Recep Adiyaman
bioinformatics

Issue #37: Efficacy of Ipflufenoquin against Strawberry Gray Mold: Insights from AlphaFold- Based Structural Modeling and Genome-Wide Transcriptomic Analysis.

January 31, 2026 Daily Intelligence
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Efficacy of Ipflufenoquin against Strawberry Gray Mold: Insights from AlphaFold- Based Structural Modeling and Genome-Wide Transcriptomic Analysis.

🧬 Abstract

Gray mold caused by Botrytis cinerea significantly threatens strawberry production. This study evaluated the efficacy of ipflufenoquin, a novel dihydroorotate dehydrogenase (DHODH) inhibitor, against fungal pathogens isolated from Korean strawberry fields in 2023. Ipflufenoquin demonstrated a high in vitro sensitivity to B. cinerea and broad activity against other pathogens. Fruit and greenhouse trials confirmed its robust control of gray mold, including strains resistant to multiple fungicide classes. However, treatment shifted the fungal community, promoting less sensitive genera, such as Cladosporium and Rhizopus. Structural modeling with AlphaFold2 and molecular docking confirmed that ipflufenoquin binds to the quinone binding tunnel of DHODH, correlating binding affinity with susceptibility. Additionally, RNA-seq analysis revealed that ipflufenoquin suppresses primary metabolic pathways while triggering a robust stress response, up-regulating detoxification and efflux transporter genes. This integrated study confirms the efficacy of ipflufenoquin against gray mold and elucidates its molecular impacts, offering essential data for sustainable management strategies.

Why it matters: Enhances small-molecule or peptide docking accuracy for targeted drug discovery.

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Comparison of In Vitro Multiple Physiological Activities of Cys-Tyr-Gly-Ser-Arg (CYGSR) Linear and Cyclic Peptides and Analysis Based on Molecular Docking.

Peptide cyclization is a strategy to improve biological stability and functional activity, but direct comparison between linear and cyclic peptides with the same sequence is still limited. In this study, linear (L-CR5) and cyclic (C-CR5) forms were synthesized, and biological functions such as antioxidant, whitening, and anti-wrinkle activity were compared and evaluated. C-CR5 showed about 22.3 times of DPPH radical scavenging activity, which was significantly stronger than L-CR5, and tyrosinase inhibition increased rapidly in C-CR5 to reach inhibition of 95% or more, whereas L-CR5 showed only moderate activity in the same range (about 6.5 times). MMP-1 expression in the evaluation of anti-wrinkle activity did not show a decreasing trend in L-CR5 at all, while C-CR5 showed an anti-wrinkle effect, which was reduced by about 92.8% at 400 μg/mL. As a result of molecular docking analysis, C-CR5 exhibited lower MolDock scores than L-CR5 toward both tyrosinase and MMP-1, indicating a potentially higher binding affinity and improved binding stability. This is expected to be due to reduced structural flexibility and optimized residue directions (especially Tyr and Arg). These results indicate that peptide cyclization is an example of enhanced functional bioactivity of CYGSR and provides a positive case for the structure-activity relationship.

Decrypting potential mechanisms linking ochratoxin A to hepatocellular carcinoma: an integrated approach combining toxicology, machine learning, molecular docking, and molecular dynamics simulation.

Background Ochratoxin A (OTA), a common food-borne mycotoxin, is a potential human carcinogen, yet the specific molecular mechanisms linking it to hepatocellular carcinoma (HCC) remain unclear. Methods We integrated network toxicology to predict OTA targets and intersected them with HCC transcriptomic data to identify key candidate genes. Functional enrichment analysis was then conducted. Multiple machine learning algorithms were applied to screen and validate core genes. Furthermore, molecular docking and molecular dynamics (MD) simulations were employed to evaluate the binding stability between OTA and key target proteins. Results A total of 50 key genes were identified as potential targets for potential OTA-associated hepatocarcinogenesis. Enrichment analysis revealed their significant involvement in critical processes such as xenobiotic metabolism and oxidative stress response. Machine learning analysis prioritized eight core genes (AURKA, GABARAPL1, CA2, PARP1, LMNA, SLC27A5, EPHX2, and GSTP1), and a combined diagnostic model demonstrated outstanding performance (AUC = 0.986). Structural analyses via molecular docking and MD simulations confirmed stable binding interactions between OTA and these core targets. Conclusions This integrated computational study identifies a set of candidate genes through which OTA may potentially interact with HCC-associated molecular networks. The robust binding predicted between OTA and the core targets provides a structural basis for these interactions. These findings offer a prioritized list of targets and a theoretical framework for subsequent experimental validation and investigation into OTA’s toxicological role in HCC.

Study on the Mechanism of Ku Diding in the Treatment of Diabetes based on Network Pharmacology, Molecular Docking Technology, and Molecular Dynamics.

Introduction To explore how Ku Diding (KDD) works in managing Diabetes Mellitus (DM), researchers utilized network pharmacology, molecular docking, and molecular dynamics methodologies. Methods Key active components of KDD were identified using the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP). Data for diabetesrelated targets were retrieved from the Human Genetic Comprehensive Databases (Genecards) and the Online Mendelian Inheritance in Man (OMIM) database. The intersection of these targets was analyzed to determine potential therapeutic targets for diabetes treatment. Proteinprotein interaction networks (PPI) were constructed using the STRING database and Cytoscape software, followed by Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Molecular docking between the components and key targets was performed using the AutoDock Vina platform. Results This study identified that Dihydrosanguinarine, (S)-Scoulerine, among others, are the main active ingredients of KDD for treating DM, showing high affinity for critical targets like PTGS2 and PRKACA, through multiple pathways including vascular regulation, neuromodulation, metabolic regulation, and endocrine regulation. The molecular docking results showed that there are interactions between the active ingredients and the key targets, with the majority of the effective components exhibiting a stronger binding affinity than Metformin. Among them, (S)-Scoulerine and Dihydrosanguinarine demonstrated high docking affinity with the key target proteins PTGS2 and PRKACA. Discussion DM is closely linked to oxidative stress, chronic inflammation, and insulin signaling dysregulation. This study reveals that KDD exerts anti-diabetic effects via a multi-target network involving proteins such as PRKACA, PTGS2, ESR1, FOS, and DRD2. These targets are associated with glucose metabolism, inflammation, oxidative stress, and neural regulation. Modulation of these pathways likely enhances insulin sensitivity, lowers blood glucose, suppresses inflammation, and protects against oxidative damage. GO and KEGG analyses further indicate involvement in MAPK signaling, synaptic transmission, and vascular regulation, forming a multidimensional “metabolism-inflammation-neural” regulatory network. Compared to Metformin, most KDD-derived compounds showed stronger binding, highlighting their therapeutic potential. Molecular dynamics simulations support the stability of the observed binding conformations, suggesting their potential as therapeutic targets. These findings underscore KDD’s ability to simultaneously target multiple pathological mechanisms, offering a holistic treatment strategy for DM. Conclusion This study provides preliminary evidence that KDD is characterized by a multicomponent, multi-target, and multi-pathway approach in the treatment of diabetes mellitus (DM), thereby establishing a scientific foundation for further in-depth exploration of KDD’s molecular mechanisms.

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Investigation of the Interaction between Platinum Nanozymes and Serum Albumin by Multispectroscopic Approach and Molecular Docking Simulation.

The interaction between platinum nanozymes (PtNZs) and serum albumin (SA), including human serum albumin (HSA) and bovine serum albumin (BSA), was comprehensively analyzed through a combination of ultraviolet-visible (UV-vis) absorption, fluorescence (FL), circular dichroism (CD) spectroscopy, and molecular docking simulation. The differences in the binding between SA and PtNZs were compared based on thermodynamic data. The spectral experimental results indicated that PtNZs interact with HSA and BSA to varying degrees, with BSA exhibiting a stronger binding affinity for PtNZs than HSA. The fluorescence results indicate that the fluorescence intensity of SA is quenched by platinum nanozymes, with static quenching being the main mechanism. The Ka values of HSA binding with PtNZs is smaller than that of BSA at the same temperature, indicating a relatively weak affinity between HSA and PtNZs. Negative ΔG values suggest that this interaction is a spontaneous process, while positive ΔH values are classified as endothermic processes. ΔH > 0 and ΔS > 0 proved that hydrophobic interactions were the primary driving forces in the binding processes. Synchronous fluorescence and excitation emission matrix spectroscopy indicated that the structure of tyrosine (Tyr) and tryptophan (Trp) residues in HSA/BSA had undergone slight changes, with their secondary structure also exhibiting subtle alterations. Molecular docking simulations yielded the number and type of amino acids bound to the surface of PtNZs within 3 Å as well as the energy of the binding system. Analysis of the CD spectra shows that the interaction with PtNZs causes secondary structural changes in HSA/BSA.

Immunomodulatory Peptides Derived from <i>Tylorrhynchus heterochaetus</i>: Identification, In Vitro Activity, and Molecular Docking Analyses.

Tylorrhynchus heterochaetus is an aquatic food with both edible and medicinal value in China. With a protein-rich body wall, it has strong potential for producing bioactive peptides. To explore its potential as a source of immunomodulatory peptides, in this study, flavor enzymes were selected as the optimal hydrolases, and the hydrolyzed products were subjected to ultrafiltration fractionation. The α ), interleukin-6 (IL-6) and nitric oxide (NO) in a concentration dependent manner. Peptide omics analysis, combined with the activity and safety screened by bioinformatics, identified 43 candidate peptides. Molecular docking predicts that three novel peptides, LPWDPL, DDFVFLR and LPVGPLFN, exhibit strong binding affinity with toll-like receptor 4/myeloid differentiation factor-2 (TLR4/MD-2) receptors through hydrogen bonding and hydrophobic/π stacking interactions. Synthetic verification confirmed that these peptides were not only non-toxic to cells at concentrations ranging from 62.5 to 1000 µg/mL, but also effective in activating macrophages and stimulating the release of immune mediators. This study successfully identified the specific immunomodulatory peptides of the Tylorrhynchus heterochaetus , supporting its high-value utilization as a natural source of raw materials for immunomodulatory functional foods.

Neurotoxicity Mechanisms of Per- and Polyfluoroalkyl Substances: An Integrated Study of Network Toxicology, Molecular Docking, and Mendelian Randomization.

Observational studies have shown that exposure to per- and polyfluoroalkyl substances can lead to neurotoxicity. We focus on whether perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) affect brain morphology and the potential molecular mechanisms of toxicity. Causal relationship between exposure to both PFOA and PFOS and brain morphology was explored based on Mendelian randomization (MR), and the toxic molecular mechanism was investigated using network toxicology. MR analysis indicated PFOA exposure reduced brain volume in left parahippocampal (p = 0.018) and right rostral anterior cingulate (p = 0.007), while PFOS exposure decreased volume in left middle temporal (p = 0.036), paracentral (p = 0.022), postcentral (p = 0.014), posterior cingulate (p = 0.002), rostral middle frontal (p = 0.040), superior frontal (p = 0.027), superior parietal (p = 0.033), and in the right hemisphere: inferior parietal (p = 0.017), superior frontal (p = 0.030), superior parietal (p = 0.025), and caudal middle frontal (p = 0.041). GO/KEGG analyses revealed 161 targets linked to the neurotoxicity of PFOA and PFOS, primarily associated with fatty acid metabolism, GABA signaling, neurotransmitter receptor activity, ferroptosis, and PPAR pathways. Molecular docking verified key targets (PPARG, FASN, SCD, CD36, GOT2) underlying the toxicity mechanism. Exposure to PFOA and PFOS leads to reduced brain volume - neurotoxicity at the macroscopic level. At the molecular level, we identified PPARG, FASN, SCD, CD36, and GOT2 as key targets implicated in the pathology of brain damage induced by PFOA and PFOS.

Tunable triazole-based cholera toxin inhibitors: A QSAR-guided design and evaluation approach.

Cholera toxin B subunit (CTB) is a validated target for anticholera therapeutics, but current inhibitors often suffer from synthetic complexity and limited tunability. This study aimed to develop a compact, tunable triazole scaffold exhibiting low-micromolar potency combined with high synthetic tractability. We applied a unified, QSAR-driven, multiscale computational-experimental workflow integrating molecular docking, induced fit docking (IFD), molecular dynamics (MD), QM/MM calculations, and descriptor-based QSAR modeling to prioritize a focused library of 44 N-sulfonyl triazole inhibitors. QSAR modeling employed multiple regression techniques on molecular descriptors derived from E-Dragon software and quantum mechanical (QM/QM-MM) parameters. Models including Ordinary Linear Regression, LASSO, Ridge, Elastic Net, Random Forest, Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) were developed. The best predictive performance was achieved by SVM (test R2 = 0.79, RMSE = 0.49) and Random Forest (test R2 = 0.77, RMSE = 0.52) on E-Dragon descriptors, while multiple linear regression yielded outstanding fits on QM descriptors (test R2 up to 0.98, RMSE ∼0.14). Key molecular descriptors influencing activity included hydrogen bond donor count (ndonr), polarizability (AlogP), and topological indices (Jhetv). Guided by these QSAR models, lead candidates were synthesized via regioselective Cu(I)/Ru-catalyzed click chemistry, with experimental CTB-ELISA screening confirming compound 5d as the most potent inhibitor (IC₅₀ = 11.78 ± 1.2 μM). Computational studies consistently supported these findings, demonstrating favorable binding energetics, dynamic adaptability, and optimal electronic complementarity for lead compounds. This integrated strategy not only delivers a potent, synthetically accessible monovalent CTB inhibitor but also provides a rational, data-driven platform for rapid design and optimization for multivalent cholera toxin antagonists with improved efficacy.

Uralenol, Glycyrol, and Abyssinone II as potent inhibitors of fibroblast growth factor receptor 2 from anti-cancer plants: A deep learning and molecular dynamics approach.

Fibroblast Growth Factor Receptor 2 (FGFR2) plays a critical role in cellular proliferation and differentiation, and its dysregulation is associated with multiple cancers. This study integrates molecular docking, deep learning, pharmacokinetic profiling, and molecular dynamics (MD) simulations to identify potential FGFR2 inhibitors from a library of 1,350 phytochemicals derived from 51 anti-cancer medicinal plants that were traditionally used for anticancer purposes. Initial screening through AutoDock Vina revealed several top candidates with high binding affinities to FGFR2. The top three compounds, uralenol, glycyrol, and abyssinone II, underwent further evaluation via deep learning models, which predicted the potential efficacy of the pIC₅₀ (negative logarithm of the half-maximal inhibitory concentration) values. The ADME/T (absorption, distribution, metabolism, excretion, and toxicity) analysis confirmed favorable pharmacokinetic profiles and low toxicity risks. MD simulations validated the stability and compactness of protein-ligand complexes, with principal component analysis (PCA) and free energy landscape analyses confirming these interactions’ conformational stability and thermodynamic favorability. These findings suggest that uralenol, glycyrol, and abyssinone II are potential FGFR2 inhibitors and need further experimental validation for potential therapeutic use in cancer treatment.

Distal design improves thermostability and enzyme activity of type III tyrosinase from Nitrosospira.

Tyrosinase (TYR), a copper-containing oxidase pivotal in melanin synthesis, is widely distributed across animals, plants, and microorganisms. Despite its significant potential in biotechnology and industry, its practical application is hampered by limitations such as low catalytic efficiency and poor stability. To address these constraints, a highly active type III TYR from Nitrosospira (Sp2) was identified through systematic genomic mining in this study. Based on the structural features of type III TYR, two C-terminal truncated mutants were constructed. Among them, the truncated mutant TYR-Sp2-276 achieved an enzyme activity of 317 U/mg, which is a 15% increase compared to the wild-type. Subsequent protein engineering adopted a distal design strategy, which rationally targets residues remote from the catalytic center coupled with computational simulations to construct a combinatorial mutant library. The combinatorial mutant TYR-Sp2-276-G73A/M106D/Q152A/M231P exhibited a 2.37-fold enhancement in enzymatic activity, reaching 654 U/mg. Its melting temperature (Tm) increased by 4.59 °C, while the kcat value showed a 2.55-fold improvement. Structural predictions from AlphaFold 3 and molecular docking indicate that changes in structural rigidity and microscopic interactions such as hydrogen bonding may be responsible for the enhancement of its catalytic activity and thermal stability. This work demonstrates that rational distal design is an effective strategy for optimizing enzyme properties, offering valuable insights for engineering industrially relevant microbial enzymes.

Synthesis and integrative multimodal evaluation of cholic acid-based hydrazone conjugates: in vitro, in silico, and in vivo studies.

Cholic acid-derived hydrazones represent a promising scaffold for multifunctional drug discovery. We synthesized and characterized two novel cholic acid-based hydrazone derivatives: DFBCH (2,4-difluorobenzylidene conjugate) and MEBCH (3-methoxybenzylidene conjugate) through various spectroscopic techniques, confirming the structural integrity of the synthesized compounds, followed by their comprehensive biological evaluation. In vitro screening showed a greater antibacterial zone of inhibition of MEBCH compound as compared to DFBCH against the tested strains. MEBCH demonstrated potent antioxidant activity (IC50 1.87 ± 0.20 mg/mL) relative to ascorbic acid (IC50 7.3 ± 1.40 mg/mL) by DPPH assay illustrating superior antioxidant activity. Both the compounds exhibited greater % inhibition against Hela cell line as compared to doxorubicin, suggesting greater inhibitory activity. MEBCH was the superior inhibitor of acetylcholinesterase (IC50 1.67 ± 0.61 µM), surpassing the standard donepezil (IC50 3.13 ± 0.1 µM), and demonstrated effective tyrosinase inhibition (IC50 1.87 ± 0.88 µM), comparable to kojic acid (IC50 2.38 ± 0.75 µM). Both of these compounds demonstrated effective β-glucosidase inhibition with an IC50 ≈ 2.23 µM, comparable to standard miglustat (IC50 2.02 ± 1.05 µM). Molecular docking (Glide) revealed MEBCH producing a favorable docking score with AChE (- 6.711; E-model - 57.480), indicating a stable predicted binding pose relative to DFBCH. BioTransformer 3.0 predicted CYP 450-mediated pathways for both analogues; docking of predicted metabolites showed retained or improved affinity (MEBCH metabolite to AChE - 6.3 kcal/mol) and lower RMSD bounds (AChE; lb/ub ≈ 5.8/6.2 Å; β-glucosidase lb/ub ≈ 5.25/5.73 Å). DFT calculations revealed small HOMO-LUMO gaps and solvent-sensitive dipole moments consistent with moderate chemical reactivity and solvent stabilization. Single-dose oral pharmacokinetics (10 mg/kg) in rats revealed MEBCH with higher Cmax, AUC, longer t1/2, and greater oral bioavailability than DFBCH. Collectively, these data nominate MEBCH as a lead cholic-hydrazone for further preclinical development against enzyme targets relevant to neurological and oxidative-stress pathways.

Molecular insights into the bioactivity of H-thiazine compounds against breast cancer cells: a computational study.

Breast cancer, a leading cause of global mortality, necessitates novel therapies targeting key drivers like the epidermal growth factor receptor (EGFR). This computational study evaluates nine 4-phenyl-2H-[1,3]thiazino[3,2-a]benzimidazol-2-imine (H-thiazine) derivatives as potential EGFR inhibitors. Using molecular docking, ADMET profiling, molecular dynamics simulations, and binding energy calculations, we identified methyl- and bromine-substituted derivatives as probable candidates that outperform the reference drug Olmutinib in terms of binding affinity, pharmacokinetics, and stability. Although these compounds showed promising bioactivity, in silico toxicity screening indicated potential AMES mutagenicity and hERG-II inhibition, highlighting important safety liabilities. Overall, thiazine derivatives represent viable scaffolds for EGFR-targeted anti-cancer development; however, further optimization and experimental validation, including biochemical assays and genotoxicity testing, are required to confirm their therapeutic potential. Supplementary information The online version contains supplementary material available at 10.1007/s40203-025-00542-y.

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Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman

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