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PubMed2026

Rational design of high-productivity perfusion processes for CHO Cells: From growth inhibitory strategies to model-driven optimization.

While perfusion culture for Chinese hamster ovary (CHO) cells offers advantages such as continuous operation and flexibility, it suffers from product loss through cell bleeding and difficulties in reaching high productivity due to sustained rapid cell growth. Growth inhibitory strategies are widely used to enhance productivity in fed‑batch processes; however, their practical implementation and comparative effectiveness in perfusion processes remain insufficiently explored. Meanwhile, process development often relies on costly trial‑and‑error approaches. Here, we systematically compared three growth inhibitory strategies in perfusion culture-low cell‑specific perfusion rate (CSPR), sodium butyrate, and mild hypothermia-with respect to cell growth, metabolism, productivity, and product quality. Genome‑scale metabolic flux sampling analysis revealed that low‑CSPR and sodium butyrate induce a convergent up‑regulation of energy metabolism, correlating with greater gains in specific productivity (qp). Building on this insight, we developed a growth‑kinetic model for the combined low‑CSPR + butyrate strategy, incorporating parameter uncertainty. This model‑guided framework enabled the rational design of two distinct high‑productivity perfusion processes: a sustained mode that achieved robust long‑term stability alongside substantial productivity gains, and a high‑intensity mode that pushed qp and daily volumetric titer to their maxima, with increases of up to 108.94% and 190.36%, respectively, in a model CHO cell line with a moderate baseline productivity. Our study provides a proof‑of‑concept framework for perfusion intensification, from strategy selection to rational process design.

Animals↗

PubMed2011

Phase 1 gene therapy for Duchenne muscular dystrophy using a translational optimized AAV vector.

Efficient and widespread gene transfer is required for successful treatment of Duchenne muscular dystrophy (DMD). Here, we performed the first clinical trial using a chimeric adeno-associated virus (AAV) capsid variant (designated AAV2.5) derived from a rational design strategy. AAV2.5 was generated from the AAV2 capsid with five mutations from AAV1. The novel chimeric vector combines the improved muscle transduction capacity of AAV1 with reduced antigenic crossreactivity against both parental serotypes, while keeping the AAV2 receptor binding. In a randomized double-blind placebo-controlled phase I clinical study in DMD boys, AAV2.5 vector was injected into the bicep muscle in one arm, with saline control in the contralateral arm. A subset of patients received AAV empty capsid instead of saline in an effort to distinguish an immune response to vector versus minidystrophin transgene. Recombinant AAV genomes were detected in all patients with up to 2.56 vector copies per diploid genome. There was no cellular immune response to AAV2.5 capsid. This trial established that rationally designed AAV2.5 vector was safe and well tolerated, lays the foundation of customizing AAV vectors that best suit the clinical objective (e.g., limb infusion gene delivery) and should usher in the next generation of viral delivery systems for human gene transfer.

Amino Acid Sequence↗

PubMed2026

The TRIM-cancer paradox: BCG as a programmable vaccine platform and a mechanistic probe for rational immunotherapy design.

BCG, a first-generation live vaccine, is being reconsidered as an immunological platform. Interest in its heterologous protection intensified during the pandemic. However, large-scale clinical trials revealed inconsistencies in the efficacy of native BCG. This review argues that BCG's main value lies in its potential as a modifiable vector platform and in its ability to reveal tractable molecular pathways for therapeutic design. This review summarizes the molecular basis of BCG-induced trained immunity (TRIM), focusing on PRR-driven signaling, metabolic rewiring, and epigenetic remodeling in innate immune cells and hematopoietic progenitors. It also maps their convergence with pathways that sustain pro-tumorigenic inflammation. The original conceptual paradigm of the "TRIM-Cancer Paradox" is presented. This paradigm posits that the same innate immune circuits that mediate protective heterologous responses can drive tumor-promoting inflammation and immune escape under conditions of chronic dysregulation. Recombinant BCG (rBCG) is further analyzed as a strategy to rationally amplify or redirect these circuits, the current clinical landscape of BCG-based interventions across various diseases and oncological malignancies is highlighted, and specific molecular nodes that could be exploited to increase the precision, efficacy, and safety of rBCG-based therapies are identified. Overall, this review proposes BCG a programmable immunological platform and to use the TRIM-Cancer Paradox as a novel design principle for next-generation rBCG platforms that transcend traditional vaccinology and cancer immunotherapy applications.

Humans↗

PubMed2026

Rational and computation-assisted engineering of a compact and efficient CRISPR-Cas12f genome editor.

The CRISPR-Cas12f system is an ultracompact genome-editing platform, yet only a few orthologs exhibit robust activity in mammalian cells. Here, we systematically screened 23 Cas12f orthologs and identified two active nucleases, PspCas12f1 and TcCas12f1, capable of genome editing in human cells. Single guide RNA (sgRNA) scaffold optimization enhanced the basal activity of PspCas12f1. To further improve its performance, we combined structure-guided rational design with protein language model-assisted filtering. Candidate mutations predicted by SaProt were further screened based on structural proximity to the DNA-binding interface and electrostatic compatibility. This integrative strategy identified Q100R and E293R, whose combination yielded the optimized variant enPspCas12f1. enPspCas12f1 achieved genome-editing efficiencies comparable to SpCas9 across multiple endogenous loci while maintaining high specificity. Collectively, our results demonstrate that integrating protein language model-assisted filtering with structure-guided rational design provides an effective strategy for engineering PspCas12f1 and may facilitate the optimization of additional compact CRISPR nucleases.

CRISPR-Cas12f↗

PubMed2026

Collateral sensitivity-harnessing microbial vulnerabilities as a solution to antimicrobial resistance.

Bacteria exhibit an evolutionary trade-off through their development of collateral sensitivity (CS) which allows them to resist one antibiotic while becoming more vulnerable to another. This vulnerability offers a compelling therapeutic opportunity by selecting against resistant isolates. Laboratory evolution studies, genome sequencing, deep mutagenesis and use of artificial intelligence and machine learning can design the bespoke strategy against multi-drug-resistant bacteria. This review discusses about recent studies that are rationally designed to harness this evolutionary trade-off for the development of alternative antimicrobial strategies. The translational barriers to the clinical implementation of CS are addressed and evidence-based design principles for optimization of CS-guided therapy are discussed.

Bacteria↗

PubMed2026

EPIC: multi-objective guided diffusion for epitope design in TCR-pMHC complexes.

MOTIVATION: T cell receptor (TCR) recognition of peptide-major histocompatibility complex (pMHC) complexes is central to adaptive immunity, yet rational design of immunogenic epitopes remains elusive due to complex triplet binding constraints and data scarcity. No existing method can generate epitopes satisfying simultaneous requirements for antigenicity, MHC presentation, and TCR specificity. RESULTS: We present EPIC, a multi-objective diffusion framework that decomposes TCR-pMHC binding into three biologically grounded sub-tasks, enabling training-free gradient guidance without end-to-end retraining. By integrating ESM-based classifiers with a peptide diffusion generator, EPIC leverages heterogeneous immunological interaction datasets to generate diverse, context-aware epitopes. EPIC-designed top-three epitopes achieve lower predicted interface energies compared to ground-truth epitopes in 78.31% of test cases, while maintaining 80.1% sequence novelty and comparable structural confidence. Generated epitopes exhibit 100% uniqueness, high diversity (64.05%), and high antigenicity scores (0.4723). To our knowledge, EPIC is the first computational framework capable of de novo epitope design while explicitly integrating the triplet constraints of TCR-pMHC binding. This paradigm shift from discovery to design unlocks new potential for personalized cancer vaccines, precision adoptive T cell therapy, and rapid response to emerging infectious diseases. AVAILABILITY AND IMPLEMENTATION: The source code of EPIC is available at https://github.com/Octopus125/EPIC and archived on Zenodo (DOI: 10.5281/zenodo.18537646).

Receptors, Antigen, T-Cell↗

PubMed2026

Precisely designed keystone metabolites boost shrimp disease resistance by recruiting symbionts via the lipoxin A4-AP-1 pathway.

BACKGROUND: Gut metabolites and symbionts are indispensable for host health, yet the precise identification of keystone metabolites and construction of synthetic microbial communities (SynComs) to enhance disease resistance remains limited. RESULTS: Using Litopenaeus vannamei as a model, we identified pyruvic acid and DL-glutamine (1:2) as keystone metabolites by borrowing the microbial ecology principles of bio-indicators and driver taxa. Dietary supplementation with these metabolites sufficiently protected shrimp from white feces syndrome (WFS). Multi-omics analyses demonstrated that keystone metabolites exerted positive effects by enriching beneficial Ruegeria lacuscaerulensis, Bacillus subtilis and Nioella nitratireducens, strengthening the gut network stability, and enhancing shrimp immunity, which collectively potentiated WFS resistance. The recruited three strains were consumers and producers of the two keystone metabolites, and discriminative strains between healthy and diseased shrimp across global datasets. A SynCom constructed from the three strains (4:3:2) replicated the efficacy of keystone metabolites. Both keystone metabolites and SynCom elevated shrimp gut and hepatopancreas lipoxin A4 (LXA4) levels, which suppressed the pro-inflammatory transcription factor AP-1, as validated by in vivo inhibition assay. CONCLUSIONS: Our findings demonstrate that precisely designed keystone metabolites enhance shrimp disease resistance through the recruitment of key symbionts-LXA4-AP-1 axis. The rationally designed keystone metabolites and SynCom are compelling biocontrol solutions in improving host disease resistance. Video Abstract.

Animals↗

PubMed2026

Recent advances in the chemical synthesis of Glycosaminoglycans.

Glycosaminoglycans (GAGs) are complex carbohydrates ubiquitously expressed on cell surfaces and within the extracellular matrix, where they regulate essential biological processes through sequence- and sulfation-dependent interactions. Major GAG classes, including heparan sulfate (HS), chondroitin sulfate (CS), dermatan sulfate (DS), keratan sulfate (KS), and hyaluronic acid (HA), exhibit diverse sulfation patterns that encode specific molecular recognition events. Deciphering their structure-activity relationships has been hindered by intrinsic heterogeneity and limited access to well-defined materials. This review focuses on the recent advances in the chemical synthesis of GAGs, highlighting strategies that enable precise control over GAG structure and sulfation patterns. Recent innovations in protecting group design, stereoselective glycosylation, and automated assembly have significantly improved synthetic efficiency, facilitating the construction of increasingly complex and biologically relevant structures and advancing the rational design of GAG-based tools and therapeutics.

Glycosaminoglycans↗

PubMed2026

Systematic mapping of insertion-tolerant regions enables capsid engineering of an infectious RNA phage.

RNA phages are attractive platforms for the design of programmable bioparticles, but their development has been constrained by limited knowledge of genomic sites that can tolerate sequence insertion. Here, we combined MuA transposase-mediated in vitro insertion mutagenesis with our established reverse genetics systems to systematically identify insertion-tolerant regions (ITRs) in the RNA phages MS2 and PP7. Screening of 4,555 MS2 and 2,228 PP7 random insertion clones identified 29 and 26 non-redundant ITRs, respectively. We further analyzed and compared these ITRs in the context of RNA genome organization and virion architecture. Both phages contained ITRs within the maturation protein, whereas only PP7 tolerated insertions within the coat protein (CP). On the basis of structural location and plaque-forming capacity, an ITR situated between Gly74 and Glu75 (GGC^GAG) in the PP7 CP was selected for further study. Infectious phage particles generated from complementary DNA clones retained the 15-bp insertion at both the RNA and protein levels. Engineered PP7 phages carrying an Arg-Gly-Asp motif inserted into the CP at this ITR displayed enhanced in vivo clearance in a Drosophila model, despite having in vitro stability comparable to that of the wild type. These findings provide the first example of CP engineering in an infectious RNA phage and establish a framework for engineering RNA phages for biological and biotechnological applications.IMPORTANCEA major obstacle to developing RNA phages as synthetic biology platforms is the lack of design principles for genomic insertion. Here, we address this limitation by establishing a mutagenesis-and-recovery workflow that systematically identifies insertion-tolerant regions (ITRs) in the RNA phages MS2 and PP7. The resulting maps reveal distinct structural constraints in the two phages and enable rational engineering of a peptide-display site in the PP7 capsid. Using this approach, we generated an engineered infectious phage with a modified capsid, thereby providing the first demonstration of capsid engineering in an infectious RNA phage, to our knowledge. This study lays the groundwork for the rational design of live RNA phage virions as tractable and engineerable scaffolds for future biological and biotechnological applications.

Animals↗

PubMed2026

abCRISPR: deep learning-based design of abasic gRNA sequences for specific CRISPR-Cas9 genome editing.

SUMMARY: CRISPR-Cas9 has become a widely used tool for genome editing. However, its off-target cleavage caused by partial sequence matches with guide RNAs (gRNAs) remains a critical limitation. Recently, abasic gRNAs (ØXØ) have been developed to enhance target specificity, but their effects vary depending on the positional sequence context. Here, we present abCRISPR, a deep neural network (DNN) framework for the rational design of ØXØ sequences with minimized off-target activity. abCRISPR leverages informative few-shot training with paired datasets of abasic and unmodified gRNAs, using high-quality random mismatch target libraries, exhaustively sequenced for mismatched off-target substrates (n = 97583) in in vitro CRISPR-Cas9 cleavage experiments. Predicted off-target activities for both abasic and unmodified gRNAs showed strong correlation with experimental data (r ≥ 0.95, 10-fold cross-validation). Notably, these comprehensive training sets provide robust ground-truth negatives, enabling accurate and sensitive prediction of off-targets. For unmodified gRNAs, abCRISPR (AUC = 0.98) was validated to outperform existing deep learning-based methods (AUC = 0.45-0.68). When applied to the human genome, abCRISPR generated ØXØ sequences, covering 58 875 004 potent CRISPR-targetable sites with improved target specificity. Together, this work provides a comprehensive bioinformatics resource for safe and precise CRISPR-Cas9 genome editing. AVAILABILITY AND IMPLEMENTATION: The source code for abCRISPR and training data are available at https://doi.org/10.5281/zenodo.20398246. abCRISPR results for the human genome are available at http://clip.korea.ac.kr/abCRISPR/.

Deep Learning↗

PubMed2026

Recent advances in supramolecular macrocycle-based artificial light-harvesting systems.

Artificial light-harvesting systems (ALHSs) inspired by the antenna function of natural photosynthesis provide molecular platforms for collecting excitation energy and directing it to emissive or reactive acceptors. In many supramolecular ALHSs, however, practical performance is limited by poorly defined donor-acceptor orientation, aggregation-caused quenching (ACQ), interfacial defects, and limited stability in aqueous or complex media. Supramolecular macrocycles-particularly pillar[n]arenes (PAs), cucurbit[n]urils (CBs), calixarenes (CAs), cyclodextrins (CDs), and supramolecular coordination complexes (SCCs)-offer a useful design space because their cavities, pre-organized scaffolds, and reversible non-covalent interactions can confine chromophores, tune local donor/acceptor ratios, and modulate Förster resonance energy transfer (FRET). This Review systematically examines the unique structural advantages and assembly mechanisms of the five macrocyclic families, with an emphasis on their use in constructing ALHSs-from single-step to cascaded FRET-and in advancing aqueous photocatalysis, near-infrared bioimaging, panchromatic fluorescence modulation, and singlet oxygen generation. The resulting structure-property-application framework is intended to guide the rational design of macrocycle-assisted photofunctional materials while avoiding overextension of the photosynthesis analogy.

Journal Article↗

PubMed2025

Granular Hydrogels as Brittle Yield Stress Fluids.

While granular hydrogels are increasingly used in biomedical applications, methods to capture their rheological behavior generally consider shear-thinning and self-healing properties or produce ensemble metrics (e.g., dynamic moduli) while neglecting transient yielding and unyielding processes. Combining oscillatory shear testing with Brittility (Bt) via the Kamani-Donley-Rogers (KDR) model, this work shows that granular hydrogels behave as brittle yield stress fluids. This work quantifies steady and transient rheology as a function of microgel properties and granular composition for polyethylene glycol and gelatin microgels. The KDR model with Bt captures granular hydrogel behavior for a wide range of design parameters, reducing the complex rheology to a determination of model parameters. In granular mixtures, this work observes monotonic dependencies of the elastic modulus, structural viscosity, and brittility upon granular composition, while the yield stress is lower for mixtures. Microgel size distribution and polymer fraction are the most influential parameters in monolithic granular hydrogels, while microgel size and packing density are less impactful. The model robustly captures self-healing behavior and reveals that granular hydrogel relaxation accelerates with an increased small-amplitude strain rate. This quantitative framework is an important step toward rational design of granular hydrogels for applications ranging from injection and in situ stabilization to 3D bioprinting.

brittility↗

PubMed2026

NAViFluX: a visualization‑centric platform for interactive analysis, refinement and design of genome‑scale metabolic networks.

MOTIVATION: Genome-scale metabolic network (GSMN) models enable flux-based metabolite fate discovery, metabolic engineering, drug target identification, and multi-omics integration. However, programming requirements, architectural complexity, and limited visualization support impede its adoption by the broader scientific community. Existing tools exclusively specialize in GSMN analyses or visualization while lacking important features such as pathway-specific views, database-integrated refinement, and comprehensive enrichment and perturbation analyses. RESULTS: Here, we present NAViFluX (metabolic Network Analysis and Visualization of Flux), a visualization-centric, web browser-based tool that unifies native pathway/subsystem map generation, interactive model refinement via KEGG/BiGG, pathway merging and modules for flux computations, topology, and functional enrichment all within network views. Using three independent case studies on Escherichia coli, the utility of NAViFluX for characterization of nutrient-specific metabolic adaptations, enhancing gene essentiality predictions and interpretability, and rational design of an optimized carbon-fixing metabolic state is demonstrated. AVAILABILITY AND IMPLEMENTATION: All source code and supplementary files associated with the case studies are publicly available via Zenodo at https://zenodo.org/records/19107831. NAViFluX can be easily installed as a standalone software through https://github.com/bnsb-lab-iith/NAViFluX.

Metabolic Networks and Pathways↗

PubMed2025

Quantifying Protein-Nucleic Acid Interactions for Engineering Useful CRISPR-Cas9 Genome-Editing Variants.

Numerous high-specificity Cas9 variants have been engineered for precision genome editing. These variants typically harbor multiple mutations designed to alter the Cas9-single guide RNA (sgRNA)-DNA complex interactions for reduced off-target cleavage. By dissecting the contributions of individual mutations, we attempt to derive principles for designing high-specificity Cas9 variants. Here, we computationally modeled the specificity harnessing mutations of the widely used Cas9 isolated from Streptococcus pyogenes (SpCas9) and investigated their individual mutational effects. We quantified the mutational effects in terms of energy and contact changes by comparing the wild-type and mutant structures. We found that these mutations disrupt the protein-protein or protein-DNA contacts within the Cas9-sgRNA-DNA complex. We also identified additional impacted amino acid sites via energy changes that constitute the structural microenvironment encompassing the focal mutation, giving insights into how the mutations contribute to the high-specificity phenotype of SpCas9. Our method outlines a strategy to evaluate mutational effects that can facilitate rational design for Cas9 optimization.

Gene Editing↗

PubMed2026

In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.

BACKGROUND: Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity hinders direct comparison. RESULTS: We introduce NIS (Neidhardt-Ingraham-Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence. CONCLUSIONS: NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications. Video Abstract.

Metabolic Networks and Pathways↗

PubMed2026

seq2ribo: structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

MOTIVATION: Ribosome dynamics are vital in the process of protein expression. Current methods rely on ribosome profiling (Ribo-seq), RNA-seq profiles, and full genomic context. This restricts their use in de novo sequence design, like messenger RNA (mRNA) vaccines. Simulation-only approaches like the Totally Asymmetric Simple Exclusion Process (TASEP) oversimplify translation by focusing solely on codon elongation times. RESULTS: We present seq2ribo, a hybrid simulation and machine learning framework that predicts ribosome A-site locations using only an mRNA sequence as input. Our method first employs a novel structure-aware TASEP (sTASEP), which models translation using a comprehensive set of fitted parameters that include codon wait times and structural features, such as local angles, base-pairing, and discrete positional buckets. The ribosome locations generated by sTASEP are then processed by a polisher model, which learns to refine the simulated ribosome distributions. seq2ribo provides high-fidelity predictions of ribosome locations across diverse cell types (iPSC, HEK293, LCL, and RPE-1), significantly outperforming baselines. seq2ribo is the first method to achieve meaningful positional correlation with observed ribosome profiles from sequence alone, reaching transcript-level Pearson correlations up to 0.920 and within-transcript shape correlations up to 0.186, where all baselines yield near-zero values on these metrics. seq2ribo also reduces elementwise error by up to 37.7% relative to the sequence-only Translatomer baseline. By adding a task-specific head, seq2ribo achieves Pearson correlations up to 0.732 with experimental translation efficiency (TE) across several cell lines, and up to 0.903 with measured protein expression. By operating from sequence alone, seq2ribo provides a new tool for synthetic biology, enabling the rational design and optimization of mRNA sequences without the need for expression-level data or genomic context. AVAILABILITY: seq2ribo is available at https://github.com/Kingsford-Group/seq2ribo.

Machine Learning↗

PubMed2026

seq2ribo: Structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

Journal Article↗

PubMed2026

Minimal Transport Units Govern Oxygen-Defect Stabilization and Transport for Lightweight Solid Electrolytes.

Solid electrolytes are central to electrochemical energy technologies, including fuel cells, sensors, catalysis, membrane separation, and electrolyser. However, most established oxide-ion solid electrolytes are built around heavy B-site cations embedded in rigid and highly connected coordination frameworks, leading to widespread high-weight and sluggish ionic transport that is strongly coupled to large-amplitude lattice relaxations. This intrinsic challenge hinders further performance optimization and constrains the rational design of lightweight electrolytes. Herein, we propose a minimal transport unit-based design paradigm that combines simplified structural motifs with light-element chemistry, enabled by the exceptional flexibility of B-O polyhedra in coordination, rotation, deformation, and connectivity. As a proof of concept, Sc1- xZnxBO3- x /2, constructed from isolated BO3 units, exhibits high oxide ion conductivity (σ(1000°C) ∼ 1.5 × 10-2 S/cm), alongside excellent thermo-mechanical stability. Oxygen vacancies are stabilized through the formation of B2O5 units rather than isolated BO2 species. Long-range oxide-ion migration is mediated by dynamic oxygen exchange between minimal BO3 and B2O5 units via continuous breaking and reforming of B2O5 units, with transient BO2 configurations as intermediates. This study demonstrates minimal transport units as a governing principle for defect stabilization and ionic conduction in lightweight solid electrolytes, offering a general design framework for portable and scalable high-temperature energy technologies.

NMR spectroscopy and variable‐temperature P↗