Recep Adiyaman
Vision About Experience Education Skills Publications Tools Newsletter Updates Podcasts Blog Contact CV Subscribe
All Issues
Older Newer
Daily Signal January 31, 2026 · 9 min read

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

Protein Design Digest - 2026-01-31 - Efficacy of Ipflufenoquin against Strawberry Gray Mold: Insights from AlphaFold- Based Structural Modeling and Genome-Wide Transcriptomic Analysis.

Share X LinkedIn
Protein Design Daily

Building something in Protein Design?

I love collaborating on new challenges. Let's build together.

LinkedIn Email

Subscribe to Protein Design Digest

Daily curated signals from arXiv, PubMed, and BioRxiv.

Signal of the Day

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

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

Also Worth Reading

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.

Research & AI Updates

From the Industry

Quick Reads

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. Read more →

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. Read more →

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. Read more →

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. Read more →

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. Read more →

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. Read more →

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. Read more →

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). Read more →

Pipeline Tip

Use Snakemake for reproducible end-to-end protein design workflows.

Resources & Tools

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

BS HF DK