ObjectiveThe exacerbating trend of global population aging poses profound socioeconomic and public health challenges, making the comprehensive elucidation of biological aging mechanisms and the discovery of effective anti-aging interventions an urgent priority in the life sciences. Based on our previous serum metabolomics findings that dimethylglycine, an intermediate metabolite of amino acid metabolism naturally present in the human body, was significantly enriched in the serum of longevity families, this study aimed to systematically investigate the anti-aging effects of dimethylglycine both in living organisms and in controlled laboratory environments, and to preliminarily elucidate its underlying molecular mechanisms. While existing literature indicates that dimethylglycine possesses antioxidant and immunomodulatory properties, its direct anti-aging efficacy and the specific molecular pathways through which it operates remain largely unexplored. MethodsTo comprehensively evaluate the anti-aging properties of dimethylglycine, we utilized replicative senescent human embryonic lung fibroblasts, specifically the WI-38 cell line, as an experimental model in a controlled laboratory environment. Cell viability and safety were thoroughly assessed using Cell Counting Kit-8 and lactate dehydrogenase release assays across various concentrations of dimethylglycine. The impact of dimethylglycine on cellular senescence phenotypes, oxidative stress, and proliferative capacity was evaluated via senescence-associated beta-galactosidase staining, reactive oxygen species fluorescence detection, and 5-ethynyl-2'-deoxyuridine incorporation assays. Furthermore, the molecular alterations of senescence-associated secretory phenotype factors and core senescence signaling pathways were quantified using quantitative reverse transcription polymerase chain reaction for the messenger RNA levels of interleukin-6, interleukin-8, p21, and matrix metalloproteinase-1, and enzyme-linked immunosorbent assay for the measurement of p16 and p21 protein expression levels. For the living organism model, the wild-type nematode Caenorhabditis elegans was used to evaluate systemic physiological effects. We conducted a comprehensive lifespan analysis at 20°C, heat stress resistance survival assays at 35℃, senescence-associated beta-galactosidase staining, lipofuscin accumulation tracking, intracellular reactive oxygen species measurement, and Oil Red O staining to ascertain systemic lipid accumulation. Additionally, network pharmacology bioinformatics tools, including PharmMapper and STRING databases, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were utilized to predict target pathways, alongside highly detailed molecular docking simulations utilizing SwissDock and Protein-Ligand Interaction Profiler to examine interactions with the cytochrome P450 family 2 subfamily C member 9 protein. ResultsThe experimental outcomes robustly demonstrate the potent anti-aging capabilities of dimethylglycine. At the cellular level, toxicity analyses firmly confirmed that dimethylglycine is highly safe; continuous treatment with 50 mol/L and 70 mol/L of dimethylglycine for 5 d did not induce any cellular membrane damage or cytotoxicity, but rather actively promoted cellular proliferation. Utilizing the optimal standardized concentration of 50 mol/L, dimethylglycine treatment significantly ameliorated senescent phenotypic markers in human embryonic lung fibroblasts, which was evidenced by a drastic and highly significant reduction in the senescence-associated beta-galactosidase positive cell percentage (P<0.000 1) and intracellular reactive oxygen species levels (P<0.000 1), alongside a marked increase in the 5-ethynyl-2'-deoxyuridine-positive proliferation rate (P=0.003 5). On a molecular expression scale, dimethylglycine significantly downregulated the messenger RNA expression of multiple core senescence-associated secretory phenotype inflammatory factors, including interleukin-6, interleukin-8, p21, and matrix metalloproteinase-1. Concurrently, it effectively suppressed the protein expression of critical cell cycle arrest markers, diminishing p16 protein levels by 57.3% (P=0.000 4) and p21 protein levels by 27.2% (P=0.000 7). In the nematode Caenorhabditis elegans animal model, dimethylglycine significantly extended the mean lifespan from 20.402 d to an impressive 23.066 d (P<0.000 1) and notably enhanced overall survival rates under severe heat stress environmental conditions (P=0.017). Furthermore, systemic dimethylglycine intervention significantly mitigated age-related physiological decline by decreasing bodily lipofuscin accumulation (P<0.000 1), significantly reducing senescence-associated beta-galactosidase activity, lowering systemic reactive oxygen species fluorescence (P=0.008), and effectively alleviating overall fat accumulation (P<0.000 1). Mechanistically, extensive network pharmacology and Kyoto Encyclopedia of Genes and Genomes analyses strongly revealed that the potential targets of dimethylglycine are significantly enriched in fundamental drug metabolism and oxidative stress response pathways. Precision molecular docking simulations conclusively demonstrated that dimethylglycine forms highly stable structural interactions with the cytochrome P450 family 2 subfamily C member 9 protein, specifically highlighting the definitive formation of 5 stable hydrogen bonds involving serine 365, leucine 366, and serine 429 residues, as well as two critical salt bridge formations with arginine 97 and histidine 368 residues. It is additionally predicted to interact favorably with glutathione S-transferase family proteins. ConclusionDimethylglycine exhibits a profoundly significant and multifaceted anti-aging activity at both the cellular and entire living animal levels. By powerfully alleviating oxidative stress, heavily suppressing the core p16 and p21-dependent cellular senescence signaling pathways, and substantially mitigating the detrimental senescence-associated secretory phenotype, dimethylglycine effectively delays fundamental cellular senescence processes and drastically extends whole-organism lifespan. The biological mechanisms driving these robust protective effects are highly likely closely associated with its direct stable interactions with crucial metabolic and detoxifying enzyme systems, such as cytochrome P450 family 2 subfamily C member 9 and glutathione S-transferase family proteins, thereby systemically improving metabolic dysregulation and restoring critical redox homeostasis. This comprehensive study provides highly solid experimental evidence supporting dimethylglycine as a highly potent and safe potential anti-aging intervention agent, while simultaneously offering a clear molecular mechanistic explanation for the previously documented high abundance of dimethylglycine observed within exceptionally long-lived human populations.

This study aims to comprehensively analyze the material basis of toad visceral oil(hereafter referred to as toad oil), and explore the pharmacological effect of toad oil on atopic dermatitis(AD). Ultra-high performance liquid chromatography-linear ion trap/orbitrap high-resolution mass spectrometry(UHPLC-LTQ-Orbitrap-MS) and gas chromatography-mass spectrometry(GC-MS) were employed to comprehensively identify the chemical components in toad oil. The animal model of AD was prepared by the hapten stimulation method. The modeled animals were respectively administrated with positive drug(0.1% hydrocortisone butyrate cream) and low-and high-doses(1%, 10%) of toad oil by gavage. The effect of toad oil on AD was evaluated with the AD score, ear swelling rate, spleen index, and pathological section results as indicators. A total of 99 components were identified by UHPLC-LTQ-Orbitrap-MS, including 14 bufadienolides, 7 fatty acids, 6 alkaloids, 10 ketones, 18 amides, and other compounds. After methylation of toad oil samples, a total of 20 compounds were identified by GC-MS. Compared with the model group, the low-and high-dose toad oil groups showed declined AD score, ear swelling rate, and spleen index, alleviated skin lesions, and reduced infiltrating mast cells. This study comprehensively analyzes the chemical composition and clarifies the material basis of toad oil. Meanwhile, this study proves that toad oil has a good therapeutic effect on AD and is a reserve resource of traditional Chinese medicine for external use in the treatment of AD.
Animals ; Dermatitis, Atopic/immunology* ; Disease Models, Animal ; Mice ; Male ; Gas Chromatography-Mass Spectrometry ; Humans ; Bufonidae ; Oils/administration & dosage* ; Chromatography, High Pressure Liquid ; Female ; Mice, Inbred BALB C

ObjectiveTraditional Chinese medicine (TCM) constitutes a valuable cultural heritage and an important source of antitumor compounds. Poria (Poria cocos (Schw.) Wolf), the dried sclerotium of a polyporaceae fungus, was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia. Traditionally recognized for its diuretic, spleen-tonifying, and sedative properties, modern pharmacological studies confirm that Poria exhibits antioxidant, anti-inflammatory, antibacterial, and antitumor activities. Pachymic acid (PA; a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid), isolated from Poria, is a principal bioactive constituent. Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms, though these remain incompletely characterized. Neuroblastoma (NB), a highly malignant pediatric extracranial solid tumor accounting for 15% of childhood cancer deaths, urgently requires safer therapeutics due to the limitations of current treatments. Although PA shows multi-mechanistic antitumor potential, its efficacy against NB remains uncharacterized. This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking, dynamic simulations, and in vitro assays, aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays. MethodsThis study employed network pharmacology to identify potential targets of PA in NB, followed by validation using molecular docking, molecular dynamics (MD) simulations, MM/PBSA free energy analysis, RT-qPCR and Western blot experiments. Network pharmacology analysis included target screening via TCMSP, GeneCards, DisGeNET, SwissTargetPrediction, SuperPred, and PharmMapper. Subsequently, potential targets were predicted by intersecting the results from these databases via Venn analysis. Following target prediction, topological analysis was performed to identify key targets using Cytoscape software. Molecular docking was conducted using AutoDock Vina, with the binding pocket defined based on crystal structures. MD simulations were performed for 100 ns using GROMACS, and RMSD, RMSF, SASA, and hydrogen bonding dynamics were analyzed. MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex. In vitro validation included RT-qPCR and Western blot, with GAPDH used as an internal control. ResultsThe CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability. GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress, vesicle lumen, and protein tyrosine kinase activity. KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/AKT, MAPK, and Ras signaling pathways. Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1, EGFR, SRC, and HSP90AA1. RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1, EGFR, and SRC while increasing the HSP90AA1 mRNA and protein levels. ConclusionIt was suggested that PA may exert its anti-NB effects by inhibiting AKT1, EGFR, and SRC expression, potentially modulating the PI3K/AKT signaling pathway. These findings provide crucial evidence supporting PA’s development as a therapeutic candidate for NB.

Objective: To explore the potential category patterns of risk prevention and control behaviors of HIV infection among young students who have sex with men (MSM) and their impact on HIV infection and late detection, aiming to optimize intervention strategies. Methods: From September 2017 to December 2024, a total of 1 637 MSM young students in Tianjin were recruited through both online and offline channels. Latent class analysis was applied to classify 11 HIV risk prevention and control behaviors [condom use during the most recent anal sex in the past 6 months, consistent condom use, use of water based lubricants, abstinence from recreational drugs, regular on site professional testing, fixed sexual partners, partner testing, awareness of partner s HIV testing results, testing before sexual activity, nucleic acid testing, and use of pre exposure prophylaxis (PrEP) or post exposure prophylaxis (PEP)]. Multivariate Logistic regression analyzed associations between demographic characteristics/intervention services factors and latent classes. Differences in HIV infection and late detection across behavior patterns were compared. Results: HIV risk prevention and control behaviors among MSM students were classified into three latent classes:condom dependent group (38.42%), low prevention group (27.73%), and comprehensive prevention group (33.85%). Students who received condom promotion/testing services were more likely to belong to the comprehensive prevention group ( OR =5.58), while those who received peer education were less likely to the comprehensive prevention group ( OR =0.43) (both P <0.01). Among the MSM student population, the HIV infection rate was 4.83%, with 2.26% of cases detected late. The HIV infection rate (1.45%) and late detection proportion (0.82%) in the comprehensive prevention group were lower than those in the low prevention group (7.89% and 3.83%, respectively) ( χ 2=16.20, 7.31, both P <0.01). Conclusions HIV risk prevention and control behaviors among MSM young students exhibit significant heterogeneity. Comprehensive prevention strategies can effectively reduce HIV infection and late detection risks. It is necessary to optimize peer education content and improve the accessibility of diversified prevention measures such as PrEP/PEP to enhance HIV prevention and control.

Objective: To understand the incidence risk and risk factors of tuberculosis (TB) among middle school students in Chongqing, so as to provide a basis for formulating TB prevention and control strategies. Methods: From September to December 2022, 32 181 middle school students were selected as the study cohort from 15 administrative districts in Chongqing by using the stratified cluster random sampling method. All cohort members were screened with the tuberculin skin test (TST), and relevant information was collected from January 1, 2023 to December 31, 2024. On the basis of active screening, the follow up data of the participants were compared with the National Tuberculosis Management Information System to obtain the incidence status of the study subjects. The Log rank test was used to compare the TB incidence rates among students with different characteristics, and a Cox proportional hazards model was established to analyze the incidence risk and risk factors of TB. Results: The TST screening rate of the cohort members was 93.0%. During the 2 year follow up period, a total of 36 TB cases occurred, with a cumulative incidence rate of 111.87/100 000 and an incidence density of 55.95/100 000. Among them, the cumulative incidence rate of students from public schools (170.44/ 100 000 ) was higher than that of students from private schools (41.16/100 000), the cumulative incidence rate of students in schools located in high epidemic areas (153.95/100 000) was higher than that in medium epidemic areas (69.00/100 000), and the difference was statistically significant ( χ 2=11.49, 4.73, both P <0.05). The Log-rank test for different TST results showed that the difference in TB comulative incidence rate between students with strongly positive TST results (216.55/ 100 000 ) and those with negative TST results (81.40/100 000) was statistically significant ( χ 2=5.85, P <0.05). Univariate analysis using the Cox proportional hazards model revealed that the risk of TB was lower in students from private schools ( HR=0.25, 95% CI = 0.10-0.59) and students in medium epidemic areas ( HR=0.46, 95%CI =0.23-0.94); whereas the risk of TB was increased in students with strongly positive TST results ( HR=1.39, 95%CI =1.05-1.84) (all P <0.05). Multivariate Cox regression analysis showed that the risk of TB in students from private schools was lower than that of students from public schools ( HR=0.23, 95%CI=0.08-0.62, P <0.05). Conclusions The annual average incidence rate of TB among middle school students in Chongqing is at a relatively high level. It is necessary to strengthen the management and intervention for student groups, including those in public schools, those in schools located in high epidemic areas, and those with strongly positive TST results, so as to reduce the incidence rate of TB.

Objective To investigate the effect and mechnism of betaine(BET)in reversing chemotherapy resistance in prostate cancer(PCa)by inhibiting ATP-binding cassette subfamily B member 1(ABCB1).Methods The PCa chemotherapy-sensitive C4-2B cells were cultured,and the TaxR cells resistant to docetaxel(DTX)were established by gradient increase the concentration of DTX.The drug resistance of C4-2B and TaxR cells against DTX was assessed using CCK-8 and the colony formation experiment.Western blotting and qRT-PCR were used to detect ABCB1 expression.The TaxR cells were divided into:(1)Control group,negative control group(NC),siABCB1-1 group(transfected with siABCB1-1),and siABCB1-2 group(transfected with siABCB1-2).Western blotting was used to detect the effect of small interfering RNA on silencing ABCB1,and CCK-8 was used to detect the differences in DTX resistance between each group.(2)Different concentrations of BET(0,100,200,400,600,800 mmol/L)groups.These groups were subjected to CCK-8 to detect cell viability,and Western blotting was used to detect the protein expression of ABCB1.(3)Control group,DTX group(20 nmol/L DTX),BET group(200 mmol/L BET),and DTX+BET group(20 nmol/L DTX+200 mmol/L BET),flow cytometry was used to detect apoptosis rate and cell cycle,and Western blotting to detect the protein expression of apoptosis-related proteins(Bcl2,BAX,c-caspase-3).(4)Control group,BET group(200 mmol/L BET),wortmannin(WM)group(100 μmol/L WM),and BET+WM group(200 mmol/L BET+100 μmol/L WM).Western blotting was used to detect the protein expression of PI3K,Akt,and ABCB1.(5)Control group,BET group(200 mmol/L BET),and BAY group(10 μmol/L BAY),BAY+BET group(200 mmol/L BET+10 μmol/L BAY).Western blotting was used to detect the protein expression of NF-κB p65,p-ikBα and ABCB1.Network pharmacology combined with transcriptome sequencing was used to predict the possible pathways for BET to reverse chemotherapy resistance.Results Compared with C4-2B cells,TaxR cells showed significantly increased resistance to DTX(P<0.01),and high expression of ABCB1(P<0.01).After silencing ABCB1 with siRNA,TaxR cells'resistance to DTX was significantly inhibited(P<0.01).The inhibition rate of TaxR cells treated with 200 mmol/L BET was less than 20%,and it significantly decreased the expression of ABCB1 protein in TaxR cells(P<0.05).Compared with control group,the combination of 200 mmol/L BET and 20 nmol/L DTX resulted in higher apoptosis rate and higher S stage cell ratio,lower expression of Bcl-2 protein and higher expression of BAX and c-caspase-3 proteins than the two drugs used alone(P<0.05).Compared with control group,the combination of 200 mmol/L BET and 100 μmol/L WM significantly down-regulated the protein expression of PI3K,Akt and ABCB1(P<0.01).The combination of 200 mmol/L BET and 10 μmol/L BAY significantly down-regulated the protein expression of NF-κB p65,p-ikBα and ABCB1(P<0.01).Conclusion BET may reverse TaxR cells'chemotherapy resistance by down-regulating ABCB1 expression through the PI3K/Akt/NF-κB signaling pathway.

The inflammatory status in patients with chronic kidney disease(CKD)is closely associated with cardiovascular events,infections,and other complications,and is a powerful indicator for prognosis assessment.The core view of the"gut-kidney axis"theory reveals the relationship among inflammatory state,microbiota dysbiosis,and deterioration of renal function.The microbiota alters the microenvironment through structural changes and metabolites with different properties,subsequently leading to microbiota translocation,inducing inflammatory lesions,and damaging the kidneys.Recent studies have proposed that targeted microbiota intervention strategies such as probiotics,prebiotics,and synbiotics can modulate the microbiota structure,regulate the microenvironment,relieve renal inflammation,and affect the progression of renal disease,representing a potentially promising research direction in the future.This review discusses the characteristics of how intestinal microbiota influence the inflammatory status in CKD,focusing on the research progress of targeted microbiota intervention,aiming to discuss the effectiveness and scientific basis of these strategies,providing a foundation for the treatment of CKD and the expansion of targeted microbiota research,as well as offering references for the clinical application of probiotics,prebiotics,and synbiotics.

Objective To investigate the potential mechanisms by which the natural herbal monomer scutellarin alleviates ischemic stroke(IS)using network pharmacology and in vivo experimental validation.Methods Potential targets of scutellarin were predicted using SwissTargetPrediction and PharmMapper,and standardized via UniProt.IS-related differentially expressed genes(DEGs)were obtained from the GSE22255 dataset in the GEO database,with screening criteria of|log10FC|≥1 and P＜0.05.Venny 2.1.0 analysis was used to identify overlapping targets.Protein-protein interaction(PPI)networks were constructed using STRING and visualized in cytoscape.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses were performed.Molecular docking was conducted using AutoDock Vina 1.5.6 to assess the binding affinity between scutellarin and hub targets.Middle cerebral artery occlusion(MCAO)mouse model was established and divided into sham,MCAO,and MCAO+scutellarin groups.Real-time PCR was used to detect mRNA expression of hub genes and phosphatidylinositol 3-kinase(PI3K)/Akt pathway components in the ischemic cortex.Results A total of 325 scutellarin targets and 2168 IS-related DEGs were identified,with 29 overlapping targets.GO analysis yielded 51 biological processes,5 cellular components,and 8 molecular functions.KEGG enrichment highlighted PI3K/Akt and metabolic pathways.PPI analysis identified Caspase-3,epidermal growth factor receptor(EGFR),prostaglandin-endoperoxide synthase 2(PTGS2),peroxisome proliferator activated receptor alpha(PPARA),and interleukin-2(IL-2)as key hub proteins.Molecular docking showed strong binding affinities between scutellarin and these proteins.Real-time PCR result confirmed that scutellarin modulated the expression of hub genes and activated the PI3K/Akt pathway.Conclusion In the MCAO mouse model,scutellarin exerts neuroprotective effects by modulating targets such as CASP3,EGFR,PTGS2,PPARA,and IL-2,and activating the PI3K/Akt signaling pathway,exhibiting multi-target and multi-pathway characteristics.

Objective To investigate the status of population dynamics and distribution changes of Aedes aegypti in Guangdong Province.Methods Continuous monitoring was conducted from May 2018 to July 2024 in Wushi Town and Qishui Town,Leizhou City,Zhanjiang City,Guangdong Province.Additionally,a survey of the distribution of Ae.aegypti along the Leizhou Peninsula coast was carried out.Results The density of Ae.aegypti in Zhanjiang showed a gradual decline from 2018 to 2024.The last detection of adult Ae.aegypti in Wushi Town was in September 2021,and the last larva was found in October 2023.No Ae.aegypti was detected in Qishui Town during surveys from 2021 to 2024.A survey of 18 coastal villages in the Leizhou Peninsula revealed no detections of Ae.aegypti.Conclusions This study provides a basis for understanding the distribution and population density fluctuations of Ae.aegypti,assessing its invasion risk,and scientifically conducting relevant prevention and control efforts.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.