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Daily Signal April 15, 2026 · 8 min read

Issue #89: Binding Affinity and Interaction Profiles of Erinacines and Erinacerins with iNOS and NF-κB Revealed by Molecular Dynamics Simulations.

Protein Design Digest #89: Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of L…

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Binding Affinity and Interaction Profiles of Erinacines and Erinacerins with iNOS and NF-κB Revealed by Molecular Dynamics Simulations.

Chronic neuroinflammation driven by microglial activation is a pathological hallmark of neurodegenerative diseases, and the NF-κB/iNOS signaling axis plays a central role in propagating this damage. NF-κB-mediated iNOS transcription generates excessive nitric oxide, causing oxidative neuronal injury. The medicinal mushroom Hericium erinaceus produces cyathane diterpenoid erinacines and isoindolinone erinacerins, both reported to attenuate neuroinflammation; however, the molecular basis of their interactions with iNOS and NF-κB remains poorly characterized. We screened 21 erinacerins and 18 erinacines against both targets using validated molecular docking, then subjected top-ranked candidates and negative controls to 100 ns molecular dynamics simulations, MM-PBSA binding free energy calculations (±SEM), per-residue energy decomposition, backbone RMSD, and ligand-protein minimum distance analyses, with quercetin as reference. The analysis revealed scaffold-dependent target selectivity: erinacerins exhibited preferential stability with iNOS (erinacerin L: RMSD 0.185 nm), whereas erinacines formed more stable complexes with NF-κB (erinacines G and J: RMSD < 0.36 nm). Minimum-distance monitoring confirmed that the elevated ligand RMSD in iNOS predominantly reflected surface relocation rather than dissociation. Erinacine S emerged as the most promising dual-target candidate (ΔGbind: -24.31 ± 0.16 and -14.24 ± 0.11 kcal/mol for iNOS and NF-κB, respectively), over twofold stronger than quercetin for iNOS. Negative controls revealed that docking-based ranking was target-dependent in its discriminative capacity, underscoring the need for MD-based refinement. These results identify erinacine S as a priority candidate for experimental validation.

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Also Worth Reading

Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.

De novo protein design has enabled the creation of proteins with diverse functionalities that are not found in nature. Despite recent advances, experimental success rates remain inconsistent and context-dependent, posing a bottleneck for broader applications of de novo design. To overcome this, structure and sequence prediction models have been applied to assess design quality prior to experimental testing to save time and resources. In this study, we examined the extent to which AlphaFold, Protein MPNN, and ESMFold can discriminate between experimentally successful and unsuccessful designs. We first curated a benchmark dataset of 614 experimentally characterized de novo designed monomers from 11 different design studies between 2012 and 2021. All predictive models demonstrated moderate ability to discriminate experimental successes (expressed, soluble, monomeric, and fold with the correct secondary structure) from failures. Still, many failed designs have better confidence metrics than successful designs, and confidence metrics were topology-dependent. Among all computational models evaluated, ESMFold average predicted local-distance difference test (pLDDT) yielded the best individual performance at distinguishing between successful and unsuccessful designs. A logistic regression model combining all confidence metrics provided only modest improvement over ESMFold pLDDT alone. Overall, these results show that these models can serve as an initial filtering strategy prior to experimental validation; however, their utility at accurately predicting experimentally successful designs remains limited without task-specific training.

Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors of HER2 L755S, T798I, and T798M based on a Large Database of Curcumin Derivatives.

Objective This study presents a methodology employing virtual screening to identify curcumin derivatives with selective affinity for the HER2 mutations L755S, T798I, and T798M. Methods Curcumin derivatives were retrieved from the ChEMBL database and filtered using KNIME. HER2 mutations were modeled in silico using MOE software with PDB ID 3RCD. Molecular docking and dynamics simulations were conducted to screen high-affinity compounds and evaluate binding interactions. Result From 505 curcumin derivatives, the RDKit module implemented in KNIME successfully filtered 317 compounds. Subsequent molecular docking against wild-type HER2 identified 100 curcumin derivatives with low docking scores, among which the top 20 compounds exhibited better binding affinities than Lapatinib. Further molecular docking screening against the three HER2 mutations identified five lead compounds with the lowest docking scores. Molecular docking and molecular dynamics simulation revealed critical binding interactions with residues essential for kinase domain stability. Chemical structural analysis revealed key modifications, such as geranyl and tripeptide modifications. CHEMBL3758656 and CHEMBL3827366, two curcumin derivatives, demonstrated consistent binding across HER2 mutations and a favorable ADMET profile. Conclusion This study successfully identified CHEMBL3758656 and CHEMBL3827366 as promising HER2 inhibitors through comprehensive virtual screening. Their high binding affinity against L755S, T798I, and T798M mutations and favorable ADME and toxicity properties underscore their potential as alternative therapeutics for HER2-positive breast cancer.

Exploration of potential neuroprotective agents from medicinal plants for the treatment of Alzheimer’s disease-approach through in silico ADMET, network pharmacology, docking, and dynamics studies.

Context A degenerative brain disorder that causes memory loss is Alzheimer’s disease (AD). Phytoconstituents represent a promising therapeutic strategy due to their diverse bioactivities and favourable safety profiles. This study aimed to identify potential neuroprotective phytoconstituents for AD by pharmacokinetic screening, network pharmacology, molecular docking and molecular dynamics simulation. Among 22 phytoconstituents analysed, the network revealed GSK3β, STAT3, MAOB, ESR1, and PTGS2 as key AD-associated targets. Docking results were supported by dynamic stability analysis. The combined computational results support rosmariquinone as a potential neuroprotective lead compound for AD treatment. Methods Pharmacokinetic and toxicity profiling of 22 phytoconstituents was performed using Swiss ADME and ProTox III software. Target prediction and construction of the phytoconstituent-disease target-gene interaction network were conducted using Swiss Target Prediction, Gene Card and Cytoscape. Pathway enrichment was evaluated via KEGG and GO analysis. Molecular docking of all shortlisted phytoconstituents against AD-related targets was carried out using AutoDock Vina, while 500 ns molecular dynamics simulations were performed using the Desmond module of the Schrödinger Suite to assess complex stability, RMSD, RMSF and hydrogen-bond fluctuation.

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Binding Affinity and Interaction Profiles of Erinacines and Erinacerins with iNOS and NF-κB Revealed by Molecular Dynamics Simulations.

Chronic neuroinflammation driven by microglial activation is a pathological hallmark of neurodegenerative diseases, and the NF-κB/iNOS signaling axis plays a central role in propagating this damage. Read more →

Integrative computational elucidation of molecular mechanisms and multi-target interactions in paraquat-induced pulmonary fibrosis.

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