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Proteome-wide C-degron activity profiling connects conditional regulation of the CTLH E3 ligase complex to ribosome biogenesis 蛋白质组C-degron活性分析将CTLH E3连接酶复合物的条件调节与核糖体生物发生联系起来
IF 16 1区 生物学
Molecular Cell Pub Date : 2026-10-20 DOI: 10.1016/j.molcel.2026.09.036
Drew W. Grant,Shengjiang Tan,Dylan E. Ramage,Mamie Z. Li,Ying Di,Iva A. Tchasovnikarova,Michael P. Weekes,Stephen J. Elledge,Alan J. Warren,Richard T. Timms
{"title":"Proteome-wide C-degron activity profiling connects conditional regulation of the CTLH E3 ligase complex to ribosome biogenesis","authors":"Drew W. Grant,Shengjiang Tan,Dylan E. Ramage,Mamie Z. Li,Ying Di,Iva A. Tchasovnikarova,Michael P. Weekes,Stephen J. Elledge,Alan J. Warren,Richard T. Timms","doi":"10.1016/j.molcel.2026.09.036","DOIUrl":"https://doi.org/10.1016/j.molcel.2026.09.036","url":null,"abstract":"Many E3 ubiquitin ligases recognize degrons located at protein termini, but the paucity of bona fide substrates of N-degron and C-degron pathways hampers our understanding of their physiological significance. Devising an expression screening strategy to assess the effect of C-terminal “capping” on the stability of thousands of human proteins, we systematically identify substrates harboring C-degrons. Interrogating one leading candidate, ZMYND19, we characterize a C-degron pathway governed via the C-terminal to LisH (CTLH) E3 ligase complex subunit Muskelin. Cell-to-cell variability in ZMYND19 stability uncovered conditional regulation, with CTLH-mediated degradation impaired by TNF-α stimulation but enhanced by mTOR inhibition. Parallel genetic and proteomic screens identified angio-associated migratory protein (AAMP) and apoptosis-enhancing nuclease (AEN) as additional substrates of the CTLHMuskelin C-degron pathway, leading us to define an essential role for AAMP in ribosome maturation through chaperone activity toward uL16. Altogether, these data define a C-degron pathway connecting conditional regulation of the CTLH E3 ligase complex to ribosome biogenesis.","PeriodicalId":18950,"journal":{"name":"Molecular Cell","volume":"30 1","pages":""},"PeriodicalIF":16.0,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221456","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
Regulatable in vivo gene expression via adaptamers 可调节的体内基因表达通过适应子
IF 16 1区 生物学
Molecular Cell Pub Date : 2026-10-20 DOI: 10.1016/j.molcel.2026.09.037
Jack Bryant,Laura Herron,Yesh Doctor,Finn McRae,Jocelyn Wang,Theodore Hirsch,Andrew Portell,Satheesh Kumar,Prashant Mali
{"title":"Regulatable in vivo gene expression via adaptamers","authors":"Jack Bryant,Laura Herron,Yesh Doctor,Finn McRae,Jocelyn Wang,Theodore Hirsch,Andrew Portell,Satheesh Kumar,Prashant Mali","doi":"10.1016/j.molcel.2026.09.037","DOIUrl":"https://doi.org/10.1016/j.molcel.2026.09.037","url":null,"abstract":"Precise, reversible control of transgene expression is essential for safe and durable gene therapy, yet current inducible systems remain difficult to translate in vivo due to large size, limited induction duration, and dependence on immunogenic regulators. Here, we present the adaptamer (adenosine deaminases acting on RNA [ADAR]-modulatable aptamer), a compact (<120 base pair) RNA switch that couples FDA-approved small-molecule-responsive aptamers with endogenous ADAR-mediated RNA editing to regulate expression. Ligand binding stabilizes a double-stranded RNA structure that recruits ADAR to convert a stop codon into a sense codon, restoring downstream protein translation. This cleavage-free, post-transcriptional mechanism enables precise, small-molecule-dependent modulation without exogenous protein machinery. We demonstrate that adaptamers are highly functional across multiple cell lines, including human T cells. In mice, adeno-associated virus (AAV)-delivered adaptamer-controlled FGF21 expression induced metabolic remodeling, reversing obesity and hepatic steatosis. This minimal, programmable system offers a clinically compatible approach for tunable genetic medicines and safe deployment of pleiotropic and dose-limited proteins.","PeriodicalId":18950,"journal":{"name":"Molecular Cell","volume":"81 1","pages":""},"PeriodicalIF":16.0,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221459","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
Learning how single cells respond to perturbations 学习单个细胞对扰动的反应
IF 16 1区 生物学
Molecular Cell Pub Date : 2026-10-15 DOI: 10.1016/j.molcel.2026.09.033
Ekin Deniz Aksu,Bonnie Berger
{"title":"Learning how single cells respond to perturbations","authors":"Ekin Deniz Aksu,Bonnie Berger","doi":"10.1016/j.molcel.2026.09.033","DOIUrl":"https://doi.org/10.1016/j.molcel.2026.09.033","url":null,"abstract":"In a recent issue of Cell, Adduri et al.1 introduce State, a machine learning model that generalizes across different cellular contexts and predicts how single-cell transcriptomes respond to genetic or chemical perturbations.","PeriodicalId":18950,"journal":{"name":"Molecular Cell","volume":"13 1","pages":""},"PeriodicalIF":16.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149173063","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
Hidden in plain sight: dsRNAscan shines light on the human dsRNAome 隐藏在显而易见的地方：dsRNAscan可以照亮人类的dsRNAome
IF 16 1区 生物学
Molecular Cell Pub Date : 2026-10-15 DOI: 10.1016/j.molcel.2026.09.029
Meera K. Singh,Hachung Chung
{"title":"Hidden in plain sight: dsRNAscan shines light on the human dsRNAome","authors":"Meera K. Singh,Hachung Chung","doi":"10.1016/j.molcel.2026.09.029","DOIUrl":"https://doi.org/10.1016/j.molcel.2026.09.029","url":null,"abstract":"In this issue of Molecular Cell, Andrews and Bass1 introduce dsRNAscan: a computational method uncovering putative dsRNA-forming sequences in the human genome hidden from traditional dsRNA-identifying methods. Using this tool, they build the Human dsRNAome Resource, a database where users can analyze the human dsRNAome.","PeriodicalId":18950,"journal":{"name":"Molecular Cell","volume":"53 1","pages":""},"PeriodicalIF":16.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149173064","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
Emerging frontiers for AI in single-cell genomics 单细胞基因组学中人工智能的新兴领域
IF 16 1区 生物学
Molecular Cell Pub Date : 2026-10-15 DOI: 10.1016/j.molcel.2026.09.014
Sneha Mitra,Jiaxin Li,Christina S. Leslie
{"title":"Emerging frontiers for AI in single-cell genomics","authors":"Sneha Mitra,Jiaxin Li,Christina S. Leslie","doi":"10.1016/j.molcel.2026.09.014","DOIUrl":"https://doi.org/10.1016/j.molcel.2026.09.014","url":null,"abstract":"We are just over a decade into the single-cell revolution in biology, which has been coupled to the growing use of artificial intelligence (AI) in single-cell genomics. Most early single-cell RNA embedding models and subsequent “foundation models”—representational models pretrained on large single-cell datasets—did not gain wide traction among experimental biologists. However, as massive single-cell RNA-sequencing (scRNA-seq) atlas efforts ramp up, there is a clear rationale for exploiting AI models to interpret new datasets in the context of vast existing data resources. Meanwhile, the advent of single-cell epigenetic/multiomic assays and Perturb-seq technologies presents new opportunities for AI modeling. Here, we review AI embedding and foundation models for scRNA-seq and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq), extensive machine-learning efforts to exploit single-cell multiome in regulatory genomics, and AI strategies to learn “virtual cell” models from Perturb-seq, ending with a description of current limitations of single-cell AI models and an outlook for future advances.","PeriodicalId":18950,"journal":{"name":"Molecular Cell","volume":"13 1","pages":""},"PeriodicalIF":16.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149173121","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
dsRNAscan maps human dsRNAome, revealing conservation, intermolecular dsRNA, and correlates of ADAR dependency. dsRNAscan可以绘制人类dsRNAome，揭示dsRNA的保守性、分子间性和ADAR依赖性。
IF 16 1区 生物学
Molecular Cell Pub Date : 2026-10-15 Epub Date: 2026-08-28 DOI: 10.1016/j.molcel.2026.08.001
Ryan J Andrews, Brenda L Bass
{"title":"dsRNAscan maps human dsRNAome, revealing conservation, intermolecular dsRNA, and correlates of ADAR dependency.","authors":"Ryan J Andrews, Brenda L Bass","doi":"10.1016/j.molcel.2026.08.001","DOIUrl":"10.1016/j.molcel.2026.08.001","url":null,"abstract":"<p><p>The human transcriptome contains millions of A-to-I editing sites arising from an unclear number of poorly characterized dsRNAs. Editing sites reveal the presence of dsRNA, but this method is limited by transcription levels, read depth, and ADAR expression and cannot identify unedited dsRNA. To address these limitations, we developed dsRNAscan. Applying dsRNAscan to the human genome predicted 5 million dsRNAs, mostly in repetitive and intergenic regions. Machine learning models trained on A-to-I editing and RNA structure-probing data identified ∼2.4 million high-confidence predictions, which were enriched at dsRNA-binding protein binding sites. Additionally, we predicted hundreds of dsRNAs conserved across vertebrates and observed thousands of editing-enriched regions suspected to arise from intermolecular dsRNAs formed with sense-antisense transcripts. Quantifying expression of intramolecular and intermolecular dsRNAs accessible to cytoplasmic immune sensors revealed that their ratio correlated with ADAR dependency across cancer cell lines. The human dsRNAome is available as a resource at https://dsrna.chpc.utah.edu/.</p>","PeriodicalId":18950,"journal":{"name":"Molecular Cell","volume":" ","pages":"4200-4218.e4"},"PeriodicalIF":16.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576434/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850824","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}
引用次数: 0
Programming biological behavior with AI-guided de novo protein design 用人工智能引导的从头蛋白质设计编程生物行为
IF 16 1区 生物学
Molecular Cell Pub Date : 2026-10-15 DOI: 10.1016/j.molcel.2026.08.027
Philipp Huettemann,Tanja Kortemme
{"title":"Programming biological behavior with AI-guided de novo protein design","authors":"Philipp Huettemann,Tanja Kortemme","doi":"10.1016/j.molcel.2026.08.027","DOIUrl":"https://doi.org/10.1016/j.molcel.2026.08.027","url":null,"abstract":"Driven by advances in artificial intelligence, de novo protein design can now generate diverse molecular functions from the ground up, achieving remarkable success in engineering binders against many macromolecular targets. Here, we provide a perspective on how de novo design can progress beyond bimolecular interactions toward the bottom-up construction of protein reaction networks. We highlight how programmable, modular, and controllable de novo components can drive complex, emergent biological behaviors. Overcoming current challenges—including designable protein dynamics, multi-step functionalities, and non-equilibrium systems—is essential for this transition. With continued foundational method improvements, the field is poised to pioneer precise protein perturbations for systems-level insight and to engineer emergent cellular behaviors like signal amplification, decision-making, and error correction de novo to fundamentally advance synthetic biology, biotechnology, and medicine.","PeriodicalId":18950,"journal":{"name":"Molecular Cell","volume":"100 1","pages":""},"PeriodicalIF":16.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149173057","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
Structural compatibility is not enough: Current challenges in protein-protein interaction prediction 结构相容性是不够的：蛋白质-蛋白质相互作用预测的当前挑战
IF 16 1区 生物学
Molecular Cell Pub Date : 2026-10-15 DOI: 10.1016/j.molcel.2026.08.030
Arne Elofsson
{"title":"Structural compatibility is not enough: Current challenges in protein-protein interaction prediction","authors":"Arne Elofsson","doi":"10.1016/j.molcel.2026.08.030","DOIUrl":"https://doi.org/10.1016/j.molcel.2026.08.030","url":null,"abstract":"Since the introduction of AlphaFold2, AI-driven methods have greatly advanced protein structure and complex prediction. However, the performance varies strongly across interaction classes. The performance drops when dynamics are important and when interactions lack a strong co-evolutionary history. We propose to organize these limitations into distinct regions: interactions that are largely tractable by current methods; interactions in which structural compatibility is ambiguous due to competition, paralogy, or stoichiometry; interactions that lack sufficient co-evolutionary signal; and interactions whose biological relevance depends on cellular context, dynamics, or disorder. These limitations define distinct categories of interaction prediction challenges, each requiring different computational and experimental approaches. Future progress will require methods that rely less on multiple sequence alignments, better distinguish interaction strength and specificity, and move beyond static structure prediction toward modeling interaction landscapes in their cellular contexts.","PeriodicalId":18950,"journal":{"name":"Molecular Cell","volume":"11 1","pages":""},"PeriodicalIF":16.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149173061","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
Looking to the future of AI and molecular biology 展望人工智能和分子生物学的未来
IF 16 1区 生物学
Molecular Cell Pub Date : 2026-10-15 DOI: 10.1016/j.molcel.2026.09.031
The Molecular Cell editorial team
{"title":"Looking to the future of AI and molecular biology","authors":"The Molecular Cell editorial team","doi":"10.1016/j.molcel.2026.09.031","DOIUrl":"https://doi.org/10.1016/j.molcel.2026.09.031","url":null,"abstract":"","PeriodicalId":18950,"journal":{"name":"Molecular Cell","volume":"10 1","pages":""},"PeriodicalIF":16.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149173066","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 prediction: The evolving role of artificial intelligence in genome editing 超越预测：人工智能在基因组编辑中的演变作用
IF 16 1区 生物学
Molecular Cell Pub Date : 2026-10-15 DOI: 10.1016/j.molcel.2026.08.025
Soochul Shin,Heesoo Uhm,Yong-Woo Kim,Hyeon Woo Im,Sangsu Bae
{"title":"Beyond prediction: The evolving role of artificial intelligence in genome editing","authors":"Soochul Shin,Heesoo Uhm,Yong-Woo Kim,Hyeon Woo Im,Sangsu Bae","doi":"10.1016/j.molcel.2026.08.025","DOIUrl":"https://doi.org/10.1016/j.molcel.2026.08.025","url":null,"abstract":"Since the discovery of the clustered regularly interspaced short palindromic repeat (CRISPR)-associated (Cas) system, genome-editing technologies have advanced rapidly, and the field has expanded substantially. In parallel, artificial intelligence (AI) has progressed at an unprecedented pace and is transforming genetic engineering. Early AI applications primarily addressed predictive tasks, including guide RNA (gRNA) activity, off-target effects, and editing outcomes for CRISPR-Cas nucleases, base editors, and prime editors. Recently, transformer-based protein language models (PLMs) and generative AI have expanded the role of AI beyond prediction to direct molecular design. Thus, AI-driven frameworks can now evolve genome-editing proteins, design molecular architectures, and generate de novo genome-editing enzymes. This review discusses how AI is reshaping genome engineering, from gRNA activity prediction to the design of next-generation editors.","PeriodicalId":18950,"journal":{"name":"Molecular Cell","volume":"30 1","pages":""},"PeriodicalIF":16.0,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149173060","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
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