Synthetic Biology

Engineering G protein-coupled receptors (GPCRs) for biosensing applications remains challenging due to structural and functional constraints when such proteins are expressed in a heterologous host. OrthoRep offers the continuous accumulation and selection of signal-enhancing mutations in yeast, yet it has not been applied to GPCR evolution. We apply OrthoRep-driven mutagenesis to evolve opioid GPCRs and improve yeast-based biosensors: first, we express the human -opioid receptor (OPRM1) on the OrthoRep p1 plasmid and couple ligand activation to growth. Serial cell passaging of the biosensor generated functionally diverse mutants. However, mutations that decouple receptor activation from selection arose over longer passaging campaigns, halting evolution. We resolve this issue using a site-specific recombinase that re-introduces evolved variants onto the p1 landing pad of an unmutated biosensor, allowing continued selection. This led to an ~18-fold improvement in sensitivity of a mutant OPRM1 over wt-OPRM1. Additional polymerases were added to the platform, broadening the mutational profiles available. Assembling these components forms the platform we call HERO (Heterologous Engineering of Receptors using OrthoRep). Lastly, we deploy HERO using automation to improve the human {delta}-opioid receptor's weak response to an agonist by ~40-fold. Sensitivity also improved for a structurally dissimilar ligand assayed on the same evolved variants.

Genome transplantation is a key technology for synthetic genomics, enabling entire genomes to be transferred into recipient cells. Despite its importance, genome transplantation remains confined to a small number of wall-less Mollicute species and is often characterized by low efficiencies, limiting the development and testing of synthetic genomes. Because genome transplantation relies on the same polyethylene glycol mediated DNA delivery process used for plasmid transformation, improving DNA uptake is an important step toward more efficient transplantation systems. Here, we systematically optimized polyethylene glycol-mediated transformation in Mesoplasma florum, a fast-growing, non-pathogenic Mollicute with a reduced genome that represents an attractive chassis for synthetic genomics. We evaluated 20 parameters spanning cell physiology, DNA preparation, membrane conditioning, and recovery conditions. DNA topology emerged as the strongest determinant of transformation efficiency, with highly compacted DNA preparations producing up to three orders of magnitude more transformants than conventional plasmid preparations. Growth phase and polyethylene glycol concentration also strongly influenced transformation outcomes. To identify potential recipients for future genome transplantation experiments, we further screened ten strains belonging to the Mesoplasma lineage. Transformation efficiencies varied widely among strains, and Mesoplasma entomophilum W17 emerged as a particularly promising candidate. Moreover, W17 supported replication of plasmids carrying the M. florum oriC, suggesting compatibility between the replication systems of the two species. Together, these results establish an improved transformation workflow for M. florum, identify DNA topology as a major determinant of DNA uptake, and reveal a new candidate recipient for future genome transplantation studies. These advances provide practical tools and biological insights for synthetic genome engineering in Mollicutes.

While directed differentiation of human pluripotent stem cells commonly relies on the timed delivery of extracellular factors, these uniform treatments often yield heterogeneous responses across cell populations. Linking intracellular gene regulation directly to an emerging developmental state offers a complementary strategy to coordinate these differentiation signals from within the cell. Here, we explored this approach by coupling a T/Brachyury promoter to CRISPR interference targeting CTNNB1, which encodes the canonical Wnt signaling mediator {beta}-catenin. A T-promoter EGFP reporter line exhibited transiently increased activity during early differentiation, supporting the use of this promoter as a developmentally responsive input. We then combined the promoter with dCas9-KRAB and a CTNNB1-targeting guide RNA. Cas9-mediated integration was accompanied by indels at the CTNNB1 target site, whereas a Cas12a-mediated integration strategy yielded clones with no indels detected by ICE analysis. During differentiation, the selected T-dCas9 CTNNB1 clone showed reduced CTNNB1 expression and attenuated induction of Wnt associated genes. Together, these findings provide a proof of concept for combining a developmental promoter with a programmable intracellular regulator and identify a strategy for separating circuit integration from unintended target-site editing. This modular approach provides a foundation for developing genetic interventions whose expression is linked to developmental state.

Autonomous experimentation, where an automated system designs, performs, analyzes, and iteratively improves experiments with minimal human intervention represents a transformative scientific research paradigm. However, it remains hampered by the disconnect between computational design and experimental execution. Here we present iCloudBiofoundry, a cloud-accessible, scalable, and self-evolving platform that bridges this divide. A scientific multi-agent system, iBioGenie, plans research workflows by converting natural language research objectives into testable hypotheses and experimentally grounded protocols using literature, databases, and computational tools. A physical AI layer, BiofoundryAI, translates them into executable workflows that run autonomously on distributed robotic biofoundries, and collects the data for iterative design. We demonstrate the end-to-end capabilities of iCloudBiofoundry across three representative case studies spanning enzyme retrieval, protein engineering, and metabolic engineering. By integrating agentic AI with physical AI, iCloudBiofoundry provides a cloud accessible platform that unifies computational design and robotic execution, enabling autonomous biological research.

At-home sampling can address obstacles to participation in transcriptomics research. We previously developed the homeRNA and homeRNAmax platforms; these custom-designed tubes containing RNA stabilizer interface with a blood tube compatible with upper-arm blood collection devices, allowing for blood collection and RNA stabilization to occur from a participant's home. In this work we introduce homeRNAdrop, an insert for a commercially available blood tube (BD Microtainer) that divides the tube into two compartments: the top compartment holds 200 L blood and the bottom compartment holds ~590 L liquid stabilizer (RNAlater). By combining blood collection and RNA stabilization into the same tube, homeRNAdrop streamlines sample preparation for participants and allows for more compact sample storage for the lab. In a remote pilot study, n=25 participants from across the United States tested the homeRNAdrop kit. The n=23 samples successfully received by the lab show that samples collected using homeRNAdrop are of sufficient RNA quality (all RINs > 6, a common cutoff for analysis) and yield (mean yield=1.44 g) for downstream gene expression analysis. Almost all participants who successfully collected blood using the Tasso Mini device found the homeRNAdrop device easy to use (n=23/24). Overall, this work adds a simplified, user-friendly platform to the growing suite of tools for remote blood RNA stabilization.

In vitro-transcribed (IVT) messenger RNA (mRNA) therapeutics require rigorous quality control of 5' capping efficiency and 3' poly(A) tail length, both of which constitute critical quality attributes (CQAs). Current analytical workflows for these CQAs rely on hybridization with nucleic acid probes and biotin-based affinity purification, which constrain throughput and increase per-assay cost. Here, we report a rationally designed 5' untranslated region (5' UTR) that sustains high translational efficiency while enabling probe-free, one-step purification for rapid quantification of capping efficiency and poly(A) tail length distribution. This approach exploits a strategically engineered 5' UTR incorporating a purine-only RNase A-resistant fragment terminating at a single pyrimidine cleavage site, together with a translation-enhancing element comprising an N6-methyladenosine (m6A) deposition motif and an 18S rRNA complementarity sequence. This dual-function design accommodates GA-rich sequence segments without compromising mRNA translational output. Compared with conventional probe- and biotin-dependent workflows, the present method substantially reduces cost and processing time and is compatible with simultaneous detection of segmented AmGn poly(A) tail variants. This integrated platform provides a simplified, probe-free and construct-universal for high-throughput in-process control (IPC) of mRNA therapeutics.

Numerous CRISPR-Cas systems have been developed for molecular detection of genetic elements exploiting the trans-cleavage activity of LbCas12a combined with short fluorescent probes. Alongside, a large variability of buffer conditions has been reported. However, how solution chemistry balances enzymatic turnover, signal stability, and detection performance have not been systematically defined. Here, we evaluated how the interplay between anions (chloride vs. acetate), additives, sequence probes, and ionic strength contributes to LbCas12a trans-cleavage activity. Buffer composition screening revealed that chloride-based buffers maintain a low background but slow catalytic rates, meanwhile acetate-based buffers accelerate enzymatic turnover with and without the target DNA sequence, increasing non-specific signals. Reducing agents and surfactants were the best enhancer combinations, improving cleavage performance by increasing ternary complex formation (Km). High salt concentrations only slightly improved the reaction kinetics. Finally, the 5FAM-TTATT-3BHQ-1 reporter showed the best balance between signal intensity and nonspecific background. Our findings indicate that LbCas12a tolerates a vast buffer composition landscape, maintaining robust trans-cleavage activity and detection performance. However, conditions that enhance catalytic activity simultaneously increase nonspecific probe cleavage and higher background, resulting in lower detection performance. Therefore, assay optimization should prioritize detection performance rather than maximal fluorescence output, where solution chemistry is a critical determinant.

L-Threonine is an essential amino acid widely used in the feed, food, and pharmaceutical industries, and microbial fermentation is now the dominant route for its production. Further increases in producer titers depend on random mutagenesis coupled with high-throughput screening (HTS), but progress has been limited by the lack of a biosensor with a broad operating range, high sensitivity, and target specificity. Here, we show that L-threonine induces the tryptophan-repressed trp promoter of Escherichia coli, a response that, to our knowledge, has not been described previously. Deletion analysis showed that both the trpO operator and the trpL attenuation element contribute to induction, and that the lowest basal expression occurs only when both elements are present. By optimizing the ribosome-binding site, the promoter -10 region, and TrpR expression, we constructed an L-threonine biosensor on a pACYC backbone. The sensor responded linearly to L-threonine from 0.1 to 30 g/L (R2 = 0.9895), with an induction ratio of up to 51.7-fold. Among the 20 proteinogenic amino acids tested, only L-threonine produced a detectable response. The biosensor distinguished strains with different L-threonine production capacities and guided the directed evolution of homoserine kinase (ThrB) at a conserved motif, yielding variants with brighter reporter fluorescence and increased L-threonine production. This work provides a high-performance biosensor based on native E. coli regulatory elements for screening L-threonine overproducers.

Many therapeutically relevant signaling pathways are sensed inside the cell, yet a modular synthetic biology platform for responding to intracellular signaling at the post-translational level is lacking. Signaling pathways control the nuclear localization and activity of native transcription factors via phosphorylation of nuclear localization/export or cytoplasmic anchor sequences. Here, we apply this concept to develop a platform of nucleocytoplasmic localization tags (NCtags) that control the nuclear translocation of a synthetic transcription factor (synTF) for transgene induction in engineered cells. We identify human-based synTF components that are basally cytoplasmic and induce transgenes under the control of NCtags with minimal background. We demonstrate the modular nature of this platform through transcriptional reporter induction in response to a calcium-inducible NFAT-derived NCtag, and development of novel NCtags responsive to hyperosmolarity, cAMP, or logical combinations of cAMP and calcium. Multiple factors in the tumor microenvironment elevate cAMP in T cells, leading to reduced function. We demonstrate that NCtag-synTF fusions expressed in T cells can respond to endogenous sources of cAMP or calcium in breast cancer spheroids to induce transgene expression. Our use of human components and an all-in-one lentiviral vector encoding the NCtag-synTF-transgene system establishes this as a novel platform for sense-and-response inside therapeutic cells.

Electromicrobial production (EMP) technologies aim to combine renewable electricity, CO2, and engineered microbes to make energy-dense molecules at efficiencies exceeding photosynthesis. CO2 can be electrochemically reduced to formate, which is far easier to handle at the bench than H2 or an electrode, but formate carries only two electrons per carbon against the six in a biofuel. The remaining electrons must come from oxidizing additional formate, from H2 oxidation, or from extracellular electron uptake (EEU), and no rigorous comparison of these options coupled to the choice of carbon assimilation pathway currently exists. We calculate upper-limit efficiencies for butanol production by six carbon assimilation pathways, each paired with all three electron delivery mechanisms, using electrochemical parameters drawn from a survey of the recent literature. Electrical to butanol energy conversion efficiencies range from 35.5 to 51.7%, corresponding to solar-to-fuel efficiencies of 11.7 to 17%, so even the least efficient route exceeds the 8% theoretical ceiling of algal photosynthesis. The serine variant of the reductive glycine pathway reaches an electrical energy conversion efficiency of when using H2 oxidation, within 1.9 points of the most efficient pathway, and is the only high-efficiency option that tolerates O2. This makes an EMP system that combines electron delivery by formate coupled with the serine variant of reductive glycine pathway highly attractive, as it presents few barriers to rapid, iterative engineering in the lab, and a high theoretical ceiling. Drawing both carbon and electrons from formate costs 6.2 points against H2 at a state-of-the-art whole-cell voltage (2.2 V). However, this small penalty is amplified three-fold by any rise in the CO2-to-formate cell voltage, and reaches 13.5 points at the highest whole-cell voltages reported for scaled-up CO2-to-formate electrolyzers, where formate-only operation falls to 11.2 electrical-to-fuel and 3.7% solar-to-fuel efficiency, below the ceiling of photosynthesis, against 24.7 and 8.1% for H2 (only just above algal photosynthesis). Our choice between a formate-only system and one coupled to H2 oxidation or EEU therefore depends on our belief about the trajectory of CO2 reduction technology. If whole-cell voltages continue to fall at the rate of the past decade, formate alone is the right target, and the simplicity of its workflow is bought at low cost. However, if that improvement plateaus, the electron delivery mechanism must be swappable, and a system should be designed from the outset so that it can be. At the US Department of Energy SunShot target of 2 cents per kilowatt hour, the electricity to make a US gallon of butanol costs $1.40 for a formate-only system at the state of the art, rising to $5.45 at the highest scaled-up electrolyzer voltage reported, against $1.23 and $2.47 for formate and H2 system.

Directed evolution enables the engineering of proteins with novel or improved functions, yet existing approaches often require extensive manual intervention and are difficult to sustain over long evolutionary trajectories. Here we introduce PRIME (Protein-primed Replication for In vivo Mutagenesis and Evolution), a plasmid-based orthogonal DNA replication system for continuous directed evolution in Escherichia coli. PRIME harnesses protein-primed DNA replication to establish an autonomous replicon that operates independently of the host genome. Coupling this system to an error-prone DNA polymerase enables targeted diversification of constructs encoded on the orthogonal plasmid while preserving genomic integrity. Using only standard laboratory equipment, we demonstrate the evolution of a construct expressing msfGFP with an 11.8-fold increase in fluorescence. The platform is fully compatible with standard molecular biology workflows and requires no specialised instrumentation. We further demonstrate its portability across bacterial hosts by establishing PRIME in Pseudomonas putida. PRIME thus provides a broadly accessible framework for continuous in vivo evolution, with wide-ranging applications in protein engineering and synthetic biology.

Genome engineering via CRISPR/Cas9 has been widely applied in biological research and developing gene therapies. However, genome editing flexibility is strongly constrained by the position and quality of guide RNAs relative to the intended edit: desired edit sites are frequently positioned far from efficient protospacer adjacent motifs (PAM). SMART (Silently Mutate And Repair Template) technique relaxes this constraint by silently mutating the gap between the Cas9 cut and the edit in the repair donor, preserving the encoded protein while expanding the usable guide-RNA design space. Designing such donors manually requires coordinated reasoning over transcript structure, reading frame, candidate protospacers, codon usage, homology arms and the expected edited allele. Auto-SMART is a web platform that automates this process for CRISPR/Cas9-mediated insertion, deletion, point mutation and sequence replacement in coding exons and in introns. Users choose a genome assembly, search a gene symbol or transcript, select a modification site and select an editing operation. Auto-SMART finds nearby Streptococcus pyogenes Cas9 (SpCas9) guides, ranks them by predicted on-target activity and cut-to-edit geometry, builds repair templates with synonymous substitutions, automatically recodes residual on-target sites to prevent re-cutting and renders interpretable panels for the target locus, donor template and locus after HDR, together with a downloadable report and a Primer3 genotyping handoff. The web application for Auto-SMART is freely available at https://auto-smart.chuanping.org/.

Motivation: Real-time qPCR data are commonly analyzed to obtain a Cq values, which are used to compare the relative abundance of a targeted template. However, improper background subtraction distorts Cq and changing the assigned threshold can alter experimental conclusions. In addition, a Cq values provide no information on the quality of the qPCR for a given sample. Alternatively, global fitting of qPCR data provides agnostic quantification without the use of Cq or thresholds, and the fitting terms provide a useful metric of reaction performance. Current implementations of global fitting are limited to custom in-house programs. Results: qPyCR is comprehensive analysis tool written in Python that performs background signal subtractions, computes a threshold, and reports Cq values in a traditional manner. The program also performs global fitting and reports template abundances along with reaction quality values. The quality values can be used to assign performance standards, which allow researchers to distinguish aberrant reactions from true changes in template abundance. Availability and Implementation: Code and example data are freely available on GitHub (https://github.com/sdmoore-labs/qPyCR) and on Zenodo (https://doi.org/10.5281/zenodo.21115342).

The valorisation of biological polymers requires microbial systems that can both access recalcitrant substrates and convert the resulting carbon into useful products. Although microbial genome and metagenome resources provide an expanding reservoir of candidate depolymerizing and modifying enzymes, most discovery workflows remain disconnected from enzyme optimisation and host adaptation. Here we present a coupled sequence-based enzyme discovery, enzyme evolution and synthetic yeast chassis adaptation strategy for microbial biopolymer valorisation. Focusing on laccases for the depolymerisation of lignin as a proof of concept, we combine sequence data mining for enzyme discovery, modular yeast surface display for functional screening, directed evolution for enzyme optimisation and synthetic yeast genome diversification for chassis improvement. In our study, surface display enabled functional benchmarking and recovery of improved laccase variants and synthetic-genome-enabled diversification provided a route to explore host configurations that influence display and enzyme performance. By integrating enzyme-level and chassis-level optimisation, this framework addresses a central bottleneck in converting microbial biodiversity by computational sequence repository mining into deployable biomanufacturing systems. Our results establish laccases as tractable entry points for oxidative biopolymer conversion and provide a generalizable platform for engineering yeast systems for sustainable carbon valorisation.

Human Ca2+/calmodulin (CaM)-dependent protein kinase II alpha (CaMKII) is a highly abundant and conserved protein in the brain. It is a multifunctional serine/threonine kinase that phosphorylates cellular substrates across subcellular compartments, activating signaling pathways essential for long-term memory storage. However, a detailed understanding of protein structure and enzyme-substrate interaction mechanisms require a method to produce active, stable, and pure proteins at scale. Since phosphorylation can alter catalytic activity and substrate affinity, examining the non-phosphorylated state enables an unbiased characterization of basal enzymatic function. Therefore, producing the non-phosphorylated form of the kinase is important for biochemical and structural studies aimed at understanding kinase activation mechanisms. We describe a detailed protocol for the expression and high-yield purification of full-length, non-phosphorylated human CaMKII in bacterial cells. CaMKII was co-expressed with {lambda}-phosphatase to maintain it in a non-phosphorylated state. Protein expression was induced by autoinduction at low temperature to enhance the stability and solubility of CaMKII. Recombinant CaMKII was purified by Ni-NTA affinity chromatography, followed by gel filtration chromatography. Although CaMKII has been considered difficult to purify in its non-phosphorylated form, we obtained it in high yield, and the protein was predominantly non-phosphorylated. Our results show that combining co-expression with autoinduction improves both the solubility and yield of the homogeneous non-phosphorylated form of CaMKII, which could be suitable for subsequent biochemical and structural studies.

Kinetic parameters in engineered biomolecular circuits are typically known only within intervals and are not precisely tunable; controller synthesis is therefore naturally posed over sets of gains rather than at a single nominal design. We formulate robust controller tuning under parametric plant uncertainty as a universally quantified set-inversion problem: given a plant-parameter box, compute the controller gains that stabilize every plant in the box. Robust stability is decided by Hurwitz sign conditions, which are bounded over the joint gain-parameter domain using validated range enclosures, yielding machine-checkable certificates of local exponential stability of the deterministic equilibrium. Numerical experiments show that the certified region depends strongly on the range enclosure. For an antithetic integral feedback (AIF) controller acting on a two-species gene-expression cascade, a tensor-product Bernstein enclosure certifies 83.2% of the design plane at bisection tolerance epsilon=0.05 with 324 terminal boxes, whereas four baseline enclosures certify between 50.9% and 58.9% on the same paving. For a fifth-order loop, the certified fraction increases from 34.4% to 73.8%, and the certified and excluded terminal boxes confine the robustly stabilizing area to a window of about 2.1 percentage points. Finally, for a refining variant of the algorithm in which the plant box is refined alongside the design box, the undecided area satisfies |U(epsilon)|=O(epsilon) under a non-degeneracy assumption on the stability boundary.

Protein-cofactor selectivity prediction is important for enzyme annotation, metabolic engineering, and biocatalyst discovery, but it remains challenging because experimentally supported associations are limited and unevenly distributed, and because biologically relevant differences can be subtler than those captured by general protein-ligand interaction models. Here, we present MINT (Multimodal Interaction Network with Teacher supervision), a multimodal framework for cofactor specificity prediction. MINT combines protein sequence features with cofactor sequence and graph representations and is trained with a gradual adaptation strategy that transfers useful information from a pretrained protein-ligand interaction model to the cofactor prediction task. On a fixed balanced held-out test set, MINT achieved the best overall performance among all compared methods (AUC = 0.932), and this ranking was reproduced in an independent family-stratified evaluation at the natural positive rate (pooled AUC = 0.939). Further analyses suggest that the pretrained model provides transferable structural information, while the gradual training scheme helps adapt that information to cofactor specificity. In a case study of the chemically near-identical NAD/NADP pair, MINT recovered 95.0-98.6% of true binders and resolved a single-phosphate-level distinction with direct relevance to engineering enzyme cofactor specificity. These results show that gradual transfer learning is an effective approach to cofactor specificity prediction when annotation is limited.

Plasmid copy number (PCN) is a central determinant of recombinant gene expression levels and metabolic burden in bacterial cell factories. However, dynamic real-time control of PCN during microbial cultivation remains largely unexplored. In the ColE1/pUC19 replication system, PCN is governed by an antisense RNAI/RNAII circuit, but this native mechanism provides no external handle for process-level intervention. Here, we engineer and characterize a dual-layer control architecture that combines two orthogonal perturbations to direct plasmid replication in Escherichia coli populations i.e., temperature shifts, which modulate the secondary structure of RNAII and, consequently, its binding to RNAI and its inhibitory potency (ex vivo control), and arabinose-inducible overexpression of a supplementary RNAI cassette, integrated either on a secondary low-copy plasmid or directly on the high-copy target plasmid (in vivo control). Using a superfolder GFP validated fluorescent reporter and automated flow cytometry in continuous cultures, we track PCN dynamics at the population level in real time under chemostat conditions. Preliminary screening in microplates high-throughput cultivation device across three strains, two carbon sources, and three temperatures (30, 37, and 42C) reveals that the two control layers act synergistically. Neither temperature alone nor inducible RNAI alone achieves the dynamic range obtained by their combination. This synergistic effect is sustained during continuous cultivation at a fixed dilution rate of 0.1/h, with carbon source and temperature transitions producing predictable and reproducible shifts in population-level PCN. These results establish a scalable, dual-input framework for on-demand PCN tuning in continuous bioprocesses, with direct relevance to biopharmaceutical plasmid production and synthetic biology applications.

Forster resonance energy transfer (FRET) is commonly used to monitor protein-protein interactions in situ. The high spatiotemporal resolution and facile implementation inside complex molecular environments have spearheaded FRET's widespread adoption in biosensing. Despite these advantages, current FRET biosensors are largely restricted to the detection of the presence/absence of individual inputs and are thus unable to sense several multiplexable inputs simultaneously within complex milieu of biological environments. In this work, we introduce a generalizable strategy to construct genetically encoded protein-based FRET biosensors capable of recognizing multiple inputs following Boolean logic-type (YES/OR/AND) operations. These topologically specified FRET sensors powerfully expand the input capacity in sensing protein-protein interactions while providing a user-programmable platform for monitoring heterogeneous biological activities both in vitro and in living cells.

Glucoamylase is essential for industrial starch saccharification, but the limited thermostability and near-neutral pH tolerance of fungal glucoamylases necessitate cooling and acidification of liquefied starch. Here, we developed an artificial intelligence-guided strategy to simultaneously improve the thermostability, pH tolerance, and catalytic activity of glucoamylase from Penicillium oxalicum (PoGA). Two property-specific machine-learning models, CASPE-T and CASPE-A, identified substitutions associated with thermostability and pH tolerance, respectively. Experimental screening identified beneficial substitutions in 11 of 21 CASPE-T and 12 of 22 CASPE-A candidates. Folding-energy-guided recombination integrated the two traits while maintaining structural compatibility. The optimal variant, PoGA T513E/Q305N, exhibited 2.21-fold higher specific activity than the wild type, with half-life extended from 22.3 to 57.9 min at 60 degrees C and from 16.6 to 64.7 min at pH 8.0. Molecular dynamics simulations attributed these improvements to reinforcement of high-occupancy hydrogen-bonding networks, suppression of conformational fluctuations in the linker and carbohydrate-binding module, enhanced long-range dynamic coordination, and preservation of a compact catalytic architecture. At 60 degrees C and pH 6.5 without acidification, PoGA T513E/Q305N produced 219.9 g/L glucose and achieved 89.1% starch conversion, 31.4% higher than the wild type. This work provides an efficient framework for multi-objective enzyme engineering and sustainable starch biorefining.