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.

The long-standing challenge of seasonal influenza vaccines providing poor protection is attributed to the rapid and continuous evolution of the virus. In theory, the recommendation of vaccine strain is to predict the dominant strain with best fitness in the upcoming season. Here, we develop FutureFlu, a biologically-grounded framework that quantifies the fitness score of influenza variants in upcoming seasons, by integrating metrics on three levels: molecular genetic divergence, individual immune escape, and population-scale transmission dynamics. The fitness scores of influenza variants present strong positive correlation with their actual observed frequencies in the next seasons. Validation across 24 seasons of three influenza subtypes shows FutureFlu recommends antigenically matched vaccine strains more frequently than annual recommendations, particularly for challenging subtypes: 83.3% versus 45.8% seasons for A/H3N2, and 75.0% versus 33.3% seasons for B/Victoria. Furthermore, FutureFlu significantly outperforms the currently used methods in vaccine strain selection whether or not there is available antigenic data from hemagglutination inhibition (HI) assay, which provides a valuable supplement for WHO vaccine recommendation. To support global public health implementation, an open online platform (futureflu.com.cn) has been established to offer real-time viral fitness predictions and vaccine recommendations.

Background Alcohol-associated liver disease (ALD) has emerged as a major cause of chronic liver disease and liver-related mortality in China. This study aimed to project the future burden of ALD in Chinese adults from 2020 to 2050, including prevalence of ALD, number of alcoholic steatohepatitis (ASH) cases, incident hepatocellular carcinoma (HCC) cases, liver transplantation (LT) demand, liver-related deaths, and disability-adjusted life years (DALYs). Methods We developed an agent-based state-transition microsimulation model with yearly cycles and a lifetime horizon. The model simulated 5,678,912 representative Chinese adults (mean age 36.2 years, 51.2% male). Health states included no steatosis, alcohol-associated steatotic liver, ASH, fibrosis stages F0-F4, decompensated cirrhosis, HCC, LT, and liver-related death. Model inputs were derived from the China Kadoorie Biobank, Global Burden of Disease Study 2021, China's national surveys, published meta-analyses, and transplant registry data. Projections incorporated demographic shifts, alcohol consumption trends, and calibrated transition probabilities. Uncertainty was assessed via 1,000 Monte Carlo simulations generating 95% uncertainty intervals. Results ALD prevalence was projected to increase from 4.8% (55 million individuals) in 2020 to 8.5% (94 million individuals) by 2050. ASH cases rose from approximately 18 million to 20 million. Annual incident HCC cases nearly doubled from 20,500 in 2020-2025 to 45,200 by 2046-2050. LT demand quadrupled from 2,300 to 9,800 cases. Liver-related deaths increased from 50,000 in 2020 to 85,000 in 2050, while DALYs rose from 1.5 million to 2.6 million. Conclusions In the absence of strengthened alcohol control policies, ALD will impose a substantial and growing burden on China's health system by 2050, with marked increases in HCC incidence, LT demand, and liver-related mortality.

Abstract Objective Commercially available next-generation sequencing (NGS) platforms in China routinely adopt a lung cancer-derived tumor microenvironment (TME) subtyping signature from a European cohort to classify colorectal cancer (CRC), yet its diagnostic performance in Chinese CRC patients remains unvalidated. This study aimed to evaluate the subtyping efficiency of the lung cancer TME signature in a Chinese CRC cohort, screen CRC-specific immune mRNA biomarkers for TME subtyping, and explore the clinical utility of IRF1, CD8A and LAG3 for distinguishing immune-enriched (IE) and immune-desert plus fibrotic (D+F) subtypes. Methods A total of 87 FFPE CRC specimens with complete NGS and clinicopathological data were retrospectively enrolled, including 15 IE subtype and 72 D+F subtype patients. Thirty-one mRNA transcripts covering 13 immune-metabolic homeostasis genes and 18 immune checkpoint/infiltration-related genes were divided into two functional modules. Spearman correlation analysis was performed to assess co-expression patterns among candidate genes. Receiver operating characteristic (ROC) curves combined with five-fold cross-validation were used to compare the discriminatory efficacy of single-gene markers and the three-gene combined panel. Results Strong positive co-expression was observed between IRF1, CD8A and LAG3 (IRF1-CD8A: r=0.93; IRF1-LAG3: r=0.84; CD8A-LAG3: r=0.73). Nominal P-values indicated elevated expression of IRF1, CD8A and LAG3 in IE subtype, though no intergroup significance remained after Benjamini-Hochberg FDR correction, largely attributed to the limited sample size of IE cases. Single-gene ROC analysis showed AUC values of 0.763 (IRF1), 0.752 (CD8A) and 0.771 (LAG3), with LAG3 exhibiting the best individual discriminatory capacity. The three-gene combined panel yielded a cross-validated AUC of 0.717, inferior to single LAG3, due to severe collinearity that generated redundant predictive information. Conclusions The lung cancer-originated TME subtyping system cannot be directly extrapolated to Chinese CRC patients. LAG3 serves as a promising independent transcriptomic candidate marker for distinguishing CRC TME subtypes. The robust collinearity among IRF1, CD8A and LAG3 eliminates additional predictive benefits of the combined signature. Large independent multi-center Chinese CRC cohorts are required to construct population-specific immune transcriptomic biomarkers for standardized clinical NGS TME stratification.

Epigenetic clocks based on DNA methylation patterns are among the most accurate molecular correlates of chronological age, yet widely used clocks are predominantly empirical models with limited explicit characterization of the underlying methylation variability, lacking a direct connection to the physical mechanisms of aging. In this work, we bridge this gap by introducing an information-theoretic framework for DNA methylation dynamics combined with nonlinear machine learning to develop a competitive and interpretable age predictor. We model the population distribution of methylation {beta}-values at each CpG site using a reparameterized three-parameter Generalized Gamma Distribution (GGD) and derive a closed-form expression for its differential Shannon entropy. The resulting CpG-level entropy is used to characterize methylation variability and as a criterion for locus filtering. We introduce the Stacy Gradient Boosting Clock (Stacy-GB), which combines this GGD-based representation with a LightGBM regressor. The model was evaluated across independent cohorts using the ComputAgeBench epigenetic clock benchmark. Stacy-GB achieved a mean absolute error (MAE) of 3.74 years and a median error (bias) of 2.41 years, significantly outperforming state-of-the-art epigenetic clock baselines. Furthermore, age acceleration estimated by Stacy-GB was associated with several clinical pathologies, including ischemic heart disease, HIV infection, multiple sclerosis, and Werner syndrome, supporting its potential as an accurate and biophysically grounded tool for clinical aging research.

Neurotrophins regulate neuronal survival, differentiation, and synaptic plasticity through activation of tropomyosin receptor kinase (Trk) receptors, and their dysregulation is strongly implicated in neurodegenerative disorders, including Alzheimer's disease. However, the therapeutic application of recombinant neurotrophins remains limited by poor bioavailability, rapid degradation, and inadequate blood brain barrier permeability. Here, we report the discovery of Bavachin as a dual Trk-A/Trk-B neurotrophin mimetic via an integrated computational screening and experimental validation approach. Structure-based virtual screening of a bioactive natural product library derived from Traditional Chinese Medicine, combined with a novel consensus-ranking strategy, enabled the identification of flavonoid scaffolds targeting neurotrophin-binding sites of Trk receptors. Among these, Bavachin demonstrated robust, dose-dependent activation of both Trk-A and Trk-B receptors, but not Trk-C, indicating receptor selectivity. Induced-fit docking and molecular dynamics simulations revealed stable binding of Bavachin within conserved and receptor-specific ligand-binding pockets, consistent with its dual agonist activity. Collectively, this study identifies Bavachin as a putative small-molecule neurotrophin mimetic and establishes an integrated computational and experimental framework for the discovery of dual Trk-A/Trk-B agonists for neurodegenerative disease 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

Inhibitory neurotransmission, essential for neural circuit homeostasis and proper neurological function, depends on the efficient sequestration of {gamma}-aminobutyric acid (GABA) and glycine into synaptic vesicles. This critical process is mediated exclusively by the vesicular inhibitory amino acid transporter (VIAAT). Despite extensive biochemical and physiological investigation, the molecular mechanism governing VIAAT-mediated transport has remained incompletely understood. Here, we determined structures of human VIAAT in multiple functional states, including apo, GABA-bound, and glycine-bound states, as well as apo state in chloride-free condition. VIAAT adopts a classical LeuT-fold and we elucidated how its large, electronegative binding pocket accommodates both GABA and glycine. Moreover, we resolved two previously unidentified chloride-binding sites. Through integrative molecular dynamics simulations and functional mutagenesis, our results support roles for chloride and the conserved residue E213 in substrate binding and proton-coupled transport. Together, our findings establish a structural framework for VIAAT-mediated inhibitory neurotransmitter transport that illuminates the molecular basis of inhibitory synaptic transmission and provides a foundation for understanding VIAAT dysfunction in epilepsy and related neurodevelopmental disorders.

Extensive studies suggest the brain performs Bayesian inference to infer the latent world states. It is a fundamental neuroscience question that how canonical recurrent neural circuits in the brain implement Bayesian inference. Many existing theoretical studies focused on how the recurrent circuits compute the posterior, while largely overlooking how the circuits compute the model evidence (normalization constant in Bayes' theorem), a key quantity that measures how well a model explains the observed data.Thus, it remains largely unknown about how the recurrent circuits compute the model evidence. The present study performs rigorous theoretical analyses of the continuous attractor networks, a canonical recurrent circuit model, and reveals that the nonlinear circuit dynamics can simultaneously compute the posterior and model evidence in first two dominant subspaces within the circuit dynamics. Specifically, the circuit dynamics in the stimulus feature subspace implements the Langevin posterior sampling, and the circuit dynamics in the subspace of total neuronal activity computes the model evidence in a way analogous to the evidence lower bound in stochastic variational inference. We further extend the circuit model to compute the model evidence of multiple inputs, and simulations validate the computation in the network. Our work for the first time reveals the concurrent model evidence and posterior sampling in subspaces in continuous attractor networks, significantly deepen our understanding of the computational algorithms adopted by the neural circuits.

Real-world decision-making rarely occurs with perfect information. Instead, individuals must constantly weigh potential rewards against the probability of adverse outcomes.1 Failures of this process can lead to maladaptive decisions associated with reduced lifetime success, and numerous psychiatric disorders such as gambling addictions, bulimia nervosa, and substance use disorder.2,3 The neural computations that facilitate inference about the landscape of potential outcomes remain unclear, but are thought to occur in distributed frontotemporal circuits.4 Here we used deep reinforcement learning agents to predict distinct behavioral strategies and their underlying neural population dynamics during a risky decision-making task. Across a range of training conditions, deep reinforcement learning agents separated into strategies marked by either overly cautious exploration of the reward contingency space or a high-performing, risk-adaptive Bimodal strategy. The internal dynamics of high-performing Bimodal agents formed low-dimensional representations that segregated safe and risky states. In contrast, the cautious exploration agents were associated with more skewed and entangled neural representations. We found remarkably similar dynamical representations and their associated behavioral strategies in neuronal ensemble recordings from human epilepsy patients performing a similar risky decision-making task. These results reveal the structure of dynamical computations that underlie inferences about uncertain outcomes and their associated behavioral strategies.

Despite significant advancements in microsurgical techniques in recent years, the treatment and prognosis of craniopharyngiomas remain unsatisfactory. As a central nervous system tumor located adjacent to important brain structures such as the hypothalamus-pituitary axis and accompanied by a highly inflammatory microenvironment, the tumor heterogeneity and tumor microenvironment characteristics of papillary craniopharyngiomas (PCPs) remain unclear. In this study, we integrated multimodal single-cell and spatial profiling from PCP tissue and peripheral blood mononuclear cells (PBMCs) to elucidate the tumor heterogeneity and microenvironment characteristics of PCP. Our single-cell and spatial analyses defined four specific tumor cell states in PCP, representing specific transcriptional regulatory programs and spatial heterogeneity characteristics during tumor progression. By constructing a spatial niche composed of tumor, immune, and stromal cells, we analyzed the cellular and spatial ecosystem of PCP at multiple levels to further assess the communication relationships between different tumor cell states and microenvironment cells. This study established a multidimensional molecular atlas of PCP from the perspectives of cell state, spatial structure, and microenvironment interactions, providing a foundation for understanding its biological behavior and exploring new intervention strategies.

Programmable transcription activators are central to functional genomics, gene therapy, and synthetic biology. However, application of the most versatile CRISPR activators (CRISPRa) is hampered by the bulky size and compact CRISPR systems are constrained by the requirement of long protospacer adjacent motifs (PAMs). To address these limitations, we repurposed the TIGR-Tas RNA-guided DNA-targeting system as a versatile platform for gene activation. By systematically optimizing the fusion architecture of a nuclease-inactivated TasR with gene activation domains, we achieved robust, specific, and multiplexable transcriptional activation of endogenous human genes. This TIGR activation (TIGRa) system could be a superior alternative to CRISPRa systems due to its extreme compactness and potentially PAM-independent targeting capability.

Hepatocellular carcinoma (HCC) is particularly aggressive and difficult to treat. Due to the lack of early clinical diagnosis and the unsatisfactory clinical treatment effect, it is particularly important to identify novel markers that can predict tumor behavior in HCC. biogenesis of ribosomes BRX1 (BRIX1) is abundant in various tissues of the human body. However, the regulatory mechanisms and its role in various tissues are not fully understood. Here, we analyzed the expression pattern of BRIX1 in HCC from public gene expression databases and tissue samples from clinical HCC. We confirmed that BRIX1 was upregulated in both HCC cell lines and HCC paraffin section samples. BRIX1 depletion significantly dicreased the capacity of cells to grow and migrate in vitro, and knockdown BRIX1 suppressed tumor growth in xenograft tumor model. Mechanistically, BRIX1 depletion suppressed the MAPK/ERK pathway, as reflected by reduced phosphorylated ERK (p-ERK) levels. In summary, we provide a rational clue for the further investigation of BRIX1 as an invaluable biological marker for diagnosing and predicting prognosis of patients with HCC.

The canonical genetic code is used by most known forms of life, yet explaining its historical origin and present day functional performance requires comparison with the enormous space of possible codon-to-output assignments. Here, the code is formulated as a hierarchy of constrained mapping problems spanning codeword length, degeneracy composition, synonymous-block partitioning, semantic assignment and a coarse decoder layer. Exact structural analyses identify triplets as a Pareto choice under a fixed-length full-codebook model and show that anonymous degeneracy statistics alone do not explain the canonical profile. Within a fixed canonical block architecture and under specified objective functions, recurrent AAindex-based rule learning contracts the 20! amino-acid assignment space to 2.72 ** 1011 admissible mappings, from which 108 complete codes are sampled. In this screened conditional candidate library, the standard genetic code ranks in the best 0.9749% under the equal-weight three-objective score and in the best 1.801% when accessible replacement diversity is added. Sensitivity analyses show that this position is broad across many, but not all, tested objective weights and aggregation rules. These results describe a conditional multi-objective compromise; they do not establish global optimality, historical inevitability or cellular feasibility of decoder redesign.

Accurately predicting the effects of mutations on protein-RNA binding is crucial for elucidating disease mechanisms. Yet, exhaustively exploring the space of all possible variants is prohibitively expensive, motivating computational methods that can quantify mutation-induced changes in binding affinity (aka {Delta}{Delta}G) accurately and efficiently. We present iSCALE, an interpretable and generalizable deep learning method that adopts an implicit Spatial Coupling-Aware Ligand Encoding strategy to predict mutation-induced binding affinity changes. By injecting this implicit multiscale encoding scheme into a bidirectional state space modeling architecture, iSCALE learns a generalizable multiscale coupling pattern that achieves superior performances on not only the protein-RNA binding {Delta}{Delta}G, but also the protein stability {Delta}{Delta}G and protein-protein binding {Delta}{Delta}G predictions. Detailed analyses demonstrate that the model attention scores align well with structural characteristics. In addition, iSCALE shows good discriminative ability when predicting close samples such as complexes of same mutation but with different ligands or the same complex but with different mutation sites. In summary, iSCALE serves as an effective in silico tool for large-scale protein-RNA binding {Delta}{Delta}G prediction, which pushes the border of understanding in mutation-induced pathological outcomes.

SynNotch-CAR circuits improve T cell specificity by coupling antigen recognition to inducible CAR expression. However, basal CAR expression without receptor activation, termed here as leakiness, can reduce the separation between killing of intended target cells and sparing of antigen-positive off-target cells, limiting target-cell discrimination. Here, we systematically quantified basal CAR expression for several synNotch-CAR designs and developed a coupled ordinary differential equation model to show that discrimination depends on basal output, CAR potency, and effector-to-target ratio. We introduced C-terminal tags such as fluorescent proteins, degron domains, endocytosis signals, and endoplasmic reticulum retention motifs as a strategy to reduce CAR leakiness. We found that fluorescent proteins and degron-containing tags reduced basal CAR surface expression while preserving antigen-induced CAR expression, improving discrimination of antigen-density sensing and combinatorial circuits in vitro. In xenograft models, fluorescent protein-tagged CARs improved discrimination by reducing activity against off-target cells while retaining activity against high-antigen tumors. Degron-containing constructs reduced basal CAR expression in vitro but showed suboptimal performance in vivo, revealing a trade-off between basal CAR suppression and induced CAR persistence. Together, these findings demonstrate that basal output expression is a key parameter for inducible genetic circuit designs and establish layered transcriptional and post-translational regulation as a strategy to improve the fidelity of inducible T cell circuits.

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.

An accurate and tractable approximation of the single-point mutation-induced change in protein thermodynamic stability, denoted by DDG, is critical for understanding the genotype-phenotype relationship. Several computational methods have been proposed for this problem; however, limited and error-prone training data and the difficult-to-predict magnitude of structural perturbations make this a challenging task. Consequently, the computational predictors proposed throughout the past decade incrementally improved prediction performance by proposing novel features, combining existing features, task-adapted neural network architectures, loss functions, data augmentation techniques, and pre-training procedures. In this work, we propose PMPNN-DDG, a Random Forest-based DDG prediction model, trained on a novel set of interpretable features extracted from the recently proposed message-passing neural network-based fixed backbone protein design model, ProteinMPNN. On the S669 independent test set, PMPNN-DDG achieves rF +R = 0.64 and RMSE = 1.45, outperforming all compared baseline methods across the reported evaluation measures. On the Ssym independent test set, it achieves rF +R = 0.81, rF -R = -0.99, and RMSE = 1.10, showing competitive performance relative to the compared baselines. PMPNN-DDG is publicly available at https://github.com/dRanger666/PMPNN-DDG.

IL-4Ra is a key regulatory receptor for type 2 inflammatory responses, signal transduce from IL-4 and IL-13 through binding with IL-13Ra or the gamma c chain to activate the downstream JAK1-STAT6 pathway. IL-4Ra is currently the most successful "golden target" in the field of allergic disease therapeutics. Its representative monoclonal antibody drug, dupilumab, through the dual blockade mechanism of IL-4/IL-13 has pioneered a new era of precision therapy for type 2 inflammation. In our manuscript, we employed large-scale deep learning-based computational design methods to de novo design mini-protein antagonists specific for both human and mouse IL-4Ra. The binding affinity was improved from 22.1 nM to 569 pM through partial diffusion. The design accuracy and binding specificity were verified through X-ray crystallography and biochemical studies. In vitro IL4/IL13 signal blockade assays revealed that de novo designed monomeric mini-protein antagonist exhibited comparable blockade ability to bivalent dupilumab. In vivo pharmacokinetic half-life studies demonstrated that fusion to an HSA-binding domain extended the half-life of the mini-protein antagonist from 2.7 hours to 60.6 hours. The IL-4Ra mini-protein antagonist had excellent expression levels, solubility and thermal stability. The IL4/IL13 signal blockade ability remained unchanged even after being heating to 95 degrees. In conclusion, through large-scale cluster computing and deep learning-based de novo design, we developed well-performed IL-4Ra mini-protein antagonist, and demonstrates certain potential for drug development.