bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.02.28.640864

Targeting D-Ribose-Binding Proteins in Brucella melitensis: A Novel Frontier Against Antibiotic Resistance

Abstract

BackgroundAntibiotic resistance among pathogens common to human beings and animals, which include Brucella melitensis, has end up a significant worldwide health task. Traditional antibiotic treatments for brucellosis, along with lengthy-time period regimens of doxycycline and rifampicin, are going through increasing boundaries because of rising resistance, affected person adherence issues, and considerable side results. MethodsThis observe investigates the capacity of targeting the periplasmic D-ribose-binding protein (DBP), a key component of the bacterial ATP-binding cassette (ABC) delivery system, as a unique healing technique. Protein structural modeling was performed to use of superior computational tools together with AlphaFold, Swiss-Model, and Phyre2, followed by validation via Ramachandran plots and energy minimization techniques. Molecular docking analyses recognized D-Talopyranose as a promising ligand with a high binding affinity of -5.8 kcal/mol. Subsequent ADMET profiling found out favorable pharmacokinetic and toxicological results, assisting its potential as a drug candidate. Molecular dynamics simulations similarly evaluated the stability and dynamics of the protein-ligand interplay complex, confirming its suitability for therapeutic programs. ResultsAdvanced computational tools were used to analyze the proteins structure, and key modifications that influence its stability and function were identified. AlphaFold was recognized as the most reliable model for predicting the proteins 3D architecture, with its predictions being validated by metrics such as Ramachandran plots and error assessments. D-Talopyranose, a sugar molecule, was revealed as a top candidate through molecular docking due to its strong binding affinity with DBP. Promising drug-like properties, including balanced solubility, low toxicity risks, and minimal interactions with metabolic enzymes, were highlighted in further analysis, though environmental concerns around biodegradability were noted. The stability of the protein-ligand complex was tracked through simulations, showing consistent structural integrity despite minor flexibility in certain regions. While frequent dosing may be required due to the compounds rapid clearance from the body, its safety profile and synthetic accessibility are positioned as a viable starting point for drug development. Computational predictions are bridged with practical insights in this work, offering a roadmap for targeted therapies against antibiotic-resistant infections, while the need to balance efficacy with environmental safety in future optimizations is underscored. Our outcomes reveal that targeting DBP could offer a unique mechanism to combat antibiotic-resistant lines of Brucella melitensis by using disrupting essential metabolic pathways. ConclusionThis study affords a promising street for revolutionary brucellosis treatments by way of addressing the challenges posed using antibiotic resistance and paves the manner for experimental validation and optimization of the identified ligands. Such focused strategies may also notably improve ailment control and reduce the worldwide burden of brucellosis, mainly in areas where traditional antibiotics are losing their efficacy.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Moradi, O., Maghsoudi, A., Masoudi, A. A., Torshizi, R. V.. 2025-03-06. Targeting D-Ribose-Binding Proteins in Brucella melitensis: A Novel Frontier Against Antibiotic Resistance. https://doi.org/10.1101/2025.02.28.640864

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

KEEP EXPLORING

Related preprints

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↗

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↗

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↗