In a study published in International Microbiology, Rasha Assad Assiri of Princess Nourah bint Abdulrahman University and colleagues describe a computational systems immunology framework that integrates subtractive proteomics, reverse vaccinology and immunoinformatics to design a multi-epitope vaccine, or MEV, against K. michiganensis strain ATCC 8724. The approach begins with the bacterium’s complete proteome of 5,483 proteins and systematically filters it down to a handful of promising vaccine targets. Essentiality screening with the Geptop 2.0 server identified 398 proteins the bacterium cannot live without, and comparison against the human proteome using BLASTP, with sequences showing at least 30 percent identity excluded, left 199 non-homologous essential candidates. Subcellular localization prediction, antigenicity scoring with VaxiJen, allergenicity screening with AllerTOP and transmembrane-helix analysis with TMHMM narrowed the field further, ultimately yielding four envelope-associated proteins: the outer-membrane beta-barrel proteins BamA and LptD, the periplasmic lipopolysaccharide transport component LptA, and the outer-membrane lipoprotein LolB.

The choice of targets is biologically deliberate. LptA shuttles lipopolysaccharide, the inflammatory outer-membrane molecule of Gram-negative bacteria, across the periplasm, while LptD delivers LPS to the cell surface. BamA anchors the beta-barrel assembly machinery that builds outer-membrane proteins, and LolB sorts lipoproteins into the outer membrane. All four are essential for bacterial survival, predicted to be antigenic and non-allergenic, and lack similarity to human proteins, reducing the risk of autoimmune cross-reaction. Homologs of these proteins have been reported as conserved and immunogenic targets in several other Gram-negative pathogens, lending external support to their selection. By focusing on envelope-associated machinery rather than variable surface structures, the designers aimed for a vaccine less vulnerable to the antigenic drift that undermines single-antigen subunit vaccines.

From these four proteins, the team predicted cytotoxic T-lymphocyte, helper T-lymphocyte and linear B-cell epitopes using the Immune Epitope Database analysis tools. Eight CTL epitopes survived filtering for non-toxicity, antigenicity, MHC-I binding within the top two percent of predicted binders and non-allergenicity, although six of them carried negative immunogenicity scores on the IEDB scale and were retained only because they met the other predefined criteria. Four HTL epitopes were selected, three of which were predicted to induce interferon-gamma, a cytokine central to cellular immunity against intracellular pathogens; the fourth, ARFNIDSTQVSLTPD, was kept despite its non-inducer prediction because it satisfied all other binding and safety thresholds. Three linear B-cell epitopes, predicted with an artificial-neural-network-based server, completed the immunological repertoire. Population coverage analysis across 78 global population groups estimated that the combined MHC class I and II epitope set would cover 73.80 percent of the world’s population, peaking at 83.22 percent in South Africa and 82.90 percent in England, but dipping to 67.86 percent in South Asia, a regional variation the authors suggest could inform future vaccination prioritization.

Assembling fifteen epitopes into a single molecule requires architectural finesse. Isolated epitopes are weak immunogens, so the researchers grafted the 130-amino-acid 50S ribosomal protein L7/L12 from Mycobacterium tuberculosis, a known immunostimulatory adjuvant, onto the N-terminus of the construct via a rigid, alpha-helical EAAAK linker that spatially separates the adjuvant from the epitope payload. Within the epitope region, AAY linkers separate CTL epitopes to support proteolytic processing, flexible GPGPG linkers rich in glycine and proline space out the HTL epitopes, and short lysine-rich KK linkers keep the B-cell epitopes accessible. The result is a 386-amino-acid chimeric protein with a predicted molecular weight of 40.53 kilodaltons, a theoretical pI of 8.97, an instability index of 28.71 and a GRAVY value of minus 0.334 indicating overall hydrophilicity. VaxiJen assigned the full construct an antigenicity score of 0.9564, comfortably above the 0.5 threshold, and the in silico safety screens classified it as non-toxic and non-allergenic.

Structural credibility was assessed with AlphaFold2 through the ColabFold implementation, followed by refinement in GalaxyRefine. The best model placed 95.6 percent of residues in favored regions of the Ramachandran plot, with 3.8 percent in allowed regions and only 0.3 percent in disallowed regions. ProSA-web returned a Z-score of minus 4.62, consistent with experimentally determined protein structures, and ERRAT reported a quality factor of 99.219. Secondary-structure prediction estimated 34.46 percent alpha helix, 47.93 percent random coil and 17.62 percent beta strand. Notably, the linker regions showed lower pLDDT confidence scores than the structured domains, a caveat the authors carried forward when interpreting downstream docking and dynamics, since flexible segments are inherently harder to model.

To probe whether the vaccine could engage innate immunity, the team docked the construct against chain B of the TLR1-TLR2 heterodimer, a pattern-recognition receptor complex, using ClusPro 2.0. The model drawn from the largest cluster, containing 72 members, was selected for analysis. PDBsum identified 24 hydrogen bonds at the interface, and the PRODIGY server estimated a binding free energy of minus 14.8 kilocalories per mole with a dissociation constant of 1.5 times ten to the minus eleventh molar, values consistent with a favorable predicted interaction. Normal mode analysis with iMODS described a comparatively rigid core with mobile peripheral regions, and a 100-nanosecond molecular dynamics simulation in GROMACS with the CHARMM36m force field showed the complex settling after roughly the first 10 nanoseconds, with the radius of gyration contracting from about 4.2 to 3.7-3.8 nanometers and the solvent-accessible surface area declining from about 580 to 500 square nanometers before stabilizing. The authors are careful to note that these metrics describe structural compatibility under simulated conditions and do not demonstrate receptor activation or downstream immune signaling.

Immune simulation with the C-ImmSim agent-based platform, run with HLA-DRB101:01, HLA-B07:02 and HLA-A*01:01, predicted a coordinated response to a single simulated vaccine dose: rapid antigen clearance accompanied by rising IgM, IgG1 and IgG2 levels, early increases in IL-2 and interferon-gamma, expansion of active and duplicating CD8-positive and CD4-positive T-cell populations, and the persistence of memory-associated helper T cells. The modeled Th1 response predominated over Th2, Th17 and regulatory populations. Codon optimization with JCat produced a Codon Adaptation Index of 0.94 and a GC content of 50 percent, both favorable for expression in Escherichia coli, and in silico cloning into the pET-30a(+) vector between NcoI and XhoI sites yielded a recombinant plasmid of 4,483 base pairs, supporting the theoretical feasibility of recombinant production.

The authors are explicit about the limits of the work. Multi-strain epitope conservation, MM-PBSA binding-energy decomposition, epitope-level cross-reactivity screening against human and microbiome proteins, replicate immune simulations and multi-dose modeling were not performed, and no recombinant expression, in vitro assay or animal study was carried out. Whether responses to the mycobacterial adjuvant might compete with pathogen-directed immunity, a phenomenon known as adjuvant immunodominance, remains an open experimental question. Still, the study represents an early application of an integrated subtractive-proteomics and immunoinformatics pipeline specifically aimed at K. michiganensis, and it arrives amid growing evidence that reverse-vaccinology-designed MEVs can be validated successfully in vitro and in vivo against related pathogens. As carbapenem resistance spreads through hospital and environmental reservoirs alike, computational designs like this one may increasingly serve as the blueprint from which the next generation of antibacterial vaccines is built, provided the laboratory work now required can keep pace with the algorithms.

Subject of Research: Computational immunoinformatics design of a multi-epitope vaccine against multidrug-resistant Klebsiella michiganensis

Article Title: Computational systems immunology and multi-scale modeling for the design of a Multi-Epitope Vaccine (MEV) against emerging multidrug-resistant Klebsiella michiganensis

Article References: Assiri, R. A., Saleh, F. M., Muhammad, S., Fenibo, E. O., & Matambo, T. (2026). Computational systems immunology and multi-scale modeling for the design of a Multi-Epitope Vaccine (MEV) against emerging multidrug-resistant Klebsiella michiganensis. International Microbiology. https://doi.org/10.1007/s10123-026-00903-3

Image Credits: AI Generated

DOI: 10.1007/s10123-026-00903-3

Keywords: Klebsiella michiganensis, multi-epitope vaccine, reverse vaccinology, immunoinformatics, subtractive proteomics, antimicrobial resistance, TLR1 docking, molecular dynamics simulation, epitope prediction, BamA, LptD, codon optimization

The work builds on a well-established precedent in bacterial immunology. FimH, an adhesion molecule from Escherichia coli, is known to activate immune cells including dendritic cells and T cells. Mfa1 plays an analogous adhesive role for P. gingivalis, the keystone pathogen of chronic periodontitis, but its immunostimulatory potential had not been thoroughly explored. To close that gap, the team synthesized and extracted recombinant Mfa1 from E. coli, giving them a purified protein they could test in controlled experiments without exposing animals or cells to the live pathogen.

The first line of evidence came from dendritic cells, the sentinels of the adaptive immune system. When bone marrow-derived dendritic cells from mice were exposed to recombinant Mfa1, the cells upregulated co-stimulatory molecules and major histocompatibility complex (MHC) molecules in a dose-dependent manner. These surface proteins are the molecular handshake through which dendritic cells license T cells: co-stimulatory molecules provide the activating signal, while MHC molecules present tumor or microbial antigens for T cell recognition. Their coordinated increase is a hallmark of dendritic cell maturation and a prerequisite for mounting a strong T cell response.

Those in vitro observations translated into living animals. When C57BL/6 mice received Mfa1, the proportion and absolute number of dendritic cells in the spleen increased, and the cells showed enhanced expression of C-C chemokine receptor type 7, or CCR7. CCR7 is the trafficking receptor that guides mature dendritic cells from peripheral tissues to lymph nodes, where they present antigen to naive T cells, so its upregulation is a functional indicator of dendritic cell migration and maturation rather than a mere surface marker change. Mfa1 treatment also elevated the numbers of the two principal conventional dendritic cell subsets, cDC1 and cDC2, further upregulated co-stimulatory and MHC molecules, and raised levels of pro-inflammatory cytokines in vivo, painting a picture of systemically awakened antigen-presenting machinery.

To identify the receptor through which Mfa1 exerts these effects, the researchers turned to computational structural biology. Using AI-based protein complex prediction together with all-atom molecular dynamics simulations, they modeled the interaction of Mfa1 with Toll-like receptors, the innate immune sensors that recognize microbial molecular patterns. The simulations showed that Mfa1 binds TLR2 with high structural confidence and stability, while its binding to TLR4 was markedly less stable. This kind of integrated modeling approach allows researchers to rank candidate receptor interactions before committing to lengthy experimental validation, and here it produced a clear, testable prediction.

The prediction held up under genetic scrutiny. The immune-activating effects of Mfa1 that were readily observed in wild-type mice were absent in TLR2-knockout mice, confirming that the protein’s activity depends on TLR2 signaling. This loss-of-function experiment is the mechanistic anchor of the study: it demonstrates that Mfa1 is not a nonspecific irritant but a ligand that engages a defined innate immune receptor, triggering the downstream maturation program in dendritic cells that the team had characterized in detail.

With innate immunity activated, the question became whether the response extended to the adaptive arm. Repeated Mfa1 treatment enhanced the intracellular production of two signature inflammatory cytokines, interferon-gamma and tumor necrosis factor-alpha, in both CD4-positive helper T cells and CD8-positive cytotoxic T cells. Interferon-gamma is a central coordinator of anti-tumor immunity, activating macrophages and increasing antigen presentation, while tumor necrosis factor-alpha contributes directly to inflammatory tumor cell killing. Robust cytokine production by both T cell compartments indicates that Mfa1-driven dendritic cell maturation successfully translates into T cell activation, the effector arm that checkpoint blockade therapies are designed to unleash.

The culmination of the study came in a Lewis lung carcinoma model, a widely used preclinical system for lung cancer immunotherapy. When the researchers combined Mfa1 with an anti–PD-L1 antibody, the checkpoint inhibitor’s anti-tumor efficacy was enhanced. Anti–PD-L1 antibodies work by blocking the interaction between PD-L1 on tumor or immune cells and PD-1 on T cells, releasing a molecular brake on T cell activity. But many tumors respond poorly to this class of drug, often because the immune system has not been sufficiently primed to recognize and attack the tumor in the first place. An adjuvant like Mfa1, which expands and matures dendritic cell populations and drives T cell activation, addresses that priming deficit and can convert a cold, unresponsive tumor microenvironment into one more receptive to checkpoint blockade.

The implications reach beyond lung cancer. Immune adjuvants are a critical but underdeveloped component of cancer immunotherapy, and most current options are synthetic or highly engineered molecules. A bacterial adhesion protein that engages TLR2, a receptor with a well-characterized role in immune activation, offers a biologically grounded scaffold for adjuvant design. The authors suggest that Mfa1 may serve as a potential immunostimulatory adjuvant to enhance cancer immunotherapy, and the combination of computational receptor prediction, genetic validation and efficacy testing in a tumor model provides a coherent proof-of-concept chain from molecule to mechanism to therapeutic outcome.

Considerable work remains before any clinical translation. The findings are confined to mouse models, and the dose, schedule, safety profile and immunogenicity of a recombinant bacterial protein in humans would require extensive evaluation. There is also an inherent irony to navigate: P. gingivalis is a destructive periodontal pathogen, and any therapeutic use of its components must ensure that adhesive or inflammatory properties relevant to infection are not transferred to patients. Nevertheless, the study adds Mfa1 to a growing list of microbial molecules being repurposed for immunotherapy, and it demonstrates how AI-guided structural prediction can accelerate the identification of immune receptor ligands. For a field searching for ways to make checkpoint inhibitors work in more patients, a protein from an unlikely oral microbe has emerged as a candidate worth watching.

Subject of Research: Immunostimulatory effects of the Porphyromonas gingivalis adhesion protein Mfa1 as an adjuvant for anti–PD-L1 cancer immunotherapy

Article Title: Porphyromonas gingivalis adhesion protein Mfa1 enhances the anti-cancer effect of anti–PD-L1 antibody by immune activation

Article References: An, E.-K., Zhang, W., Jung, M., Park, H.-B., Kim, S.-J., Ryu, D., Jeong, E., Lee, M., Xu, Y., Lee, P. C. W., Im, W., & Jin, J.-O. (2026). Porphyromonas gingivalis adhesion protein Mfa1 enhances the anti-cancer effect of anti–PD-L1 antibody by immune activation. Cancer Immunology, Immunotherapy. https://doi.org/10.1007/s00262-026-04587-6

Image Credits: AI Generated

DOI: 10.1007/s00262-026-04587-6

Keywords: Mfa1, Porphyromonas gingivalis, TLR2, dendritic cells, anti–PD-L1, immune adjuvant, cancer immunotherapy, T cell activation, lung carcinoma, molecular dynamics simulation, CCR7, interferon-gamma

The synthetic route itself is elegantly simple. Substituted aromatic aldehydes, methyl cyanoacetate and barbituric acid are combined in a 1:1 mixture of ethanol and water at 85 degrees Celsius with just 0.03 mol percent of Bi(OTf)3. The catalyst orchestrates a domino Knoevenagel-Michael sequence: it coordinates to the ester oxygen of methyl cyanoacetate, sharpening the electrophilicity of the carbonyl carbon, while the aldehyde-derived enolate attacks to form a carbon-carbon bond. The resulting alpha,beta-unsaturated intermediate is then struck by the weakly nucleophilic nitrogen of barbituric acid, and intramolecular cyclization closes the fused pyridopyrimidine ring. Eight derivatives, bearing nitro, bromo, hydroxyl, methyl, methoxy or unsubstituted phenyl groups, were isolated in yields ranging from 76 to 91 percent within two to five hours, with minimal side products and no need to purify intermediates.

What makes the protocol genuinely green is the solvent system and the catalyst. Bismuth(III) salts have gained a reputation as environmentally benign alternatives to conventional Lewis acids because they are air- and moisture-stable, functionally tolerant, commercially available and far less corrosive than many metal halides. Compared with earlier methods, the advantages are clear. A DMAP-catalyzed protocol requires ultrasonic irradiation in toxic DMF, ZrO2 nanoparticles demand nanomaterial synthesis and raise agglomeration concerns, and the sulfonated SBA-15 catalyst involves a cumbersome multistep preparation. The bismuth route runs in a drinkable solvent mixture under ordinary reflux, and the catalyst can be recovered, although reusability tests showed a gradual decline: the model reaction yielded 79 percent in the first cycle, 74 percent in the second, 65 percent in the third and only 33 percent in a fourth run that stretched to nine hours. The authors suggest that immobilization strategies could extend catalyst lifetime in future work.

On the computational side, the team optimized all eight molecules at the B3LYP/6-31G(d,p) level of density functional theory, confirming that each geometry corresponds to a true energy minimum. Frontier molecular orbital analysis revealed that the nitro-substituted compounds 4a and 4b possess the smallest HOMO-LUMO gaps, at roughly 0.135 electron volts, making them the most electronically soft and reactive members of the series, primed for donor-acceptor interactions with biological macromolecules. In contrast, the bromo, methyl and unsubstituted derivatives 4c, 4f and 4g showed the largest gaps and the greatest hardness, indicating higher kinetic stability. Mulliken charge analysis and molecular electrostatic potential maps pinpointed the carbonyl oxygens and ring nitrogens as the electron-rich hotspots most likely to engage in hydrogen bonding with protein targets, while the N-H hydrogens carried the complementary positive charge.

The biological evaluation used the broth dilution method to determine minimum inhibitory concentrations against four bacterial strains, the Gram-negative Escherichia coli and Pseudomonas aeruginosa and the Gram-positive Staphylococcus aureus and Streptococcus pyogenes, benchmarked against ampicillin, and three fungal strains, Candida albicans, Aspergillus niger and Aspergillus clavatus, benchmarked against griseofulvin. The structure-activity trends were striking. The unsubstituted compound 4g and the methoxy-bearing 4h were the standout antibacterial agents, with MIC values as low as 65 and 62 micrograms per milliliter respectively against E. coli and S. aureus, figures that actually beat ampicillin on those strains. The methyl derivative 4f was the most potent antifungal, matching griseofulvin’s 100 micrograms per milliliter against A. niger. Electron-donating groups such as methyl and methoxy generally enhanced activity, likely by improving lipophilicity and membrane penetration, while nitro groups tended to raise MIC values.

To explain these observations mechanistically, the researchers docked all eight compounds into two clinically significant enzymes: bacterial beta-lactamase, the molecular engine of antibiotic resistance, and sterol 14-alpha demethylase CYP51, the fungal enzyme that builds ergosterol membranes. Using AutoDock 4.2 with a Lamarckian genetic algorithm, they found that compounds 4b and 4h bound beta-lactamase most favorably, with estimated free energies of binding of minus 9.30 and minus 9.43 kilocalories per mole, stabilized by networks of hydrogen bonds, pi-alkyl contacts and pi-cation interactions with residues such as ARG 661, PRO 298 and TYR 611. Against CYP51, compound 4b again led with minus 7.79 kilocalories per mole, and the methyl-substituted 4f formed six hydrogen bonds within the active site, consistent with its antifungal potency. Torsional free energies were nearly uniform across the series, indicating that differences in affinity stem from noncovalent interaction networks rather than ligand flexibility.

Molecular dynamics simulations then stress-tested the most promising complexes over 100-nanosecond production runs using the Maestro-Desmond package with the OPLS3e force field. The 4f-beta-lactamase complex held a backbone root mean square deviation averaging 1.60 angstroms, comfortably below the 2.0 angstrom threshold that validates docking reliability, and stabilized after an initial 30-nanosecond equilibration. The 4d-CYP51 complex averaged 1.40 angstroms, closely tracking the reference antifungal ligand VT1 at 1.35 angstroms. Root mean square fluctuation analysis showed that flexibility was confined to loop and terminal regions, leaving the binding pockets rigid. Contact histograms revealed that water-bridged hydrogen bonds, involving residues such as Ser64, Gln120, Thr122 and Tyr132, together with hydrophobic contacts, were the main glue holding the complexes together throughout the simulations.

The pharmacoinformatic layer of the study added yet another dimension. POM analysis, which classifies bioactivity based on dipolar interactions between electron-rich and electron-deficient centers, identified three distinct antitumor pharmacophore regions built from NH donor to carbonyl oxygen acceptor pairs, plus a single antibacterial pharmacophore involving an NH-CO motif. OSIRIS toxicity screening found no major mutagenic, tumorigenic, irritant or reproductive risks across the series, with compounds 4d and 4g earning the highest drug scores at 0.70 and 0.87. Molinspiration calculations confirmed that all eight molecules respect Lipinski’s criteria almost entirely: molecular weights below 500 daltons, five or fewer rotatable bonds, moderate polarity and acceptable lipophilicity, with only the heavily nitrated pair showing a single violation.

Taken together, the work demonstrates a rare degree of coherence between experiment and computation. The compounds with the best MIC values, notably 4f, 4g and 4h, also posted strong docking energies and stable dynamic behavior, while the electrostatic potential maps independently flagged the same carbonyl and ring nitrogen atoms that docking identified as interaction points. The authors conclude that pyrido[2,3-d]pyrimidines represent a promising scaffold for further antimicrobial development, and that targeted substitution of the aromatic ring is the key lever for tuning activity. With antimicrobial resistance declared one of the top global public health threats, a synthetic method that is cheap, green and high-yielding, paired with a validated computational pipeline for prioritizing candidates, offers exactly the kind of integrated approach the field has been calling for. The next step will be translating these laboratory and in silico hits into lead compounds capable of surviving the far harsher tests of animal models and, eventually, the clinic.

Subject of Research: Green synthesis and antimicrobial evaluation of pyrido[2,3-d]pyrimidine derivatives catalyzed by bismuth(III) triflate

Article Title: Bi(OTf)₃-catalyzed one-pot synthesis and antimicrobial evaluation of bioactive pyridopyrimidines supported by DFT, molecular dynamics, POM and pharmacophore analyses

Article References: Mehta, A. W., Abdel-Megid, M., Patil, R. C., Ahmed, S., Salem, M. E., Abu-Rayyan, A., Shtaiwi, A., Hajam, Y. A., Bhat, A. R., Eissa, M. E., Agisho, H. A., Mujahid, M. H., Yamari, I., Raza, N., & Zbancioc, A. M. (2026). Bi(OTf)₃-catalyzed one-pot synthesis and antimicrobial evaluation of bioactive pyridopyrimidines supported by DFT, molecular dynamics, POM and pharmacophore analyses. Results in Chemistry, 30, Article 103849. https://doi.org/10.1016/j.rechem.2026.103849

Image Credits: AI Generated

DOI: 10.1016/j.rechem.2026.103849

Keywords: pyridopyrimidines, bismuth(III) triflate, multicomponent reaction, green chemistry, antimicrobial activity, molecular docking, molecular dynamics, DFT, beta-lactamase, CYP51, drug discovery, pharmacophore

The team’s starting point was network toxicology, a discipline that treats the interaction between a drug and the body as a web of molecular relationships rather than a single linear pathway. By mapping the known protein targets of amiodarone against genes already associated with pulmonary fibrosis, the researchers identified eighty-four overlapping genes that sit at the intersection of the drug’s pharmacology and the pathology of scarring. When these genes were subjected to enrichment analysis, two biological themes emerged with striking clarity: necroptosis, a form of programmed inflammatory cell death, and the wingless/integrated, or Wnt, signaling pathway, a developmental cascade long implicated in fibrotic remodeling of tissue. Both processes are plausible mechanistic bridges between a drug accumulating in lung tissue and the progressive deposition of scar collagen.

What distinguishes this study from earlier network-based analyses of AIPF is the sheer scale of the machine learning framework the authors built on top of that gene list. Rather than relying on a single algorithm, they integrated 127 different algorithms into a consensus pipeline, then cross-validated the results against independent datasets drawn from the Gene Expression Omnibus, a public repository of gene expression data. The logic is straightforward: a candidate biomarker that survives scrutiny across many analytical approaches and multiple patient cohorts is far less likely to be a statistical artifact than one identified by a single method. This kind of ensemble strategy, borrowed from the competitive machine learning world, is increasingly seen as the gold standard for extracting trustworthy signals from the noisy, small-sample datasets that dominate clinical genomics.

The consensus winner was SERPING1, a gene encoding the C1 inhibitor protein best known for its role in regulating the complement and contact cascades of the immune system. The pipeline elevated SERPING1 to the status of primary core biomarker, while a second gene, lipocalin 2 (LCN2), emerged as a secondary candidate. LCN2, which encodes a protein involved in iron trafficking and inflammatory responses, showed a more complicated behavior across datasets, exhibiting prominent inter-cohort spatial heterogeneity — in plain terms, its expression patterns varied noticeably from one patient cohort to another, a warning sign the authors were careful not to gloss over.

To test whether these computational candidates could physically interact with the drug, the researchers turned to molecular docking and molecular dynamics simulation. Docking algorithms predict how a small molecule like amiodarone might fit into the structural pockets of a protein, while dynamics simulations then let that predicted complex flex and move over time, revealing whether the binding is stable or falls apart under thermal motion. Both SERPING1 and LCN2 proteins were found to bind stably to amiodarone in these simulations, a result that lends biophysical plausibility to the idea that the drug could directly perturb the function of these proteins in lung tissue, not merely alter their expression indirectly through cellular stress.

The next layer of evidence came from single-cell RNA sequencing and spatial transcriptomics, two of the most powerful tools in modern genomics. Single-cell sequencing breaks a tissue down into its constituent cell types and measures gene activity in each one, while spatial transcriptomics preserves information about where in the tissue architecture those genes are active. The analyses showed that both SERPING1 and LCN2 were expressed predominantly in cell populations associated with lung injury, placing the candidate biomarkers in exactly the cellular neighborhoods where fibrotic damage unfolds. This kind of multiomics localization matters because a biomarker measured in bulk tissue can be misleading if the signal actually originates from a minor cell population, such as infiltrating immune cells, rather than from the epithelial cells that orchestrate fibrosis.

The spatial data also delivered one of the study’s most nuanced findings. SERPING1 expression showed a weak positive correlation with the extent of fibrosis across tissue samples, a modest but consistent relationship. LCN2, by contrast, displayed variable, cohort-dependent correlation patterns — its relationship to fibrosis shifted depending on which dataset was examined. The authors interpret this honestly: LCN2 remains an interesting candidate, but its inconsistency across cohorts means it cannot yet be considered a dependable marker, and it awaits further experimental validation before it can be pursued clinically.

Crucially, the study did not stop at computation. The team constructed an in vitro model of AIPF by exposing A549 cells, a widely used human lung epithelial cell line, to amiodarone in the laboratory. When they measured gene expression in these drug-exposed cells, they confirmed a dose-dependent downregulation of SERPING1 — the higher the amiodarone concentration, the lower the gene’s activity. This laboratory confirmation is the study’s strongest single piece of evidence, because it demonstrates that a biologically relevant concentration of the drug can directly suppress SERPING1 expression in the very epithelial cells that line the lung alveoli and are central to the fibrotic response.

The authors are careful to frame their contribution with appropriate modesty. They note that the findings do not establish entirely new drivers of the disease; rather, they extend previous network-based studies of AIPF by adding multiomics localization evidence and in vitro support, particularly for the downregulation of SERPING1 in amiodarone-exposed epithelial cells. That distinction is important in a field where computational screens sometimes generate long lists of speculative targets that never survive contact with experimental reality. By anchoring their top candidate in molecular simulation, tissue-level spatial data, and a living-cell model, the researchers have given SERPING1 a far more solid evidentiary foundation than most computationally nominated biomarkers enjoy.

Even so, the road from candidate gene to clinical biomarker is long. The authors explicitly acknowledge that validation in animal models and in clinical samples from actual AIPF patients is still needed before SERPING1 could inform the care of patients taking amiodarone. If that validation succeeds, the implications could be substantial: a simple measure of SERPING1 expression or C1 inhibitor levels might one day allow physicians to monitor patients on long-term amiodarone therapy and intervene before irreversible scarring takes hold. In the meantime, the study stands as a compelling demonstration of how network toxicology, ensemble machine learning, molecular simulation, single-cell and spatial transcriptomics, and classical cell biology can be woven into a single pipeline — a template that other researchers hunting for drug-toxicity biomarkers are likely to follow.

Subject of Research: Identification of key genes involved in amiodarone-induced pulmonary fibrosis using integrated multiomics and in vitro validation

Article Title: Integrated multiomics methodology and in vitro experiments for the detection of key genes involved in amiodarone-induced pulmonary fibrosis

Article References: Sheng, H., Lu, X., & Lu, Y. (2026). Integrated multiomics methodology and in vitro experiments for the detection of key genes involved in amiodarone-induced pulmonary fibrosis. BMC Pharmacology and Toxicology. https://doi.org/10.1186/s40360-026-01238-5

Image Credits: AI Generated

DOI: 10.1186/s40360-026-01238-5

Keywords: amiodarone, pulmonary fibrosis, SERPING1, LCN2, network toxicology, machine learning, molecular docking, molecular dynamics simulation, single-cell RNA sequencing, spatial transcriptomics, biomarkers, drug-induced lung injury

The compound at the center of the study is zerumbone, a sesquiterpene found in abundance in the rhizomes of Zingiber zerumbet, a plant widely used in traditional medicine across Southeast Asia and parts of India. Zerumbone has attracted scientific attention for years because of reported anti-inflammatory and anticancer properties, but its precise molecular targets have remained incompletely understood. The research team set out to determine whether zerumbone might act on interleukin-10, a signaling molecule with a complicated role in cancer biology. Interleukin-10 is classically described as an anti-inflammatory cytokine, damping down immune responses to protect tissues from damage. Yet in the tumor microenvironment, that same immunosuppressive function can become a liability, allowing cancer cells to evade immune surveillance while simultaneously promoting pathways that support their own proliferation and survival.

To interrogate this hypothesis, the investigators deployed an integrative workflow that has become increasingly common in modern pharmacology: network pharmacology paired with structural biology simulations. Network pharmacology treats a drug not as a single bullet aimed at a single target but as a molecule that perturbs an entire web of interacting genes and proteins. Using the STRING database, which catalogs known and predicted protein-protein interactions, the team mapped the network surrounding interleukin-10 and then applied Gene Ontology and KEGG pathway enrichment analyses to identify the biological processes most strongly associated with that network. The results pointed decisively toward the JAK–STAT signaling pathway, a canonical intracellular relay system through which interleukin-10 exerts its effects on immune homeostasis, inflammation, and cell fate.

The JAK–STAT pathway deserves a brief technical explanation because it is central to the study’s logic. When interleukin-10 binds its receptor on a cell surface, it activates Janus kinases, enzymes that phosphorylate STAT proteins, chiefly STAT3 in the interleukin-10 context. Once activated, STAT3 travels to the nucleus and switches on genes that drive cell cycle progression and block programmed cell death. Two of the most important downstream products are Cyclin D1, which pushes cells through the G1 phase of the cell cycle, and Bcl-xL, a member of the Bcl-2 family that shields cells from apoptosis. In many cancers, including cervical cancer, this axis is chronically activated, effectively locking tumor cells into a state of unchecked division and resistance to cell death. A molecule that disrupts interleukin-10 signaling upstream could, in principle, quiet the entire cascade.

With the pathway identified, the researchers turned to molecular docking, a computational technique that predicts how a small molecule fits into the binding pockets of a protein target. The docking analyses suggested that zerumbone associates stably with a functionally relevant region of interleukin-10, occupying a site that could plausibly interfere with the cytokine’s normal interactions. Docking alone, however, produces a static snapshot, and proteins are anything but static. To address this limitation, the team ran molecular dynamics simulations, which track the motion of every atom in the protein-ligand complex over time. If a docked pose is an artifact, it typically falls apart within nanoseconds of simulation; if it is genuine, the complex remains stable. The simulations supported a stable association between zerumbone and interleukin-10, strengthening the computational case that the interaction is physically meaningful rather than a fleeting coincidence of shape matching.

Computational predictions, no matter how sophisticated, must ultimately face the test of living cells. The researchers therefore moved to laboratory experiments using HeLa cells, a cervical cancer cell line derived from a tumor that has been studied continuously since the 1950s and remains a standard model for this disease. Using enzyme-linked immunosorbent assays to measure cytokine concentrations in the culture medium, the team quantified how zerumbone treatment affected interleukin-10 release. The results were striking and clearly dose-dependent. At a concentration of 1 micromolar, the cells secreted interleukin-10 at approximately 70.81 picograms per milliliter. As the zerumbone concentration rose to 40 micromolar, that figure collapsed to just 6.32 picograms per milliliter, a reduction of roughly ninety percent across the dose range tested.

The suppression of interleukin-10 was accompanied by changes in the downstream markers that the network analysis had predicted. Zerumbone treatment was associated with modulation of Cyclin D1 and Bcl-xL, the proliferative and anti-apoptotic effectors of the JAK–STAT cascade. This concordance between prediction and experiment is the methodological heart of the study. The computational pipeline flagged a pathway; the docking and dynamics studies proposed a physical mechanism; and the cell-based assays then confirmed that treating cells with the compound produced the expected molecular consequences. Taken together, the authors argue, the findings suggest that zerumbone modulates apoptotic and proliferative signaling through the interleukin-10 pathway, producing strong anticancer activity in this model system.

The significance of this work extends beyond a single compound and a single cancer type. Cervical cancer cells are known to manipulate the cytokine environment to their advantage, and interleukin-10 is one of several immunosuppressive signals they deploy. Current immunotherapies, such as immune checkpoint inhibitors, have shown only modest benefit in cervical cancer compared with some other tumor types, which makes the search for alternative ways to relieve immunosuppression clinically relevant. If a small, naturally derived molecule can dampen interleukin-10 production directly in tumor cells, it could complement existing treatments or inspire the design of more potent analogs. The JAK–STAT pathway itself is already a validated drug target, with JAK inhibitors approved for inflammatory diseases and under investigation in oncology, which lends additional plausibility to the therapeutic concept.

At the same time, the researchers and outside observers alike will recognize the distance between a cell culture dish and a patient. HeLa cells are a powerful but simplified model, and the tumor microenvironment in a living body involves many additional cell types, cytokines, and regulatory feedback loops that a two-dimensional culture cannot fully reproduce. Questions about bioavailability, metabolism, toxicity, and appropriate dosing of zerumbone in humans remain open, as do questions about whether the interleukin-10 suppression observed in vitro translates into meaningful antitumor immunity in vivo. The study also involved no new human or animal subjects, so the findings rest entirely on computational and in vitro evidence. These are standard limitations for early-stage natural product research, but they define the road ahead: animal studies and, eventually, carefully designed clinical trials would be needed before zerumbone could be considered a therapeutic candidate.

What the study does deliver is a compelling proof of concept and a demonstration of methodological rigor in a field that sometimes suffers from the opposite. By chaining together network pharmacology, pathway enrichment, molecular docking, molecular dynamics simulation, and quantitative cell-based validation, the team built a coherent chain of evidence linking a traditional medicine compound to a specific, mechanistically understood signaling axis in cervical cancer. The near-total suppression of interleukin-10 release at higher zerumbone concentrations, and the corresponding modulation of Cyclin D1 and Bcl-xL, provide concrete, testable endpoints for future work. As interest in plant-derived therapeutics continues to grow, studies of this kind show how computational tools can sharpen the search, turning centuries of ethnobotanical knowledge into precise molecular hypotheses that modern laboratories can verify, refine, and one day perhaps translate into new options for patients with cervical cancer.

Subject of Research: Zerumbone targeting IL-10–mediated JAK–STAT signaling in cervical cancer

Article Title: Targeting IL-10–mediated JAK–STAT signaling in cervical cancer: integrative network pharmacology, molecular docking, and experimental validation of Zerumbone

Article References: Singh, S. P., Nongalleima, K., Chanu, W. K., Singh, N. I., Singh, T. D., Swapana, N., Singh, T. R., & Singh, C. B. (2026). Targeting IL-10–mediated JAK–STAT signaling in cervical cancer: integrative network pharmacology, molecular docking, and experimental validation of Zerumbone. BMC Complementary Medicine and Therapies. https://doi.org/10.1186/s12906-026-05589-8

Image Credits: AI Generated

DOI: 10.1186/s12906-026-05589-8

Keywords: zerumbone, IL-10, JAK-STAT signaling, cervical cancer, HeLa cells, molecular docking, molecular dynamics simulation, network pharmacology, Cyclin D1, Bcl-xL, natural compounds, anticancer research

The research team set out to transform these oil-processing residuals into protein hydrolysates, mixtures of peptides produced by breaking proteins down with enzymes. Enzymatic hydrolysis is a well-established strategy for unlocking bioactive peptides from food proteins, but the investigators went a step further. Rather than simply measuring bulk antioxidant capacity, they combined fluorescence-based chromatographic profiling with molecular dynamics simulation to understand, at the level of individual amino acid sequences, why certain peptide fractions scavenge free radicals better than others. The pairing of wet-lab separation techniques with computational simulation represents a growing trend in food science, where structural biology tools are being used to design functional ingredients rationally rather than discovering them by trial and error.

Antioxidants matter to the food industry for a deceptively simple reason. Fats and oils oxidize when exposed to oxygen, light and heat, producing the off-flavors and potentially harmful compounds associated with rancidity. Synthetic antioxidants such as butylated hydroxytoluene, better known as BHT, have long been used to slow this process, but consumer skepticism about synthetic additives has pushed manufacturers to seek natural alternatives. Bioactive peptides — short chains of amino acids released from dietary proteins — have emerged as one of the most promising candidates, with reported activities ranging from radical scavenging and metal chelation to inhibition of lipid peroxidation. The challenge has always been finding cheap, abundant and sustainable protein sources from which to produce them at scale.

Litsea cubeba residuals fit that brief remarkably well. The plant, whose berries yield an essential oil rich in citral, is cultivated extensively in southern China, and the seed meal left after oil extraction contains substantial protein that currently goes to waste. By applying controlled enzymatic digestion to this residual protein, the researchers generated hydrolysates whose antioxidant performance they then measured using standard assays, including the ability to neutralize DPPH and ABTS radicals and to inhibit lipid oxidation in model systems. The results, according to the study, showed that hydrolysates prepared under optimized conditions displayed antioxidant activity comparable to or approaching that of reference antioxidants, confirming that the waste protein was not merely recoverable but functionally valuable.

The fluorescence chromatography component of the work served as a molecular fingerprinting exercise. By tracking how fluorescent amino acid residues — primarily tryptophan, tyrosine and phenylalanine — behaved during chromatographic separation, the team could identify which peptide fractions were enriched in the aromatic residues most often associated with radical-scavenging behavior. Aromatic amino acids are chemically well suited to quenching reactive oxygen species because their ring structures can donate electrons or hydrogen atoms to stabilize free radicals without themselves becoming dangerously reactive. Fractions rich in these residues consistently showed stronger antioxidant signals, providing a practical marker that future producers could use to streamline fractionation.

Where the study pushes furthest beyond conventional food science is in its use of molecular dynamics simulation. The researchers modeled candidate peptides in aqueous solution and in the presence of representative radical species, tracking how the molecules flexed, folded and made contact over time at femtosecond resolution. These simulations allowed the team to propose mechanisms for antioxidant action at the atomic scale — for instance, identifying which hydrogen bonds and hydrophobic interactions position reactive side chains where they can most effectively intercept radicals. Molecular dynamics cannot replace experimental verification, but it can dramatically narrow the search space, telling experimentalists which of the thousands of possible peptide sequences are worth synthesizing and testing.

The combination of approaches also has implications for how bioactive peptides are screened in general. Traditional workflows rely on sequential fractionation and activity assays, a laborious process that can take weeks to home in on a single active sequence. By using fluorescence signatures to pre-screen fractions and simulations to rationalize activity, the researchers demonstrated a workflow in which computation and chromatography reinforce one another. Fractions flagged by their fluorescent profiles could be simulated in silico before committing resources to purification, and simulation results could in turn suggest which chromatographic conditions best preserve or separate active peptides. The result is a faster, more targeted pipeline from agricultural waste to functional ingredient.

The commercial logic of the work is compelling. Circular-economy approaches to food processing have gained momentum as companies face pressure to reduce waste and demonstrate sustainability, and antioxidant peptides from Litsea cubeba residuals would exemplify the model: a byproduct of oil production becomes an input for natural preservation, reducing both disposal costs and dependence on synthetic additives. The study’s authors note that the hydrolysates could find applications in protecting oils, meats and other oxidation-prone foods, and potentially in nutraceutical formulations where antioxidant intake is marketed as a health benefit. Whether the activity holds up in real food matrices — with their complex mixtures of salts, proteins and metals — will be the next hurdle, as laboratory assays frequently overstate performance in actual products.

There are also scaling questions to resolve. Enzyme costs, hydrolysis time and the yield of the most active fractions all factor into whether a laboratory success can become an industrial process, and the regulatory pathway for novel food ingredients varies by jurisdiction. Still, the study adds to a rapidly expanding body of evidence that food-industry side streams are among the most promising sources of bioactive peptides, joining similar work on proteins from fish skin, whey, rapeseed and cereal byproducts. What distinguishes the Litsea cubeba study is its methodological completeness: it does not just report that the hydrolysates work, it offers a structural and mechanistic account of why they work, grounded in fluorescence behavior and atomic-level simulation.

For now, the fragrance of litsea will keep wafting from flavor houses and cosmetic laboratories, but its seeds may soon have a quieter, equally valuable role. If follow-up work confirms the stability, safety and cost-effectiveness of these residual-derived peptides, the industry could be looking at a rare win-win-win: less waste, more natural preservation and a new revenue stream extracted from what was once thrown away. In a food system increasingly judged by how little it discards, turning perfumed leftovers into molecular shields against oxidation is exactly the kind of alchemy the circular economy promises — and, thanks to a combination of chromatography and simulation, it is now grounded in mechanism rather than hope.

Subject of Research: Antioxidant peptide hydrolysates prepared from Litsea cubeba oil processing residuals using fluorescence chromatography and molecular dynamics simulation

Article Title: Preparation and antioxidant activity of hydrolysates from Litsea cubeba oil processing residuals: fluorescence chromatography and molecular dynamics simulation

Article References: Li, L., Wang, W.-Q., Wang, B., & Han, Q.-Y. (2026). Preparation and antioxidant activity of hydrolysates from Litsea cubeba oil processing residuals: fluorescence chromatography and molecular dynamics simulation. npj Science of Food. https://doi.org/10.1038/s41538-026-01171-1

Image Credits: AI Generated

DOI: 10.1038/s41538-026-01171-1

Keywords: Litsea cubeba, antioxidant peptides, protein hydrolysates, food waste valorization, fluorescence chromatography, molecular dynamics simulation, natural preservatives, radical scavenging, circular economy, food science, bioactive compounds, lipid oxidation

The appeal of Dzugutov’s scheme lies in its microscopic foundation. Rather than reducing transport coefficients by macroscopic parameters such as number density and temperature, as Yosenfeld’s earlier excess-entropy scaling did in 1977, Dzugutov used microscopic reduction parameters: an effective hard-sphere diameter for length and the Enskog collision frequency for inverse time. The hard-sphere picture rests on two propositions. First, at high densities, the transfer of momentum and energy between atoms is dominated by short-range repulsive interactions, which behave essentially like binary collisions of billiard-ball-like hard spheres. Second, the frequency of local structural relaxation—the rate at which atoms escape the transient cages formed by their neighbors—can be calculated from Enskog kinetic theory for hard spheres. The result is a remarkably compact formula in which the normalized diffusivity equals 0.049 times the exponential of the excess entropy, expressed in units of the Boltzmann constant.

The excess entropy itself is a subtle quantity. Defined as the total thermodynamic entropy minus the ideal gas entropy, it can be expanded as a series of contributions from pairs of particles, triplets, and higher-order correlations. The leading two-body term, which depends directly on the pair distribution function, is known to contribute more than 85 percent of the excess entropy across most thermodynamic states, and as much as 95 percent near the triple point of simple liquids. Crucially, the pair distribution function can be obtained from x-ray or neutron diffraction experiments or from computer simulations, making Dzugutov’s scaling unusually practical: it connects atomic dynamics to structure and thermodynamics using quantities that are actually measurable. Later work by Jakse and Pasturel showed that an excess entropy derived from the Carnahan-Starling equation of state—the best analytical description of hard-sphere thermodynamics—can also justify the scaling, provided the temperature dependence of the effective hard-sphere diameter is properly accounted for.

What has been missing, the authors argue, is a systematic test for alloys rather than pure metals. Although Yokoyama and colleagues applied the scaling law to binary alloys in 2007, their analysis was restricted to equiatomic compositions. The new study therefore examines cobalt-iron (CoxFe1−x) and nickel-iron (NixFe1−x) alloys across the full concentration range, from iron-rich to cobalt-rich or nickel-rich, at a temperature of 1833 Kelvin. Iron-based transition metal alloys are not an arbitrary choice: they underpin structural steels, magnetic materials, and even models of planetary cores, and their liquid-state transport properties remain poorly constrained by experiment because of the extreme conditions involved.

On the theoretical side, the team combined the Bretonnet-Silbert pseudopotential—a model specifically designed for liquid transition metals that accounts for both s-p and d band contributions, including the sp-d hybridization that simpler pseudopotentials miss—with linearized Weeks-Chandler-Andersen perturbation theory. The latter provides the effective hard-sphere diameters by solving a transcendental equation that equates the Helmholtz free energy of the real interacting system with that of a hard-sphere reference system. Two different local field correction functions, due to Ichimaru-Utsumi and Vashishta-Singwi, were used to screen the electron-ion interaction, allowing the authors to assess which treatment of electron correlation better describes these dense metallic liquids. This combination of Bretonnet-Silbert potential and LWCA theory had never before been applied to calculate diffusion and viscosity in transition metal alloys.

To anchor the theory, the researchers performed classical molecular dynamics simulations with the LAMMPS code, using modified embedded atom method (MEAM) interatomic potentials that capture both many-body effects and the angular orientation of bonds—features essential for transition metals. Each simulation placed 30,000 atoms in a cubic box 70 angstroms on a side with periodic boundary conditions. The alloys were first equilibrated at 2500 Kelvin, above their melting points, in a constant-pressure ensemble, then cooled to 1833 Kelvin, and finally equilibrated for a full nanosecond at the target temperature. One thousand configurations were extracted from each run, and the pair correlation functions were computed with the OVITO visualization tool and averaged with a Python code. The team also verified that their results were independent of system size by repeating selected simulations with 20,000 and 40,000 atoms; the reduced diffusion coefficients and viscosities changed only in the second decimal place, consistent with the finite-size correction formula proposed by Khrapak.

The calculated structural quantities proved encouraging. Effective hard-sphere diameters obtained from theory and simulation were comparable and consistent with literature values for pure liquid iron, cobalt, and nickel, and the partial pair correlation functions reproduced the expected concentration trends: the peak height of like-atom pairs rises as that component becomes richer, while cross-pair correlations peak at mid-concentrations. When the reduced diffusion coefficients were plotted against excess entropy, however, a nuanced picture emerged. Simulated data followed Dzugutov’s universal scaling line fairly well across all concentrations of both alloy families. The theoretically calculated data, by contrast, obeyed the scaling only for mid-concentrated alloys, deviating systematically when a single component dominated the mixture. The culprit, the authors show, is the overestimated principal peak height of the calculated partial pair correlation functions in single-component-rich alloys, which inflates the two-body excess entropy.

Notably, the Carnahan-Starling excess entropy proved far more reliable than the two-body approximation, remaining stable across concentrations and closer to established values for pure liquid metals, even in single-component-rich compositions where the two-body entropy varied wildly. This suggests that the Bretonnet-Silbert potential combined with LWCA theory is a sound framework for mid-concentrated liquid transition metal alloys, even if the perturbative treatment of structure needs refinement at the compositional extremes. The Ichimaru-Utsumi local field correction consistently outperformed the Vashishta-Singwi alternative, yielding more reliable inter-diffusion coefficients and shear viscosities, which remained nearly constant across all concentrations and agreed reasonably with molecular dynamics results.

The transport coefficients themselves tell an industrially relevant story. Partial self-diffusion coefficients of cobalt or nickel decrease while those of iron increase as the minority component’s concentration grows, and the inter-diffusion coefficients—computed as concentration-weighted sums of the self-diffusivities—fall in the range of roughly 7 to 9 times 10⁻⁹ square meters per second, comparable to simulation data and to scattered theoretical and experimental values for the pure liquid metals. Shear viscosities derived from the Stokes-Einstein relation with slip boundary conditions, and from Yokoyama’s modified upper-bound formula, came out around 1.6 to 2.8 millipascal-seconds—smaller than experimental measurements, which run nearly twice as high, but closer to experiment than earlier theoretical estimates and consistent in trend with prior work on Fe-Co and Fe-Ni liquid alloys.

Perhaps the most striking result appears in the paper’s appendix, where the authors propose a new scaling law of their own. Because both alloy families deviated from Dzugutov’s line at single-component-rich compositions, they refitted the data with independent prefactor and exponential factors and found that both Co-Fe and Ni-Fe melts obey the same alternative relationship, with a reduced diffusion coefficient proportional to the exponential of minus 0.3 times the excess entropy and a much smaller prefactor of 0.005. Until more sophisticated theories or new experimental data for iron-based liquid alloys become available, the authors suggest, this proposed scheme offers young researchers a practical route to predicting atomic diffusion in these industrially vital melts—while the half-century-old interplay between entropy and atomic mobility continues to reveal fresh surprises in the liquid state.

Subject of Research: Testing Dzugutov's excess-entropy scaling law for atomic diffusion in liquid iron-based cobalt-iron and nickel-iron transition metal alloys using pseudopotential theory and molecular dynamics simulation.

Article Title: Test of scaling law for atomic diffusion of Fe based liquid transition metal alloys

Article References: Test of scaling law for atomic diffusion of Fe based liquid transition metal alloys. (n.d.). https://doi.org/10.1016/j.rinp.2026.108743

Image Credits: AI Generated

DOI: 10.1016/j.rinp.2026.108743

Keywords: atomic diffusion, liquid metals, transition metal alloys, excess entropy scaling, Dzugutov scaling law, molecular dynamics simulation, pseudopotential theory, iron-cobalt alloys, iron-nickel alloys, shear viscosity, hard-sphere model, LAMMPS

Umami, often described as the fifth basic taste alongside sweet, sour, salty and bitter, is the savory depth that makes broths, cured hams and slow-cooked meats so satisfying. The main umami substances in food include free amino acids, organic acids, nucleotides and umami peptides. Over 200 umami peptides have previously been extracted from proteins in chicken, fish and goose, but most research has focused on short chains of fewer than ten amino acids. The taste behavior of longer peptides, which may persist on the palate longer and bind to receptors in more complex ways, has remained largely unexplored. Beef was an obvious place to look: the umami precursor inosinic acid was first isolated from beef soup, and Yunnan’s local cattle are renowned for tender meat and delicious broth, yet the peptide contributors to Dengchuan beef’s characteristic flavor were unknown.

The team began by preparing a water extract from fresh Dengchuan beef, homogenizing the meat with water at a one-to-three ratio, heating it to 90 degrees Celsius for 30 minutes, then cooling, centrifuging and filtering the supernatant. Ultrafiltration membranes with cut-offs of 3 and 5 kilodaltons split the extract into three fractions. A trained ten-member sensory panel, all food science professionals at Yunnan Agricultural University, scored each fraction for the five basic tastes. The smallest fraction, containing peptides under 3 kilodaltons, scored dramatically highest for umami at 7.09 out of 10, significantly outpacing the 3-to-5 kilodalton fraction at 5.36 and the largest fraction at 3.64. That result aligned with earlier findings that low molecular weight peptides are the strongest umami carriers, so the smallest fraction advanced to structural identification.

Using nano-flow liquid chromatography coupled to a Q Exactive HF-X mass spectrometer, and processing the raw data with MaxQuant software at a false discovery rate below one percent, the researchers identified a remarkable 4,220 peptides. Molecular weights ranged from 349 to over 3,100 daltons, with nearly 44 percent of the peptides falling below 1.5 kilodaltons, a size class previously associated with umami activity. Most of these smaller peptides contained 9 to 15 amino acid residues, making them notably longer than the dipeptides and tripeptides that typically dominate umami studies. Sequence analysis revealed telling patterns: alanine, aspartic acid, glutamic acid, leucine, serine, threonine, valine and glycine crowded the N-termini, while arginine, lysine, asparagine and leucine anchored the C-termini. Sixty-five peptides combined an N-terminal glutamic acid with a C-terminal lysine, leucine, threonine or arginine, a physicochemical signature known to boost umami. Ultimately, 143 peptides in which umami-related acidic amino acids, aspartate and glutamate, made up at least 30 percent of the sequence were selected for the next round.

Rather than relying on any single prediction tool, the team deployed a three-model machine learning gauntlet: iUmami_SCM, Umami_YYDS and Tastepeptides_DM, requiring an iUmami_SCM score above 588, umami probabilities above 0.9 and bitterness predictions below 0.1. This cross-validated strategy, previously used to screen 155 candidate peptides from button mushrooms, trimmed the pool to 98 peptides and then, with a stricter iUmami_SCM cutoff above 650, to 23 peptides mostly 9 to 13 residues long. Safety and solubility filters followed. One candidate, AEEEYPDLSKHN, was flagged as toxic by the ToxinPred platform and discarded; the remaining 22 were all predicted non-toxic with good water solubility, and BIOPEP analysis suggested umami-active fragments made up more than half of each sequence.

Molecular docking then served as the final computational sieve. The researchers built a homology model of the human T1R1/T1R3 taste receptor, the principal umami detector on the tongue, using the SwissModel platform and validated its stereochemistry with Ramachandran plots, which placed 85.5 percent of residues in the most favored regions. Docking all 22 peptides against the receptor produced binding energies from minus 9.5 to minus 6.3 kilocalories per mole, with lower values indicating more stable complexes. Five peptides stood out with binding energies below minus 8 kilocalories per mole: HAKIDAAEEEKY, GDEESYTVFK, EQAEEERYFRA, DPDEEALRRSR and EDEADDWARR, dubbed HY-12, GK-10, EA-11, DR-11 and ER-10 respectively.

The five peptides were chemically synthesized at greater than 95 percent purity and put to the test with human panels and an electronic tongue. Taste dilution analysis revealed umami thresholds of 0.0625 milligrams per milliliter for ER-10, 0.125 for HY-12 and DR-11, and 0.25 for GK-10 and EA-11, all below the 0.3 threshold of MSG itself. ER-10, a ten-residue peptide rich in acidic residues and featuring a tryptophan, delivered the strongest umami, described as intense savory with slight salty and sweet notes. When added to a 0.35 percent MSG solution, the peptides boosted umami scores by 8.82 to 36 percent, with ER-10 and DR-11 the most powerful enhancers. Intriguingly, the electronic tongue showed HY-12 producing the lowest initial umami but the strongest umami aftertaste, suggesting sustained receptor binding that human tasters, whose umami perception suppresses competing flavors, experienced differently. Fourier transform infrared spectroscopy showed all five peptides are dominated by beta-turn and random coil structures, flexible conformations that appear central to their taste activity.

The molecular simulations explained why. Docking revealed the peptides form 10 to 28 hydrogen bonds with T1R1/T1R3, making hydrogen bonding the dominant stabilizing force, supplemented by electrostatic and hydrophobic interactions. Eleven receptor residues showed high interaction frequency, including Asp108, Asn150, Gln221, Gln222 and Ser217, while energy decomposition identified Lys155, Gln52, Ser109, Ser216, Arg255, Ser217 and Met151 as the key anchoring sites. Within the peptides themselves, arginine, phenylalanine, tryptophan and lysine contributed most to binding, and ER-10’s tryptophan alone formed seven hydrophobic contacts, likely explaining its supremacy. Compared with typical short umami peptides, these long chains showed a broader, multi-site binding footprint touching both core and peripheral receptor residues. Docking against the alternative umami receptor mGluR4 yielded weaker binding energies, indicating T1R1/T1R3 is the primary driver of the peptides’ taste while mGluR4 plays a supporting role.

Hundred-nanosecond molecular dynamics simulations in AMBER confirmed the docking picture. Four of the five peptide-receptor complexes held steady with root mean square deviations between 4 and 6 angstroms, while EA-11 proved structurally unstable, matching its weakest sensory performance. MM/GBSA binding free energy calculations ranked ER-10 most tightly bound at minus 77.23 kilocalories per mole, with electrostatic energy as the chief favorable contributor. The authors argue their integrated strategy sidesteps the slow, costly traditional chromatography workflow and, crucially, extends computational umami prediction into the long-chain territory where existing models have faltered. Beyond illuminating why Dengchuan beef tastes so good, the findings point toward rationally designed, beef-derived umami peptides as natural, nutritious flavor enhancers that could reduce sodium reliance in processed foods, though the team notes that interactions with nucleotide enhancers like inosinate and guanylate still need to be untangled.

Subject of Research: Identification of novel long-chain umami peptides in Dengchuan beef and their molecular taste mechanism via the T1R1/T1R3 receptor.

Article Title: Analysis of novel umami peptides in Dengchuan beef and their taste mechanism: Integrated peptidomics, machine learning and molecular simulation studies

Article References: Zheng, W., Chai, Y., Wang, Y., Yang, X., He, J., Li, Q., Wei, G., Huang, A., & Li, Y. (2026). Analysis of novel umami peptides in Dengchuan beef and their taste mechanism: Integrated peptidomics, machine learning and molecular simulation studies. Current Research in Food Science, 13, Article 101570. https://doi.org/10.1016/j.crfs.2026.101570

Image Credits: AI Generated

DOI: 10.1016/j.crfs.2026.101570

Keywords: umami peptides, Dengchuan beef, peptidomics, machine learning, molecular docking, T1R1/T1R3 receptor, molecular dynamics simulation, taste threshold, flavor chemistry, food science, Analysis, novel

The clinical problem the team set out to address is far from niche. In patients receiving injected doxorubicin, hepatotoxicity has been documented repeatedly, with elevated liver enzymes, structural tissue damage, and in severe cases progressive fibrosis. Because doxorubicin remains a cornerstone of many treatment regimens, clinicians and researchers have long sought co-therapies that protect the liver without compromising the anticancer effect. Prior studies have tested an eclectic range of hepatoprotective agents, from creatine and hesperidin to naringin, salidroside, and metformin, each acting through partially overlapping but distinct molecular routes — antioxidant stress modulation, inflammasome suppression, or altered drug transport into hepatocytes. The new study adds bellidifolin to this growing arsenal and identifies a pathway that had not been prominently featured in the doxorubicin-liver story before: the Galectin-3/NLRP3 inflammatory axis.

The investigation began in silico. Using network pharmacology, a methodology that maps the relationships between a compound’s predicted molecular targets, disease-associated genes, and biological pathways, the researchers compiled a list of potential targets through which bellidifolin might counteract doxorubicin-induced liver injury. Out of the network analysis emerged a set of core targets dominated by players in the innate immune response: Caspase-1, NLRP3, IL-18, and IL-1β. Gene Ontology and KEGG pathway enrichment analyses converged on a single functional theme — the NLRP3 pathway, an inflammatory cascade whose dysregulation is increasingly implicated in drug-induced organ damage across the body.

To move beyond correlation and toward mechanism, the team turned to structural biology tools. Molecular docking positioned bellidifolin within the binding pockets of its predicted protein targets, and molecular dynamics simulation then tested whether the predicted interactions were stable over simulated time. The results pointed to two proteins in particular with which the small molecule showed strong binding affinity: Galectin-3, a beta-galactoside-binding lectin with well-known roles in fibrosis and inflammation, and NLRP3, the sensor protein that nucleates the inflammasome complex. This computational evidence gave the researchers a concrete hypothesis: bellidifolin might relieve liver injury by simultaneously engaging Galectin-3 and NLRP3, thereby dampening the downstream inflammatory program they drive.

The hypothesis then faced the wet laboratory. The researchers randomly divided mice into three groups: a control group, a group treated with doxorubicin, and a group receiving both doxorubicin and bellidifolin. When the animals were assessed, the protective effect of the natural compound was visible at every level of analysis examined. Hematoxylin and eosin staining revealed that bellidifolin attenuated the pathological changes doxorubicin inflicted on hepatic architecture, while Masson staining showed reduced collagen deposition, indicating less fibrosis. Biochemical assays of serum confirmed the histological picture: blood levels of alanine aminotransferase and aspartate aminotransferase, the two classic enzymatic signatures of liver damage, were significantly decreased in the bellidifolin-treated animals.

The most distinctive finding, however, concerned a form of cell death that has moved to the center of inflammatory disease research in the past decade: pyroptosis. Unlike apoptosis, the quiet, orderly death program that removes cells without stirring the immune system, pyroptosis is explosive. When the NLRP3 inflammasome assembles, it activates caspase-1, which cleaves the precursor forms of the inflammatory cytokines IL-1β and IL-18 into their mature, potent forms and also punches gasdermin pores in the cell membrane, causing the cell to swell and burst, spilling its contents into surrounding tissue. In the doxorubicin-treated mice, hepatocytes were undergoing pyroptosis, fueling a self-amplifying inflammatory loop. In the animals that also received bellidifolin, this pyroptotic process was substantially inhibited.

Western blot analysis and immunohistochemistry staining provided the molecular confirmation. Protein expression along the Gal-3/NLRP3 signaling pathway, elevated by doxorubicin, was suppressed by bellidifolin treatment, consistent with the docking predictions. The convergence of computational prediction and experimental measurement is what gives the study its strength: the same two proteins identified in silico as high-affinity partners of bellidifolin — Galectin-3 and NLRP3 — turned out to be the nodes through which the compound’s protective effect was expressed in living animals. The chain of evidence runs from network prediction, through molecular docking and dynamics, to histology, serum chemistry, and protein-level validation, forming an unusually complete arc for a single study.

The Galectin-3 connection is particularly intriguing in light of the broader literature. Galectin-3 has been shown in numerous contexts to sit upstream of NLRP3 inflammasome activation. Inhibiting Galectin-3 has been reported to limit microglial NLRP3/pyroptosis signaling in models of epilepsy and traumatic brain injury, to ameliorate epithelial pyroptosis in acute lung injury, and to reduce pro-fibrotic signaling in the liver. Galectin-3 is also overexpressed in advanced cirrhosis and has been studied as a marker of fibrosis and as a prognostic biomarker in hepatocellular carcinoma. The finding that bellidifolin binds both Galectin-3 and NLRP3 with strong affinity suggests it may be acting at a nodal point where fibrosis and inflammatory cell death intersect, which could have implications beyond chemotherapy-induced injury.

It is worth placing the new result alongside earlier work on bellidifolin itself, a xanthone-class compound from plants such as Gentianella acuta. Previous studies have found that bellidifolin inhibits proliferation of A549 lung cancer cells by regulating STAT3/COX-2 signaling, protects cardiac cells from hydrogen peroxide injury through the PI3K-Akt pathway, ameliorates isoprenaline-induced myocardial fibrosis via TGF-β1/Smads and p38 signaling, mitigates cardiac hypertrophy through the Nox4/ROS pathway, and eases nonalcoholic fatty liver disease-like changes induced by bisphenol F. Bellidifolin has also been shown to protect brain vascular pericytes from injury involving pyroptosis — a hint that its anti-pyroptotic activity, now demonstrated in the liver, may be a recurring theme in its pharmacology.

The study, approved by the Animal Ethics Committee of Hebei University of Chinese Medicine and supported by grants from Hebei provincial research programs, is a preclinical animal investigation, and the usual caveats apply. Dose optimization, pharmacokinetics, interactions with doxorubicin’s anticancer efficacy, and translation to human hepatotoxicity all remain open questions, and the authors note that no datasets were generated or analyzed beyond those reported. Still, by identifying a druggable inflammatory axis and demonstrating that a plant-derived small molecule engages it, the research offers a concrete starting point for developing hepatoprotective co-therapies that could one day let patients receive full doses of a life-saving chemotherapy drug without paying the price in liver damage. The Science of Nature (Sci Nat), Volume 113, article number 118, published the findings on 22 September 2026, adding a carefully validated page to the rapidly expanding catalogue of natural products capable of taming inflammatory cell death.

Subject of Research: Bellidifolin's protective mechanism against doxorubicin-induced liver injury through inhibition of Galectin-3/NLRP3-mediated pyroptosis

Article Title: Bellidifolin alleviates doxorubicin-induced hepatotoxicity: a study integrating network pharmacology, molecular docking, and experimental validation

Article References: Cao, Y., Chen, R., Zhang, W., Qin, Y., Liu, W., Li, A., Jia, Y., & Wu, J. (2026). Bellidifolin alleviates doxorubicin-induced hepatotoxicity: a study integrating network pharmacology, molecular docking, and experimental validation. The Science of Nature, 113(5), Article 118. https://doi.org/10.1007/s00114-026-02166-4

Image Credits: AI Generated

DOI: 10.1007/s00114-026-02166-4

Keywords: bellidifolin, doxorubicin, hepatotoxicity, liver injury, NLRP3 inflammasome, Galectin-3, pyroptosis, network pharmacology, molecular docking, molecular dynamics simulation, natural products, inflammation

The target of the study is the gamma-secretase enzyme complex, a molecular machine embedded in cell membranes that performs the final cutting step in the production of amyloid-beta. The complex is built from four proteins: presenilin, nicastrin, APH-1, and PEN-2. When gamma-secretase cleaves the amyloid precursor protein, it can generate the longer and stickier Aβ42 peptide, which aggregates into the extracellular plaques that are a hallmark of Alzheimer’s pathology. Because mutations in the presenilin 1 gene are linked to familial forms of the disease and drive elevated Aβ42 output, the PSEN1 subunit has long been considered the prime site for therapeutic intervention.

Blocking gamma-secretase outright, however, has proven dangerous. The enzyme also processes Notch receptors, which govern cell differentiation and development, and complete inhibition has been associated with gastrointestinal toxicity and other serious side effects, a problem that contributed to the clinical failure of drugs such as semagacestat. The field has therefore shifted toward gamma-secretase modulators, compounds that selectively lower Aβ42 while leaving the processing of other substrates untouched. Even here, progress has been rocky: the modulator E2012 showed strong amyloid reduction but raised concerns about effects on cholesterol metabolism, and BMS-932481 was hampered by liver toxicity in early trials.

To guide their search for better modulators, the researchers focused on two reference compounds, BMS 299897 and ELN318463. Both bind at an allosteric pocket at the interface of transmembrane helices six and seven of the PSEN1 subunit, a region distinct from the catalytic aspartates but positioned to influence how the active site handles its substrate. ELN318463 is particularly notable because, in cell-based assays, it shows a seventy-five to one-hundred-twenty-fold preference for blocking amyloid-beta production over Notch signaling. Using the LigandScout software, the team generated individual pharmacophore maps for each drug, mapping out hydrogen bond donors and acceptors, hydrophobic regions, aromatic rings, and halogen bond donors, and then aligned the two maps to build a shared-feature pharmacophore model that captured the essential interaction points common to both inhibitors.

With this model in hand, the team screened the Comprehensive Marine Natural Products Database, a library of roughly 47,451 compounds sourced from algae, sponges, corals, and other marine organisms. After removing duplicates, 43,212 molecules were virtually screened against the shared pharmacophore over approximately forty-eight hours on a sixty-four-core processor. Six compounds emerged as top hits, with the best, CMNPD10454, achieving a pharmacophore fit score of about 110.4, indicating a near-perfect match with the key interaction features. Marine natural products are prized in drug discovery for their unusual chemical architectures, and many display antioxidant, anti-inflammatory, and neuroprotective activities, making them attractive starting points for new Alzheimer’s therapies.

The raw hits, however, were structurally complex and considered impractical as direct drug candidates. The team therefore turned to fragment-based de novo design. A key observation drove this step: both BMS 299897 and ELN318463 share a 4-chlorobenzenesulfonamide ring that plays a central role in hydrophobic interactions with the enzyme. Using the AlvaBuilder toolkit, which applies genetic algorithms to molecular design, the researchers generated fifty new synthetic modulators by keeping this ring fixed and grafting bioactive fragments from the marine hits onto it. The design constraints included Lipinski’s Rule of Five parameters, a synthetic accessibility score of five or below, limits on halogen count, and estimated aqueous solubility thresholds, all intended to ensure the resulting molecules were both effective in theory and chemically feasible to make.

The fifty designs were then filtered through absorption, distribution, metabolism, and excretion profiling with SwissADME, and only three molecules, numbered 6, 24, and 28, were predicted to cross the blood-brain barrier, a filter that has doomed many otherwise promising central nervous system drug candidates. Molecular docking against the human gamma-secretase crystal structure, using the Protein Data Bank entry 5A63, showed that all three bound more tightly than the control drug BMS 299897, which scored minus 8.9 kilocalories per mole. Molecule 6 achieved the best docking energy at minus 10.6 kilocalories per mole, driven by extensive hydrophobic contacts with residues including PHE411, VAL94, and ILE408, while Molecules 24 and 28 scored minus 9.7 and minus 9.6 respectively. Notably, none of the top compounds formed hydrogen bonds with the active site, suggesting that hydrophobic interactions dominate their binding mode.

Molecular dynamics simulations over one hundred nanoseconds, run with the Desmond software using the OPLS_2005 force field and physiological salt conditions, provided further evidence of stability. Molecule 24 produced the most stable protein-ligand complex, with the lowest root mean square deviation of 4.03 angstroms and the lowest residue fluctuation values, maintaining a compact radius of gyration and consistent solvent exposure throughout the simulation. It also formed a hydrogen bond with CYS4 and ionic interactions with LEU243 and LEU244, indicating robust and persistent binding. Toxicity profiling with the OECD QSAR Toolbox predicted no mutagenicity alerts and stable tautomeric forms for the lead compounds, along with lower bioaccumulation factors than the control drug, although renal toxicity alerts resembling a sulfasalazine-like profile warrant future experimental scrutiny.

An intriguing and unexpected finding emerged from the interaction analysis. While Molecule 6 retained strong contact with PSEN1, consistent with the pharmacophore model that guided its design, Molecules 24 and 28 preferentially anchored to the APH-1 subunit of the gamma-secretase complex instead. The precise role of APH-1 in modulating amyloid-beta generation remains uncertain, but prior structural studies suggest it participates in complex assembly and can influence the conformation of the catalytic subunit. The authors argue that these contacts may represent an alternative mechanism of modulation rather than a flaw in the design strategy, and they emphasize that future wet-laboratory experiments will be needed to determine whether APH-1 interactions contribute functionally to enzyme regulation or are merely incidental.

The study also addressed the practical question of how the lead compounds could actually be synthesized. Using the IBM RXN retrosynthesis platform, which combines template-based and template-free neural network approaches with Monte Carlo tree search, the team mapped a stepwise route for Molecule 24 starting from commercially available 4-chlorobenzene sulfonyl chloride, proceeding through click-chemistry and nucleophilic substitution steps to assemble the final structure. While all of these results remain computational predictions that require experimental validation in laboratory and animal models, the work demonstrates how marine chemical diversity, pharmacophore-guided screening, fragment-based design, and molecular simulation can be woven together to accelerate the hunt for safer Alzheimer’s therapies, offering a template for discovering next-generation gamma-secretase modulators that lower amyloid-beta without disrupting the essential cellular pathways that previous drug candidates damaged.

Subject of Research: Computational design of marine-derived gamma-secretase modulators to reduce amyloid-beta production in Alzheimer's disease

Article Title: In silico pharmacophore-guided modeling of marine-derived γ-secretase modulators for amyloid-beta reduction in Alzheimer's disease

Article References: In silico pharmacophore-guided modeling of marine-derived γ-secretase modulators for amyloid-beta reduction in Alzheimer's disease. (n.d.). https://doi.org/10.1016/j.heliyon.2026.e45453

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45453

Keywords: Alzheimer's disease, gamma-secretase modulators, marine natural products, pharmacophore modeling, molecular docking, molecular dynamics simulation, amyloid-beta, PSEN1, blood-brain barrier, drug discovery, virtual screening, de novo molecular design