Skip to main content
recep.adiyaman
Vision About Experience Education Skills Publications Tools Newsletter Updates Podcasts Blog Contact CV Subscribe
All Issues
Older Newer
Daily Signal June 05, 2026 · 9 min read

Issue #124: Unveiling the Potential Therapeutic Efficacy of Gold Ceria Nanohybrid as an Anticancer Agent: A Special Insight to Molecular Docking Study.

Protein Design Digest #124: Computational Assessment of Phytochemical Inhibitors of Cytochrome P450 …

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

Unveiling the Potential Therapeutic Efficacy of Gold Ceria Nanohybrid as an Anticancer Agent: A Special Insight to Molecular Docking Study.

Over the last decades, nanoformulations embody a versatile and highly promising approach in cancer therapeutics by enabling targeted, controlled, and multimodal treatment strategies. To copiously comprehend their clinical potential, further research, standardization, and toxicological assessment are essential. A green synthetic approach was employed to develop gold-core cerium oxide-shell (Au/CeO2) nanohybrid with enhanced anticancer properties. Comprehensive physicochemical characterization confirmed successful nanoparticle formation. Improved cellular uptake of Au/CeO2 nanoparticles in MDA-MB-231 triple-negative breast cancer (TNBC) cells was attributed to enhanced permeability and retention (EPR) effects and gold-mediated synergistic internalization. JC-1 staining indicated significant mitochondrial membrane depolarization following treatment, suggesting induction of apoptosis via mitochondrial dysfunction. The micrographs were also validated; scanning electron microscopy and phalloidin staining revealed disrupted cytoskeletal architecture, correlating with reduced cellular migration. Western blot analysis showed suppression of nuclear NFκB-p65 expression, upregulation of PTEN, and downregulation of pAKT (Ser473), implicating nanoparticle-mediated modulation of the NFκB/PTEN/pAKT signaling axis. Henceforth, molecular docking studies further supported these findings, revealing favorable binding of Au/CeO2 nanocargo and 5-fluorouracil to cancer-associated targets such as p53, NFκB, and kinase proteins. Ramachandran plot analyses validated the structural integrity of the selected target proteins. Finally, in vivo cytotoxicity profiling of the Au/CeO2 nanohybrid under control and LPS-treated conditions not only validates the non-toxic nature but also intimates its anti-inflammatory potency indicating a distinctive avenue for further investigations. Concomitantly, these findings highlight the potential of Au-CeO2 nanoparticles as a multifunctional nanoplatform for targeted cancer therapy through synergistic cellular uptake, apoptotic induction, and signaling pathway modulation in aggressive TNBC models.

Why this matters:

Also Worth Reading

Tocotrienol as a multi-target inhibitor of ICAM-1, VCAM-1, and E-selectin: Comparison using AutoDock and GNINA docking with molecular dynamics simulation.

Atherosclerosis is a chronic inflammatory disease characterized by endothelial dysfunction and leukocyte adhesion, mediated by cell adhesion molecules such as E-selectin, intercellular adhesion molecule-1 (ICAM-1), and vascular cell adhesion molecule-1 (VCAM-1). Tocotrienols, a subgroup of vitamin E, exhibit potent antioxidant and anti-inflammatory properties, suggesting their potential role in attenuating atherosclerosis. This study comparatively evaluated the binding affinities and molecular interaction profiles of α-, β-, γ-, and δ tocotrienol isomers towards E-selectin, ICAM-1, and VCAM-1 using molecular docking approaches, followed by molecular dynamic simulation to assess the stability of the top-ranked protein-ligand complexes. The docking experiment was conducted using MolModa, an automated molecular docking platform based on AutoDock Vina and convolutional neuronal network (CNN)-based AI-assisted GNINA. Overall, the conventional molecular docking tool AutoDock Vina results showed that all tocotrienol isomers exhibited the strongest average binding affinities to VCAM-1. Among the isomers, α-tocotrienol displayed the highest binding affinity towards E-selectin (-6.69 ± 0.00 kcal/mol) and ICAM-1 (-6.79 ± 0.00 kcal/mol), whereas β-tocotrienol exhibited the strongest affinity toward VCAM-1 (-7.59 ± 0.00 kcal/mol) in the molecular docking analysis using conventional molecular docking tool AutoDock Vina. In contrast, the AI-assisted molecular docking tool GNINA leveraging deep learning, demonstrated a more accurate and consistent affinity profile by consistently identified β-tocotrienol as the most favorable binder toward E-selectin (-6.91 ± 0.01 kcal/mol) and ICAM-1 (-7.08 ± 0.90 kcal/mol), characterized by hydrogen bonding, hydrophobic interactions, and extensive van der Waals forces, that are crucial for the lipid-soluble ligand. The AI-assisted molecular docking tool GNINA docking for VCAM-1 was not generated due to structural limitations of the receptor model. Molecular dynamics (MD) simulations over 200 ns demonstrate a significant stabilizing interaction with GLU87, whereas the hydrogen bonding at ASP178 was found to be intermittent and contributory throughout the trajectory. This study provides the first comprehensive computational evidence differentiating the multi-target potency of tocotrienol isomers in targeting inflammatory and vascular-related pathways. Further experimental validation is warranted to confirm these in silico predictions and explore their biological significance.

Decoding the Grammar of Protein-Protein Interaction Interfaces with Multimodal Representations

Protein-protein interactions (PPI) govern essential cellular processes, making the computational identification of interacting sites a central challenge in structural biology, with important implications for protein engineering and the development of targeted therapeutics. Existing prediction algorithms include sequence-based methods, which lack structural information, or structure-based approaches, which often struggle to effectively integrate evolutionary context. Here, we present ESM3-PPISites, a supervised model for residue-level classification of PPI interfaces, leveraging the multimodal representations of the ESM3 Protein Language Model. To ensure a bias-free evaluation, we adopt a stringent redundancy filtering protocol, systematically eliminating latent homology between the training data and a curated benchmark set in both sequence and structural space. Our findings demonstrate that while ESM3 largest proprietary version yields the highest predictive power, targeted fine-tuning of its small open-weight counterpart significantly narrows the performance gap. Requiring only primary sequence data at inference, ESM3-PPISites achieves unprecedented accuracy, vastly outperforming current approaches. Crucially, we demonstrate the practical impact of these predictions by integrating them as spatial restraints within the HADDOCK3 docking platform. When evaluated on an independent subset of 12 complexes from the Docking Benchmark v5, our prediction-guided pipeline strongly enhances the identification of near-native binding poses over ab initio blind docking, while reducing computational runtime by an order of magnitude. This framework establishes a scalable paradigm for high-throughput structural interactomics.

Elucidating the molecular mechanisms linking bisphenol A to breast cancer: an integrated study of bioinformatics, machine learning, and molecular docking.

Bisphenol A (BPA) is a prevalent environmental endocrine disruptor linked to breast cancer. However, the precise molecular mechanisms and core therapeutic targets remain to be fully elucidated. This study employed an integrative multi-omics approach to explore the potential mechanism of BPA-associated breast cancer. We integrated multiple transcriptomic datasets from the Gene Expression Omnibus (GEO) database and identified intersection targets between BPA and breast cancer through differential expression analysis, WGCNA, and multi-source database predictions (ChEMBL, PharmMapper, SEA). Pathway enrichment analyses revealed that these targets are predominantly involved in key signaling cascades, such as MAPK and PI3K/Akt. To identify robust biomarkers, we constructed a diagnostic model using machine learning algorithms and prioritized core genes via SHapley Additive exPlanations (SHAP) value analysis. Five core genes (EGFR, PPARG, MMP12, ADRB2, and KIF11) were identified, all of which demonstrated high diagnostic accuracy (AUC > 0.7) in validation sets. Subsequent molecular docking and molecular dynamics simulations predicted that BPA exhibits strong binding affinity (binding energy < - 5 kcal/mol) to these core proteins. Collectively, our findings suggest that BPA may promote breast cancer progression by modulating these core targets and interfering with the MAPK/PI3K/Akt pathways. This study provides a data-driven theoretical basis for elucidating the molecular link between BPA and breast cancer, proposing potential biomarkers that warrant further investigation for clinical diagnosis and intervention.

Research & AI Updates

From the Industry

Quick Reads

Leveraging the antibacterial and antibiofilm activities of Cymbopogon flexuosus essential oil against multidrug-resistant bacteria recovered from avian colibacillosis and bovine mastitis: in vitro and molecular docking insights.

This study investigated the chemical composition and antibacterial and antibiofilm activities of Cymbopogon flexuosus essential oil (CFEO) against multidrug-resistant (MDR) bacteria within a One Health framework. Read more →

Acetylcholinesterase inhibitory activity of phthalimide derivatives as anti-alzheimer agents: QSAR, ARKA, Hybrid ARKA-RASAR, virtual screening, molecular docking and ADMET studies.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder and a leading cause of dementia worldwide, characterized by progressive cognitive and memory decline. Read more →

Unveiling the Potential Therapeutic Efficacy of Gold Ceria Nanohybrid as an Anticancer Agent: A Special Insight to Molecular Docking Study.

Over the last decades, nanoformulations embody a versatile and highly promising approach in cancer therapeutics by enabling targeted, controlled, and multimodal treatment strategies. Read more →

Network Pharmacology and Molecular Docking-Based Investigation of Empagliflozin’s Therapeutic Potential in Chronic Kidney Disease.

Chronic kidney disease (CKD) is a progressive global health challenge. Read more →

Extending structural surfaceomics to identify aberrant conformations of tumor surface proteins as potential immunotherapy targets.

The complement of tumor cell surface proteins, or “surfaceome”, is a rich source of potential immunotherapy targets. Read more →

First investigation on the chemical composition, antioxidant, antimicrobial and molecular docking studies of the essential oil obtained from Peganum harmala L. seeds at different stages of maturation.

This is the first report on the chemical composition, antimicrobial and antioxidant activities of the essential oil of Peganum harmala L. Read more →

The Molecular Targets and Mechanisms of Mume Fructus in the Treatment of Radiation Induced Oral Mucositis Based on Network Pharmacology and Molecular Docking

Abstract Purpose The network pharmacology analysis and molecular docking were performed to explore the molecular targets and the pharmacological mechanisms of Mume Fructus (MF) in the treatment of radiation induced oral mucositis (RIOM). Read more →

Design, synthesis, and biological evaluation of novel imidazole-morpholinone hybrids as broad-spectrum antimicrobial agents: molecular docking, DFT, and ADMET studies.

A novel series of five imidazole-morpholinone hybrid compounds (10a-e) was designed, synthesised, and evaluated as broad-spectrum antimicrobial agents. Read more →

Pipeline Tip

Always validate pLDDT scores before using AlphaFold models for docking.

Resources & Tools

The protein structure is the language of life; design is its poetry. — Recep Adiyaman

BS HF DK