HER2 plays a crucial role in breast cancer (BC) progression, with the D769H and D769Y mutations significantly influencing its structural integrity, drug-binding dynamics, and therapeutic response. This study employs molecular docking and molecular dynamics simulations (MDS), with trajectories propagated for 1000 ns, to examine their distinct effects. Root mean square deviation (RMSD) analysis indicates increased conformational deviations in mutant structures, signifying heightened instability, while root mean square fluctuation (RMSF) reveals enhanced flexibility near the mutation site. Solvent accessible surface area (SASA) calculations highlight changes in solvent exposure, directly affecting ligand accessibility, while radius of gyration (Rg) assessments suggest structural loosening or tightening in response to mutation-induced alterations. Binding free energy calculations using MM-PBSA indicate variability in drug affinity, with mutations disrupting hydrogen-bonding networks and altering ligand stability. Principal Component Analysis (PCA) delineates distinct motion trajectories in mutant proteins, revealing shifts in conformational behavior. Umbrella sampling simulations indicate that while the wild-type HER2-drug complex requires 150 ps to reach equilibrium, the D769H mutant stabilizes within 100 ps, suggesting diminished drug retention. Conversely, the D769Y mutation enhances ligand binding, surpassing wild-type interaction strength. These findings elucidate mutation-specific effects on HER2 structural dynamics and drug interactions, underscoring the need for mutation-tailored therapeutic strategies to mitigate the impact of these variants.

Head and Neck Squamous Cell Carcinoma (HNSCC) diagnosis remains a challenge for clinicians, with human papillomavirus (HPV) status long associated with HNSCC prognosis and response to therapy. Small non-coding molecules, such as microRNAs (miRNAs), significantly alter gene expression, particularly of immune-modulatory genes. In the current study, an effort to map the interaction patterns between miRNAs and their target genes was carried out using diverse computational tools. A microarray-based study was retrieved and analysed using GEO2R to identify ubiquitously expressed miRNAs in HPV-associated HNSCC samples, with HPV-negative samples used as controls. Seven miRNAs were identified, namely hsa-miR-150-5p, hsa-miR-142-5p, hsa-miR-142-3p, hsa-miR-1-3p, hsa-miR-133b, hsa-miR-206, and hsa-miR-1260b. Functional annotation using miRNet identified numerous significant signalling pathways dysregulated by the aforementioned miRNAs. miRDB was used to map miRNA target genes, which were visualised in Cytoscape; among these, key immune-modulatory genes were analysed in a comprehensive meta-analysis. Furthermore, the GEPIA2 tool was used to perform survival analysis and generate Kaplan-Meier plots correlating survival percentages in HNSCC patients with gene expression patterns. The viral infectious cycle was dysregulated by miR-1260b expression. DKK1, STC2, SPOCK1, and TP53 were among the genes whose aberrant expression was associated with a significant reduction in survival in HNSCC-affected individuals. This report elucidates the pivotal role of miRNAs in modulating the expression of key immune-modulatory genes, thereby influencing the prognosis of HNSCC and HPV infection.

The rise of multidrug resistance (MDR) in Acinetobacter baumannii has severely compromised the efficacy of carbapenem antibiotics. This resistance is primarily driven by class B1 metallo-β-lactamases (MBLs), including IMP, VIM, and NDM, which hydrolyze a broad spectrum of β-lactams, including carbapenems. To address this challenge, we conducted an extensive structure-based virtual screening campaign specifically targeting IMP-2, VIM-1, and NDM-1, integrating high-throughput docking, ADME/T filtering, and molecular dynamics validation to identify novel inhibitors. In total, 66,734 compounds from the ENAMINE, CMNPD, ASINEX, and ChemDiv libraries were evaluated, with marine-derived CMNPD molecules showing superior drug-likeness and binding potential. Virtual screening was performed using the Schrödinger suite, and top-ranked hits were validated using GROMACS-based molecular dynamics simulations, MM-GBSA binding free energy calculations, and essential dynamics analysis to confirm stability and binding efficiency. Three lead inhibitors, CMNPD29415 (IMP-2; XP G-score: -14.17 kcal/mol; MM-GBSA: -84.53 kcal/mol), CMNPD8077 (VIM-1; -11.76 kcal/mol; MM-GBSA: -50.11 kcal/mol), and BDE_30700625 (NDM-1; -13.80 kcal/mol), exhibited stable protein-ligand interactions and robust dynamic behavior. Key interactions were observed with catalytic residues, including GLU24, HIS35, GLU153, ASP224, and HIS240, supporting their inhibitory potential. MD trajectories revealed low RMSD fluctuations and reduced active-site mobility, further strengthening the therapeutic relevance of these compounds. Overall, this study highlights structurally unique marine metabolites as promising inhibitory scaffolds against Class B1 MBLs, and upcoming experimental validation will determine their translational applicability.

Acinetobacter baumannii is a significant, multidrug-resistant pathogen that is increasingly recognized as an agent associated with hospital infections. Its increasing resistance to carbapenems and other necessary antibiotics poses a serious threat to public health worldwide. The present study provides an integrative analysis of the pan-resistome and transcriptomic landscape of carbapenem-resistant A. baumannii (CRAB) under sub-minimum inhibitory concentrations (sub-MICs) of clinically relevant antibiotics, i.e., ciprofloxacin, amikacin sulfate, meropenem, and polymyxin B. A focused investigation was conducted into the transcriptional modulation of efflux transport systems and cellular stress-response mechanisms. To identify differentially expressed genes (DEGs) among the CRAB strains, parallel comparative RNA-Seq analysis with already available public datasets was undertaken. Concurrently, high-throughput virtual screening against the comprehensive marine natural product database (CMNPD) was done to identify inhibitors for MacB, a major ABC-type efflux transporter. Binding stability and interaction profiles of lead compounds were assessed via molecular dynamics simulations of 1000 ns. Transcriptomic profiling consistently showed increased levels of MacA-MacB efflux components, RcnB an intracellular stress-response protein LolA an outer membrane chaperone and surface antigen protein 1, especially under polymyxin B exposure. CMNPD27284 is the best candidate, having a strong binding affinity and stable interaction networks with critical MacB residues (-7.20 kcal/mol). These results highlight that efflux-mediated resistance and stress adaptation are crucial factors in CRAB, and they also indicate CMNPD27284 as a potential candidate for marine-derived scaffolding in developing drugs targeting the efflux pump.

Staphylococcus aureus is the leading pathogen responsible for hospital- and community-acquired infections. The increasing prevalence of nosocomial infections in healthcare settings presents a significant challenge, particularly due to the strong biofilm-forming capability of clinical strains, which contributes to biofilm-mediated multidrug resistance. The biofilm-associated protein (BAP) plays a pivotal role in the initial adhesion and maturation of biofilms, significantly increasing the likelihood of failure of conventional antimicrobial therapies. Given its crucial function in biofilm formation, BAP represents a promising target for anti-biofilm drug development. A high-throughput virtual screening technique was implemented to identify potent BAP inhibitors, utilizing triple-mode docking with the Glide module of the Schrödinger Maestro suite. About 28,831 compounds from the ENAMINE-targeted antibacterial library were screened against BAP in S. aureus. Among the selected ligands, Z1430813924 and Z1738791774 exhibited the lowest binding energy, demonstrating superior docking scores alongside favorable ADME and physicochemical properties, which suggests an enhanced inhibitory potential. To validate the docking findings, a 100-ns molecular dynamics simulation was employed to assess the stability of the protein-ligand complex within a dynamic environment. The essential dynamics analysis, including free energy landscape (FEL) and principal component analysis (PCA) evaluations, affirmed the stability and efficacy of the top compounds, Z1430813924 and Z1738791774, as promising BAP inhibitors. These insights provide a strong foundation for subsequent experimental validation and the potential development of novel anti-biofilm therapeutics targeting S. aureus infections.

The RET V804M gatekeeper mutation is a clinically significant resistance mechanism that reduces the efficacy of several kinase inhibitors. To support rapid insilico prediction of inhibitory potency against this variant, we built an interpretable QSAR model using a curated dataset of experimentally reported RET V804M inhibitors. Following descriptor preprocessing and filtering, 140 RDKit descriptors were retained for model development. Among the algorithms evaluated, Gradient Boosting Regression provided the most reliable performance. Its parameters were optimized using an extensive grid search involving 1728 combinations with 5-fold cross-validation. The resulting model achieved a Pearson correlation coefficient (r) of 0.737 and a Root Mean Squared Error (RMSE) of 0.564, indicating good agreement between the predicted and experimental activities. A leverage-residual Williams plot confirmed that most of the test compounds reside within the applicability domain (AD) of the model. Evaluation of newly reported RET inhibitors further showed that reliable predictions are achieved for compounds contained within this established chemical space. To ensure interpretability, model behaviour was examined using tree-based feature importances, which identified that electronegativity patterns, hydrophobic surface distribution, and molecular flexibility and polarizability are major drivers of RET V804M inhibition. To facilitate practical use, the fine-tuned Gradient Boosting Regressor has been deployed as an interactive web application ( https://ret-biopredictor.streamlit.app/ ), enabling users to predict pIC50 values for candidate RET V804M inhibitors.

The opportunistic pathogen Acinetobacter baumannii, a major cause of nosocomial infections, has exhibited a rapid increase in resistance to conventional antimicrobial therapies, emphasizing the urgent need for alternative strategies such as anti-virulence approaches. The response regulator BfmR is a critical mediator of biofilm formation and virulence, making it an attractive yet underexplored therapeutic target. In this study, we established a comprehensive in silico pipeline to identify potential BfmR inhibitors through large-scale virtual screening and advanced computational analyses. The crystal structure of BfmR (PDB ID: 5HM6) was prepared using Schrödinger's Protein Preparation Wizard with the OPLS3 force field. A compound library comprising 66,734 molecules from CMNPD, Enamine, ChemDiv, and Asinex databases was processed using LigPrep and Epik to generate appropriate protonation states and stereoisomers at physiological pH. Virtual screening was performed using GLIDE in a hierarchical workflow including HTVS, SP, and XP docking. Pharmacokinetic and toxicity profiles were assessed using QikProp to ensure drug-likeness. Top-scoring compounds were further evaluated using triplicate 500 ns molecular dynamics simulations in GROMACS 2023 with the CHARMM36 force field, employing TIP3P water models and system neutralization. Post-simulation analyses included RMSD, PCA, free energy landscape mapping, dynamic cross-correlation matrices, covariance analysis, and MM-PBSA binding energy calculations. Six lead compounds demonstrated stable binding, favorable energetics, and consistent interactions with key regulatory residues THR23, ARG29, and VAL109, highlighting their potential as promising anti-virulence agents against A. baumannii.

Bladder cancer (BC) poses a significant global health and economic burden due to its high recurrence rates, progression risks, and the need for lifelong surveillance. Despite advances in diagnosis and treatment, reliable molecular biomarkers for prognosis and therapeutic targeting remain limited. In this study, we used an integrative bioinformatics approach to identify key dysregulated nuclear genes in BC, focusing on histone variants because of their essential role in chromatin organization and gene regulation. Using RNA-seq data, we performed a quality assessment, aligned reads with STAR, and conducted differential expression analysis using DESeq2. A unique gene expression pattern was seen between the cancer and control groups. From the DEGs, nuclear genes were curated using the NCBI Gene database and analysed their network topology via STRING and Cytoscape. MCODE found the complex cluster, and CytoHubba identified key hub genes, notably histone genes HIST1H3D (H3C4), HIST1H4E (H4C5), and HIST1H4B (H4C2). Functional enrichment via clusterProfiler highlighted roles in chromatin assembly, nucleosome organization, and centromeric dynamics. Notably, pathway analysis revealed links to systemic lupus erythematosus (SLE), neutrophil extracellular trap formation, and transcriptional misregulation, suggesting immunomodulatory roles. Kaplan-Meier survival analysis of patient cohorts using the KM-plotter tool revealed that higher expression of HIST1H4E and HIST1H4B was associated with improved survival outcomes. These findings emphasize the epigenetic and immunological roles of nuclear histone genes in BC progression and lay the groundwork for future translational research aimed at diagnostic and therapeutic advancements.

Diabetic Nephropathy (DN) affects over half of the diabetic population, is a significant global health concern and a leading cause of end-stage renal disease (ESRD). Recent studies emphasised the potential of herbal medicinal plants, particularly those with antioxidant properties and anti-inflammatory properties, in treating DN. This research examines the active phytochemicals and molecular mechanisms of D5 chooranam in the treatment of DN. Data on these phytochemicals were sourced from the IMPPAT database. Sixteen compounds were selected for further analysis based on ADMET profiling criteria. Target prediction analysis was performed for the selected phytochemicals. Disease-associated targets were identified using multiple databases, and comparison of the phytochemical and disease target datasets revealed 462 overlapping targets. Functional analysis revealed enrichment in pathways related to insulin resistance, PI3K-AKT signaling, and AGE-RAGE metabolism. Hub targets were identified using degree measures, with the top 10 genes selected for further analysis. The compound kaempferol showed strong interactions with genes such as TP53, MAPK1, MAPK3, AKT1, and SRC. Molecular docking and 500 ns molecular dynamics simulation studies of these five complexes showed that the SRC–kaempferol complex exhibited stable interactions, structural stability throughout the simulation period, and favorable binding energies. This study elucidates the pharmacologically active compounds in D5 chooranam by combining traditional knowledge with modern scientific insights using network pharmacology, thereby informing treatment strategies.

The F₀F₁ ATP synthase of Mycobacterium tuberculosis (M. tuberculosis) is an essential membrane-embedded rotary motor responsible for ATP synthesis and maintenance of the proton motive force in bacteria. The transmembrane F₀ domain comprises the c-subunit (atpE) and the a-subunit (atpB). Their coordinated interactions are needed for proton translocation and torque generation. Bedaquiline (BDQ), FDA-approved diarylquinoline for the treatment of multidrug-resistant tuberculosis (MDR-TB), targets the F₀ motor by binding at the a–c interface and inhibiting rotary catalysis. To the best of our knowledge, this study represents the first attempt to analyze the effects of mutations in the atpB protein on its structural stability in the F₀ domain, thereby highlighting the novelty of this work. In this study, we integrated Indian whole-genome sequencing (WGS) datasets (PRJNA37907) with long-timescale (1000 ns) membrane-embedded molecular dynamics (MD) simulations. Among 57 atpB mutations identified from WGS analysis, L173I was selected for structural and MD analysis. L173I is located at the atpB-atpE interface near the BDQ-binding region, despite V177L and S184A showing higher prevalence. Comparative MD simulations encompassed four systems: wild-type apo, wild-type with BDQ, L173I apo, and L173I with BDQ. Structural interrogation revealed that the L173I substitution induces subtle destabilization of the global fold of the atpB-atpE complex relative to the apo state, while more critically attenuating inter-subunit contacts between the a-subunit and the c-ring. These perturbations provide a mechanistic rationale for reduced BDQ susceptibility, arising from altered interfacial dynamics rather than complete abrogation of drug binding. This integrative genomic–structural framework advances our understanding of ATP synthase-mediated resistance in M. tuberculosis.

The extracellular domain (ECD) of the RET receptor tyrosine kinase depends on its cysteine-rich domain (CRD) for calcium coordination, structural stability, and assembly with GFRα1 and GDNF. Mutations close to the CRD CaLM motif have been associated with disease, but their molecular effects remain understudied. In this study, we analyzed two clinically reported variants, T564N and T564P, using all-atom molecular dynamics simulations of both the isolated CRD and the RET/GFRα1/GDNF ternary complex. Our analysis showed that both the mutations introduced localized structural changes in the CRD monomer. T564N caused increased residue fluctuations at the mutation site and solvent exposure, whereas T564P enhanced flexibility across all calcium-coordinating residues and slightly decreased stabilizing contacts. These effects became more noticeable in the ternary complex. Within the complex, interactions with the neighbouring domains caused the CRD to adopt conformations that compensated for the structural changes observed in the CRD monomer. In this context, each mutation affected calcium-binding energetics differently, resulting in more favourable binding in the mutants than in the wild-type. Although calcium binding was energetically favourable, the overall interaction energy within the complex was still affected. The complex highlighted mutation-specific differences in RET's interactions with GFRα1 and GDNF. The comparison between monomeric and complex simulations indicates that the functional impact of T564 mutations cannot be inferred from the isolated CRD. Together, these results show that the structural and energetic consequences of CRD CaLM mutations depend strongly on the full signaling assembly. This underscores the need to assess RET variants within their native multiprotein environment to understand how disease-associated mutations may alter receptor function.

BackgroundNon-Small Cell Lung Cancer (NSCLC), the most prevalent form of pulmonary malignancy, is primarily classified into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). TP53 gene is the most frequently mutated gene across numerous cancers. p53, a metalloprotein is stabilized by a tetrahedral Zn2+ binding motif involving Cys176, Cys238, Cys242, and His179. The His179 site, despite its structural importance, remains underexplored.MethodsTCGA mutational profiles were evaluated for 616 LUAD and 544 LUSC individuals. This study focuses on mutational perturbations at the His179 locus, a key residue within the protein’s zinc-binding motif. Frequent substitutions at H179 (Y/R/N/L/D) were identified across LUAD and LUSC cohorts. The structural and functional ramifications of these mutations were studied using combinatorial static structural analysis and atomistic molecular dynamics simulations (MDS). Conformational trajectories were analyzed to assess alterations in protein flexibility and functionally critical regions. Binding affinity values of the protein with Zn2+ were also evaluated for all mutants.ResultsC > A was the predominant single-nucleotide substitution observed, with TP53 gene mutations present in 50% of LUAD and 81% of LUSC cases. All five H179 (Y/R/N/L/D) variants exhibited distinct conformational signatures and resulted in compromised protein stability. Contact maps indicated altered residue-level interaction patterns in the mutants as compared to and the wildtype. The energy landscape of the mutants was also observed to be altered in comparison to the wildtype. Structural perturbations were evident in L1 and L2 loops, indicating that these regions are involved in mutation-induced structural plasticity.DiscussionThe results observed underscore the pathogenic potential of His179 mutations within the p53 Zinc-binding motif. The findings highlight the critical role of the Zinc-binding motif in maintaining p53’s conformational fidelity and suggest that specific substitutions may differentially modulate its tumor-suppressive function.

Tuberculosis is a deadly airborne disease caused by Mycobacterium tuberculosis . Drug-resistant tuberculosis presents significant challenges for treatment and control of the disease. Resistant strains of Mycobacterium tuberculosis arise from specific mutations in the bacterium. Identification and characterization of resistance-associated mutations are crucial for effective treatment strategies because the first- and second-line drugs for the disease target distinct genes in the bacterium and serve different purposes. Our study developed a machine learning prediction model to analyze mutations across multiple drug-resistance types. The proposed framework predicts drug-resistance mutations across four drug-resistance types, including Rifampicin Resistance, Isoniazid Resistance, Multidrug Resistance, and Pre-extensively Drug-Resistant tuberculosis. The NIAID-NIH TB portal is a publicly available dataset of tuberculosis patients, including drug-resistance information. Our study analyzed 3,065 cases of drug-resistant TB. Eight supervised ML algorithms were implemented for the study. A Random Forest classifier with 10-fold cross-validation shows higher predictive performance than the other seven algorithms considered for further analysis. Significant drug resistance mutations were identified using SHapley Additive exPlanations feature importance. The World Health Organisation mutation catalogues, considered the gold standard for drug-resistant mutations, were used to evaluate prediction results. Mutations not reported in the WHO catalogues were identified during the post-prediction comparative analysis stage, as they may represent potential resistance-conferring markers warranting further investigation, including structural and functional validation or experimental validation. The mutations are rpoB- I480T, rpoC -G332R, L527V, gyrA -D94V, KatG -G99E, A106V, W191R, W328C, T380I, and M420T. The study further checks the stability and pathogenicity of the mutations using computational tools, including I-Mutant 2.0 and PredictSNP. The findings added more clarity and further evidence for the significance of the mutation, based on its contribution to drug resistance.

Background Antifungal resistance is an emerging but underrecognized public health threat. It receives limited attention in global antimicrobial resistance surveillance and stewardship efforts. While growing evidence links environmental pollution to bacterial resistance, the role of pollution in driving antifungal resistance remains largely unexplored. We investigated whether prolonged exposure to ambient air pollutants is associated with increased drug resistance in Candida glabrata. Methods We performed exploratory and spatiotemporal analyses of 4,055 C. glabrata isolates collected from the U.S. and Western Europe between 2010 and 2023, as part of the Pfizer-Atlas surveillance initiative. We constructed a composite resistance phenotype based on resistance information for four antifungals: anidulafungin, caspofungin, micafungin, and fluconazole. In parallel, we extracted annual concentrations for three air pollutants, NO 2 , PM 2.5 , and PM 10 , from the WHO Ambient Air Quality Database (v6.1, 2024). We then assessed the association between pollutant levels and the composite resistance phenotype using time-series regression analysis. Lastly, we also evaluated the delayed effect of pollution on antifungal resistance by introducing a 1- to 3-year lag. Results Our spatiotemporal analysis revealed global increase in fluconazole resistance. In the U.S., we found a significant positive correlation between NO 2 and isolates that were resistant to fluconazole but sensitive to the other three antifungals. We observed a consistent increase in the proportion of these isolates in the U.S. over time, while the increase in Western Europe was variable but notable. Our time-series regression analysis indicated that even after the introduction of a time-lag, the U.S. had stronger and more consistent links between air pollutants and antifungal resistance than Western Europe. Conclusions Our study is the first of its kind to provide large-scale, data-driven evidence linking prolonged presence of air pollution with time-lagged antifungal resistance in C. glabrata. Our preliminary findings emphasize the necessity of including environmental factors in antifungal stewardship and surveillance.

Klebsiella pneumoniae, a Gram-negative bacterium, poses a significant public health threat due to its resistance to various antibiotics, including β-lactams and carbapenems. This resistance is mainly due to the production of Klebsiella pneumoniae carbapenemases (KPCs). The issue of KPC-2 and its variant, KPC-3, by K. pneumoniae strains, results in resistance to the substrate imipenem and β-lactamase inhibitors. Using Schrodinger software, we performed a high-throughput virtual screening of 374 compounds from the ChemDiv natural compound library in this study, targeting KPC-2 and KPC-3. The top compounds were identified using Extra Precision (XP) mode. Molecular dynamics simulations (MDS) were performed for 500 ns using GROMACS. Among the compounds, N075-0013 and N098-0051 for KPC-2 and N025-0014 and N099-0011 for KPC-3 exhibited binding energies ranging from -5.40 to -7.01 kcal/mol against both KPC-2 and KPC-3. The complexes formed with these compounds remained stable in their dynamic environments, suggesting their potential as effective inhibitors of KPC-2 and KPC-3. These results underscore the potential therapeutic promise of these compounds, justifying further in vitro and in vivo validation for their development as inhibitors of Klebsiella pneumoniae carbapenemases.

The RET receptor tyrosine kinase is essential for cell growth, differentiation, and survival. Its cysteine-rich domain (CRD) is crucial for ligand-induced dimerization, activation, and structural stability, significantly influenced by calcium ion coordination. Mutations in key cysteine residues can disrupt disulfide bonds, alter calcium binding, and destabilize the CRD, leading to oncogenic transformations. This study investigates the impact of cysteine mutations on calcium ion binding and the structural stability of the RET receptor's CRD. Using molecular dynamics simulations and free energy calculations, the research examines the structural effects of specific cysteine mutations (C565F, C581F, and C585S) in the CRD. The findings indicate that these mutations disrupt disulfide bonds, alter calcium binding, and destabilize the CRD. RMSD and RMSF analyses show that each mutant affects structural dynamics and flexibility differently. The C581F mutant exhibited the most significant effect, with average RMSD values of 0.21 nm compared to the wild-type (0.19 nm) and other mutants (C565F, 0.14 nm; C585S, 0.17 nm). Higher residue fluctuations were observed in C581F and C585S, particularly in the calcium-coordinating residues. Binding free energy analysis indicates reduced calcium-binding stability in the mutants, while weighted contact maps reveal altered residue interaction patterns and new contact formations. These results suggest that while global structural changes are minimal, cysteine mutations cause localized destabilization of calcium ion binding sites. The disruption of key disulfide bonds and reduced residue contacts likely contribute to decreased binding stability in the mutants, underscoring the importance of cysteine residues and calcium coordination in maintaining the integrity of the RET-CRD.

This study aimed to identify B-cell epitope candidates using multiple epitope identification software and in silico analysis of the modeled B19 V protein against specific antibodies using molecular docking and dynamics simulation. Materials and Methods : Full-length amino acid sequences of the VP1 protein of B19 V were retrieved from NCBI. A consensus sequence was generated using CLC sequence viewer. Linear B cell epitopes were identified using Bepipred 2.0, ABCpred, and LBTope. The linear epitope was synthesized and validated against B19 V-specific antibodies. A 3D model of the B19 V VP1 consensus protein was generated using the ITASSER server. Discontinuous B cell epitopes were identified using Discotope 2.0 and Ellipro. Molecular docking and molecular dynamics simulation was performed to investigate the interaction between the modeled B19 V protein and specific anti-B19 V antibody. Results : The identified epitope was 100% conserved and similarly identified through ABCpred and LBTope. The HADDOCK score and MDS analysis, such as hydrogen bond interactions and MMPBSA analysis, revealed that the VP1 and mAb H chains formed a significantly stable complex. The MDS demonstrated that the VP1-mAb H chain complexes had lower RMSF values around 130 to 200 residues, a region responsible for the catalytic network for enzyme activity; as a result, the flexibility of the antibody-bound VP1 decreased when compared to Apo-VP1. Conclusion: A viable epitope identified through this process was synthesized and validated using ELISA, which highlighted the role of the epitope identification process in diagnostics. This study also sheds light on the complex interplay between VP1 and the mAb H chain and highlights key binding specificity and stability determinants.

Nephrotic syndrome (NS), a complex renal disorder characterized by proteinuria, oedema and hypoalbuminemia, exhibits a multifactorial aetiology, including genetic factors and race. Further understanding the prominent gene expression and microRNA (miRNA) patterns associated with drug metabolism may aid in the development of treatment options for children with NS. The present study aimed to determine the gene expression profiles of the cytochrome 450 3 A family of genes, CYP2C8, multidrug resistance 1 and glucocorticoid receptor (NR3C1), through the comparisons of patients with steroid-sensitive NS (SSNS) and steroid-resistant NS (SRNS), using the prediction of miRNAs as a prognostic biomarker. Reverse transcription-quantitative PCR was used to identify gene expression patterns in each group, and the Gene Expression Omnibus database was used to predict groups of genes with co-expression patterns. Moreover, a network-based interaction analysis was performed using Human Protein Atlas and STRING databases, to identify the specific gene group involved in NS. MiRDB was also used to determine the specific miRNA associated with the aforementioned gene groups. Results of the present study demonstrated that male patients were more susceptible to NS development. Moreover, in both males and females in the SRNS group, NR3C1, CYP2C8, CYP3A7, CYP3A5 and CYP3A4(statistically insignificant) were upregulated. In addition, results of the present study revealed 10 unique hub miRNAs in the SRNS group that exhibited potential as gene targets for above genes. Collectively, results of the present study may provide novel insights into the genetic mechanisms underlying NS development, which may aid in the development of personalized therapeutic strategies.