Nature biotechnology最新文献_第9页

Cellular shuttles put mislocalized proteins in their right place 细胞穿梭机将定位错误的蛋白质送回正确位置

IF 33.1 1区生物学

Nature biotechnology Pub Date : 2024-10-14 DOI: 10.1038/s41587-024-02444-4

Iris Marchal

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Pan-KRAS destruction with a single PROTAC 使用单个 PROTAC 销毁泛 KRAS

IF 33.1 1区生物学

Nature biotechnology Pub Date : 2024-10-14 DOI: 10.1038/s41587-024-02445-3

Iris Marchal

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Five questions with Weixin Tang 唐伟新的五个问题

IF 33.1 1区生物学

Nature biotechnology Pub Date : 2024-10-14 DOI: 10.1038/s41587-024-02419-5

Michael Francisco

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Bispecific upstages Keytruda 双特异性超越 Keytruda

IF 33.1 1区生物学

Nature biotechnology Pub Date : 2024-10-14 DOI: 10.1038/s41587-024-02451-5

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From Bench to Podcast: Chloe Fishman 从工作台到播客克洛伊-菲什曼

IF 33.1 1区生物学

Nature biotechnology Pub Date : 2024-10-14 DOI: 10.1038/s41587-024-02433-7

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A strategy to load, rethread and read protein sequences through a nanopore 通过纳米孔加载、重读和读取蛋白质序列的策略

IF 33.1 1区生物学

Nature biotechnology Pub Date : 2024-10-14 DOI: 10.1038/s41587-024-02443-5

Iris Marchal

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T cells T 细胞

IF 33.1 1区生物学

Nature biotechnology Pub Date : 2024-10-14 DOI: 10.1038/s41587-024-02415-9

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Niemann–Pick double win 尼曼-皮克双赢

IF 33.1 1区生物学

Nature biotechnology Pub Date : 2024-10-14 DOI: 10.1038/s41587-024-02450-6

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A community effort to optimize sequence-based deep learning models of gene regulation 优化基于序列的基因调控深度学习模型的社区努力

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2024-10-11 DOI: 10.1038/s41587-024-02414-w

Abdul Muntakim Rafi, Daria Nogina, Dmitry Penzar, Dohoon Lee, Danyeong Lee, Nayeon Kim, Sangyeup Kim, Dohyeon Kim, Yeojin Shin, Il-Youp Kwak, Georgy Meshcheryakov, Andrey Lando, Arsenii Zinkevich, Byeong-Chan Kim, Juhyun Lee, Taein Kang, Eeshit Dhaval Vaishnav, Payman Yadollahpour, Sun Kim, Jake Albrecht, Aviv Regev, Wuming Gong, Ivan V. Kulakovskiy, Pablo Meyer, Carl G. de Boer

{"title":"A community effort to optimize sequence-based deep learning models of gene regulation","authors":"Abdul Muntakim Rafi, Daria Nogina, Dmitry Penzar, Dohoon Lee, Danyeong Lee, Nayeon Kim, Sangyeup Kim, Dohyeon Kim, Yeojin Shin, Il-Youp Kwak, Georgy Meshcheryakov, Andrey Lando, Arsenii Zinkevich, Byeong-Chan Kim, Juhyun Lee, Taein Kang, Eeshit Dhaval Vaishnav, Payman Yadollahpour, Sun Kim, Jake Albrecht, Aviv Regev, Wuming Gong, Ivan V. Kulakovskiy, Pablo Meyer, Carl G. de Boer","doi":"10.1038/s41587-024-02414-w","DOIUrl":"https://doi.org/10.1038/s41587-024-02414-w","url":null,"abstract":"A systematic evaluation of how model architectures and training strategies impact genomics model performance is needed. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. All top-performing models used neural networks but diverged in architectures and training strategies. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide models into modular building blocks. We tested all possible combinations for the top three models, further improving their performance. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets, demonstrating the progress that can be driven by gold-standard genomics datasets.","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"59 1","pages":""},"PeriodicalIF":46.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142404936","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 comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy 用于发现新抗原的综合蛋白质基因组管道，推动个性化癌症免疫疗法的发展

IF 46.9 1区生物学

Nature biotechnology Pub Date : 2024-10-11 DOI: 10.1038/s41587-024-02420-y

Florian Huber, Marion Arnaud, Brian J. Stevenson, Justine Michaux, Fabrizio Benedetti, Jonathan Thevenet, Sara Bobisse, Johanna Chiffelle, Talita Gehert, Markus Müller, HuiSong Pak, Anne I. Krämer, Emma Ricart Altimiras, Julien Racle, Marie Taillandier-Coindard, Katja Muehlethaler, Aymeric Auger, Damien Saugy, Baptiste Murgues, Abdelkader Benyagoub, David Gfeller, Denarda Dangaj Laniti, Lana Kandalaft, Blanca Navarro Rodrigo, Hasna Bouchaab, Stephanie Tissot, George Coukos, Alexandre Harari, Michal Bassani-Sternberg

{"title":"A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy","authors":"Florian Huber, Marion Arnaud, Brian J. Stevenson, Justine Michaux, Fabrizio Benedetti, Jonathan Thevenet, Sara Bobisse, Johanna Chiffelle, Talita Gehert, Markus Müller, HuiSong Pak, Anne I. Krämer, Emma Ricart Altimiras, Julien Racle, Marie Taillandier-Coindard, Katja Muehlethaler, Aymeric Auger, Damien Saugy, Baptiste Murgues, Abdelkader Benyagoub, David Gfeller, Denarda Dangaj Laniti, Lana Kandalaft, Blanca Navarro Rodrigo, Hasna Bouchaab, Stephanie Tissot, George Coukos, Alexandre Harari, Michal Bassani-Sternberg","doi":"10.1038/s41587-024-02420-y","DOIUrl":"https://doi.org/10.1038/s41587-024-02420-y","url":null,"abstract":"The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc’s multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.","PeriodicalId":19084,"journal":{"name":"Nature biotechnology","volume":"64 1","pages":""},"PeriodicalIF":46.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142404934","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