying the age-related changes in human hematopoietic stem and progenitor cells from gestation through aging.
{"title":"The dynamics of hematopoiesis over the human lifespan","authors":"Hojun Li, Parker Côté, Michael Kuoch, Jideofor Ezike, Katie Frenis, Anton Afanassiev, Laura Greenstreet, Mayuri Tanaka-Yano, Giuseppe Tarantino, Stephen Zhang, Jennifer Whangbo, Vincent L. Butty, Enrico Moiso, Marcelo Falchetti, Kate Lu, Guinevere G. Connelly, Vivian Morris, Dahai Wang, Antonia F. Chen, Giada Bianchi, George Q. Daley, Salil Garg, David Liu, Stella T. Chou, Aviv Regev, Edroaldo Lummertz da Rocha, Geoffrey Schiebinger, R. Grant Rowe","doi":"10.1038/s41592-024-02495-0","DOIUrl":"10.1038/s41592-024-02495-0","url":null,"abstract":"Over a lifetime, hematopoietic stem cells (HSCs) adjust their lineage output to support age-aligned physiology. In model organisms, stereotypic waves of hematopoiesis have been observed corresponding to defined age-biased HSC hallmarks. However, how the properties of hematopoietic stem and progenitor cells change over the human lifespan remains unclear. To address this gap, we profiled individual transcriptome states of human hematopoietic stem and progenitor cells spanning gestation, maturation and aging. Here we define the gene expression networks dictating age-specific differentiation of HSCs and the dynamics of fate decisions and lineage priming throughout life. We additionally identifiy and functionally validate a fetal-specific HSC state with robust engraftment and multilineage capacity. Furthermore, we observe that classification of acute myeloid leukemia against defined transcriptional age states demonstrates that utilization of early life transcriptional programs associates with poor prognosis. Overall, we provide a disease-relevant framework for heterochronic orientation of stem cell ontogeny along the real time axis of the human lifespan. This Resource presents an atlas classifying the age-related changes in human hematopoietic stem and progenitor cells from gestation through aging.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"422-434"},"PeriodicalIF":36.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785755","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 : 2024-12-05DOI: 10.1038/s41592-024-02528-8
Srijit Seal, Maria-Anna Trapotsi, Ola Spjuth, Shantanu Singh, Jordi Carreras-Puigvert, Nigel Greene, Andreas Bender, Anne E. Carpenter
Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell’s state, introduced in 2013 to optimize and standardize image-based profiling. Cell Painting’s ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types. This Review synthesizes the literature from over 10 years of Cell Painting for image-based profiling and highlights how advances in this technology enable new biological discovery of cellular phenotypes and cell responses to perturbations.
{"title":"Cell Painting: a decade of discovery and innovation in cellular imaging","authors":"Srijit Seal, Maria-Anna Trapotsi, Ola Spjuth, Shantanu Singh, Jordi Carreras-Puigvert, Nigel Greene, Andreas Bender, Anne E. Carpenter","doi":"10.1038/s41592-024-02528-8","DOIUrl":"10.1038/s41592-024-02528-8","url":null,"abstract":"Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell’s state, introduced in 2013 to optimize and standardize image-based profiling. Cell Painting’s ability to capture cellular responses to various perturbations has expanded owing to improvements in the protocol, adaptations for different perturbations, and enhanced methodologies for feature extraction, quality control, and batch-effect correction. Cell Painting is a versatile tool that has been used in various applications, alone or with other -omics data, to decipher the mechanism of action of a compound, its toxicity profile, and other biological effects. Future advances will likely involve computational and experimental techniques, new publicly available datasets, and integration with other high-content data types. This Review synthesizes the literature from over 10 years of Cell Painting for image-based profiling and highlights how advances in this technology enable new biological discovery of cellular phenotypes and cell responses to perturbations.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"254-268"},"PeriodicalIF":36.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785810","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 : 2024-12-02DOI: 10.1038/s41592-024-02561-7
Yazhou Guo, Tao Xu, Jie Luo, Zhuqing Jiang, Wenhao Chen, Hui Chen, Ting Qi, Jian Yang
{"title":"SMR-Portal: an online platform for integrative analysis of GWAS and xQTL data to identify complex trait genes","authors":"Yazhou Guo, Tao Xu, Jie Luo, Zhuqing Jiang, Wenhao Chen, Hui Chen, Ting Qi, Jian Yang","doi":"10.1038/s41592-024-02561-7","DOIUrl":"10.1038/s41592-024-02561-7","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"220-222"},"PeriodicalIF":36.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142770663","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 : 2024-12-02DOI: 10.1038/s41592-024-02543-9
Fan Bu, Yagoub Adam, Ryszard W. Adamiak, Maciej Antczak, Belisa Rebeca H. de Aquino, Nagendar Goud Badepally, Robert T. Batey, Eugene F. Baulin, Pawel Boinski, Michal J. Boniecki, Janusz M. Bujnicki, Kristy A. Carpenter, Jose Chacon, Shi-Jie Chen, Wah Chiu, Pablo Cordero, Naba Krishna Das, Rhiju Das, Wayne K. Dawson, Frank DiMaio, Feng Ding, Anne-Catherine Dock-Bregeon, Nikolay V. Dokholyan, Ron O. Dror, Stanisław Dunin-Horkawicz, Stephan Eismann, Eric Ennifar, Reza Esmaeeli, Masoud Amiri Farsani, Adrian R. Ferré-D’Amaré, Caleb Geniesse, George E. Ghanim, Horacio V. Guzman, Iris V. Hood, Lin Huang, Dharm Skandh Jain, Farhang Jaryani, Lei Jin, Astha Joshi, Masha Karelina, Jeffrey S. Kieft, Wipapat Kladwang, Sebastian Kmiecik, Deepak Koirala, Markus Kollmann, Rachael C. Kretsch, Mateusz Kurciński, Jun Li, Shuang Li, Marcin Magnus, BenoÎt Masquida, S. Naeim Moafinejad, Arup Mondal, Sunandan Mukherjee, Thi Hoang Duong Nguyen, Grigory Nikolaev, Chandran Nithin, Grace Nye, Iswarya P. N. Pandaranadar Jeyeram, Alberto Perez, Phillip Pham, Joseph A. Piccirilli, Smita Priyadarshini Pilla, Radosław Pluta, Simón Poblete, Almudena Ponce-Salvatierra, Mariusz Popenda, Lukasz Popenda, Fabrizio Pucci, Ramya Rangan, Angana Ray, Aiming Ren, Joanna Sarzynska, Congzhou Mike Sha, Filip Stefaniak, Zhaoming Su, Krishna C. Suddala, Marta Szachniuk, Raphael Townshend, Robert J. Trachman III, Jian Wang, Wenkai Wang, Andrew Watkins, Tomasz K. Wirecki, Yi Xiao, Peng Xiong, Yiduo Xiong, Jianyi Yang, Joseph David Yesselman, Jinwei Zhang, Yi Zhang, Zhenzhen Zhang, Yuanzhe Zhou, Tomasz Zok, Dong Zhang, Sicheng Zhang, Adriana Żyła, Eric Westhof, Zhichao Miao
RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson–Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest. The results of the Fifth RNA-Puzzles contest highlights advances in RNA three-dimensional structure prediction and uncovers new insights into RNA folding and structure.
{"title":"RNA-Puzzles Round V: blind predictions of 23 RNA structures","authors":"Fan Bu, Yagoub Adam, Ryszard W. Adamiak, Maciej Antczak, Belisa Rebeca H. de Aquino, Nagendar Goud Badepally, Robert T. Batey, Eugene F. Baulin, Pawel Boinski, Michal J. Boniecki, Janusz M. Bujnicki, Kristy A. Carpenter, Jose Chacon, Shi-Jie Chen, Wah Chiu, Pablo Cordero, Naba Krishna Das, Rhiju Das, Wayne K. Dawson, Frank DiMaio, Feng Ding, Anne-Catherine Dock-Bregeon, Nikolay V. Dokholyan, Ron O. Dror, Stanisław Dunin-Horkawicz, Stephan Eismann, Eric Ennifar, Reza Esmaeeli, Masoud Amiri Farsani, Adrian R. Ferré-D’Amaré, Caleb Geniesse, George E. Ghanim, Horacio V. Guzman, Iris V. Hood, Lin Huang, Dharm Skandh Jain, Farhang Jaryani, Lei Jin, Astha Joshi, Masha Karelina, Jeffrey S. Kieft, Wipapat Kladwang, Sebastian Kmiecik, Deepak Koirala, Markus Kollmann, Rachael C. Kretsch, Mateusz Kurciński, Jun Li, Shuang Li, Marcin Magnus, BenoÎt Masquida, S. Naeim Moafinejad, Arup Mondal, Sunandan Mukherjee, Thi Hoang Duong Nguyen, Grigory Nikolaev, Chandran Nithin, Grace Nye, Iswarya P. N. Pandaranadar Jeyeram, Alberto Perez, Phillip Pham, Joseph A. Piccirilli, Smita Priyadarshini Pilla, Radosław Pluta, Simón Poblete, Almudena Ponce-Salvatierra, Mariusz Popenda, Lukasz Popenda, Fabrizio Pucci, Ramya Rangan, Angana Ray, Aiming Ren, Joanna Sarzynska, Congzhou Mike Sha, Filip Stefaniak, Zhaoming Su, Krishna C. Suddala, Marta Szachniuk, Raphael Townshend, Robert J. Trachman III, Jian Wang, Wenkai Wang, Andrew Watkins, Tomasz K. Wirecki, Yi Xiao, Peng Xiong, Yiduo Xiong, Jianyi Yang, Joseph David Yesselman, Jinwei Zhang, Yi Zhang, Zhenzhen Zhang, Yuanzhe Zhou, Tomasz Zok, Dong Zhang, Sicheng Zhang, Adriana Żyła, Eric Westhof, Zhichao Miao","doi":"10.1038/s41592-024-02543-9","DOIUrl":"10.1038/s41592-024-02543-9","url":null,"abstract":"RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson–Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest. The results of the Fifth RNA-Puzzles contest highlights advances in RNA three-dimensional structure prediction and uncovers new insights into RNA folding and structure.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 2","pages":"399-411"},"PeriodicalIF":36.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02543-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142770662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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