Search Results (958)

Antigenicity and immunogenicity define a potent immunogen in vaccinology. Nowadays, there are simplified platforms to produce nanocarriers for small-peptide antigen delivery, derived from various infectious agents for the treatment of a variety of diseases, based on virus-like particles (VLPs). They have good cell-penetrating properties and protective action for target molecules from degradation. Human papillomavirus (HPV) causes anogenital warts and six types of cancer in infected women, men, or children, posing a challenge to global public health. The HPV capsid is composed of viral type-specific L1 and evolutionarily conserved L2 proteins. Produced in heterologous systems, the L1 protein can self-assemble into VLPs, nanoparticles sized around 50–60 nm, used as prophylactic vaccines. Devoid of the viral genome, they are safe for users, offering no risk of infection because VLPs do not replicate. The immune response induced by HPV VLPs is promoted by conformational viral epitopes, generating effective T- and B-cell responses. Produced in different cell systems, HPV16 L1 VLPs can be obtained on a large scale for use in mass immunization programs, which are well established nowadays. The expression of heterologous proteins was evaluated at various transfection times by transfecting cells with vectors encoding codon-optimized HPV16L1 and HPV16L2 genes. Immunological response induced by chimeric HPV16 L1/L2 VLP was evaluated through preclinical assays by antibody production, suggesting the potential of broad-spectrum protection against HPV as a prophylactic nanovaccine. These platforms can also offer promising therapeutic strategies, covering the various possibilities for complementary studies to develop potential preventive and therapeutic vaccines with broad-spectrum protection, using in silico new epitope selection and innovative nanotechnologies to obtain more effective immunobiologicals in combating HPV-associated cancers, influenza, hepatitis B and C, tuberculosis, human immunodeficiency virus (HIV), and many other illnesses. Full article

Nano-based drug delivery systems present a promising approach to improve the efficacy and safety of therapeutics by enabling targeted drug transport and controlled release. In parallel, computational approaches—particularly Molecular Dynamics (MD) simulations and Artificial Intelligence (AI)—have emerged as transformative tools to accelerate nanocarrier design and optimise their properties. MD simulations provide atomic-to-mesoscale insights into nanoparticle interactions with biological membranes, elucidating how factors such as surface charge density, ligand functionalisation and nanoparticle size affect cellular uptake and stability. Complementing MD simulations, AI-driven models accelerate the discovery of lipid-based nanoparticle formulations by analysing vast chemical datasets and predicting optimal structures for gene delivery and vaccine development. By harnessing these computational approaches, researchers can rapidly refine nanoparticle composition to improve biocompatibility, reduce toxicity and achieve more precise drug targeting. This review synthesises key advances in MD simulations and AI for two leading nanoparticle platforms (gold and lipid nanoparticles) and highlights their role in enhancing therapeutic performance. We evaluate how in silico models guide experimental validation, inform rational design strategies and ultimately streamline the transition from bench to bedside. Finally, we address key challenges such as data scarcity and complex in vivo dynamics and propose future directions for integrating computational insights into next generation nanodelivery systems. Full article

The purpose of this in silico study was to evaluate the main difference of the adhesion strength of direct and semi-direct composite resin restorations in dentin using micro-tensile testing (μTBS) and finite element analysis (FEA). This in silico study employed cohesive zone traction and shear laws to investigate interfacial damage in both restoration groups. Tridimensional finite element models of both restoration specimens were created. A 20 μm thick resin cement layer was created for the semi-direct case. The Clearfil SE Bond 2 adhesive system and the restorative material, Ceram X Spectra ST HV composite resin, were used on both restorations. The numerical bond strength of both restoration techniques was evaluated using two different analysis assumptions. In the first assumption, the numerical analysis procedure included only the non-linear behavior of dentin and the von Mises damage criterion, whereas cohesive zone models were included in the second analysis assumption. The influence of dentin-adhesive cohesive mechanical properties was studied using values reported in the literature, and a sensitivity study helped improve the correlation between experimental and numerical results. The mechanical properties of the composite cohesive zone were defined assuming that the interface strength of dentin and composite follows the values reported by the manufacturer of Spectra ST. Damage initiation and progression were analyzed, and strains and stresses of the cohesive zone models (CZM) were compared with the corresponding perfect bonded models. The experimental µTBS results for the direct restoration strategy showed an adhesive strength of 38.156 ± 10.750 MPa, while the CZM predicted a slightly higher value of 40.4 ± 10.8 MPa. For the indirect restoration strategy, the experimental adhesive strength was 25.449 ± 10.193 MPa, compared to a numerically predicted strength of 28.1 ± 9.3 MPa. Overall, the CZM tends to overestimate the adhesive strength relative to experimental values. The statistical analysis of dentin extension strains for direct (DR) and semi-direct (SR) group models reveals that the SR configuration yields higher strain levels. Hence, these results suggest that, assuming identical dentin properties across both restoration groups, the material configuration in the direct restoration offers better mechanical protection to the dentin. These findings highlight the critical role of incorporating damage mechanics to more accurately characterize stress distribution during tooth rehabilitation. Full article

Rugulopteryx okamurae is an invasive brown alga that has colonised Mediterranean and northeastern Atlantic coastlines, posing significant ecological and economic challenges. Its biomass is rich in structurally diverse metabolites—including polysaccharides (alginate, fucoidan, laminaran), phlorotannins, diterpenoids, fatty acids, and peptides—many of which exhibit notable antioxidant, anti-inflammatory, antimicrobial, and anticancer activities. Comparative assessment of extraction yields, structural features, and bioactivity data highlights phlorotannins and diterpenoids as particularly promising, demonstrating low-micromolar potencies and favourable predicted interactions with key inflammatory and apoptotic targets. Algal polysaccharides exhibit various bioactivities but hold strong potential for scalable and sustainable industrial applications. Emerging compound classes such as fatty acids and peptides display niche bioactivities; however, their structural diversity and mechanisms of action remain insufficiently explored. Insights from in vitro and in silico studies suggest that phlorotannins may modulate NF-κB and MAPK signalling pathways, while diterpenoids are implicated in the induction of mitochondrial apoptosis. Despite these findings, inconsistent extraction methodologies and a lack of in vivo pharmacokinetic and efficacy data limit translational potential. To overcome these limitations, standardized extraction protocols, detailed structure–activity relationship (SAR) and pharmacokinetic studies, and robust in vivo models are urgently needed. Bioactivity-guided valorisation strategies, aligned with ecological management, could transform R. okamurae biomass into a sustainable source for functional foods, cosmetics, and pharmaceuticals applications. Full article

Time-lapse microscopy of mesenchymal stem cell (MSC) cultures allows for the quantitative observation of their self-renewal, proliferation, and differentiation. However, the rigorous comparison of two conditions, baseline (A) versus perturbation (B) (the addition of molecular factors, environmental shifts, genetic modification, etc.), remains difficult because morphology, division timing, and migratory behavior are highly heterogeneous at the single-cell scale. MSCs can be used as an in vitro model to study cell morphology and kinetics in order to assess the effect of, for example, gene therapy and prime editing in the near future. By combining static, frame-wise morphology with dynamic descriptors, we can obtain weight profiles that highlight which morphological and behavioral dimensions drive divergence. In this study, we present A/B Cells Policy: a modular, open-source Python package implementing a robust cell tracking pipeline. It integrates a YOLO-based architecture as a two-stage assignment framework with fallback and recovery passes, re-identification of lost tracks, and lineage reconstruction. The framework links descriptive statistics to a transferable system, opening up avenues for regenerative medicine, pharmacology, and early translational pipelines. It does this by providing an interpretable, measurement-based bridge between in vitro imaging and in silico intervention strategy planning. Full article

Liposomes are nanoscale, spherical vesicles composed of phospholipid bilayers, typically ranging from 50 to 200 nm in diameter. Their unique ability to encapsulate both hydrophilic and hydrophobic molecules makes them powerful nanocarriers for drug delivery, diagnostics, and vaccine formulations. Several FDA-approved formulations such as Doxil^® (Baxter Healthcare Corporation, Deerfield, IL, USA), AmBisome^® (Gilead Sciences, Inc., Foster City, CA, USA), and Onivyde^® (Ipsen Biopharmaceuticals, Inc., Basking Ridge, NJ, USA) highlight their clinical significance. This review provides a comprehensive synthesis of how molecular dynamics (MD) simulations, particularly coarse-grained (CG) and atomistic approaches, advance our understanding of liposomal membranes. We explore key membrane biophysical properties, including area per lipid (APL), bilayer thickness, segmental order parameter ($S_{CD}$), radial distribution functions (RDFs), bending modulus, and flip-flop dynamics, and examine how these are modulated by cholesterol concentration, PEGylation, and curvature. Special attention is given to curvature-induced effects in spherical vesicles, such as lipid asymmetry, interleaflet coupling, and stress gradients across the leaflets. We discuss recent developments in vesicle modeling using tools such as TS2CG, CHARMM-GUI Martini Maker, and Packmol, which have enabled the simulation of large-scale, compositionally heterogeneous systems. The review also highlights simulation-guided strategies for designing stealth liposomes, tuning membrane permeability, and enhancing structural stability under physiological conditions. A range of CG force fields, MARTINI, SPICA, SIRAH, ELBA, SDK, as well as emerging machine learning (ML)-based models, are critically assessed for their strengths and limitations. Despite the efficiency of CG models, challenges remain in capturing long-timescale events and atomistic-level interactions, driving the development of hybrid multiscale frameworks and AI-integrated techniques. By bridging experimental findings with in silico insights, MD simulations continue to play a pivotal role in the rational design of next-generation liposomal therapeutics. Full article

As an emerging natural source of DPP-IV inhibition strategy, we report for the first time the use of Lithobates catesbeianus skin gelatin (LSG) as a novel source for DPP-IV inhibitory peptides in this study. Through enzymatic hydrolysis with multiple proteases, the papain-treated hydrolysate exhibited superior performance in hydrolysis degree, protein recovery, and DPP-IV inhibition, with 93.47% of peptides under 1 kDa. Subsequent separation and peptidomics analysis identified 13 previously unreported peptides. Molecular docking and in silico screening pinpointed four candidate peptides, i.e., LGPQR, RGFDQ, RGPVGP, and RLDDVT, which were then synthesized and functionally validated. Enzyme kinetic studies revealed that these peptides acted via competitive or mixed-type inhibition mechanisms. Notably, this study uncovered the bio-functional potential of amphibian-derived gelatin and provided a new strategy for natural DPP-IV inhibitor discovery through integrated enzymatic, computational, and biochemical approaches. This work pioneered the use of amphibian skin gelatin in antidiabetic peptide discovery and laid the foundation for its application in functional foods. Full article

Human Papillomavirus (HPV), particularly high-risk strains such as HPV16 and HPV18, is a leading cause of cervical cancer and a significant risk factor for several other epithelial malignancies. While the oncogenic mechanisms of viral proteins E6 and E7 are well characterized, the broader effects of HPV infection on host transcriptional regulation remain less clearly defined. This study explores the hypothesis that conserved genomic motifs within the HPV genome may act as molecular decoys, sequestering human transcription factors (TFs) and thereby disrupting normal gene regulation in host cells. Such interactions could contribute to oncogenesis by altering the transcriptional landscape and promoting malignant transformation.We conducted a computational analysis of the genomes of high-risk HPV types using MEME-ChIP for de novo motif discovery, followed by Tomtom for identifying matching human TFs. Protein–protein interactions among the predicted TFs were examined using STRING, and biological pathway enrichment was performed with Enrichr. The analysis identified conserved viral motifs with the potential to interact with host transcription factors (TFs), notably those from the FOX, HOX, and NFAT families, as well as various zinc finger proteins. Among these, SMARCA1, DUX4, and CDX1 were not previously associated with HPV-driven cell transformation. Pathway enrichment analysis revealed involvement in several key biological processes, including modulation of Wnt signaling pathways, transcriptional misregulation associated with cancer, and chromatin remodeling. These findings highlight the multifaceted strategies by which HPV may influence host cellular functions and contribute to pathogenesis. In this context, the study underscores the power of in silico approaches for elucidating viral–host interactions and reveals promising therapeutic targets in computationally predicted regulatory network changes. Full article

Background: Despite clinical and pathological risk tools, predicting outcomes in non-muscle-invasive bladder cancer (NMIBC), particularly high-grade (HG) cases, remains challenging due to its unpredictable recurrence and progression. There is an urgent need for molecular biomarkers to enhance risk stratification and guide treatment. Methods: We assessed the prognostic potential of eight miRNAs (miR-9, miR-143, miR-182, miR-205, miR-27a, miR-369, let-7c, and let-7g) in a cohort of ninety patients with primary bladder cancer. Expression data were retrieved from our previously published studies. Kaplan–Meier’s and Cox’s regression analyses were used to evaluate the associations with overall survival (OS), metastasis-free survival (MFS), and clinical outcomes. Principal component analysis (PCA) was performed to identify informative miRNA combinations. Target gene prediction, pathway enrichment (DAVID), and drug–gene interaction mapping (DGIdb) were conducted in silico. Results: A high expression of let-7g and miR-9 was significantly associated with better OS in HG NMIBC and MIBC, respectively (p = 0.013 and p = 0.000). MiR-9 downregulation correlated with metastasis in MIBC (p = 0.018). Among all combinations, miR-205 and miR-27a best predicted intermediate-risk NMIBC progression and recurrence (r² = 0.982, p = 0.000). A functional analysis revealed that these miRNAs regulate key cancer-related pathways (MAPK, mTOR, and p53) through genes such as TP53, PTEN, and CDKN1A. Drug interaction mapping identified nine target genes (e.g., DAPK1, ATR, and MTR) associated with eight FDA-approved bladder cancer therapies, including cisplatin and gemcitabine. Conclusions: Let-7g, miR-9, miR-143, miR-182, and miR-205 emerged as promising biomarkers for outcome prediction in NMIBC. Their integration into liquid biopsy platforms could support non-invasive monitoring and personalized treatment strategies. These findings warrant validation in larger, prospective studies and through functional assays. Full article

Background: Trimetazidine is a clinically established cardioprotective agent with anti-ischemic and antioxidant properties, widely used in the management of coronary artery disease. Combining its metabolic and cytoprotective effects with the potent anti-inflammatory activity of profens presents a promising therapeutic strategy. Methods: Five novel trimetazidine–profen hybrid compounds were synthesized using N,N′-dicyclohexylcarbodiimide-mediated coupling and structurally characterized by NMR and high-resolution mass spectrometry. Their antioxidant activity was evaluated by hydroxyl radical scavenging assays (HRSA), and the anti-inflammatory potential was assessed via the inhibition of albumin denaturation (IAD). Lipophilicity was determined chromatographically. Molecular docking and 100 ns molecular dynamics simulations were performed to investigate the binding modes and stability in human serum albumin (HSA) binding sites. The acute toxicity of the hybrid molecules was predicted in silico using GUSAR software. Results: All synthesized hybrids demonstrated varying degrees of biological activity, with compound 3c exhibiting the most potent antioxidant (HRSA IC₅₀ = 71.13 µg/mL) and anti-inflammatory (IAD IC₅₀ = 108.58 µg/mL) effects. Lipophilicity assays indicated moderate membrane permeability, with compounds 3c and 3d showing favorable profiles. Docking studies revealed stronger binding affinities of S-enantiomers, particularly 3c and 3d, to Sudlow sites II and III in HSA. Molecular dynamics simulations confirmed stable ligand–protein complexes, highlighting compound 3c as maintaining consistent and robust interactions. The toxicity results indicate that most hybrids, particularly compounds 3b–3d, exhibit a favorable safety profile compared to the parent trimetazidine. Conclusion: The hybrid trimetazidine–profen compounds synthesized herein, especially compound 3c, demonstrate promising dual antioxidant and anti-inflammatory therapeutic potential. Their stable interaction with serum albumin and balanced physicochemical properties support further development as novel agents for managing ischemic heart disease and associated inflammatory conditions. Full article

Background/Objectives: Leishmaniasis is an infectious disease caused by digenetic protozoa of the genus Leishmania, transmitted by infected female sandflies of the Phlebotominae subfamily. Current treatments are limited, relying on drugs that were not specifically developed for this disease and are often associated with high toxicity and elevated costs. Among alternative therapeutic strategies, antifolate compounds have been investigated due to their ability to inhibit dihydrofolate reductase (DHFR), an enzyme essential for folate metabolism in the parasite. However, the parasite circumvents DHFR inhibition through the activity of pteridine reductase-1 (PTR-1), which maintains folate reduction and ensures parasite survival. In this context, this study aimed to identify potential PTR-1 inhibitors in Leishmania major through in silico approaches. Methods: The methodology included virtual screening of molecular databases, Tanimoto similarity analysis, pharmacokinetic and toxicological predictions, and biological activity evaluation in silico. The most promising compounds were further analyzed via molecular docking. Results: The virtual screening resulted in 474 molecules, of which 4 structures (M601, M692, M700, and M703) showed high potential as PTR-1 inhibitors in Leishmania major throughout all stages of the methodology employed, especially in the results of molecular docking where they exhibited strong binding affinities and significant interactions with key residues of the target enzymes. Conclusions: This work provides a solid foundation for advancing these molecules into experimental validation, contributing to the development of safer and more effective therapeutic alternatives for the treatment of leishmaniasis. Full article

Type 2 diabetes is a multifactorial disease characterized by chronic hyperglycemia, insulin resistance, oxidative stress, inflammation, and dyslipidemia, factors that contribute to the development of long-term complications. In this context, the 2-aminobenzothiazole scaffold has emerged as a promising candidate due to its broad spectrum of biological properties. In this study, we performed a multidisciplinary evaluation of benzothiazole derivatives (5a–d, 8a–d, 11a–d, and 12c–d), starting with the in silico prediction of their properties, along with molecular docking against aldose reductase (ALR2) and peroxisome proliferator-activated receptor gamma (PPAR-γ). All compounds complied with the main rules of pharmacological similarity and optimal affinity, highlighting 8d (ΔG = −8.39 kcal/mol for ALR2 and −7.77 kcal/mol for PPAR-γ). Selected compounds from families C and D were synthesized in moderate yields (~60%) and showed low acute oral toxicity (LD₅₀ > 1250 mg/Kg). Compounds 8c and 8d inhibited ALR2 at concentrations below 10 µM. In vivo studies using a streptozotocin-induced diabetic rat model with a high-fat diet revealed that compound 8d produced sustained antihyperglycemic effects and reduced insulin resistance, dyslipidemia, and polydipsia, without inducing hepatotoxicity or displaying intrinsic antioxidant or anti-inflammatory activity. These findings suggest that 8d is a promising candidate for further development in diabetes-related therapeutic strategies. Full article

Background/Objectives: Antibody–drug conjugates are a rapidly evolving class of cancer therapeutics that combine the specificity of monoclonal antibodies with the potency of cytotoxic drugs. This review explores experimental and computational advances in ADC design, focusing on structural elements and optimization strategies. Methods: We examined recent developments in the mechanisms of action, antibody engineering, linker chemistries, and payload selection. Emphasis was placed on experimental strategies and computational tools, including molecular modeling and AI-driven structure prediction. Results: ADCs function through both internalization-dependent and -independent mechanisms, enabling targeted drug delivery and bystander effects. The therapeutic efficacy of ADCs depends on key factors: antigen specificity, linker stability, and payload potency. Linkers are categorized as cleavable or non-cleavable, each with distinct advantages. Payloads—mainly tubulin inhibitors and DNA-damaging agents—require extreme potency to be effective. Computational methods have become essential for antibody modeling, developability assessment, and in silico optimization of ADC components, accelerating candidate selection and reducing experimental labor. Conclusions: The integration of experimental and in silico approaches enhances ADC design by improving selectivity, stability, and efficacy. These strategies are critical for advancing next-generation ADCs with broader applicability and improved therapeutic indices. Full article

Background: While most Escherichia coli strains are harmless members of the gastrointestinal microbiota, certain pathogenic variants can cause severe intestinal and extraintestinal diseases. A notable outbreak of E. coli O104:H4, involving both enteroaggregative (EAEC) and enterohemorrhagic (EHEC) strains, occurred in Europe, resulting in symptoms ranging from bloody diarrhea to life-threatening colitis and hemolytic uremic syndrome (HUS). Since treatment options remain limited and have changed little over the past 40 years, there is an urgent need for an effective vaccine. Such a vaccine would offer major public health and economic benefits by preventing severe infections and reducing outbreak-related costs. A multiepitope vaccine approach, enabled by advances in immunoinformatics, offers a promising strategy for targeting HUS-causing E. coli (O104:H4 and O157:H7 serotypes) with minimal disruption to normal microbiota. This study aimed to design an immunogenic multiepitope vaccine (MEV) construct using bioinformatics and immunoinformatic tools. Methods and Results: Comparative proteomic analysis identified 672 proteins unique to E. coli O104:H4, excluding proteins shared with the nonpathogenic E. coli K-12-MG1655 strain and those shorter than 100 amino acids. Subcellular localization (P-SORTb) identified 17 extracellular or outer membrane proteins. Four proteins were selected as vaccine candidates based on transmembrane domains (TMHMM), antigenicity (VaxiJen), and conservation among EHEC strains. Epitope prediction revealed ten B-cell, four cytotoxic T-cell, and three helper T-cell epitopes. Four MEVs with different adjuvants were designed and assessed for solubility, stability, and antigenicity. Structural refinement (GALAXY) and docking studies confirmed strong interaction with Toll-Like Receptor 4 (TLR4). In silico immune simulations (C-ImmSim) indicated robust humoral and cellular immune responses. In Conclusions, the proposed MEV construct demonstrated promising immunogenicity and warrants further validation in experimental models. Full article

Escherichia coli biofilms are implicated in the development of persistent infections and increased antibiotic resistance, posing a significant challenge in clinical settings. These biofilms enhance bacterial survival by forming protective extracellular matrices, rendering conventional treatments less effective. Serrapeptase (SPT), a proteolytic enzyme, has emerged as a potential anti-biofilm agent due to its ability to degrade biofilm components and disrupt bacterial adhesion. In this study, we report the inhibitory effect of SPT against E. coli biofilm and its effect on key virulence factors. In vitro assays, including crystal violet staining, optical and fluorescence microscopy, and viability measurements, revealed the dose-dependent inhibition of biofilm formation (IC₅₀ = 14.2 ng/mL), reduced biofilm (−92%, 500 ng/mL) and planktonic viability (−45%, 500 ng/mL), and a marked loss of amyloid curli fibers. SPT treatment also lowered the levels of key virulence factors: cellular and secreted lipopolysaccharides (−76%, 8 ng/mL; −94%, 32 ng/mL), flagellin (−63%, 8 ng/mL), and peptidoglycan (−29%, 125 ng/mL). Mechanistically, SPT induced a phosphate-dysregulating response: secreted alkaline phosphatase activity rose (+70%, 125 ng/mL) while cellular DING/PstS proteins declined (−84%, 64 ng/mL), correlating strongly with biofilm inhibition. In silico docking further suggests direct interactions between SPT and the curli subunits CsgA and CsgB, potentially blocking fiber polymerization. Together, these findings position SPT as a powerful non-antibiotic biofilm disruptor against E. coli, offering a promising strategy to undermine bacterial persistence and resistance by targeting both structural matrix components and metabolic regulatory pathways. Full article