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Daily Signal June 04, 2026 · 9 min read

Issue #123: Identification and Characterization of New Therapeutic Candidates for Naegleria fowleri- brain-eating Amoeba: A Multi-target Approach Based on 3D-QSAR, Molecular Docking, MM-GBSA, ADMET and Molecular Dynamics.

Protein Design Digest #123: Computational Assessment of Phytochemical Inhibitors of Cytochrome P450 …

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Identification and Characterization of New Therapeutic Candidates for Naegleria fowleri- brain-eating Amoeba: A Multi-target Approach Based on 3D-QSAR, Molecular Docking, MM-GBSA, ADMET and Molecular Dynamics.

Background Naegleria fowleri is the most destructive and common brain-eating amoeba because it badly affects the human Central Nervous System (CNS), when amoeba in water goes up into the nose. N. fowleri also leads to PAM (primary amoebic meningoencephalitis) in humans. The lack of effective therapies for the treatment of PAM is a great drawback. The synthesis of new drug candidates for the treatment of this disease is time consuming and costly process. Aims & objectives Therefore, to reduce time and cost, already FDA-approved brain cancer drugs can be repurposed to get an effective drug for the management of Naegleria fowleri because compounds used to treat brain cancer also have antiviral activities. Methods For this purpose, an appropriate three-dimensional (3D-QSAR) model was created using the IC50 values of anti-brain cancer drugs. The QSAR (quantitative structure activity relationship) can provide suitable input for determining the structure of brain cancer drugs against Naegleria fowleri. The model validation was used as statistical parameters like r2 and q2. Results An appropriate 3D-QSAR was carried out with q2 and r2 calculated values like 0.9935 and 0.7249. The model QSAR also showed the predicted activities of various currently available and new drugs for brain cancer. The study was followed by molecular docking and MD simulation. Discussion The results of docking study revealed that the protein 6W7G shows a high binding affinity with Harmol, the most active compound. MD simulation of 6G6R protein showed an RMSD value of about 0.35 Å. Conclusion The present study indicated that the drug harmol could be an effective treatment for PAM in consideration of the 3D-QSAR, molecular docking, MD and ADME and toxicity analysis. However, further in vivo and in vitro studies are in process for the approval of this drug for the management of Naegleria fowleri.

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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.

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Identification and Characterization of New Therapeutic Candidates for Naegleria fowleri- brain-eating Amoeba: A Multi-target Approach Based on 3D-QSAR, Molecular Docking, MM-GBSA, ADMET and Molecular Dynamics.

Background Naegleria fowleri is the most destructive and common brain-eating amoeba because it badly affects the human Central Nervous System (CNS), when amoeba in water goes up into the nose. Read more →

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