Nature biotechnology最新文献

Anatomy of the 23andMe fall and implications for consumer genomics 剖析23andMe的衰落及其对消费者基因组学的影响

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2025-05-29 DOI: 10.1038/s41587-025-02683-z

Yaniv Erlich, Dina Zielinski

引用次数: 0

Self-supervised learning from small-molecule mass spectrometry data 从小分子质谱数据中进行自我监督学习

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2025-05-23 DOI: 10.1038/s41587-025-02677-x

Wout Bittremieux, William Stafford Noble

引用次数: 0

Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS 使用DreaMS对数百万串联质谱的分子表征进行自我监督学习

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2025-05-23 DOI: 10.1038/s41587-025-02663-3

Roman Bushuiev, Anton Bushuiev, Raman Samusevich, Corinna Brungs, Josef Sivic, Tomáš Pluskal

{"title":"Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS","authors":"Roman Bushuiev, Anton Bushuiev, Raman Samusevich, Corinna Brungs, Josef Sivic, Tomáš Pluskal","doi":"10.1038/s41587-025-02663-3","DOIUrl":"https://doi.org/10.1038/s41587-025-02663-3","url":null,"abstract":"Characterizing biological and environmental samples at a molecular level primarily uses tandem mass spectroscopy (MS/MS), yet the interpretation of tandem mass spectra from untargeted metabolomics experiments remains a challenge. Existing computational methods for predictions from mass spectra rely on limited spectral libraries and on hard-coded human expertise. Here we introduce a transformer-based neural network pre-trained in a self-supervised way on millions of unannotated tandem mass spectra from our GNPS Experimental Mass Spectra (GeMS) dataset mined from the MassIVE GNPS repository. We show that pre-training our model to predict masked spectral peaks and chromatographic retention orders leads to the emergence of rich representations of molecular structures, which we named Deep Representations Empowering the Annotation of Mass Spectra (DreaMS). Further fine-tuning the neural network yields state-of-the-art performance across a variety of tasks. We make our new dataset and model available to the community and release the DreaMS Atlas—a molecular network of 201 million MS/MS spectra constructed using DreaMS annotations.","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"56 1","pages":""},"PeriodicalIF":46.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122573","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}

引用次数: 0

Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER 基于深度学习的D-I-TASSER蛋白单域和多域结构预测

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2025-05-23 DOI: 10.1038/s41587-025-02654-4

Wei Zheng, Qiqige Wuyun, Yang Li, Quancheng Liu, Xiaogen Zhou, Chunxiang Peng, Yiheng Zhu, Lydia Freddolino, Yang Zhang

{"title":"Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER","authors":"Wei Zheng, Qiqige Wuyun, Yang Li, Quancheng Liu, Xiaogen Zhou, Chunxiang Peng, Yiheng Zhu, Lydia Freddolino, Yang Zhang","doi":"10.1038/s41587-025-02654-4","DOIUrl":"https://doi.org/10.1038/s41587-025-02654-4","url":null,"abstract":"The dominant success of deep learning techniques on protein structure prediction has challenged the necessity and usefulness of traditional force field-based folding simulations. We proposed a hybrid approach, deep-learning-based iterative threading assembly refinement (D-I-TASSER), which constructs atomic-level protein structural models by integrating multisource deep learning potentials with iterative threading fragment assembly simulations. D-I-TASSER introduces a domain splitting and assembly protocol for the automated modeling of large multidomain protein structures. Benchmark tests and the most recent critical assessment of protein structure prediction, 15 experiments demonstrate that D-I-TASSER outperforms AlphaFold2 and AlphaFold3 on both single-domain and multidomain proteins. Large-scale folding experiments further show that D-I-TASSER could fold 81% of protein domains and 73% of full-chain sequences in the human proteome with results highly complementary to recently released models by AlphaFold2. These results highlight a new avenue to integrate deep learning with classical physics-based folding simulations for high-accuracy protein structure and function predictions that are usable in genome-wide applications.","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"1 1","pages":""},"PeriodicalIF":46.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122574","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}

引用次数: 0

A multi-kingdom genetic barcoding system for precise clone isolation 用于精确克隆分离的多界遗传条形码系统

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2025-05-21 DOI: 10.1038/s41587-025-02649-1

Soh Ishiguro, Kana Ishida, Rina C. Sakata, Minori Ichiraku, Ren Takimoto, Rina Yogo, Yusuke Kijima, Hideto Mori, Mamoru Tanaka, Samuel King, Shoko Tarumoto, Taro Tsujimura, Omar Bashth, Nanami Masuyama, Arman Adel, Hiromi Toyoshima, Motoaki Seki, Ju Hee Oh, Anne-Sophie Archambault, Keiji Nishida, Akihiko Kondo, Satoru Kuhara, Hiroyuki Aburatani, Ramon I. Klein Geltink, Takuya Yamamoto, Nika Shakiba, Yasuhiro Takashima, Nozomu Yachie

{"title":"A multi-kingdom genetic barcoding system for precise clone isolation","authors":"Soh Ishiguro, Kana Ishida, Rina C. Sakata, Minori Ichiraku, Ren Takimoto, Rina Yogo, Yusuke Kijima, Hideto Mori, Mamoru Tanaka, Samuel King, Shoko Tarumoto, Taro Tsujimura, Omar Bashth, Nanami Masuyama, Arman Adel, Hiromi Toyoshima, Motoaki Seki, Ju Hee Oh, Anne-Sophie Archambault, Keiji Nishida, Akihiko Kondo, Satoru Kuhara, Hiroyuki Aburatani, Ramon I. Klein Geltink, Takuya Yamamoto, Nika Shakiba, Yasuhiro Takashima, Nozomu Yachie","doi":"10.1038/s41587-025-02649-1","DOIUrl":"https://doi.org/10.1038/s41587-025-02649-1","url":null,"abstract":"Cell-tagging strategies with DNA barcodes have enabled the analysis of clone size dynamics and clone-restricted transcriptomic landscapes in heterogeneous populations. However, isolating a target clone that displays a specific phenotype from a complex population remains challenging. Here we present a multi-kingdom genetic barcoding system, CloneSelect, which enables a target cell clone to be triggered to express a reporter gene for isolation through barcode-specific CRISPR base editing. In CloneSelect, cells are first stably tagged with DNA barcodes and propagated so that their subpopulation can be subjected to a given experiment. A clone that shows a phenotype or genotype of interest at a given time can then be isolated from the initial or subsequent cell pools stored during the experiment using CRISPR base editing. CloneSelect is scalable and compatible with single-cell RNA sequencing. We demonstrate the versatility of CloneSelect in human embryonic kidney 293T cells, mouse embryonic stem cells, human pluripotent stem cells, yeast cells and bacterial cells.","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"45 1","pages":""},"PeriodicalIF":46.9,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103958","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}

引用次数: 0

Beyond cell atlases: spatial biology reveals mechanisms behind disease 超越细胞图谱：空间生物学揭示疾病背后的机制

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2025-05-21 DOI: 10.1038/s41587-025-02699-5

引用次数: 0

Retargeted retrotransposons insert multi-kilobase cargo at new sites 重靶向逆转录转座子在新的位点插入数千个碱基的货物

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2025-05-16 DOI: 10.1038/s41587-025-02682-0

Iris Marchal

{"title":"Retargeted retrotransposons insert multi-kilobase cargo at new sites","authors":"Iris Marchal","doi":"10.1038/s41587-025-02682-0","DOIUrl":"https://doi.org/10.1038/s41587-025-02682-0","url":null,"abstract":"Site-specific retroelements, such as R2 retrotransposons, have potential as programmable genome-editing systems, but so far have only been used for targeted insertion at genomic safe-harbor loci. In a paper now published in Nature, Fell et al. profile the evolution of site-specific retrotransposon families to gain insight into target site preferences, which they then used to engineer reprogramming.By surveying 4,464 animal assemblies derived from Genbank, the authors discovered several new site-specific retrotransposon families, including families with multiple integration preferences and with differing 5′ and 3′ site predictions, which might indicate retargeting mechanisms. They zoned in on a retrotransposon from the zebra finch (Taeniopygia guttata), called R2Tg, for biochemical profiling and characterized its activity in mammalian cells, which showed that R2Tg can insert payloads by reverse transcription of RNA and nicking of genomic DNA. Notably, the authors found that R2Tg can be retargeted by engineering the payload, which allowed scarless insertion of the payload at new genomic sites. The activity of the reprogrammed R2Tg was enhanced by fusing it with CRISPR–Cas9 nickase.","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"16 1","pages":""},"PeriodicalIF":46.9,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066955","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}

引用次数: 0

Fruit waste for catwalk fashion 水果废料用于t台时装

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2025-05-16 DOI: 10.1038/s41587-025-02679-9

引用次数: 0

FDA pushes to replace animal testing FDA推动替代动物试验

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2025-05-16 DOI: 10.1038/s41587-025-02690-0

引用次数: 0

Biotech news from around the world 来自世界各地的生物技术新闻

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2025-05-16 DOI: 10.1038/s41587-025-02680-2

引用次数: 0