Trends in Biotechnology

Genetic constructs meant for metabolic engineering in nonmodel microbes often use similar genetic parts to those familiar to E. coli work. The typical workflow is to clone these parts into plasmids in E. coli before they are transferred to the nonmodel host or its genome. In many cases, the metabolic burden of these constructs is stronger in the E. coli cloning phase of the workflow than in the eventual host, possibly resulting in mutation or other failure during cloning. Here, we apply generic knockdown of a range of popular expression systems, using CRISPR interference, by targeting guide RNAs to either promoters or RBSs that are commonly used in metabolic engineering. Generic targeting of a constitutive promoter series, combined with genome integration of CRISPR components, allows the use of only one or a few specific cloning strains to achieve strong knockdown of a wide range of constructs. Further, we present a recombinase-based workflow for easily adding guide RNAs with custom targets, so that users can knock down any desired promoter or ORF. Together, this group of strains comprises easy-to-use cloning strains meant for increasing success rates of difficult or burdensome cloning reactions, ultimately allowing more ambitious genetic constructs to reach their intended context.

Escherichia coli is a well-established model organism in molecular biology and biotechnology. Despite its long history as a laboratory workhorse, the efficient single-step chromosomal integration of large DNA fragments remains a challenge. Currently known methods are either simple but have limitations on insert size, or flexible but laborious requiring plasmid construction or multi-step procedures. Here, we present PhAGE (Phage-Assisted Genome Engineering), which enables the integration of [~]20 kb DNA fragments into E. coli genome within a single day. PhAGE method uses in vitro packaging of recombinant DNA into bacteriophage capsids, followed by general transduction to introduce pre-assembled DNA with flanking homology arms into recipient cells. This approach allows efficient and landing pad-free integration of large constructs into the target loci. We demonstrate its usefulness through rapid integration of multi-gene operons. PhAGE resolves the long-standing trade-off between simplicity and insert size in E. coli genome engineering, accelerating strain construction across a wide range of applications, from biosynthetic pathway engineering to genome-scale design.

The genus Mycobacterium is increasingly recognized as a major clinical concern due to diseases such as tuberculosis, along with the emergence of antimicrobial-resistant strains, underscoring the urgent need for advanced genetic tools to study mycobacterial biology and pathogenesis. Progress in this area relies heavily on the functional characterization of previously unannotated genes, which necessitates tightly regulated expression systems. Here, we report the development of an improved tetracycline-regulated vector platform, comprising the episomal pM(R)T2 and integrative pMI(R)T2 series, which builds upon the previously described pMT vector system. The T2 vector series incorporates a fine-tuned TetRO system for enhanced transcriptional control. The pMT2 vectors function as tetracycline-inducible systems, whereas the pMRT2 variants utilize a reverse tetracycline repressor (RevTetR) to enable tetracycline-repressible gene regulation. Additionally, the integrative variant, pMI(R)T2 switches the oriM element with the integrase and attP sites derived from mycobacteriophage L5, facilitating stable genomic integration and controlled expression of concentration-sensitive genes, including toxins. To expand the selection flexibility, the pAN(R)Tet series replaces the kanamycin resistance cassette with a hygromycin resistance cassette. Functional validation of gene regulation in M. smegmatis and M. bovis BCG shows that both TetR and RevTetR systems provide reliable inducible and repressible controls, respectively, upon anhydrotetracycline addition. Taken together, these vectors constitute a versatile, tightly regulated genetic toolkit with significant potential to accelerate research and therapeutic development in mycobacterial systems.

Cells are the fundamental unit of life. Yet there is no natural cell for which all its life-essential functions are understood. Here we demonstrate a complete cell cycle for a synthetic cell undergoing selection, with genome replication, growth, resource acquisition via feeding, and genetically encoded division. The cell is encoded via a 90kb genome that includes functions needed for resource uptake, transcription, translation, growth, genome replication, and division. The resulting synthetic cell is sufficiently encouraging to support routinization of synthetic cell engineering workflows, and will ultimately underlie diverse applications across all of biotechnology.

Genome synthesis is a major limitation in generative biology. Here, the half-sized genome of Escherichia coli was constructed by fleshing out an imperfect minimal genome through genome-scale debugging process. Our platform consists of integrated development environment (IDE) and runtime environment (RTE). The genome IDE supported the cell-free assembly of 200-300 kb plasmids and their in vivo fusion into a single 1.7 Mb plasmid. This imperfect genome was stably maintained in E. coli as a guest genome. The RTE relies on the restriction enzyme-mediated self-digestion of the host genome in the presence and absence of the RecA recombinase. The guest genome was tested, debugged, and partially replaced by the host genome to establish E. coli controlled by a 2.3-Mb genome. This is less than half in size of the wildtype and the smallest ever reported. Enfleshing a guest genome will facilitate genome printing that transforms AI-designed genomes into physical ones.

DNA assembly is a cornerstone of synthetic biology, enabling the construction of bespoke genetic systems for applications ranging from metabolic engineering to DNA nanotechnology. Conventional Gibson Assembly (GA), the most widely used method, relies on 5' exonucleolytic resection and elevated temperatures ([~]50 {degrees}C), which together prevent the retention of 5' modifications and restrict compatibility with temperature-sensitive functionalities. Here, we report a DNA assembly strategy, 3 exonuclease-mediated low-temperature DNA assembly (3LTDA), which generates complementary 5' overhangs while preserving 5' end integrity. This approach enables the efficient assembly of blunt-ended, 5'-functionalised DNA fragments into both linear and circular constructs at ambient temperature (21 {degrees}C), with some assembly observed at temperatures as low as 4{degrees}C. We systematically optimise reaction conditions and demonstrate that this method supports efficient plasmid re-circularisation and multi-fragment assembly, including the construction of a [~]12.5 kbp plasmid from multiple DNA components. Comparative analysis across several DNA substrates shows that, under their respective optimal conditions, this approach matches or exceeds GA performance, improving assembly efficiency by up to 12.8%. Sequence analysis confirms high fidelity with no detectable base-pairing errors across assembled junctions. Crucially, this method preserves chemically functionalised 5' termini, enabling downstream conjugation and biochemical functionality. Retention of azide and biotin modifications was verified through fluorescence imaging, bead-based co-localisation, and enzymatic activity in ELISA-based assays. This is in contrast to GA-assembled controls, which showed complete loss of functionality under comparable conditions. We further assembled 5 kbp dsDNA using 3LTDA from four independent segments, three with different fluorescence reporters, and the fourth containing a biotin group for microparticle conjugation, each on the 5 end. Under fluorescence illumination, bead-bound DNA with all three fluorescence markers were detected. Conventional GA assembled constructs, on the other hand, failed to retain the reporter groups and the fluorescent images did not show the presence of any fluorescent markers. In addition to enhanced performance, the method could also reduce reagent cost and eliminate the need for elevated temperatures, simplifying workflows and expanding the applicability of multi-functionalised DNA constructs. Collectively, this work establishes 3LTDA as a robust, low-temperature alternative to conventional GA, with advantages for applications requiring precise chemical modification, temperature-sensitive components, or deployment outside conventional laboratory environments.

Microbial cultivation optimization remains labor-intensive and inefficient, requiring extensive experimental screening to identify suitable growth conditions. Traditional one-factor-at-a-time approaches are particularly ineffective for exploring complex, multidimensional nutrient parameter spaces. We present MicroGrowAgents, an AI-driven, agent-based system that automates the design of optimized growth media through integration of knowledge graphs, metabolic modeling, and optimal experimental design. The system employs 28 specialized agents and 50 skills that query structured biological knowledge (KG-Microbe: 864,363 validated species), mine literature evidence (245+ papers), perform genome-guided design (57 genomes, 667,000+ annotated features), and generate statistically optimal experimental designs using the MaxPro algorithm. We applied the approach to Methylorubrum extorquens AM1 by cultivating 70 designed conditions in quadruplicate and assessing three concurrent objectives: biomass (OD600 at 740 nm), redox activity (Abs590 Biolog proxy), and lanthanide uptake (residual Nd measured by arsenazo III). Monte-Carlo resampling of the replicate-level uncertainty (1000 iterations) identified a single stable Pareto-optimal medium, MPOB_058 (membership frequency 0.99), together with two borderline candidates and six rare appearers, providing a robust anchor set for subsequent rounds of design-build-test-learn. The integration of chemical similarity search (208,000+ embeddings), metabolic gap analysis, and multi-modal reasoning enables evidence-based hypothesis generation that reduces experimental burden while accelerating discovery of growth-promoting conditions. MicroGrowAgents provides complete provenance tracking with cryptographic checksums and 90.5% literature citation coverage, advancing reproducible, data-driven approaches to microbial cultivation. Author SummaryGrowing microbes in the laboratory is like figuring out the right recipe: too much or too little of any nutrient and they barely grow. Scientists have traditionally tested ingredients one at a time, an approach that is slow, expensive, and poorly suited to the dozens of interacting nutrients that real microbes need. We built MicroGrowAgents, an AI system that acts like a team of specialist scientists working together. It consults structured biological databases, reads the published literature, inspects microbial genomes, and uses statistical experimental design to recommend nutrient combinations worth testing in the laboratory. Applied to Methylorubrum extorquens AM1, a methanol-eating bacterium of interest for capturing rare-earth elements, the system designed 70 growth conditions and identified one robust winner that performed well across cell growth, metabolism, and lanthanide uptake. The software is free and open-source, helping any laboratory adopt these tools.

Primary human hepatocytes (PHHs) are the gold standard for toxicology and drug metabolism studies in industry. However, their limited availability, substantial batch-to-batch variability, and high cost restrict their use. Here, we report a novel culture condition that reprograms PHHs into a proliferative state. These proliferating cells, termed precursors of chemically expanded hepatocytes (pre-cHep), expand over 106-fold within 30 days while retaining liver repopulation capacity comparable to PHHs. pre-cHep can further differentiate into chemically expanded hepatocytes (cHep) as three-dimensional (3D) spheroids within 7 days in vitro, exhibiting global gene expression profiles, albumin production, and cytochrome P450 (CYP) activities similar to 3D-cultured PHH spheroids (3D PHH). Efficient genetic manipulation of pre-cHep using CRISPR/Cas9 is also achievable. Together, pre-cHep and cHep represent a promising alternative to high-quality PHHs, providing a more affordable, reproducible, and scalable source of human hepatocytes for toxicology, drug metabolism studies, disease modelling, towards precision drug development.

The Modular Cloning (MoClo) and PhytoBrick standards have revolutionized plant synthetic biology by establishing a standardized, hierarchical assembly grammar. However, as the engineering of complex metabolic pathways, multi-trait stacks, and synthetic gene circuits expands, existing toolkits hit practical boundaries in assembly capacity and fixed grammars. To overcome these bottlenecks, we present MozClo, an expansion of the MoClo/PhytoBrick architecture. MozClo expands the standard Level 1 assembly framework to 10 positions using new L1 acceptors, end-linkers and dummy parts. We also identify and resolve a critical, sticky-end collision at L1 position 7 that has caused assembly failures during L2 cloning of large plasmids. To address commercial DNA synthesis length constraints and to lower cloning costs, we designed a universal 5-in-1 gene fragment multiplexing system. This architecture embeds up to five distinct parts flanked by orthogonal pairs of BpiI restriction sites into a single synthesized fragment, allowing them to sort independently into their respective L0 acceptor plasmids while maintaining complete modular flexibility of part types. Finally, we provide Level 2 cloning backbones with built in selection genes for common soybean transformation methods to facilitate downstream plant selection. Together, these advancements reduce DNA synthesis overhead and accelerate the construction of complex multigene payloads for plant biotechnology.

Compact tissue-specific promoters are highly desirable for gene therapy because viral vectors possess limited packaging capacity. However, existing promoter engineering strategies rely primarily on rational design or de novo sequence generation and lack efficient approaches for compressing long native promoters while preserving regulatory specificity. Although genome foundation models have substantially improved sequence-to-function prediction, they have not been effectively translated into computational platforms for promoter engineering. Here, we present VirEvo, a computational promoter engineering framework that integrates a virtual dual-luciferase assay (VirDLA), genome-foundation-model-guided genetic evolution, and an orthogonal Pan-Tissue Consistency Filter (PTCF). VirDLA introduces an internal-reference normalization strategy inspired by dual-luciferase reporter assays, enabling relative comparison of promoter activity across tissues without retraining AlphaGenome. Guided by these normalized activity scores, VirEvo iteratively optimizes promoter selectivity, off-target activity, and sequence length. Using the human p16INK4a promoter as a proof of concept, VirEvo evolved a compact synthetic promoter, SRP2M, of only 398 bp, representing an 85.9% reduction in sequence length. Experimental validation using dual-luciferase reporter assays in senescent IMR90 fibroblasts demonstrated that SRP2M retained 77% of wild-type senescence selectivity while reducing basal leakage to 52% of the wild-type level. Together, these results demonstrate the feasibility of genome-foundation-model-guided promoter engineering. VirEvo provides a generalizable framework for designing compact tissue-specific regulatory elements and extends the application of genome foundation models from functional prediction to synthetic regulatory engineering.

Translation initiation has become an attractive target for engineering orthogonal translation systems, yet the extent to which these systems retain functionality across distinct host backgrounds remains poorly defined. In bacteria, start codon recognition depends on pairing between the initiator tRNA anticodon and a suitable start codon within the appropriate distance from the Shine-Dalgarno sequence. These sequence-specific interactions enable translation initiation to be reprogrammed through anticodon engineering. What is currently missing is an understanding of how anticodon mutants of initiator tRNAs function across different bacterial strains. Here, we systematically evaluated the portability of a library of twelve i-tRNA anticodon mutants paired with their complementary non-canonical start codons. Most i-tRNA-start codon pairs supported detectable translation initiation across multiple strains, demonstrating broad functional portability. However, initiation efficiency, absolute system output, and fitness effects varied substantially between strains. Comparative genomic analyses revealed host-specific gene differences broadly, and endogenous tRNA gene sequence and copy number specifically, was associated with this variability. While most i-tRNA variants were well tolerated, a subset produced strain-dependent growth defects that primarily affected growth rate rather than final culture density. Together, these findings show that translation initiation efficacy of engineered i-tRNAs is partially strain-dependent and that host background must be considered a key design variable when deploying these translation systems. Looking forward, this study provides a framework for host-aware selection of microbial chassis for orthogonal translation applications in synthetic biology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/719103v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@118b02borg.highwire.dtl.DTLVardef@1d5dab0org.highwire.dtl.DTLVardef@1088d0borg.highwire.dtl.DTLVardef@63eb74_HPS_FORMAT_FIGEXP M_FIG C_FIG

High-quality plasmid DNA purification at high throughput remains a significant bottleneck in molecular biology and bioengineering. Current methods frequently fail to deliver sufficient yields of pure, transfection-grade DNA required for genetic engineering applications in mammalian cells. Here, we present a Biofoundry-based automated pipeline using the CyBio FeliX robotic liquid handling platform to rapidly purify plasmid DNA with minimal manual intervention. The protocol leverages Solid Phase Reversible Immobilisation (SPRI)-based magnetic bead technology to ensure consistency, scalability, and DNA purity suitable for downstream viral particle production and mammalian cell transfection. The pipeline supports flexible processing of between 8 and 96 samples per run, making it adaptable across a wide range of experimental scales. The protocol is openly available via Earlham Institute GitHub repository, enabling broad adoption across the bioscientific community and contributing to the growing toolkit of reproducible, scalable engineering biology workflows. In this work, we employed an integrated robotic pipeline to process 528 pooled DNA plasmids and built a Lentiviral DNA plasmid library for lineage tracing, validated the library by sequencing, and demonstrated efficacy in downstream mammalian cell transfection experiments.

Antimicrobial resistance (AMR) is a growing global threat to human health, and rapid methods for characterising emerging antimicrobial resistance genes (ARGs) are needed. Here, we develop a semi-automated workflow using cell-free gene expression (CFE) systems to measure the activity of two ARGs encoded on plasmid DNA that produce rifampicin-inactivating and gentamicin-inactivating enzymes. We validated the use of a small benchtop Myra liquid handling system compared to manual pipetting, with no statistical differences observed. After optimising the pre-incubation time of ARGs and dispensing protocol, expression of aac(3)-IIa increased the half-maximal inhibition concentration (IC50) of gentamicin by over 150-fold, while arr-3 increased the IC50 of rifampicin by approximately 20-fold compared to controls. Future work could extend this platform to characterise novel ARGs identified through genomic surveillance or rapidly profile activity of new or derivative antibiotics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/720151v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1a61fe3org.highwire.dtl.DTLVardef@1778eadorg.highwire.dtl.DTLVardef@380be4org.highwire.dtl.DTLVardef@194bb63_HPS_FORMAT_FIGEXP M_FIG C_FIG

Spatial organization of gene expression is a key feature of cells but remains a major challenge in bottom-up synthetic biology. While phase-separated DNA condensates have been used to spatially confine transcription, achieving efficient recruitment of protein-coding DNA for full protein biosynthesis within these membrane-less structures has remained a major challenge. Here, we present a modular DNA nanostructure that enables tunable and highly efficient partitioning of long client DNA into condensate-based synthetic nuclei, thereby surpassing present limitations. This serves as the starting point for a multimodal DNA organelle-based system for spatially regulated gene expression in synthetic cell environments. The flow of information includes localized transcription within this synthetic nucleus, followed by translation and product release into the surrounding cytosol. Furthermore, we extend the toolkit of spatiotemporal organization by designing protein targeting of cell-cycle protein ParR within parC-enriched orthogonal DNA condensates. Quantitative analysis reveals a trend toward higher protein yield in the condensate-based system compared to standard PURE expression, indicating a functional advantage of spatial organization even in such minimal systems. Hence, we demonstrate how protein expression can be engineered not only by molecular composition, but by spatial architecture itself. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/728287v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@18e40a4org.highwire.dtl.DTLVardef@35d6f6org.highwire.dtl.DTLVardef@b3d376org.highwire.dtl.DTLVardef@1fffde1_HPS_FORMAT_FIGEXP M_FIG C_FIG

Carbonic anhydrases are among the fastest known biocatalysts, reversibly facilitating the hydration of CO2 to HCO3- at rates up to 107 s-1, which warrants their investigation for industrial carbon capture technologies. However, engineering carbonic anhydrases to maintain stability under harsh industrial process conditions remains a key challenge, and sequence-to-function datasets compatible with machine learning to inform forward engineering are lacking. Here, we developed a high-throughput platform that couples cell-free gene expression with a gaseous CO2 colorimetric assay to map the fitness landscapes of carbonic anhydrases. From 96 diverse natural homologs, we identified a robust variant from the Aquificota phylum and conducted an exhaustive mutational scan and functional assessment of this enzyme at 70C and 90C, covering >99% of all single-amino acid substitutions (totaling 4,365 mutations assayed in 39,285 reactions). This biochemical landscape was used to benchmark 22 zero-shot protein fitness models and identify critical mutations that improved enzyme stability at 90C by more than three-fold. We then used both zero-shot protein language models and supervised learning to filter 419 model-generated variants from a ProteinMPNN library of 100,000 sequences, leading to a best-in-class enzyme that retained activity after incubation at 95C. This work demonstrates that integrating cell-free enzyme engineering with machine learning enables opportunities for high-throughput experimental measurements to benchmark and improve protein language models, accelerate design loops, and expand functional exploration within protein families where experimental information is limited.

Expanding genetic engineering beyond model microorganisms is critical to unlocking novel applications in biotechnology, yet the low efficiency of DNA delivery methods like conjugation, remains a major bottleneck in non-model and environmental microbes. Here, we present an automated, high-throughput droplet microfluidic platform that enhances conjugation by encapsulating donor and recipient microbes in picoliter-scale water-in-oil microdroplets, stabilizing cell-cell contact and DNA transfer. Optimization of incubation time, donor to recipient ratio, and plasmid type yielded over a 100-fold increase in conjugation efficiency compared to conventional methods and enabled delivery of complex DNA libraries in low reaction volumes, demonstrating scalability for pooled plasmid library delivery. We further utilized a synthetic biology circuit for donor removal within microdroplets without antibiotic selection, eliminating the need for host-specific selection markers or engineered auxotrophs. When applied to a soil microbial community, this platform improved community-level conjugation, preserving microbial diversity and enabling the identification of genetically accessible chassis. Collectively, this platform establishes a scalable, generalizable solution for high throughput DNA delivery in previously inaccessible microbial hosts. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=54 SRC="FIGDIR/small/721591v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@c7a8d4org.highwire.dtl.DTLVardef@1d1fbaorg.highwire.dtl.DTLVardef@e1faforg.highwire.dtl.DTLVardef@14234dc_HPS_FORMAT_FIGEXP M_FIG C_FIG

Lentiviral vectors enable efficient delivery of genetic cargoes for gene and cell therapies. With their [~]10-kb packaging limit, lentiviral vectors can encode multiple transcription units, supporting delivery of compact gene circuits. RNA-based devices offer highly compact control including ligand-responsive induction and closed-loop regulation. However, RNA devices such as ribozymes and splicing switches may interfere with vector production via activity on the single-stranded RNA genome. Here, we examine the impact of gene syntax and genetic parts to define design strategies for two-gene vectors encoding RNA devices. We find that titer decreases with genetic parts that interfere with transcription or processing of the viral transcript during production. Compared to initial vectors, our best-performing design boosts titer more than 30-fold, enabling fine-scale tuning of expression to optimize cell-fate conversion within a nonmonotonic landscape. Together, this work illuminates principles for constructing two-gene lentiviral vectors with both high titer and robust expression, enhancing efficacy for downstream applications.

Genome streamlining and pathway refactoring are powerful strategies for constructing controllable microbial chassis for both fundamental studies and applications. While rational design benefits from reduced genetic complexity, adaptive laboratory evolution (ALE) thrives on metabolic redundancy, creating a mismatch between optimal hosts for design and evolution. Here, we introduce a dual chassis framework (DUET) in which rational pathway construction and adaptive evolution are first carried out in an evolution-competent host, and the resulting optimized designs are subsequently transferred into a genetically stable chassis for deployment. Using the naturally evolvable bacterium Acinetobacter baylyi ADP1 and its genome-stabilized derivative (ISx), we applied this framework to the {beta}-ketoadipate pathway, a central hub for aromatic compound catabolism. We first streamlined the native network by deleting individual pathway branches and then engineered a minimal synthetic route that merges protocatechuate and catechol metabolism. Subsequent ALE enabled efficient growth through this synthetic pathway, and reverse-engineering identified key adaptive mutations underlying functional recovery. Both the synthetic pathway and the mutations were transferred unchanged into ISx, where robust growth was maintained without further adaptation. These results demonstrate that DUET enables portable, host-independent deployment of rational metabolic streamlining combined with evolution, providing a generalizable strategy for building reduced yet robust microbial platforms. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/729570v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@161e8b6org.highwire.dtl.DTLVardef@f5056forg.highwire.dtl.DTLVardef@37dcc0org.highwire.dtl.DTLVardef@17df4a7_HPS_FORMAT_FIGEXP M_FIG C_FIG

The rapid emergence of SARS-CoV-2 variants that evade neutralizing antibodies underscores the need for next-generation antiviral biologics that combine molecular precision with scalable, cost-effective manufacturing. Computationally designed miniproteins targeting the receptor-binding domain (RBD) of the spike protein offer a compelling alternative to monoclonal antibodies due to their small size, high thermal stability, and compatibility with microbial expression systems. Here we report the end-to-end development and cGMP production of IPD-52520, a de novo antiviral miniprotein, using an optimized E. coli platform. Two miniprotein candidates, a homotrimeric construct (Trimer is referred to as IPD-52520, 17 kDa) and a tandem fusion (Daisy is referred to as IPD-52521, 25 kDa), were evaluated in parallel through systematic optimization of strain selection, media composition, fed-batch fermentation, inclusion-body solubilization, refolding, and chromatographic purification. The Trimer was downselected as the lead molecule based on superior preclinical efficacy, favorable pharmacokinetic properties, and higher volumetric manufacturing yields. The optimized process delivers approximately 2 g/L of purified protein at greater than 90% purity. Scale-up from 5 L to 50 L under cGMP conditions demonstrated excellent batch-to-batch reproducibility across six independent batches, supporting nonclinical and Phase 1 clinical supply. Comprehensive biophysical characterization confirmed a well-folded, predominantly alpha-helical trimer (Tm = 73.4 {degrees}C; polydispersity = 1.005) with an intact primary structure and strong target-binding affinity (KD < 1 pM). Real-time stability studies indicate that the drug substance is stable at 2-8 {degrees}C for at least 12 months, with ongoing stability studies. These results demonstrate the feasibility of translating computationally designed antiviral miniproteins into manufacturable biologics and provide a platform applicable to rapid-response therapeutics against current and future pandemic threats.

Artificial intelligence has advanced from analyzing experimental data to autonomously generating hypotheses, designing experiments, and coordinating closed loop discovery. Yet the translation from computational reasoning to physical execution remains bottlenecked by the experimental protocol, which in biology still relies on ambiguous natural-language descriptions: a medium other engineering disciplines abandoned decades ago in favor of compiler verified specification languages. This deficit fragments reproducibility along three axes: protocol accuracy, pre execution verification, and cross platform portability. Existing formalisms address only subsets of these challenges, trading expressiveness for rigor, portability for standardization, or usability for provenance. Here we introduce the Biology Protocol Language (BPL), a domain specific language with a biology-native type system in which every quantity carries physical units, every reagent declares its physical form, and every container maintains compiler-tracked state, so that implicit assumptions must be stated explicitly and physically impossible operations are rejected at compile time. We further develop BPL-COGEN, a pipeline that couples a fine tuned 30 billion parameter language model with the deterministic compiler in a closed generate validate repair loop, iteratively correcting the translation from natural language SOPs to BPL through compiler diagnostics until all physical, dimensional, and state constraints are satisfied. On a benchmark of 300 published Nature Protocols papers, BPL COGEN achieved an overall fidelity score of 95.1 against the source protocols as ground truth. Wet-lab experiment and cross-platform validation in GFP expression library construction and HPLC to UHPLC method translation confirmed that a single BPL source yielded reproducible execution across manual and liquid handler assisted contexts. The results established a novel pipeline that generates compiler-verified protocols, which is an essential prerequisite for physically embodied AI in biology.