bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.01.30.635827

Comprehensive Molecular Docking on the AlphaFold-Predicted Protein Structure Proteome: Identifying Target Protein Candidates for Puberulic Acid, a Suspected Lethal Nephrotoxin

Hayama, T.·Sugawara, R.·Kamata, R.·Sekijima, M.·Takeda, K.
View original source ↗Export metadata ↓

Abstract

This study presents a novel in silico screening method for identifying potential protein-ligand interactions within the complete structural proteome, as predicted by AlphaFold2, using puberulic acid as a test case. The methodology utilizes molecular docking simulations to predict interactions based on structural compatibility and binding affinity estimations. This approach highlights proteins, such as sodium/myo-inositol cotransporters, which exhibit theoretical high affinity for puberulic acid, suggesting possible competitive inhibition or mimetic interactions that could disrupt osmoregulation. It is important to clarify that these simulations provide a hypothetical framework for understanding potential interactions. The high docking scores do not directly translate to biological effects but serve as a basis for further experimental validation. This cautious interpretation acknowledges the inherent limitations of predictive modeling while maintaining confidence in the robustness of the computational process. Moreover, the exploratory nature of this study is supported by the fact that binding affinity predictions are not influenced by protein amino acid length or the predictive accuracy (pLDDT scores) provided by AlphaFold2. This independence from structural prediction metrics suggests that the docking results offer a reliable exploration of potential interactions across a wide array of proteins, making this approach a valuable asset in the fields of drug discovery and toxicological research. The ability to comprehensively identify candidate proteins with potential affinity to chemical compounds enhances the predictive capacity of toxicological assessments and supports the strategic development of safer pharmaceutical agents.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hayama, T., Sugawara, R., Kamata, R., Sekijima, M., Takeda, K.. 2025-02-05. Comprehensive Molecular Docking on the AlphaFold-Predicted Protein Structure Proteome: Identifying Target Protein Candidates for Puberulic Acid, a Suspected Lethal Nephrotoxin. https://doi.org/10.1101/2025.01.30.635827

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↗