Molecular Systems Biology最新文献

Validation and analysis of 12,000 AI-driven CAR-T designs in the Bits to Binders competition. 在Bits to binder竞赛中验证和分析12,000个ai驱动的CAR-T设计。

IF 6.7 1区生物学

Molecular Systems Biology Pub Date : 2026-10-08 DOI: 10.1038/s44320-026-00246-1

Clayton W Kosonocky, Alex M Abel, Aaron L Feller, Amanda E Cifuentes Rieffer, Phillip R Woolley, Jakub Lála, Daryl R Barth, Tynan Gardner, Stephen C Ekker, Andrew D Ellington, Wesley A Wierson, Edward M Marcotte

{"title":"Validation and analysis of 12,000 AI-driven CAR-T designs in the Bits to Binders competition.","authors":"Clayton W Kosonocky, Alex M Abel, Aaron L Feller, Amanda E Cifuentes Rieffer, Phillip R Woolley, Jakub Lála, Daryl R Barth, Tynan Gardner, Stephen C Ekker, Andrew D Ellington, Wesley A Wierson, Edward M Marcotte","doi":"10.1038/s44320-026-00246-1","DOIUrl":"10.1038/s44320-026-00246-1","url":null,"abstract":"Artificial intelligence (AI) methods for proteins have advanced rapidly, improving structure prediction and design, particularly for de novo binders. However, most evaluations emphasize binding affinity rather than higher-order biological function. We present Bits to Binders, a global competition evaluating de novo binder design in the context of chimeric antigen receptor (CAR) T cells. Teams from 42 countries submitted 12,000 designs of 80-amino acid de novo minibinders targeting human CD20 as CAR binding domains. Designs were screened by pooled CAR-T proliferation, identifying 707 designs exhibiting significant CD20-specific enrichment, with team hit rates from 0.6% to 38.4%. Top-performing candidates were validated as individual constructs, measuring CD20-specific proliferation, expansion, cytokine production, and targeted cell lysis. We identified common design methodologies and factors correlated with DNA synthesis, expression, and target-specific T cell activation which nearly double the success rates when applied as a retrospective filter. We release this dataset as an open resource, with practical recommendations to support more effective AI-driven binder design.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149137153","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

Crowdsourcing functional cell therapy binders from generative artificial intelligence. 众包生成人工智能的功能性细胞治疗粘合剂。

IF 6.7 1区生物学

Molecular Systems Biology Pub Date : 2026-10-08 DOI: 10.1038/s44320-026-00249-y

Abdul Vehab Dozic, Caleb A Lareau

引用次数: 0

Trade-off between flux efficiency and metabolic control shapes enzyme allocation. 通量效率和代谢控制之间的权衡决定了酶的分配。

IF 6.7 1区生物学

Molecular Systems Biology Pub Date : 2026-10-04 DOI: 10.1038/s44320-026-00250-5

Kai Sun, Weiyan Zheng, Wenchao Fan, Chuyun Ding, Dan Huang, Ziwei Dai

{"title":"Trade-off between flux efficiency and metabolic control shapes enzyme allocation.","authors":"Kai Sun, Weiyan Zheng, Wenchao Fan, Chuyun Ding, Dan Huang, Ziwei Dai","doi":"10.1038/s44320-026-00250-5","DOIUrl":"https://doi.org/10.1038/s44320-026-00250-5","url":null,"abstract":"Efficient use of limited cellular resources is fundamental to metabolism. Although flux optimization is widely recognized as a central objective of metabolic networks, how flux efficiency influences the allocation of the metabolic proteome remains unclear and lacks direct validation. Here, we derive a simple analytical relationship linking the equilibrium constant (K) and the catalytic-abundance quotient (CAQ) of reactions within a pathway that defines the condition for maximal efficiency. By integrating reaction thermodynamics, enzyme kinetics, transcriptomic, and proteomic data, we compared enzyme allocation in glycolysis and the TCA cycle with this K-CAQ relationship across evolutionarily distant species, and showed that deviation from it can be explained by a trade-off between maximizing flux efficiency and concentrating flux control. Moreover, the drive to optimize glycolytic efficiency is strengthened under oncogenic signaling and a limited cellular budget for glycolytic enzymes. These findings establish a principle of Pareto optimality governing enzyme allocation in metabolic pathways and reveal key determinants of efficiency optimality in glycolysis.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149112463","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

Thermo-flux: generation and analysis of thermodynamic-stoichiometric metabolic network models. 热通量：热力学化学计量代谢网络模型的生成与分析。

IF 6.7 1区生物学

Molecular Systems Biology Pub Date : 2026-10-01 Epub Date: 2026-07-03 DOI: 10.1038/s44320-026-00227-4

Edward N Smith, Nathan Fargier, José Losa, Matthias Heinemann

{"title":"Thermo-flux: generation and analysis of thermodynamic-stoichiometric metabolic network models.","authors":"Edward N Smith, Nathan Fargier, José Losa, Matthias Heinemann","doi":"10.1038/s44320-026-00227-4","DOIUrl":"10.1038/s44320-026-00227-4","url":null,"abstract":"Metabolic modeling with stoichiometric models and flux balance analysis (FBA) has greatly advanced our understanding of metabolism. However, valid FBA predictions require mechanistically correct constraints. Thermodynamic constraints can increase the mechanistic foundations of stoichiometric models and reduce the solution space, but incorporating them has so far required cumbersome manual effort. To circumvent manual curation, we introduce 'Thermo-Flux', a semi-automated Python package that converts stoichiometric models into comprehensive thermodynamic-stoichiometric models. 'Thermo-Flux' enables (i) automated mass and charge balancing while considering physical and biochemical parameters, (ii) definition of transporter variants and Gibbs energies for transport processes, (iii) handling of metabolites with unknown structures or Gibbs energies, and (iv) integration of recent methods for determining Gibbs energies and their uncertainties. To guide users, we provide detailed instructions on how to use 'Thermo-Flux' and include background information to facilitate appropriate modeling assumptions. We highlight the applicability of 'Thermo-Flux' by converting 87 stoichiometric models from the BiGG database and demonstrate improved flux predictions for a genome-scale yeast model (iMM904). We expect 'Thermo-Flux' to support fundamental and applied metabolic research.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1650-1664"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148382446","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

Macrophage response specificity to ligand mixtures is improved by signaling pathway antagonism. 通过信号通路拮抗提高巨噬细胞对配体混合物的反应特异性。

IF 6.7 1区生物学

Molecular Systems Biology Pub Date : 2026-10-01 Epub Date: 2026-07-29 DOI: 10.1038/s44320-026-00230-9

Xiaolu Guo, Supriya Sen, Julian Gonzalez, Alexander Hoffmann

{"title":"Macrophage response specificity to ligand mixtures is improved by signaling pathway antagonism.","authors":"Xiaolu Guo, Supriya Sen, Julian Gonzalez, Alexander Hoffmann","doi":"10.1038/s44320-026-00230-9","DOIUrl":"10.1038/s44320-026-00230-9","url":null,"abstract":"Macrophages function as immune sentinels that distinguish diverse threats and mount appropriate responses. This stimulus-response specificity (SRS) is partly encoded in the dynamics of the NFκB transcription factor. While most studies examine single ligands, physiological exposures involve complex multi-ligand mixtures. Using a mathematical model that captures heterogeneous single-cell NFκB responses, we generated simulation datasets for mixtures of up to five ligands and validated key predictions with live-cell microscopy. Following iterative refinement of the model, we quantified SRS with Wasserstein distance and machine learning classification and found that NFκB temporal coding can partially convey the presence of specific ligands within mixtures. By generating simulation datasets across all ligand pairs at doses spanning the full responsiveness range, we found several cases of synergy and antagonism between stimuli. Antagonism was for example the result of a limited supply of stimulus-cofactor CD14 or endosomal transport capacity. Synergy depended on ultra-sensitive IKK activation in cells with low receptor expression. While synergy does not enhance SRS, antagonism between TLR9 and TLR3 signaling pathways due to endosomal transport competition may enhance the distinguishability of CpG-pIC from ligand mixtures. These studies therefore identified antagonism mechanisms in signaling pathways as key to maintaining immune specificity under complex conditions.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1578-1604"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148620783","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

qMAP decodes RNA fragmentation dynamics in development and disease. qMAP解码发育和疾病中的RNA断裂动力学。

IF 6.7 1区生物学

Molecular Systems Biology Pub Date : 2026-10-01 Epub Date: 2026-07-20 DOI: 10.1038/s44320-026-00237-2

Hukam C Rawal, Jiancheng Yu, Xudong Zhang, Chen Cai, Qi Chen, Tong Zhou

{"title":"qMAP decodes RNA fragmentation dynamics in development and disease.","authors":"Hukam C Rawal, Jiancheng Yu, Xudong Zhang, Chen Cai, Qi Chen, Tong Zhou","doi":"10.1038/s44320-026-00237-2","DOIUrl":"10.1038/s44320-026-00237-2","url":null,"abstract":"Noncanonical small RNAs, such as tRNA-derived (tsRNAs) and rRNA-derived (rsRNAs) fragments, are more abundant than microRNAs and arise from selective cleavage events rather than random degradation. While fragmentation of parental RNAs produces functionally diverse small RNAs, current analytical approaches are limited to abundance measures and cannot systematically quantify differential cleavage signals. Here, we present qMAP, a computational framework profiling differential fragmentation of parental RNAs from small RNA sequencing data. qMAP integrates two complementary models to identify condition-specific fragmentation patterns and includes a dedicated module to pinpoint the small RNA species driving these differences. Using qMAP, we uncover dynamic tRNA and rRNA fragmentation during mouse cell reprogramming, demonstrate the classification power of RNA fragmentation in human ulcerative colitis, develop and validate a blood-based RNA fragmentation signature of recurrent implantation failure, and identify aging-associated RNA fragmentation in sperm, which supports RNA fragmentation as a distinct regulatory dimension beyond expression/abundance information. qMAP enables systematic exploration of the regulatory \"RNA fragmentome\", providing a foundational tool for both mechanistic discovery and translational applications of noncanonical small RNAs.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1626-1649"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13638987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148536164","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

Tracking cell fate through morphodynamics. 通过形态动力学追踪细胞命运。

IF 6.7 1区生物学

Molecular Systems Biology Pub Date : 2026-10-01 Epub Date: 2026-08-27 DOI: 10.1038/s44320-026-00244-3

Julia Dorr, Brian J Mitchell

引用次数: 0

Decoding spatiotemporal fibrotic and cellular immunosuppression of therapeutic T cells in live pancreatic ductal adenocarcinoma. 解码活胰腺导管腺癌中治疗性T细胞的时空纤维化和细胞免疫抑制。

IF 6.7 1区生物学

Molecular Systems Biology Pub Date : 2026-10-01 Epub Date: 2026-09-10 DOI: 10.1038/s44320-026-00243-4

Guhan Qian, Hongrong Zhang, Ingunn M Stromnes, Kevin W Eliceiri, Paolo P Provenzano

{"title":"Decoding spatiotemporal fibrotic and cellular immunosuppression of therapeutic T cells in live pancreatic ductal adenocarcinoma.","authors":"Guhan Qian, Hongrong Zhang, Ingunn M Stromnes, Kevin W Eliceiri, Paolo P Provenzano","doi":"10.1038/s44320-026-00243-4","DOIUrl":"10.1038/s44320-026-00243-4","url":null,"abstract":"Pancreatic ductal adenocarcinoma (PDA) is profoundly immunosuppressive. To help define this behavior, we present integrated experimental and computational frameworks to elucidate therapeutic T cell dynamics. Through the development of TME-CARTographer (TME-CART), a computational pipeline integrating high-dimensional data, graph theory, behavior analysis, and deep learning (DL), we present quantitative insights on 4D T cell-TME interactions in live PDA tumors. Mapping physical immunosuppression demonstrates that collagen fiber architectures direct migration while concomitantly limiting off-axis movement, creating immune exclusion zones. Expanding these findings, we establish that the collagen matrix harbors and spatially organizes immunosuppressive myeloid cells to serve as cooperative co-modulators of T cell behaviors, including migration, sampling, repulsion, and sequestration. Consistent with these findings, DL defines both linear and nonlinear collagen matrix and cellular neighborhood interactions as drivers of T cell behavior. The TME-CART DL framework also accurately predicts shifts in immunosuppression following depletion of myeloid cells. Overall, we identify synergistic barriers impeding anti-tumor T cell behaviors and present TME-CART as a discovery platform for interpreting complex 4D data to enhance the understanding and design of immunotherapies.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1508-1530"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148930359","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

Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz. 整合代谢组学数据分析，利用MetaProViz产生机制假设。

IF 6.7 1区生物学

Molecular Systems Biology Pub Date : 2026-10-01 Epub Date: 2026-08-17 DOI: 10.1038/s44320-026-00231-8

Christina Schmidt, Jannik Franken, Denes Turei, Dimitrios Prymidis, Macabe Daley, Christian Frezza, Julio Saez-Rodriguez

{"title":"Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz.","authors":"Christina Schmidt, Jannik Franken, Denes Turei, Dimitrios Prymidis, Macabe Daley, Christian Frezza, Julio Saez-Rodriguez","doi":"10.1038/s44320-026-00231-8","DOIUrl":"10.1038/s44320-026-00231-8","url":null,"abstract":"The lack of standardised workflows and ambiguous metabolite annotations hampers metabolomics integration with prior knowledge, thus limiting the extraction of meaningful biological insights. We present MetaProViz (Metabolomics Processing, functional analysis and Visualization), an open-source Bioconductor R package for metabolomics data analysis that integrates prior knowledge to generate mechanistic hypotheses ( https://saezlab.github.io/MetaProViz/ ). MetaProViz operates on annotated intensity values and offers a flexible framework consisting of five modules: processing, differential analysis, prior knowledge integration, functional analysis and visualisation, applicable to intracellular and exometabolomics experiments. To improve functional analysis, we created the Metabolism Signature Database (MetSigDB), a collection of annotated metabolite sets. MetSigDB includes pathway-metabolite, metabolite-receptor, metabolite-transporter sets, and chemical class-metabolite sets. MetaProViz enables the conversion of gene sets to metabolite sets, metabolite identifier expansion and analyses mapping ambiguities. The MetaProViz functional analysis toolkit includes sample metadata analysis, enrichment analysis and biologically informed clustering. By applying MetaProViz to kidney cancer metabolomics data, we identified increased methionine usage in line with decreased methionine levels in tumour samples. In summary, MetaProViz facilitates and improves the analysis and interpretation of metabolomics data.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1605-1625"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148795744","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

E. coli prepares for starvation by dramatically remodeling its proteome in the first hours after loss of nutrients. 大肠杆菌在失去营养后的最初几个小时内通过剧烈地重塑其蛋白质组来为饥饿做准备。

IF 6.7 1区生物学

Molecular Systems Biology Pub Date : 2026-10-01 Epub Date: 2026-07-01 DOI: 10.1038/s44320-026-00226-5

Théo Gervais, Bjoern Kscheschinski, Michael Mell, Nevil Goepfert, Erik van Nimwegen, Thomas Julou

{"title":"E. coli prepares for starvation by dramatically remodeling its proteome in the first hours after loss of nutrients.","authors":"Théo Gervais, Bjoern Kscheschinski, Michael Mell, Nevil Goepfert, Erik van Nimwegen, Thomas Julou","doi":"10.1038/s44320-026-00226-5","DOIUrl":"10.1038/s44320-026-00226-5","url":null,"abstract":"Although in the wild bacteria likely spend most of their time deprived of nutrients and slowly starving to death, very little is known about how bacteria adapt their phenotype to starvation. Here we combine microfluidics with quantitative fluorescence microscopy of transcriptional reporters to comprehensively quantify growth and gene expression at the single-cell level in E. coli during carbon starvation. We find that all cells immediately stop growing upon loss of carbon source and that almost all remain alive for over 30 hours. Furthermore, entry into starvation triggers a dynamic expression program that is remarkably homogeneous across single cells, but highly variable across genes, causing dramatic remodeling of the proteome early in starvation. We further show that, as protein production and the rate of protein degradation both decay approximately exponentially, protein concentrations become essentially 'frozen' after the first 5 to 10 hours, setting phenotypes for several days of starvation. Finally, using experiments in which gene expression is inhibited for different periods, we show that protein production in the first 5 hours is crucial for protecting cells from stresses late in starvation.","PeriodicalId":18906,"journal":{"name":"Molecular Systems Biology","volume":" ","pages":"1531-1550"},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148369276","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