National Science Review

Japanese Encephalitis Virus (JEV) belongs to the Flavivirus family, and the RNA dependent RNA polymerase (RdRp) domain located at the C terminus of non structural protein 5 (NS5) regulates de novo viral genome synthesis. The conserved priming loop inserted in the thumb domain of RDRP initiates de novo genome synthesis. Interestingly, the reported replication process initiates with an interaction between NS5 (methyltransferase/RDRP) and NS3 (protease/helicase), and inhibiting this interaction directly correlates with the inhibition of viral replication. The function of the priming loop in the context of the NS5 NS3 interaction is not yet known. In this study, we studied the priming loop function in the NS5 NS3 interaction and viral replication. Using a structure based drug design approach, we screened the Maybridge compound library against the RdRp priming loop and identified 15 candidate compounds based on binding energy. Among them, DSHS00151 showed significant dose dependent inhibition of the NS5 NS3 interaction with an IC50 of 3.5 micromolar in the mammalian two hybrid assay, inhibited viral infectivity with an IC50 of 2.54 micromolar, and reduced viral RNA load with an IC50 of 2.9 micromolar, compared to CD10712. Molecular Dynamics (MD) simulations revealed that DSHS00151 exhibits stronger and more stable binding with the priming loop residues (790 to 812) compared to CD10712. The molecular mechanism suggested by the docking of the NS5 and NS3 proteins showed that the priming loop tends to rotate toward the NS3 protein to stabilize the complex, and compound binding restricts this loop rotation, thus compromising the stability of the NS5 and NS3 complex and affecting viral replication. Overall, our study validated the function of the NS5 priming loop as an allosteric site, and compounds binding to this allosteric site belong to the Non Nucleoside Reverse Transcriptase Inhibitors (NNRTI) class of inhibitors, which disrupt the NS5 and NS3 interaction and could be developed as novel anti JEV therapeutics.

Experimental surgery is one of the most fundamental fields of biomedical research, recreating clinical conditions on living organisms similar to clinical patients, to facilitate the design of novel therapies. For modeling neurological and psychiatric disorders, the majority of operative techniques mandatorily include craniotomy for consecutive interactions with the open brain. The most wide-spread methodology involves stereotactic surgery in rodents, implementing a specific head holder, attached to the coordinate table, providing the precise animal head positioning according to the brain structures topology. Despite the high efficiency, such systems require complex instrumental skills and long procedure duration per animal even for simple implantation protocols. Hence, novel alternative model species could be a valuable supplementary tool for such manipulations. Therefore, the zebrafish (Danio rerio) fits perfectly, due to its low cost, easy husbandry, rapid development, optic transparency, and high genetic and physiological similarity to rodents and humans. Furthermore, the everted zebrafish telencephalon provides excellent accessibility to critical mammalian-like structures involved in emotion processing and cognition. However, conventional avian- or rodent-inspired craniotomies in adult zebrafish often trigger inadvertent disruption of the brain vessels network, resulting in high operative mortality. A novel, geometry-driven, non-lethal cranial window protocol preserving neurovascular integrity and ensuring 90-100% post-operative survival is presented to overcome above mentioned limitations. This rapid, simple, and high reproducible technique opens new avenues for precise region-specific in vivo neurosurgery, high-throughput psychopharmacological screening, and neurostimulation in a powerful non-mammalian model species

Antigen-recognition histories accumulate across adult life, but existing human aging resources largely emphasize host-state measurements rather than antigen-resolved humoral memory. We present CHAIR, the Chinese Healthy Aging Immune Resource, which integrates self-antigen, human virome and toxin/virulence-factor PhIP-seq with routine clinical measurements and T/B/NK phenotyping in clinically screened adults aged 20-94 years. The resource framework includes 1,008 unique PhIP-seq samples, 995 clinical records, 977 T/B/NK records and 853 participants with all three layers. Across modeled spaces comprising 23,584 self-antigen targets, 210 virome targets and 1,258 virulence-factor source targets, most measured antibody-reactivity architecture was consistent with broad preservation within domains, whereas age- and sex-associated signals were concentrated in focal targets and modules. Together, CHAIR provides a traceable, antigen-resolved reference framework based on clinically screened adults for investigating focal humoral remodeling across healthy adult aging.

Accurate prediction of mutation-induced changes in protein-protein affinity remains a central challenge in computational biophysics and protein engineering. Here we present a physics-based scoring method for predicting the effects of single amino acid substitutions on protein-protein and protein-peptide binding affinity. The method is based on MMPBSA energy calculations and implemented in a user-friendly, publicly available workflow (https://github.com/Hoecker-Lab/mmpbsa_scoring). To derive the scoring function, MMPBSA energy terms were fitted using a large and diverse dataset of experimentally measured binding affinity changes upon point mutation. The terms were then evaluated on independent systems. Specifically, we assessed the ability to predict mutational effects at the interface of SARS-CoV-2 spike protein with its cellular receptor, as well as computationally predicted Armadillo-repeat protein-peptide complexes. The approach performs well across diverse targets and shows promising predictive performance on both experimentally determined and computationally predicted structures. It matches or exceeds the accuracy of established physics-based protein design software and state-of-the-art deep learning-based scoring functions when applied to data outside their training domain. The method provides a computationally efficient and interpretable framework for prioritizing interface mutations in protein engineering and binding affinity optimization.

Proteins function through dynamic conformational ensembles rather than a single static structure, yet accurately modeling these ensembles remains challenging. Here, we introduce TopoFlow, an evolutionarily conditioned flow-matching framework for protein conformational ensemble generation. TopoFlow uses evolutionary representations from multiple sequence alignments (MSAs) to encode global conformational heterogeneity. It combines this with structural variables from a variational autoencoder (VAE) to capture local flexibility. These complementary representations are then adaptively fused through a conditional modulation module to construct unified conditioning features that guide protein conformational ensemble generation. On the ATLAS molecular dynamics (MD) benchmark, TopoFlow improved the Jensen Shannon metrics for pairwise distances (JS-PwD), radius of gyration (JS-Rg), and time-lagged independent components (JS-TIC) by 10.73%, 5.78% and 7.75%, respectively, relative to BioEmu. Notably, this performance generalizes to experiment-based evaluation, where TopoFlow exhibits strong agreement with experimental observables, including chemical shifts (CS) and small-angle X-ray scattering (SAXS). Ablation analyses further indicated that the evolutionary representations and structural latent variables contributed complementary information to ensemble generation. Our results suggest that TopoFlow, through its integration of evolutionary and structural latent information, offers a promising strategy for generating structurally plausible and conformationally informative protein ensembles.

Protein--Protein Interactions (PPIs) are fundamental to cellular processes and represent major targets for therapeutic development. Recent advances in machine learning for protein modeling, particularly protein language models, have enabled powerful representations of protein sequences and monomeric structures. However, compared with the increasingly generalizable representations available for individual proteins, transferable representations explicitly designed to capture protein-complex and interface contexts remain less developed. Here, we developed GraPPI, a unified graph-based deep learning framework for learning transferable representations of protein--protein complexes. At its core, PPIencoder represents protein complexes as heterogeneous residual graphs and uses self-supervised masked-edge prediction to learn interaction-aware representations that integrate sequence, three-dimensional geometry, and physicochemical properties. We then evaluated the transferability of these pretrained representations across complementary PPI tasks spanning binding-mode classification, binding-affinity prediction, and mutation-associated affinity prediction. GraPPI accurately distinguished plausible binding interfaces from perturbed or decoy binding interfaces, achieving AUROC values above 0.92 on an independent benchmark. For protein--protein binding-affinity prediction, GraPPI achieved Pearson correlation coefficients of 0.690 and 0.684 on the S90 and S79 benchmarks, respectively, with performance competitive with or exceeding that of recently reported affinity-prediction methods. GraPPI further transferred effectively to mutation-associated binding-affinity prediction, achieving a Pearson correlation of 0.84 and a mean absolute error of 1.20 kcal/mol on an independent SKEMPI benchmark. Together, these results establish GraPPI as a generalizable framework for protein-complex representation learning, extending pretrained protein representations from individual proteins toward the interaction context in which protein function is realized.

Bats are recognized reservoirs for several zoonoses and have an exceptional tolerance to viruses, likely linked with strong evolutionary selection in immune genes. Notably, in light of the emergence history of severe acute respiratory syndrome coronavirus (SARS-CoV), SARS-CoV-2 is hypothesized to have originated from Rhinolophus bats as well. In-depth studies are still needed, however, to unravel how SARS-related coronaviruses (SARSr-CoVs) persist and thrive in nature through evolution, virus-host interactions, and cross-species transmission. Here we present a pioneering 10-year longitudinal study of BBCoV-EPI1, a SARSr-CoV in Rhinolophus bats that revealed the complexity of virus-host interactions and the tempo at which new viral lineages emerge. Our results demonstrated the impacts of host life cycles (hibernation, female gathering, weaning juveniles, and mating periods) on strong seasonal oscillations in viral prevalence. These pronounced fluctuations drove viral diversification through bottlenecks, founder effects and Darwinian selection, counterbalanced by high mutation rates, fast gradualism, and recombination. We also found that host-cell enzymes are major contributors to BBCoV-EPI1 evolution in bats. The antiviral effect of APOBEC3 detected here in bats is weaker than it is in humans. Conversely, the ADAR enzyme had an unexpectedly strong and beneficial influence on viral evolution, even though the high mutation rate of RNA viruses is usually attributed to viral polymerases' low replication fidelity. We discovered that key BBCoV-EPI1 innovations in Rhinolophus bats were mostly located in proteins involved in cell-cycle hijack and in blocking bat cells' antiviral response, rather than in the Spike (S). We also showed that the BBCoV-EPI1 S protein efficiently uses angiotensin-converting enzyme 2 (ACE2) receptor to enter Rhinolophus bat cells, but not to enter human or sympatric species cells, due to amino-acid differences at the S-ACE2 interface. Last, this work identifies 101 critical amino-acid changes frequently associated with viral fitness shift, and paves the way for future experimental studies focusing on the capacity of SARSr-CoVs to adapt to hosts and cross the species barrier.

Mapping the multi-mutant fitness landscape is vital to protein engineering, but is challenging due to the vast combinatorial sequence space awaiting exploration. A central difficulty lies in the accurate and efficient modeling of non-additive epistatic effects among individual mutations, which partially arise from the physical inter-residue interactions prescribed by the protein structure. Existing fitness predictors usually perform well on single mutants but become less powerful for higher-order mutants, due to the lack of explicitly considering the relationship between sequence, structure and function of the target protein. Here, we present an end-to-end framework named Cerebra-Epistasis, which couples a single-sequence structure predictor that explicitly endows the structure awareness beyond the conventional sequence-fitness mapping with a downstream fitness prediction network that deliberately models the non-linear epistatic effects beyond the traditional additive terms. When evaluated across diverse assays, Cerebra-Epistasis outperforms the other state-of-the-art baselines in multi-mutant fitness prediction, with enhanced advantage over increasing mutation orders. Moreover, our special design on the epistasis modeling allows reliable extrapolation from low-order mutant data to unseen higher-order combinations, enabling one-shot inference of the overall mutation atlas from the starting sequence, a benefit that supposedly introduces three orders of magnitude acceleration in the landscape-scale prediction.

Genetic loci influencing multiple complex traits often remain difficult to interpret because the underlying functional variants, target genes and tissue contexts are unresolved. Here, we investigated the regulatory basis of a shared signal for average daily gain (ADG) and backfat thickness (BF) in Yorkshire pigs. By integrating GWAS in 7,972 pigs, targeted capture sequencing, LD-based fine-mapping, porcine epigenomic annotations, three-dimensional chromatin interaction maps and functional assays, we narrowed the shared SSC1 signal to a 1.59-Mb interval and functionally prioritized rs334769214 (T>G) as the leading candidate variant. The T>G substitution produced marked allele-specific differences in enhancer activity and nuclear-protein binding, while transcription-factor co-transfection assays identified FOXL1-responsive, allele-dependent regulatory activity. Chromatin interaction and CRISPRa analyses supported a regulatory connection between the rs334769214-containing enhancer and PMAIP1, and PMAIP1 overexpression promoted apoptosis in porcine intestinal epithelial cells. In the capture-sequenced subset, the G allele was associated with higher ADG and BF, consistent with a relative loss-of-activation model. Together, these findings delineate a candidate FOXL1-enhancer-PMAIP1 regulatory pathway and demonstrate how multi-omics fine-mapping can advance a shared quantitative-trait locus toward a testable molecular mechanism underlying variation in growth and fatness.

The balance between excitatory and inhibitory (E/I) neurons is fundamental to cortical function, and its disruption underlies a wide range of neurological disorders, including epilepsy, Alzheimers disease and autism spectrum disorder. Cortical organoids offer a promising model to study this balance, as they recapitulate key features of brain development, including the emergence of E/I circuitry. However, no existing technology can simultaneously resolve neuronal identity, spatial organization, and dynamic network activity within intact organoids, limiting precise interrogation of E/I circuit function. Here, we report INFORM, an INtegrated Flexible Organoid Recording Multimodal neural interface that conformally wraps organoids of any size and shape, enabling long-term recordings over 371 days with above 90% of channels detecting spikes. INFORM interface combines four distinct modalities; electrophysiology, two-photon calcium imaging, targeted electrical microstimulation, and chemical intervention to monitor and modulate neural activity across multiple spatial and temporal scales spanning from action potentials from single neurons to synchronized bursts from large-scale populations. Such high-resolution interrogation enables probing of emergence of functional connectivity in fusing assembloids and resolves spatially discrete functional subnetworks that can be selectively recruited and modulated by INFORM. Neurotransmission blockade confirms that stimulus-evoked suppression of neuronal spiking is mediated by GABAergic inputs providing direct functional evidence of the presence of inhibitory signaling in organoids. This work allows single-cell dissection of functional networks in brain organoids, providing a powerful platform for modelling and studying cortical development, neurological diseases, and the E/I imbalances underlying circuit dysfunction.

Age-related cognitive decline in humans is well established, yet how neurobiological variations influence brain dynamics, and subsequently behavioral performance, remains insufficiently explored. Microscale accounts of cognitive aging revolve around attenuations in dopamine (DA) receptors and transporters (DAT). In contrast, macroscale accounts associate individual declines in cognitive performance with functional brain networks' reorganization and white-matter tracts' deterioration. We endeavor to bridge this gap with an amortized dopaminergic aging model (ADAM) which assembles aging brain twins by coupling dopaminergic modulation with structural connectomes to constrain emergent whole-brain dynamics. ADAM thereby augments established DAT decline with personalized estimates, through a reusable and generalizable estimator capable of disentangling neuromodulatory from structural contributions to age-related decline within each individual. To that end, we embed structural connectomes into a low-dimensional space exhibiting high reconstruction fidelity and -pseudonymized- individual identifiability. The spectrum of structural embeddings is subsequently updated with fMRI observations to estimate DA clearance timescales in 3,650 adults aged 36 to 95 years across five independent aging cohorts. Our analysis reveals an age-related elevation of DA clearance timescales. Critically, low performers exhibit elevated clearance in adults with marked structural degradation, and only within sliding age windows centred between 56 and 76 years. This supports a contribution of dopamine to aging, and reveals a structure- and age-conditional association between its clearance and cognitive performance. Our findings suggest that structural connectivity gates the macroscale reach of dopaminergic modulation, thus rendering the gain-control framework of neuromodulation mechanistically explicit across individuals and cohorts. ADAM therefore provides a biologically plausible framework that anchors interindividual variability in cognitive aging to perturbations in the dopaminergic system by learning cohort-scale mappings which can be shared across sites, establishing a transferable testbed for precision neuromodulation and future in-silico pharmacological trials.

Protein inverse folding, which infers compatible amino acid sequences from a given backbone structure, is central to rational protein design. Current methods rely largely on sequence recovery rate as the evaluation metric, emphasizing structural compatibility but seldom addressing function. Yet functional activity depends on the proper network of non-covalent interactions-protein-ligand contacts being a prime example. We argue that function-oriented protein design is governed by three complementary constraints: structural, interaction, and evolutionary, none of which are handled in a unified manner by existing pretrained models. To bridge this gap, we introduce zPolaron, a graph neural network built on protein-ligand complexes for functional protein design. zPolaron represents residues and ligand atoms as a heterogeneous graph and jointly encodes structural, interaction, and evolutionary information by fusing physical-interaction-enhanced graph representations with ESM-2 embeddings. Across multiple fitness prediction benchmarks, zPolaron outperforms all baselines, with gains evident across structural, interaction, and evolutionary dimensions. Wet-lab experiments further confirm its utility in functional enzyme screening and design: the optimized CalA6-D205A-S160G mutant achieves a 17.84-fold improvement in activity, while the CalB-I121M mutant yields a 1.91-fold increase. Together, these results show that integrating interaction and evolutionary information fills the missing puzzle of inverse folding, enhancing protein fitness prediction beyond structural compatibility. zPolaron is available at https://github.com/zelixirSH/zPolaron.

Decision-making requires accumulation of evidence over time, yet exactly how neural circuits implement this computation remains debated. Classical models posit that evidence accumulation is realized by ramping neural activity, whereas recent experimental findings suggest an alternative mechanism in which neural activity remains largely silent before abruptly transitioning into an active decision state. This mechanism leaves a fundamental question unanswered: how is evidence accumulated during the neural activity-silent period? To address this question, we trained excitatory-inhibitory recurrent neural networks (RNNs) with short-term synaptic plasticity (STP) on a reaction-time decision task. We found that after training, the networks learned to leverage STP to make decisions in an activity-silent manner. Choice-tuning analysis revealed a clear functional organization of the network, and connection-pruning analysis uncovered the coupling between choice-selective excitatory and inhibitory neural groups. Guided by the architecture of trained RNNs, we constructed a minimal neural circuit model, in which evidence accumulates in facilitated synaptic efficacy that progressively reshapes the circuit's phase portrait, eventually driving the network state across the stability boundary and triggering a rapid transition to a choice state. We further compared activity before a choice between the theoretical STP and Static models to examine the potential energy benefit of activity-silent decision-making. Together, our results suggest STP as a mechanism for evidence accumulation in activity-silent decision-making and offer insights into the design of energy-efficient artificial neural networks.

In the post-AlphaFold era, advances in protein complex structure prediction have enabled the generation of thousands of candidate models per target, making the accurate and efficient selection of high-quality models from large candidate pools a critical challenge. Existing model-selection approaches commonly employ estimated model accuracy (EMA) methods, which include single-model and consensus-based methods. The former lack cross-model comparative information, whereas the latter often incur high computational costs from pairwise structural alignments and are influenced by candidate-pool quality. Here, we present DeepUMQA-Selection, a two-stage framework combining single-model accuracy estimators for preliminary selection with Ultrafast Shape Recognition (USR)-based structural alignment for consensus selection. Preliminary selection enriches the candidate pool with high-quality models and reduces subsequent structural comparisons, while USR-based structural alignment enables efficient consensus selection without explicit structural superposition. On the retrospective CASP16 QMODE3 benchmark dataset, DeepUMQA-Selection outperformed all participating methods and predictor-derived confidence metrics, as measured by mean Top-5 weighted penalty. The advantage was particularly pronounced for heteromeric targets, with a 13.9% improvement in model-selection performance relative to the best-performing CASP16 participating method. Furthermore, a confidence-assisted strategy enabled the framework to accommodate larger protein complexes while maintaining superior model-selection performance over competing methods, supporting its scalability. Runtime benchmarking showed that the proposed USR-based structural alignment achieved a substantial 317-fold speedup over US-align for candidate pools containing 1,000 models, demonstrating its computational efficiency for large-scale model selection. Our results demonstrate that DeepUMQA-Selection enables accurate and computationally efficient selection of high-quality protein complex models from large-scale candidate pools.

Predicting molecular receptor status, including estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), directly from routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) could reduce reliance on costly immunohistochemistry, but image-only models have no access to the transcriptomic programs that define these subtypes. Here we show that coupling a whole-slide vision transformer (GigaPath) with bulk RNA-sequencing profiles through a training-time-only contrastive objective improves receptor-status classification from WSIs alone, without requiring any RNA-seq data at inference. Rather than training a dedicated bulk-transcriptomics encoder, we purpose the gene-sentence encoding and pretrained text encoder from BioBERT1 and apply them to bulk RNA-seq profiles from The Cancer Genome Atlas breast cancer cohort (TCGA-BRCA). Under five-fold cross-validation, this RNA-guided pre-training consistently improved AUROC for ER, PR, and HER2 relative to a contrastive-pre-training-free baseline and reproducibly introduced receptor-relevant structure into the learned slide representation. However, evaluation on independent external cohorts revealed a striking, receptor-dependent divergence: gains for ER and PR, both governed by broad, multi-gene luminal transcriptional programs, generalized robustly, whereas the apparent AUROC improvement for HER2, driven by focal ERBB2 amplification rather than a coordinated transcriptional signature, masked a severe loss of classification sensitivity at the operating threshold. These findings establish training-time transcriptomic supervision as a practical route to enhancing histology-only molecular subtyping, while showing that gene-selection strategy, and not model architecture alone, determines which molecular alterations such supervision can teach a vision model to recognize.

Genetically modified immune effector cells, including chimeric antigen receptor (CAR) T cells, have emerged as transformative anti-cancer agents. However, these autonomous, living therapies are limited by difficult-to-control hyperactivation toxicities. To achieve their full potential as therapies for a broader range of diseases, CAR T cells must target novel antigens and integrate further potency enhancements that raise the possibility of novel and intensified toxicities. High performance safety systems are needed to restrain these highly active next-generation gene- and cell-based therapies. Cell therapy suicide switches remain infrequently used, and are currently limited by inaccessible controller drugs, incomplete cell depletion, fitness cost, and immunogenicity. To address these issues, we engineered a fully human sequence-based cell therapy suicide switch based on the overexpression of Caspase-Activated DNase (CAD) and its stoichiometric chaperone/inhibitor ICAD fused to a lenalidomide-inducible degron. Acute destabilization of ICAD with the broadly available, off-patent molecular glue degrader drug lenalidomide uncaged CAD to induce DNA damage and cell death. By this degradable cage mechanism, the ICAD-CAD suicide switch achieved long-term expression in primary human T cells without detectable leakiness, and deep drug-induced cell depletion. The ICAD-CAD switch was generalizable to alternative acute destabilization systems. Genome-wide CRISPR screening established the necessity of CAD and the lenalidomide-interacting E3 ubiquitin ligase substrate adaptor CRBN for switch function. When co-delivered with a CAR, ICAD-CAD CAR T cells retained effector functions and could be rapidly depleted with lenalidomide.

Protein complex structure prediction depends on both the breadth of candidate coverage and the ability to select accurate models within a limited output budget. Most existing approaches emphasize only one stage. End-to-end models and molecular docking workflows generate candidate structures, whereas quality-assessment models primarily rerank a predefined candidate pool. A complete strategy must account for complementary sampling across generators, differences among scoring scales and the allocation of final candidate quotas. Here, EnsPlex is presented as a multi-source framework that couples structural sampling with candidate selection for protein-protein interaction complex prediction. EnsPlex combines conformation-expanded docking, AlphaFold-Multimer, AlphaFold3 and Boltz-1 to expand the sampled conformational space. Conformation-expanded docking comprises monomer conformational expansion followed by flexible HADDOCK docking. FACET is trained to predict candidate quality using DockQ and its component metrics as supervision. Across 102 antigen-antibody systems, EnsPlex achieved up to a 27.8% relative improvement in target success rate over AlphaFold3 with its built-in ranking under matched output budgets. FACET also improved within-source ranking in the internal candidate pools and in homology-filtered external data. These findings support the utility of EnsPlex for structure prediction in the evaluated antigen-antibody systems.

Directed evolution and machine-learning-assisted evolution have transformed biocatalysis by enabling rapid optimisation of catalytic performance within compatible protein scaffolds. In parallel, advances in deep-learning-driven de novo protein design have expanded accessible protein architectural space, enabling the generation of functional catalysts for increasingly challenging transformations. We herein report a synergistic design-evolution strategy to encode new-to-nature regio- and enantioselective C-H amination reactions using engineered de novo haemproteins to synthesise chiral piperidines, a privileged pharmacophore in drug discovery. By integrating motif scaffolding, computational re-design, and directed evolution, we demonstrate de novo enzyme design can enable challenging, high-energy-barrier enzyme chemistry with programmable regio- and enantioselectivity, high catalytic efficiency, excellent enzyme stability, and scaffold diversity.

Aging is a complex phenomenon, yet therapeutic discovery has largely relied on testing individual hypothesis-driven targets. Here, we performed a genome-scale open reading frame (ORF) screen for oxidative-stress resistance in retinal pigment epithelium (RPE) cells, which are vulnerable to oxidative damage during aging. The screen identified nine protective ORFs including antioxidant factors and an epithelial-to-mesenchymal transition regulator. Notably, we also identified three factors that reduce transcriptomic age including the cardiac transcription factor NKX2-5. NKX2-5 conferred robust oxidative resilience in vitro and in vivo. Domain dissection revealed that its homeodomain is dispensable for resilience, enabling development of NKX2-5{Delta}HD, which retained protective activity without detectable toxicity over 14 months. In middle-aged mice, subretinal NKX2-5{Delta}HD restored visual and electrophysiological function, and its systemic delivery improved grip strength and reduced frailty in late-aged mice. These findings highlight the value of unbiased, genome-scale gain-of-function screening, as resilience factors normally expressed in other contexts can be reengineered as potential therapeutics for age-related decline in other tissues.

Protein kinase CK2 (CK2), a ubiquitously expressed serine/threonine kinase, has emerged as a key host factor exploited by several viruses, including Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we investigate the role of CK2 subunits in SARS-CoV-2 replication and evaluate the antiviral potential and broad-spectrum anti-coronavirus activity of two CK2 inhibitors: the previously described ATP-competitive inhibitor CX-4945 and a newly developed, highly selective bivalent inhibitor, AB668. The siRNA-mediated knockdown of CK2 subunits significantly reduced SARS-CoV-2 replication, highlighting its essential role in the viral lifecycle. Co-immunoprecipitation assays revealed that the SARS-CoV-2 nucleoprotein interacts with CK2. Furthermore, bio-layer interferometry assays demonstrated that the nucleoprotein specifically binds to the tetrameric CK2 holoenzyme (CK22{beta}2) but not to individual CK2 or CK2{beta} subunits. Pharmacological inhibition of CK2 with AB668 exhibited potent antiviral activity, significantly reducing viral replication by targeting a pre-replicative or RNA-replicative step of the viral life cycle. Importantly, AB668 displayed broad-spectrum antiviral activity, effectively inhibiting both HCoV-OC43 ({beta}-CoV) and HCoV-229E (-CoV). In summary, our findings establish CK2 as a critical host factor for SARS-CoV-2 replication and identify AB668 as a promising candidate for broad-spectrum antiviral therapy. With its high selectivity, minimized off-target effects, and efficacy against multiple coronaviruses, AB668 represents a valuable chemobiological and therapeutic tool.