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Daily Signal May 13, 2026 · 10 min read

Issue #107: Simulated construction of tilmicosin nucleic acid aptamers based on molecular docking and molecular dynamics techniques.

Protein Design Digest #107: Simulated construction of tilmicosin nucleic acid aptamers based on mole…

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Signal of the Day

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.

Why this matters: Expands the searchable sequence space for novel folds and high-affinity binders.

Also Worth Reading

SENET-AOP: A computational framework model for prioritizing antioxidant protein targets in drug discovery.

Oxidative stress is closely associated with aging and a wide range of major diseases, including cancers, neurodegenerative disorders, and inflammatory conditions. Antioxidant proteins therefore constitute important functional biomolecules and potential therapeutic targets in drug discovery. Accurate and efficient identification of antioxidant proteins is thus of relevance for biomedical research and early-stage target exploration. To address the limitations of existing computational approaches, such as restricted dataset size, limited generalization ability, and simplistic feature representations, we constructed a high-quality benchmark dataset comprising 1144 antioxidant proteins and 2959 non-antioxidant proteins. On this basis, we propose SENET-AOP, an attention-based deep learning framework for antioxidant protein classification and target prioritization. The model integrates complementary semantic representations derived from two protein language models, ESM-2 and ProtT5, and adopts a dual-branch CNN-SENet architecture to capture local sequence patterns and global physicochemical properties, while adaptively recalibrating channel-wise feature importance. Experimental results demonstrate that SENET-AOP achieves an accuracy of 0.9360, a Matthews correlation coefficient of 0.8376, and an AUROC of 0.9721 under five-fold cross-validation. On the independent test set, the model attains an accuracy of 0.9367 and an AUROC of 0.9795, consistently outperforming other methods. Moreover, the proposed framework exhibits favorable interpretability. Collectively, SENET-AOP provides an effective and reliable tool for high-throughput identification and prioritization of antioxidant protein targets, supporting oxidative stress-related diseases research and medicinal chemistry-oriented drug discovery workflows. For user convenience, a freely accessible web server has been developed at: http://www.senetaop.com.cn/.

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 Molecular Mechanism of Shaoyao Gancao Decoction for Trigeminal Neuralgia Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

Background: Shaoyao Gancao Decoction (SGD) has demonstrated a broad spectrum of analgesic effects and has found application in the management of trigeminal neuralgia (TN). Nonetheless, the underlying molecular mechanisms of this therapeutic intervention remain poorly understood. Objective : This study is designed to elucidate the molecular mechanism of SGD in treating TN by employing an integrated approach that combines network pharmacology, molecular docking, and molecular dynamics simulation. Methods: The active ingredients and associated targets within SGD were screened from the TCMSP database. TN-related targets were extracted from GeneCards, OMIM, CTD, and DisGeNET databases. Subsequently, we constructed a comprehensive TN-SGD-herbs-ingredients-targets network by employing Cytoscape software for visualization. A protein-protein interaction (PPI) network was constructed using the STRING database and further analyzed with Cytoscape, from which we identified pivotal hub genes using three distinct Cytoscape plugins. GO and KEGG enrichment analyses were carried out utilizing R software. Then, molecular docking was executed using AutoDock Vina, and docking results were visualized and augmented with molecular dynamics simulations utilizing BIOVIA Discovery Studio software. Finally, in vitro experiments verified the anti-inflammatory effect of SGD on LPS-treated BV2 cells. Results: A total of 103 active ingredients within SGD, 332 targets associated with TN, and 68 potential therapeutic targets were obtained. We constructed a TN-SGD-herbs-ingredients-targets network and obtained a PPI network of potential therapeutic targets. Then, we extracted seven hub genes from the potential therapeutic targets, including ESR1, JUN, TP53, STAT3, BCL2, AKT1, and ESR2. GO enrichment analyses indicated that SGD affected multiple biological processes and functions, such as responses to xenobiotic stimuli, membrane rafts, DNA binding, and transcription factor binding. KEGG pathway analyses revealed that lipid and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, and chemical carcinogenesis-receptor activation were mainly involved in the therapeutic effects of SGD on TN. Importantly, molecular docking analysis demonstrated substantial binding affinities between the top eight ranked active ingredients and the seven identified hub genes. Furthermore, molecular dynamics simulations validated the binding activity between shinpterocarpin and ESR2. Finally, SGD decreased the levels of TNF-α, IL-1β, and IL-6 and regulated protein expression of ESR1 and ESR2 on LPS-treated BV2 cells, indicating that SGD exerted anti-inflammatory effect on microglia. Conclusion: This study offers valuable insights into the active ingredients of SGD and elucidates their potential molecular mechanisms in the treatment of TN. The findings presented herein lay the groundwork for the development of anti-TN agents rooted in the constituents of SGD.

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Pipeline Tip

Employ HADDOCK for ambiguous restraints in protein-protein docking.

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