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Daily Signal May 12, 2026 · 9 min read

Issue #106: Protocol for analyzing potential targets of environmental pollutants in human diseases using network toxicology and molecular docking.

Protein Design Digest #106: scDock: streamlining drug discovery targeting cell-cell communication vi…

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Protocol for analyzing potential targets of environmental pollutants in human diseases using network toxicology and molecular docking.

Here, we present a computational workflow for identifying and analyzing the molecular mechanisms through which environmental pollutants may contribute to human diseases. We describe steps for integrating network toxicology and molecular docking to enable systematic prediction of pollutant-target-disease relationships and structure-based plausibility assessment of molecular interactions. This protocol provides a reproducible and scalable framework applicable to diverse environmental compounds and disease models.

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Also Worth Reading

Simulated construction of tilmicosin nucleic acid aptamers based on molecular docking and molecular dynamics techniques.

Traditional aptamer screening methods often prove ineffective for small molecule targets, primarily due to the inherent structural limitations of such compounds. Their simple architecture, limited functional groups, and restricted spatial complexity drastically reduce the probability of identifying nucleic acid sequences that bind with both high affinity and specificity. Consequently, the screening process becomes inefficient and labor-intensive, frequently failing to yield aptamers of satisfactory performance for practical applications. This represents a significant technical hurdle in expanding the use of aptamers in small-molecule detection and therapeutics. Based on this, this study innovatively proposes an aptamer design method based on single-nucleotide docking assembly, using the small molecule temicloxacin as an example. Through molecular dynamics simulations (50 ns, RMSD convergence threshold of 0.15 nm), the dynamic conformational characteristics of tilmicosin were analyzed. Subsequently, saturated docking was performed on four classes of mononucleotides, screening out 32 high-affinity mononucleotides (atomic contact distance ≤4 Å). Methods such as depth-first search algorithm (DFS) and weighted graph theory model were introduced to obtain the representative single nucleotides of eight classes of functional modules and linkage assembly, and finally 63 non-redundant candidate sequences were screened. Molecular docking results indicate that the optimal aptamer Til-14 exhibits high binding affinity with tilmicosin. with an affinity of 298.16 ± 95.588 nM measured via SYBR Green I fluorescence assay. Colloidal gold colorimetric analysis confirmed its high affinity (Kd = 279.323 ± 87.234 nM) and excellent specificity. This innovative method successfully addresses the key limitations of the traditional SELEX process in screening aptamers for small molecule targets. By enhancing the efficiency and specificity of selection, it not only facilitates the discovery of high-performance aptamers but also establishes a novel, generalizable framework for the construction of nucleic acid aptamers targeting other small molecules.

A multimodal approach integrating spectroscopy, deep learning guided molecular docking, and molecular dynamics simulation for predictive assessment of pioglitazone to albumin binding for formulation development.

Binding affinity is a critical parameter that can influence the state of the drug in vivo and help to define the formulation strategy. The current study implements a multimodal approach to analyse the binding affinity between human serum albumin (HSA) and pioglitazone. Ultraviolet (UV) absorbance and fluorescence spectrometry analyses were performed on different combinations of HSA and pioglitazone complexes, and the absorbance and fluorescence intensities were mapped to calculate the binding constant. DynamicBind, a distinct deep-learning artificial intelligence tool, was implemented to perform in silico docking studies using a non-conventional approach. Furthermore, molecular dynamics simulation was also performed to generate root mean square deviation, radius of gyration, and root mean square fluctuation values, followed by principal component analysis, probability distribution function, and free energy landscape analysis. The simulation output was analysed to interpret the binding affinity and associated conformation of the protein-active pharmaceutical ingredient (API) complex. The binding constant calculated through UV analysis was 1.1 × 10 4 M -1 . Fluorescence spectroscopic analysis derived a value of 1.7 × 10 5 M -1 . At the same time, DynamicBind predicted the cLDDT score for the top predicted model to be 0.634, and a binding affinity value of greater than 5, indicating a relatively moderate binding between pioglitazone and HSA. The results from molecular dynamics simulations further complemented our earlier observations, indicating non-covalent binding interactions and a stable protein-API complex, which is desirable for developing a formulation using HSA as a carrier polymer. This orthogonal approach also provided critical information on the fate of the API and possible considerations that needed to be made during the design of the formulation process, highlighting the need for similar approaches that could provide multifaceted advantages and help in optimising R&D costs and timelines.

[Exploring the therapeutic targets and molecular mechanisms of pimecrolimus in the treatment of oral lichen planus based on network pharmacology, machine learning, and molecular docking].

Objective: To systematically investigate the potential targets and therapeutic mechanisms of pimecrolimus in the treatment of oral lichen planus (OLP) using network pharmacology, machine learning, and molecular docking approaches. Methods: Pimecrolimus and OLP-related targets were retrieved from SwissTargetPrediction, Comparative Toxicogenomics Database, and GeneCards database. Intersecting genes underwent gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. A protein-protein interaction (PPI) network was constructed, with eXtreme Gradient Boosting algorithm and SHapley Additive exPlanations employed to evaluate target importance. Molecular docking validated binding affinity between pimecrolimus and core targets. Results: Twenty intersecting targets were identified, primarily enriched in inflammatory pathways, including phosphatidylinositol 3-kinase (PI3K)-protein kinase B (Akt) signaling pathway, tumor necrosis factor (TNF) signaling pathway, and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway. PPI and machine learning identified key targets including matrix metalloproteinase 2 (MMP2), mechanistic target of rapamycin (MTOR), and erb-b2 receptor tyrosine kinase 2 (ERBB2). The expression levels of MMP2 and MTOR in erosive OLP (6.67 and 3.54, respectively) were significantly higher than those in non-erosive OLP (4.39 and 3.27, respectively) ( P P =0.012). In contrast, the expression level of ERBB2 in non-erosive OLP (5.55) was significantly higher than that in erosive OLP (4.86) ( P =0.001). Molecular docking demonstrated that pimecrolimus had good binding activity with all the above targets, with binding energies all lower than -5 kcal/mol. Conclusions: This study systematically demonstrates that pimecrolimus may interfere with OLP progression by synergistically regulating key targets such as MMP2 (acting on erosive OLP), MTOR (acting on erosive OLP), and ERBB2 (acting on non-erosive OLP), and inhibiting the activity of inflammatory pathways including PI3K-Akt, TNF, and NF-κB.

Research & AI Updates

From the Industry

Quick Reads

Protocol for analyzing potential targets of environmental pollutants in human diseases using network toxicology and molecular docking.

Here, we present a computational workflow for identifying and analyzing the molecular mechanisms through which environmental pollutants may contribute to human diseases. Read more →

MT-ConBiFormer-GPT: multi-target molecular generation for low-data drug discovery via a contrastive BiFormer-GPT architecture and curriculum learning with cross-domain generalization.

Multi-target compounds, or polypharmacological agents, hold significant potential for complex diseases like cancer, where single-target therapies are often insufficient. Read more →

Role of METTL3 Protein in Asthma: Insights from Transcriptomic Profiling and Molecular Docking Analysis.

Asthma is a chronic inflammatory disease characterized byimmune dysregulation. Read more →

Newly Synthesized Dihydroxyphenyl-Nitroaryl Schiff Base Immobilized on Silica for Green UA-DMSPE of Toxic Metals in Marine Samples: Experimental Performance, DFT Insights, Molecular Docking, and In Silico Toxicological Evaluation.

Heavy metal contamination of aquatic and marine environments continues to pose serious risks to ecosystems and human health, particularly through bioaccumulation in seafood matrices. Read more →

Development and Validation of the HUGO-SWAP Workflow for Single-Docking Robotic Colorectal Surgery.

BackgroundIndependent-arm robotic systems offer flexible instrument assignment, but standardized colorectal workflows are limited. Read more →

Folding Thermodynamics and Kinetics of the N-Terminal Domain of the Circadian Clock-Regulated Histidine Kinase SasA.

Despite groundbreaking advancements in protein structure prediction, particularly with AlphaFold2/3 and RoseTTAFold, the protein folding problem remains elusive. Read more →

CF2H: a cell-free two-hybrid platform for rapid protein binder screening.

Protein binders that detect, activate, inhibit, or otherwise modulate their targets are pivotal for biomedical applications. Read more →

Integrating network toxicology and molecular dynamics simulations to unveil the pathogenic mechanism of benzyl butyl phthalate in atopic dermatitis.

This research investigates the molecular mechanisms of benzyl butyl phthalate (BBP) in atopic dermatitis (AD) using network toxicology and molecular dynamics simulation. Read more →

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