Nature biotechnology最新文献

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Generative AI powered by nucleic acid language model enables one-round evolution of RNA aptamers. 基于核酸语言模型的生成式人工智能实现了RNA适体的一轮进化。

IF 41.7 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Nature biotechnology

Pub Date : 2026-02-06 DOI: 10.1038/s41587-026-03008-4

引用次数: 0

Single-round evolution of RNA aptamers with GRAPE-LM. 用GRAPE-LM进行RNA适体的单轮进化。

IF 41.7 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Nature biotechnology

Pub Date : 2026-02-06 DOI: 10.1038/s41587-026-03007-5

Jun Zhang, Ju Zhang, Shaoxuan Tang, Chuancheng Liu, Yushan Cai, Hao Zeng, Xiangjie Meng, Bei Liu, Yang Zhang, Yu Wang

The directed evolution of biomolecules is an iterative process. Although advancements in language models have expedited protein evolution, effectively evolving RNA remains a challenge. RNA aptamers, selected for their binding properties, provide an ideal system to address this challenge, yet traditional aptamer discovery still relies on labor-intensive, multi-round screening. Here we introduce GRAPE-LM (generator of RNA aptamers powered by activity-guided evolution and language model), a generative artificial intelligence framework designed for the one-round evolution of RNA aptamers. GRAPE-LM integrates a transformer-based conditional autoencoder with nucleic acid language models and is guided by CRISPR-Cas-based aptamer screening data derived from intracellular environments. We validate GRAPE-LM on three disparate targets: the human T cell receptor CD3ε, the receptor-binding domain of the SARS-CoV-2 spike protein and the human oncogenic transcription factor c-Myc (an intracellular disordered protein). GRAPE-LM, informed with only a single round of CRISPR-Cas-based screening, successfully obtains RNA aptamers that outperform those driven from multiple rounds of human selection and optimization.

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引用次数: 0

Pilot phase clinical trial of a wearable, electrochemical aptamer-based patch for continuous drug concentration measurement 一种可穿戴的、基于电化学适配体的连续药物浓度测量贴片的试验阶段临床试验

IF 46.9 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Nature biotechnology

Pub Date : 2026-02-04 DOI: 10.1038/s41587-026-03010-w

Marsilea A. Booth, Murat K. Erdal, Mikel Larson, Emily Birthisel, Mark Friedel, Vinicius R. Gonçales, Ali Alinezhad, Franciele Morawski, Jiezhen Li, Nicholas Hogan, Stanley Teo, Johanna Wordsworth, Sahil Khanna, Jeremy Van Eps, Stuart Boyd, Lucy Dewhirst, Julian Gerson, Dheeraj D’Souza, Wang Yin, Priya Thaivalappil Padmanabhan, Robert H. Batchelor, Ashley Farnkopf, Alastair Hodges, Garry Chambers, Michael Braude, Tod E. Kippin, Carl M. J. Kirkpatrick, J. Justin Gooding, Stephen Pianko, Sophie L. Stocker, Kevin W. Plaxco

引用次数: 0

Accurate plasmid reconstruction from metagenomics data using assembly–alignment graphs and contrastive learning 利用装配比对图和对比学习从宏基因组学数据中精确重建质粒

IF 46.9 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Nature biotechnology

Pub Date : 2026-02-04 DOI: 10.1038/s41587-026-03005-7

Pau Piera Líndez, Lasse Schnell Danielsen, Iva Kovačić, Marc Pielies Avellí, Joseph Nesme, Lars Juhl Jensen, Jakob Nybo Andersen, Søren Johannes Sørensen, Simon Rasmussen

Plasmids are extrachromosomal DNA molecules that enable horizontal gene transfer in bacteria, often conferring advantages such as antibiotic resistance. Despite their importance, plasmids are underrepresented in genomic databases because of challenges in assembling them, caused by mosaicism and microdiversity. Current plasmid assemblers rely on detecting circular paths in single-sample assembly graphs but face limitations because of graph fragmentation, entanglement and low coverage. We introduce PlasMAAG (plasmid and organism metagenomic binning using assembly–alignment graphs), a method to recover plasmids and cellular genomes from metagenomic samples. PlasMAAG complements assembly graph signals across samples by generating an ‘assembly–alignment graph’, which is used alongside common binning features for improved plasmid reconstruction. On synthetic benchmark datasets, PlasMAAG reconstructed 50–121% more near-complete plasmids than competing methods and improved the Matthews correlation coefficient of geNomad contig classification by 28–106%. On hospital sewage samples, PlasMAAG outperformed competing methods, reconstructing 33% more plasmid sequences. PlasMAAG enables the study of organism–plasmid associations and intraplasmid diversity across samples.

质粒是染色体外的DNA分子，能够在细菌中进行水平基因转移，通常具有抗生素抗性等优势。尽管质粒很重要，但它们在基因组数据库中的代表性不足，因为拼接和微多样性导致了组装质粒的挑战。目前的质粒组装器依赖于检测单样本组装图中的圆形路径，但由于图碎片、纠缠和低覆盖率而面临限制。我们介绍了PlasMAAG（质粒和有机体宏基因组结合使用装配比对图），一种从宏基因组样本中恢复质粒和细胞基因组的方法。PlasMAAG通过生成“装配比对图”来补充样品间的装配图信号，该图与改进质粒重建的常见分形特征一起使用。在合成基准数据集上，PlasMAAG重构的接近完整质粒比竞争方法多50-121%，并且将基因组序列分类的Matthews相关系数提高了28-106%。在医院污水样本中，PlasMAAG优于竞争对手的方法，多重建33%的质粒序列。PlasMAAG能够研究生物体-质粒关联和质粒内多样性。

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引用次数: 0

Balancing innovation and integrity: the biomedical research ecosystem at a crossroads. 平衡创新与诚信：十字路口的生物医学研究生态系统。

IF 41.7 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Nature biotechnology

Pub Date : 2026-02-03 DOI: 10.1038/s41587-025-02996-z

Robert G Gourdie

引用次数: 0

AAV-delivered UGA suppressor tRNA for disease-agnostic in vivo gene therapy. aav递送的UGA抑制tRNA用于疾病不可知论的体内基因治疗。

IF 41.7 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Nature biotechnology

Pub Date : 2026-02-02 DOI: 10.1038/s41587-026-02999-4

引用次数: 0

Reprogramming CRISPR-Cas enzymes for customized genome editing. 重新编程CRISPR-Cas酶用于定制基因组编辑。

IF 41.7 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Nature biotechnology

Pub Date : 2026-02-02 DOI: 10.1038/s41587-026-03002-w

引用次数: 0

Customizing CRISPR–Cas PAM specificity with protein language models 利用蛋白语言模型定制CRISPR-Cas PAM特异性

IF 46.9 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Nature biotechnology

Pub Date : 2026-02-02 DOI: 10.1038/s41587-025-02995-0

Stephen Nayfach, Aadyot Bhatnagar, Andrey Novichkov, Nahye Kim, Alexander M. Hoffnagle, Riffat Hussain, Gabriella O. Estevam, Emily Hill, Jeffrey A. Ruffolo, Rachel A. Silverstein, Joseph Gallagher, Benjamin P. Kleinstiver, Alexander J. Meeske, Peter Cameron, Ali Madani

CRISPR–Cas enzymes must recognize a protospacer-adjacent motif (PAM) to edit a genomic site, greatly limiting the range of targetable sequences in a genome. Although engineering strategies to alter PAM specificity exist, they typically require labor-intensive, iterative experimentation. We introduce an evolution-informed deep learning model, Protein2PAM, to efficiently guide the design of Cas protein variants tailored to recognize specific PAMs. Trained on a dataset of over 45,000 CRISPR–Cas PAMs, Protein2PAM rapidly and accurately predicts PAM specificity directly from Cas proteins across type I, II and V CRISPR–Cas systems. Using in silico mutagenesis, the model identifies residues critical for PAM recognition in Cas9 without using structural information. We use Protein2PAM to computationally evolve Nme1Cas9, generating variants with broadened PAM recognition and up to a 50-fold increase in PAM cleavage rates compared to the wild type in vitro. Our machine learning approach allows Cas enzymes to target sequences that were previously inaccessible because of PAM constraints, potentially increasing target flexibility in personalized genome editing.

CRISPR-Cas酶必须识别原间隔邻近基序（protospacer-邻基序，PAM）才能编辑基因组位点，这极大地限制了基因组中可靶向序列的范围。虽然存在改变PAM特异性的工程策略，但它们通常需要大量的劳动和反复的实验。我们引入了一个进化信息深度学习模型，Protein2PAM，以有效地指导Cas蛋白变体的设计，以识别特定的pam。Protein2PAM在超过45,000个CRISPR-Cas PAM数据集上进行了训练，可以快速准确地直接预测I型，II型和V型CRISPR-Cas系统中Cas蛋白的PAM特异性。利用硅诱变技术，该模型在不使用结构信息的情况下识别Cas9中PAM识别的关键残基。我们使用Protein2PAM计算进化Nme1Cas9，产生具有更宽的PAM识别的变体，与体外野生型相比，PAM切割率增加了50倍。我们的机器学习方法允许Cas酶靶向以前由于PAM限制而无法进入的序列，潜在地增加了个性化基因组编辑的目标灵活性。

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引用次数: 0

John Gurdon 1933-2025. 约翰·戈登1933-2025。

IF 41.7 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Nature biotechnology

Pub Date : 2026-01-29 DOI: 10.1038/s41587-026-03015-5

Magdalena Zernicka-Goetz

引用次数: 0

China's innovation in translational medicine: rethinking early-stage clinical development. 中国转化医学的创新：对早期临床发展的反思。

IF 46.9 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Nature biotechnology

Pub Date : 2026-01-27 DOI: 10.1038/s41587-025-02998-x

Lingshi Tan,Ken Song,Bai Lu

引用次数: 0

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