Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.04.23.538005

Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data for improving in silico herbal medicine experimentations

Park, M.·Baek, S.-J.·Park, S.-M.·Yi, J.-M.·Cha, S.
View original source ↗Export metadata ↓

Abstract

Natural products have successfully treated several diseases using a multi-component, multi-target mechanism. However, a precise mechanism of action has not been identified. Systems pharmacology methods have been used to overcome these challenges. However, there is a limitation as those similar mechanisms of similar components cannot be identified. In this study, comparisons of physicochemical descriptors, large-scale molecular docking analysis, and RNA-seq analysis were performed to compare the mechanisms of action of similar compounds and to confirm the changes observed when similar compounds were mixed and used. We propose an advanced method for in silico experiments in herbal medicine research based on the results. First, physicochemical descriptors were calculated based on the chemical structures of oleanolic acid (OA), hederagenin (HG), and gallic acid (GA). Similarities were confirmed by calculating the Euclidean, cosine, and Tanimoto distances between the descriptors. Next, the mechanisms of action of OA, HG, and GA were compared and confirmed through in silico-based systems pharmacology analysis using the BATMAN-TCM platform. The proteins interacting with the three compounds were verified through large-scale molecular docking analysis using the druggable proteome. Finally, a drug response transcriptome study was performed using OA, HG, GA, and a combination of OA and HG (COH) with similar structures.A comparison of physicochemical descriptors confirmed that OA and HG were very close. In particular, the two compounds showed a concordance rate of > 99% at cosine and Tanimoto distances. The systems pharmacology analysis results confirmed that OA and HG shared more than 86% of their predicted target proteins and differed only in GA. Systems pharmacology analysis revealed that OA and HG share the mechanisms of cardiac muscle contraction, oxidative phosphorylation, and non-alcoholic fatty liver disease. In a molecular docking analysis of the 50 major druggable proteins, OA and HG shared 38 proteins, while GA shared a few with proteins derived from the other two compounds. In addition, OA and HG were confirmed to act on gonadotropin-releasing hormone (GnRH) secretion, type 2 diabetes mellitus, cholinergic synapses, and calcium signaling pathways, and docking analysis visualization confirmed that the two components interact at the same site. RNA-seq analysis also showed that the differentially expressed genes and pathways derived from OA and HG were similar, and it was confirmed that COH had similar results to OA and HG. Our study has three novel findings. First, an advanced network pharmacology research method was suggested by partially presenting a solution to the difficulty in identifying multicomponent mechanisms. Second, a new natural product analysis method was proposed using large-scale molecular docking analysis. Finally, various biological data and analysis methods were used, such as in silico system pharmacology, docking analysis, and drug response RNA-seq. The results of this study are meaningful in that they suggest an analysis strategy that can improve existing systems pharmacology research analysis methods by showing that natural product-derived compounds with the same scaffold have the same mechanism.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Park, M., Baek, S.-J., Park, S.-M., Yi, J.-M., Cha, S.. 2023-04-25. Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data for improving in silico herbal medicine experimentations. https://doi.org/10.1101/2023.04.23.538005

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗