ACS Synthetic Biology最新文献

{"title":"Structure-Guided Hybrid GCN−Transformer Framework for Enzyme Optimal pH Prediction","authors":"Lianci Tao,&nbsp;Jilong Zhang,&nbsp;Chenxiao Xiang,&nbsp;Jun Yang,&nbsp;Li Wei,&nbsp;Shuai Kang","doi":"10.1021/acssynbio.6c00421","DOIUrl":"https://doi.org/10.1021/acssynbio.6c00421","url":null,"abstract":"<p>Accurate prediction of enzyme optimal pH (pH_opt) is fundamental to biocatalyst engineering, pharmaceutical development, and process optimization, yet developing a reliable computational method remains challenging. To address this limitation, we present trPHopt, which is based on a structure-guided hybrid framework integrating the Graph Convolutional Network (GCN) with the Transformer. At the feature representation, trPHopt introduces new structural features to better characterize residue microenvironments relevant to pH_opt. At the model architecture level, our method adopts a parallel dual-stream encoder: a structure-guided multi-head self-attention stream that incorporates geometric features as attention biases to capture long-range residue dependencies, and a Dense GCN stream that aggregates fused residue representations over distance-weighted residue graphs. The encoder-layer outputs are combined through a residual fusion gate, with the GCN contribution modulated by a learnable scalar coefficient. To alleviate the bias toward neutral predictions caused by label distribution imbalance, we further employ a two-stage calibration strategy that couples the coarse-grained acidic/neutral/alkaline classifier with a confidence-aware calibrator under a logical consistency constraint. On the independent test set, trPHopt consistently outperforms classical machine learning models and state-of-the-art methods, achieving an RMSE of 0.794 and an <em>R</em>² of 0.527. These results demonstrate that trPHopt provides an accurate and practical method for enzyme pH_opt prediction.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"15 10","pages":"4240–4251"},"PeriodicalIF":4.5,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149211008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective Sequence-to-Expression Prediction for a Model Membrane Protein Using Machine Learning and Computational Protein Design","authors":"Yuxin Shen,&nbsp;Maddie Lewis,&nbsp;Juno Underhill,&nbsp;Adrian J. Mulholland,&nbsp;Diego A. Oyarzún,&nbsp;Paul Curnow","doi":"10.1021/acssynbio.6c00206","DOIUrl":"https://doi.org/10.1021/acssynbio.6c00206","url":null,"abstract":"<p>The recombinant expression of integral membrane proteins is notoriously challenging. One way to address this challenge is via computational genotype-to-phenotype models that determine how particular sequence features correlate with protein expression levels. However, the potential of such approaches is yet to be fully realized, at least partly because so few expression datasets are available. Here, we study the sequence-to-expression relationships of a variant library originally derived from combinatorial computational design. The controlled sequence diversity of this library makes this new dataset directly compatible with lightweight off-the-shelf bioinformatic tools. The expression phenotype of the entire library was first assessed in the widely used recombinant host <em>Escherichia coli</em>. We selected a relatively small and balanced dataset of 2055 protein sequences assigned to binary classes of either “high” or “low” expression and used these sequences to train a sequence-to-expression classifier using supervised machine learning. This trained model was then used to infer the expression of &gt;10,000 unmeasured sequences, and experimental validation of these predictions for 12 test variants achieved a 100% success rate. Using tools from explainable AI, we identified specific sequence positions and substitutions that are most important in dictating cellular expression levels. This analysis was validated by model-guided protein engineering that achieved an 8-fold increase in the purification yield of a poorly expressing variant. Our results show that computational protein design in tandem with supervised learning leads to effective models for the discovery of protein variants with improved expression phenotypes and can decode the molecular basis of membrane protein expression.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"15 10","pages":"4039–4049"},"PeriodicalIF":4.5,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Dynamic Control of Functional Coacervates in Synthetic Cells”","authors":"Karthika S. Nair,&nbsp;Sreelakshmi Radhakrishnan,&nbsp;Harsha Bajaj","doi":"10.1021/acssynbio.6c00679","DOIUrl":"https://doi.org/10.1021/acssynbio.6c00679","url":null,"abstract":"","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"15 10","pages":"4382–4382"},"PeriodicalIF":4.5,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149211004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glucosamine-Producing Engineered Probiotics Enhance the Immune Function of Microglia","authors":"Yeyin Li,&nbsp;Qiushuang Zhang,&nbsp;Yicong Dai,&nbsp;Liyan Fan,&nbsp;Xucong Teng,&nbsp;Jinghong Li","doi":"10.1021/acssynbio.6c00385","DOIUrl":"https://doi.org/10.1021/acssynbio.6c00385","url":null,"abstract":"<p>Dysregulated microglial activation underlies both neuroinflammation and neurodegenerative pathology. GlcN has well-documented anti-inflammatory activity across multiple cell types, yet whether it acts on microglia in any meaningful way has not been directly tested. Here we show that GlcN shifts microglia toward an M2 anti-inflammatory state by altering their metabolic program, an effect accompanied by a clear improvement in immune function. A practical barrier complicates direct translation of this finding, oral GlcN is rapidly cleared and reaches target tissues at concentrations too low to be therapeutically useful. We therefore repurposed <em>Escherichia coli</em> Nissle 1917 (EcN) as a living production platform, introducing targeted genetic modifications to generate EcN-GlcN, a strain that continuously synthesizes GlcN within the gut lumen. In co-culture, EcN-GlcN drove the same M2 shift in microglia as exogenous GlcN. After oral gavage in mice, the strain established stable intestinal colonization and kept systemic GlcN concentrations detectable for at least 72 h. The work positions GlcN as an underappreciated neuroimmune regulator and offers a practical route to its long-term delivery through a living bacterial system.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"15 10","pages":"4186–4198"},"PeriodicalIF":4.5,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Approaches and Identifying Limitations toward Functional Yeast Surface Display of FLS2","authors":"Benedikt Dolgikh,&nbsp;Samantha Schulte,&nbsp;Daniel R. Woldring","doi":"10.1021/acssynbio.5c00884","DOIUrl":"https://doi.org/10.1021/acssynbio.5c00884","url":null,"abstract":"<p>Pattern recognition receptors such as FLAGELLIN SENSING 2 (FLS2) are central to plant immunity and attractive targets for engineering broader detection of bacterial phytopathogens for application in pest management (and diagnostics) in food crops and sustainable agriculture practices. However, evaluating numerous FLS2 variants for altered pathogen sensing specificity directly in plants is slow and has low throughput and has been seldom optimized for heterologous display systems. Here, we established conditions that enabled <em>Arabidopsis thaliana</em> FLS2 ectodomain expression on the surface of <em>Saccharomyces cerevisiae</em> and evaluated binding to its cognate ligand, flg22. We show how yeast high-mannose glycosylation of the FLS2 ectodomain contributes to inefficient folding and loss of detectable flg22 binding in standard yeast surface display conditions. Substitutions at all N-glycosylation motifs compromised surface expression, indicating that some glycosylation is required for trafficking. We tuned the extent of glycosylation using tunicamycin, an N-linked glycosylation inhibitor, in combination with thermal stress to modulate ER quality control. Under these conditions, we observed a reproducible subpopulation of cells with improved flg22 binding despite reduced overall expression, and we confirmed flg22 selectivity against non-FLS2 proteins using both flow cytometry and magnetic bead-based enrichment. Guided by structural modeling of high-mannose glycans on the FLS2 ectodomain, we then substituted asparagines at selected N-glycan sites with serine. We identified a key glycan-site variant, N388S, which lies proximal to the flg22 binding interface and increased the binding population size under stress conditions. Binding assays against FLS2 variants and a reported non-binding variant affirmed that FLS2 selectivity was specific to FLS2 display, showing that all variants maintained a low affinity interaction with flg22. Together, these results point to FLS2 display conditions, not only glycosylation state, as an underlying limitation to detect true flg22 interactions, which will require more sensitive approaches to confidently resolve true binding populations.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"15 9","pages":"3566–3578"},"PeriodicalIF":4.5,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biological Associative Memory Learning Control System Based on DNA Strand Displacement","authors":"Zicheng Wang,&nbsp;Pengpeng Zhao,&nbsp;Fei Fan,&nbsp;Junwei Sun,&nbsp;Yanfeng Wang","doi":"10.1021/acssynbio.6c00507","DOIUrl":"https://doi.org/10.1021/acssynbio.6c00507","url":null,"abstract":"<p>In recent years, artificial intelligence technology, inspired by biology, has combined control systems with associative memory to simulate human learning and memory processes. The utilization of DNA strand displacement (DSD) technology has demonstrated significant advantages in solving problems in biocomputing and control systems. In this study, the forget system, input system and output system are designed based on DSD technology. A biological associative memory learning control system is constructed by cascading three DNA control system modules. Furthermore, a multi-input biological associative memory learning control system is extended to implement the basic functions of biological associative memory, including acquisition learning, differentiation learning, generalization learning and repetition learning. Additionally, different emotional stimuli are introduced into the multi-input biological associative memory learning control system to examine the varying effects of emotional stimuli on differentiation learning and generalization learning. This work provides a direction for the integration of biocomputing and control system.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"15 9","pages":"3821–3832"},"PeriodicalIF":4.5,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing Machine Learning for Enzyme Enantioselectivity: Toward Intelligent Biocatalysis Engineering","authors":"Jie Gu,&nbsp;Yan Xu,&nbsp;Xiaoyan Sun,&nbsp;Yao Nie","doi":"10.1021/acssynbio.6c00336","DOIUrl":"https://doi.org/10.1021/acssynbio.6c00336","url":null,"abstract":"<p>Enzyme enantioselectivity remains a central challenge in the biocatalytic synthesis of chiral pharmaceuticals and fine chemicals. Conventional evolution methods, constrained by prolonged experimental cycles and computationally intensive processes, struggle to comprehensively explore complex protein sequences. Although machine learning (ML) has been successfully applied to optimize enzymatic properties, such as catalytic efficiency and stability, its potential in enantioselectivity engineering remains underexploited. This review systematically evaluates how machine learning integrates multimodal datasets, including sequence, structural, and functional performance data, to advance the discovery and engineering of naturally enantioselective enzymes, and enable the de novo design of artificial enzymes for reaction-relevant applications. Furthermore, key algorithmic frameworks are reviewed. Persistent challenges such as data heterogeneity and limited model generalization capabilities are also critically examined. Moreover, machine learning is increasingly expected to bridge molecular-level enantioselective design with pathway optimization and reactor-scale process engineering, enabling a more integrated and efficient chiral biomanufacturing pipeline. Overall, these advancements open new avenues for efficient chiral biosynthesis and provide theoretical foundations and technical blueprints for a paradigm shift toward intelligence-driven biocatalysis engineering.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"15 9","pages":"3504–3517"},"PeriodicalIF":4.5,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biosynthesis of Lycopene and Ergothioneine in Escherichia coli Using Temperature-Responsive Biosensors","authors":"Shuyi Tang,&nbsp;Chaoyong Huang,&nbsp;Zhenbang Huang,&nbsp;Min Lin,&nbsp;Jin Wang,&nbsp;Shijie Jiang","doi":"10.1021/acssynbio.6c00028","DOIUrl":"https://doi.org/10.1021/acssynbio.6c00028","url":null,"abstract":"<p>Gene-programmable expression element library is rapidly expanding, making the regulation of key genes increasingly convenient. A temperature biosensor system was constructed to utilize environmental temperature conditions for gene expression regulation, thereby reducing reliance on costly chemical inducers and enabling successful application to product synthesis. Existing biosensors based on the temperature response of CI⁸⁵⁷-P_R have shortcomings, which greatly limit their application. In this study, we constructed a dual-dependent promoter library P_38X for σ⁷⁰ and σ³⁸ and combined it with the operator gene R1 in the P_R promoter to achieve structural decoupling from the wild-type P_R promoter and improve promoter persistence in the stationary phase. The method was successfully applied to the de novo lycopene synthesis in <em>Escherichia coli</em>, obtaining a final yield of 116 mg/L within 48 h under shake-flask fermentation. Protein degradation tags were introduced to address the previously reported accumulation of repressor proteins. In addition, D91 from the degradation tag mutant library was introduced into <em>E. coli</em> to synthesize ergothioneine using a temperature-tuned expression delay timer. The yield of 307 mg/L was obtained in 48 h under shake-flask fermentation and 7.5 g/L in a 2 L bioreactor after optimizing the fermentation conditions and <em>S</em>-adenosylmethionine supply. This study provides a new approach to long-term effective gene expression at lower cost, thus enriching the library of programmable expression elements.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"15 9","pages":"3579–3592"},"PeriodicalIF":4.5,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reprogramming Enfumafungin Biosynthesis for High-Titer Production in the Endophytic Fungus Hormonema carpetanum ATCC 74360","authors":"Jing Wo,&nbsp;Haipeng Yu,&nbsp;Yige Yang,&nbsp;Hongxi Wu,&nbsp;Fangjie Xiong,&nbsp;Dekun Kong","doi":"10.1021/acssynbio.6c00248","DOIUrl":"https://doi.org/10.1021/acssynbio.6c00248","url":null,"abstract":"<p>Enfumafungin is the essential precursor for the FDA-approved antifungal agent Ibrexafungerp, yet its production is hindered by low yields, restrictive solid-state fermentation, and the genetic intractability of its producer strain, <em>Hormonema carpetanum</em> ATCC 74360. Here, we developed a CRISPR/Cas9-mediated platform that halved the genetic iteration cycle to less than 15 days. By rewiring the biosynthetic cluster through promoter engineering, we successfully transitioned production to scalable liquid fermentation, achieving an initial 21.6 mg/L. Then, the mevalonate pathway genes (<em>tHMGR</em>, <em>ERG9</em>, and <em>ERG1</em>) were overexpressed, which successfully boosted the titer to 250.1 mg/L. Following medium optimization, the engineered strain HC07 reached a record-breaking 592.4 mg/L in 250 mL shake flasks, the highest yield achieved <em>via</em> metabolic engineering to date. This study provides a robust microbial platform and a streamlined genetic toolbox, paving the way for the sustainable biomanufacturing of next-generation triterpenoid antifungals.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"15 9","pages":"3653–3663"},"PeriodicalIF":4.5,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aniline Biosynthesis and the Archival of EC 4.1.1.24","authors":"Sean A. Wirt,&nbsp;Kristala L. J. Prather","doi":"10.1021/acssynbio.6c00233","DOIUrl":"https://doi.org/10.1021/acssynbio.6c00233","url":null,"abstract":"<p>Aniline is an essential synthetic building block, with its manufacture reliant on fossil carbon feedstocks. While bioproduction could connect aniline manufacture to renewable carbon inputs, efficient metabolic pathway design remains a hurdle for aniline biosynthesis. Two alternate pathways were examined, involving the promiscuous activity of either non-oxidative phenolic acid decarboxylases or tyrosine phenol-lyases (TPLs) on an amine substrate. The decarboxylases exhibited &lt;1% aniline yields under oxygen-scarce conditions, whereas no candidate TPLs produced aniline. Further optimization of the decarboxylase reaction is necessary to construct a viable, fully biosynthetic route towards aniline. Incidentally, we observed that (1) aniline reacts abiotically with glucose in culture medium, and (2) the existing literature on aniline biosynthesis heavily references the EC 4.1.1.24 aminobenzoate decarboxylase classification, which has not been validated since first proposed almost seventy years ago. We recommend the EC 4.1.1.24 entry be archived, supporting the EC 4.1.1.61 phenolic acid decarboxylase entry instead.</p>","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":"15 9","pages":"3852–3858"},"PeriodicalIF":4.5,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}