Recep Adiyaman
bioinformatics

Issue #40: Decrypting potential mechanisms linking ochratoxin A to hepatocellular carcinoma: an integrated approach combining toxicology, machine learning, molecular docking, and molecular dynamics simulation.

February 04, 2026 Daily Intelligence
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Decrypting potential mechanisms linking ochratoxin A to hepatocellular carcinoma: an integrated approach combining toxicology, machine learning, molecular docking, and molecular dynamics simulation.

🧬 Abstract

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.

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

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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.

Exploring the Mechanism of Oral Cancer With Shikonin Based on the Network Pharmacology and Molecular Docking Technology.

To explore the underlying mechanisms of shikonin in treating oral cancer using network pharmacology and molecular docking methods. Targets of shikonin were obtained from the TCMSP, BATMAN, ChEMBL, PharmMapper and HERB databases. Targets of oral cancer were gathered from the OMIM, STITCH, GeneCards and Drugbank databases. The intersection targets of shikonin and oral cancer were obtained for subsequent analysis. The intersecting targets of shikonin and oral cancer were entered into the DAVID database and used its functions to perform Gene Ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) enrichment analysis on the intersection targets to obtain the relevant pathways and biological functions of shikonin in the treatment of oral cancer. The protein-protein interaction (PPI) network of shikonin and oral cancer targets was constructed in STRING platform. Subsequently, using Cytoscape 3.8.0 to obtain the key targets of shikonin and oral cancer. Finally, molecular docking and molecular dynamics simulations were used to evaluate the strength of binding between shikonin and key targets, as well as the hydrogen bonds involved. In total, 481 targets were screened for shikonin, and 10,058 targets were identified for oral cancer. By GO and KEGG analysis, the targets of shikonin and oral cancer may be involved in the mediation of apoptosis, inflammation and immune response. And the associated signalling pathways that targets may be involved in the treatment of oral cancer, including the FoxO signalling pathway, HIF-1 signalling pathway, TNF signalling pathway, and Th17 cell differentiation, etc. Cytoscape software screened the key genes including AKT1, MAPK1, CXCR4, CXCL8, CCL3, CCL4, CCL5, CYBB, BCL2, NOX1, HIF-1, TP53. The results of molecular docking and molecular dynamics simulations showed that shikonin exhibits good binding interactions with CCL3, AKT1 and NOX1. Mulitple molecular mechanisms involved in oral cancer management with shikonin have been elucidated providing a glimpse og the underlying therapeutic targets for the disease..

Vapour-Phase-Metalation Nanosurgery by ALD: A Tool for Single Molecular Replacement on Enterobactin for Drug Discovery and Nanomaterial Design

Abstract For the first time, the innovative vapour-phase metalation (VPM) process has been employed to functionalize soft materials such as Ferric Enterobactin (FeH x EB) and Enterobactin (H 6 EB) by linking ZnOH fragments, solely pulsing diethylzinc (DEZ). The samples were characterised through a suite of advanced techniques, including 1H and 13C 2D-HSQC nuclear magnetic resonance (NMR), infrared (IR) spectroscopy, UV-Vis spectroscopy, and MALDI-TOF mass spectrometry (MS). Groundbreaking density functional theory calculations successfully correlated the IR and NMR data, shedding light on the profound influence of the ZnOH fragments on the architectures of FeH 3 EB and H 6 EB. Moreover, insightful molecular dynamics and molecular docking studies were conducted to explore the affinity energy between the ZnOH-modified and non-modified siderophore and the ferric protein enterobactin receptor (FepA). Spectroscopic analyses not only confirmed the successful Zn-modification of FeH 3 EB and H 6 EB but also underscored the potential of the VPM process for attaching metals to soft materials. The docking results revealed a compelling interaction between the ZnOH-modified siderophore and FepA, boasting an affinity equal to or even surpassing that of their unmodified counterparts. This work positions VPM as a cornerstone technology for atomic-level molecular engineering, with transformative implications across healthcare, materials science, and environmental sustainability.

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Spectroscopic, Quantum Chemical, Docking, and Simulation Studies of a Benzothiazole-Based Derivative With Anti-Breast Cancer Potential.

This work presents an integrated spectroscopic and theoretical investigation of 2-(benzothiazolylthio)acetic acid (BTA). Experimental characterization was performed using FT-IR, FT-Raman, XRD, and UV-Vis techniques, while quantum chemical studies were carried out at the DFT/B3LYP/6-311++G(d,p) level. The simulated XRD pattern and calculated vibrational frequencies agreed well with the experimental results, validating the molecular structure. Electronic features were explored using MEP mapping and Mulliken population analysis, which revealed charge distributions and reactive regions. UV-Vis absorption bands were examined experimentally and reproduced using TD-DFT. NBO analysis identified intramolecular interactions supporting the molecule’s bioactive potential. Hirshfeld surface analysis and fingerprint plots provided further insight into intra- and intermolecular contacts, while topological descriptors, including ELF, LOL, and RDG clarified bonding characteristics. Molecular docking studies against breast cancer-related proteins revealed binding affinities ranging from -6.9 to -6.6 kcal/mol. Molecular dynamics simulations demonstrated stable ligand-protein complexes, and ADMET predictions indicated favorable pharmacokinetic and toxicity profiles. The cytotoxicity of BTA was evaluated using the MCF-7 breast cancer cell line, yielding an IC50 value of 14.15 µg/mL, which indicates favorable anticancer activity. Taken together, the combined spectroscopic, computational, docking, dynamics, and pharmacological findings highlight BTA as a promising anti-breast cancer candidate, showing comparable potential to the standard drug anastrozole.

Phenolic Profiling of Laurus nobilis by LC-ESI-MS/MS: In Vitro Bioactivities and In Silico Enzyme Docking.

This study evaluated the branch, leaf, and fruit extracts of Laurus nobilis collected from Artvin in terms of phenolic composition, antioxidant, antimicrobial, and antidiabetic activities and additionally investigated the binding of selected phenolics to α-amylase/α-glucosidase targets through molecular docking. Using LC-ESI-MS/MS analysis, 31 phenolic compounds were quantitatively identified, with epicatechin, luteolin-7-O-glucoside, and rutin predominating particularly in the branches and leaves. Antioxidant capacity was higher in branches and leaves (total phenolic content [TPC]/FRAP/DPPH IC50: branches and leaves, respectively, 95.27-60.84 mg gallic acid equivalent [GAE]/g, 206.16-162.05 mg Trolox equivalent [TE]/g, and 46.95-113.49 µg/mL). Antimicrobial activity was observed within a minimum inhibitory concentration (MIC) range of 1.25-10 mg/mL; leaves exhibited the strongest effect against Bacillus cereus (1.25 mg/mL), whereas fruits were most effective against Candida albicans (1.25 mg/mL). In antidiabetic evaluation, fruits yielded the lowest IC50 values against α-amylase and α-glucosidase (4.31 and 8.97 mg/mL, respectively), suggesting a “composition-over-quantity” effect despite low total phenolic content. Docking results supported high binding affinities of rutin (α-glucosidase -9.3 kcal/mol; α-amylase -8.1 kcal/mol) and luteolin (-8.6 kcal/mol for both enzymes). Overall, although branches and leaves exhibited strong antioxidant profiles, fruits showed more pronounced inhibitory effects on digestive enzymes; therefore, fractionation studies and in vivo/kinetic validations are recommended.

Benzo[f]chromene Derivatives as Cytotoxic Agents: Design, Synthesis, Molecular Docking, and ADMET

Polyethylene Terephthalate Microplastics Exposure Enhances the Risk of Ulcerative Colitis: Insights From Multi-Omics Integration, Machine Learning, and Molecular Docking Reveal Intestinal Toxicity Mechanisms: A Commentary.

Synthesis, Biological Evaluation, and Molecular Docking of Benzimidazole-hydrazone Derivatives as Potential Anticancer Agents Targeting Tyrosine Kinases: Induces G0-G1 Phase Arrest in HepG2 Cells.

Cancer continues to be a leading cause of morbidity and mortality worldwide, with significant variability in incidence and treatment outcomes. This study investigates novel benzimidazole-hydrazone derivatives aimed at inhibiting receptor tyrosine kinases essential for cancer progression. We synthesized a series of compounds, verifying their structures using IR, NMR, and mass spectral analyses. Biological evaluations assessed cytotoxicity against cancer cell lines (HCT-116, MCF-7, HepG2) alongside a normal cell line (WI-38). Several compounds, particularly 6f and 6h-j, demonstrated potent cytotoxicity, with IC50 values as low as 4.82-10.23 µM against MCF-7 cells, surpassing reference drugs like sorafenib and doxorubicin. Compound 6i exhibited the highest activity against kinases such as EGFR, Her2, and VEGFR2, while cell cycle analysis showed G0-G1 phase arrest in HepG2 cells. Apoptotic mechanisms were activated, with significant increases in caspase-3 and BAX levels and decrease in Bcl2. In silico studies indicated strong binding interactions of the compounds with kinases, and pharmacokinetic assessments revealed favorable properties. These findings underscore the potential of benzimidazole-hydrazone derivatives as effective cancer therapeutics, meriting further investigation into their mechanisms and clinical implications.

Dynamic Binder Exchange Improves Protein Labeling Efficiency in DNA-PAINT up to 15-Fold.

Dynamic Binder Exchange (DyBE) enhances DNA-PAINT (Point Accumulation for Imaging in Nanoscale Topography) super-resolution microscopy by exploiting transient, reversible binder-target interactions. DyBE uses DNA-conjugated binders such as nanobodies as both targeting and docking moieties, integrating their characteristic higher off-rates with DNA-PAINT blinking to efficiently sample target sites. This dual-kinetic scheme increases labeling efficiency up to 15-fold, enabling sensitive detection of targets previously inaccessible due to limitations of high-off-rate binders. Using DyBE, the study reveals pre-existing HER2 homodimers and ligand-induced EGFR-HER2 heterodimers at single-protein resolution with high fidelity. DyBE expands the usable binder repertoire, advancing spatial proteomics and enabling mechanistic drug studies of receptor organization and signaling.

Computational analysis of CCN1 as a druggable target predicts interactions with bioactive compounds.

In silico druggability assessment helps shorten early drug discovery by identifying small molecules worth experimental testing as potential protein modulators. CCN1 is a multifunctional protein involved in various physiological processes and its dysregulation has been implicated in pathological conditions such as aging, fibrosis, inflammation, and cancer. The diverse, and sometimes contradictory, functions of CCN1 make it an important candidate for druggability assessment. In this study, we evaluated its druggability by predicting its 3D structure using AlphaFold 3, identifying binding pockets with Fpocket, and assessing ligand affinity with SwissDock. Our integrative in silico workflow identified multiple high-confidence druggable pockets within the CCN1 protein, with the top-scoring site located between the thrombospondin type 1 (TSP-1) and C-terminal cystine knot (CTCK) domains. Molecular docking predicted strong interactions with several clinically relevant compounds, including antioxidants and senolytics, with Metformin showing the highest affinity (SwissDock AC score: -200.26). Importantly, these ligand-binding interactions remained stable even after deletion of amino acids forming the predicted pocket and across naturally occurring CCN1 variants arising from SNPs, indicating that CCN1 is a genetically robust drug target. This study is the first to computationally demonstrate the druggability of CCN1 and to identify candidate small molecules with the potential to modulate its activity in aging- and disease-related contexts. Our findings provide both mechanistic insight and a scalable workflow for rapid screening of CCN1-targeted therapeutics.

Exploring natural products as Bcl-2 inhibitors for acute myeloid leukemia therapy using In vitro, STD-NMR spectroscopy, and In silico approaches.

Acute myeloid leukemia (AML) is the predominant form of acute leukemia, affecting elderly individuals, typically diagnosed at an average age of 68 years. AML cells rely on the Bcl-2 protein for their survival. Overexpression of Bcl-2 protein in various cancer types renders it as a potential candidate for targeted therapies. The present study aimed to identify natural compounds as Bcl-2 inhibitors using in vitro, biophysical, and integrated computational approaches. The MTT assay was performed for cell proliferation, followed by apoptosis and gene expression analysis. STD-NMR spectroscopy, molecular docking and molecular dynamics simulations were performed for protein-ligand interactions. In the in vitro anti-proliferative assay, three natural compounds, gossypol (1), camptothecin (2), and jaceidin (3), were found active against the HL-60 cell line with IC50 concentrations of 1.634 ± 0.072, 0.137 ± 0.029, and 13.492 ± 2.292 μM, respectively. These compounds triggered apoptosis and decreased cellular viability in a dose-dependent manner. The gene expression analysis of Bax, Bcl-2, and Caspase 3 in HL-60 cells revealed that these compounds induce apoptosis by regulating essential apoptotic genes. Among the three identified potential hits, only gossypol (1) was buffer soluble and subjected to STD-NMR experiment to evaluate its protein-ligand interactions. Furthermore, molecular docking, binding free energies and MD simulation analyses demonstrated stable interactions of these compounds with the Bcl-2 protein. The three natural products showed potent to significant activity, effectively inducing apoptosis in the HL-60 cell line. Hence, this study identifies three potential lead candidates for drug discovery against Bcl-2-related cancers after further mechanistic and pre-clinical studies.

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

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