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

Targeting D-Ribose-Binding Proteins in Brucella melitensis: A Novel Frontier Against Antibiotic Resistance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Targeting D-Ribose-Binding Proteins in Brucella melitensis: A Novel Frontier Against Antibiotic Resistance Omid Moradi, Ali Maghsoudi, Ali Akbar Masoudi, Rasoul Vaez Torshizi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7618159/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Antibiotic 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. This 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 carried out the 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. 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. This study affords a promising street for revolutionary brucellosis treatments by way of addressing the challenges posed by means of 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. Biological sciences/Biochemistry Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Microbiology D-Ribose-binding periplasmic Molecular Docking Molecular Dynamics Drug Discovery D-Talopyranose Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Zoonoses are diseases that can be transmitted from animals to humans. They can be caused by various pathogens, including bacteria, viruses, parasites, and fungi. These diseases can spread through direct contact with animals, consumption of contaminated food/water, or through vectors like mosquitoes and parasites. Some common examples of zoonotic diseases are including Rabies, Lyme disease, and Avian Influenza and Brucellosis. Brucellosis is a specific type of zoonosis caused by bacteria of the genus Brucella. It primarily affects livestock such as cattle, goat, and sheep, but can also infects humans 1 . The Brucella bacteria cause this disease, which remains one of the most common zoonotic infections 2 . The World Organization for Animal Health (WOAH) views brucellosis as a significant concern due to its impacts on public health, economic losses, and disruptions to international trade 3 . This disease burdens livestock with substantial costs, as different Brucella species infect various animals: cows ( Brucella abortus ), goats ( B. melitensis ), dogs ( B. canis ), sheep ( B. ovis ), pigs ( B. suis ), and rodents ( B. neotomae ). The broad range of affected species underscores the widespread impact of brucellosis 4 . Brucellosis is prevalent among people working closely with animals, like farmers and veterinarians, due to exposure to infected animal secretions 5 . Individuals can contract the disease by handling infected animals, consuming contaminated livestock products, or inhaling the bacteria 6 . Human symptoms of brucellosis include fever, sweats, malaise, anorexia, headache, and muscle pain. However, chronic brucellosis can result in severe complications such as arthritis, endocarditis, and neurological disorders 7 . The disease starts as an acute infection and can progress to a chronic condition with various complications. Annually, around 500,000 new cases are estimated, but this number may be underestimated due to diagnostic challenges, especially in areas with limited healthcare 8 . The ATP-binding cassette (ABC) transport system is vital for transporting various molecules across cellular membranes in all species, including bacteria, archaea, and eukaryotes, using energy from ATP hydrolysis. The transport process involves substrate binding, ATP hydrolysis at nucleotide binding domains, conformational changes, substrate translocation, release into the cytoplasm, resetting for another cycle. ABC transporters handle substrates like nutrients, ions, drugs, toxins, and lipids. In bacteria, these transporters are crucial for nutrient uptake and can dismiss toxic compounds, contributing to antibiotic resistance. They also play roles in pathogen virulence by secreting virulence factors and in metabolic regulation by maintaining nutrient levels, highlighting their importance in cellular processes across diverse organisms 9 – 12 . The DBP is essential in the bacterial ABC transport system for D-Ribose uptake, a vital sugar for bacterial metabolism. It binds and transports D-Ribose across the bacterial cell membrane into the cytoplasm. This protein specifically interacts with D-Ribose in the periplasmic space to facilitate its membrane transport 13 . This process is vital for bacterial energy metabolism, converting D-Ribose to ATP necessary for cellular activities. Binding D-Ribose causes the protein to change shape, aiding interaction with the ABC transport system for efficient transport. Transporting and metabolizing D-Ribose is crucial for bacterial survival and growth, especially in sugar-rich environments. This nutrient absorption illustrates how bacteria adapt and optimize their metabolism for energy generation 9 – 11 , 14 , 15 . Brucellosis is usually treated with a combination of antibiotics, such as doxycycline and rifampicin, often alongside an aminoglycoside, over several weeks to months. However, prolonged treatment presents challenges, including difficulties in patient adherence, increased side effects like gastrointestinal issues and liver toxicity, and the emergence of antibiotic-resistant strains, complicating future treatments 11 . The financial burden on patients and healthcare systems also rises due to the long-term medication and managing side effects. Thus, while lengthy treatment is essential for completely eradicating Brucella bacteria, it significantly affects patients' quality of life and presents several challenges that need addressing. The DBP in Brucella bacteria is critical for drug discovery and understanding bacterial metabolism. Brucella melitensis , which is intracellular pathogen, utilizes sugars like D-Ribose for survival and replication within host macrophages, key for energy metabolism and pathogenicity. Targeting this protein with specific drugs could reduce off-target effects and side effects, disrupt bacterial metabolism, and potentially lead to bacterial cell death, providing a new mechanism of action against infections, especially amidst rising antibiotic resistance 5 , 16 – 18 With traditional antibiotics losing their effectiveness, targeting novel proteins like the DBP presents promising alternatives. By studying its structure and function, new drugs can be developed to inhibit its activity, offering effective treatments for antibiotic-resistant bacterial infections. For instance, research on Epigallocatechin Gallate (EGCG) from green tea shows it targets key bacterial metabolism proteins, providing insights into potential drug mechanisms against resistant strains like Mycobacterium Tuberculosis 19 . The discovery of small molecules that bind to viral proteins emphasizes the significance of targeting specific binding sites in drug discovery, similar to targeting DBPs. The creation of inhibitors for Poly Glycohydrolase showcases the value of exploring new mechanisms in drug development, paralleling the approach of targeting DBPs in bacteria. These studies highlight the crucial role of specificity and innovative mechanisms in developing new therapeutics, demonstrating how targeting specific bacterial proteins can result in effective treatments for infections 20 – 22 . The capability of Brucella to obtain essential nutrients, such as iron, during macrophage infection is vital for its survival, indicating that nutrient transport systems, including those for sugars like D-Ribose, are crucial to its pathogenic strategy 23 – 27 . This study aims to concentrating on the DBP to identify new therapeutic strategies to hinder Brucella's virulence and pathogenicity. Inhibiting the DBP could disrupt essential bacterial metabolic pathways, thus diminishing their infectious capabilities. This method also presents an alternative to traditional antibiotics, tackling the increasing issue of antibiotic resistance and offering a novel approach for effective brucellosis treatments. 2. Material And Methods 2. 1. Data retrieval The protein sequence of the DBP was retrieved from Brucella melitensis biotype 1, as documented in UniProt entry Q8YCU3 (Q8YCU3_BRUME), corresponding to strain ATCC 23456 / CCUG 17765 / NCTC 10094 / 16M. 2. 2. Gene Ontology The DBP precursor from Brucella melitensis biotype 1, identified in UniProt under entry Q8YCU3, is a notable molecular structure. This protein, which consists of 292 amino acids, is situated in the periplasmic space of the bacterium. It plays an essential role in nutrient absorption and cellular metabolism. Its functional properties include carbohydrate binding and amino acid transport, which are critical for the organism's survival and operation. Additionally Detailed structural and functional annotations from the Conserved Domain Database (CDD) reveal the protein's similarity to the Thermus maltogenic ribose-binding protein (cd19967) 28 . This similarity underscores its potential binding affinity and specificity for ribose. 2. 3. 3D Structure Prediction Six 3D Structure models of the sequence using various computational tools, including Swiss-Model server 29 , AlphaFold Colab server 30 , Robetta server 31 , I-Tasser server 32 , Phyre2 server 33 , and C-Quark server 34 was generated. Subsequently, all 3D structures were subjected to energy minimization using the Yasara web server 35 . Following this, polar hydrogens were added, and Kollman charges were assigned using AutoDock Tools software 36 . The Saves server offers several tools for validating and analyzing protein structures, The analysis includes Procheck, which evaluates several key factors. The Ramachandran Plot ensures the torsional angles of amino acid residues fall within permitted regions, indicating stable and functional conformations. The overall G-factors evaluation confirms the structural integrity of the protein model, leading to more accurate and reliable predictions. Additionally, checking planar groups to ensure they are within limits shows that most planar groups meet expected geometric standards, thereby enhancing the structural integrity, reliability of predictions, confidence in results, and overall optimization 37,38 . To identify potential errors within the protein structure, ERRAT analyzes the data of non-bonded interactions between various atom types. This analysis is crucial for ensuring the accuracy of the protein model, which is essential in drug discovery 39 . The Verify 3D evaluation produced the following outcomes for the averaged 3D-1D scores of the residues, demonstrating how well the residues align with the predicted three-dimensional structure of the protein. This evaluation is vital for confirming the protein model's accuracy and reliability, which is crucial for its application in drug discovery and other fields 40,41 . QMEAN was calculated by the SWISS-MODEL webserver 42 , and the Z-score was calculated by the ProSA-web server 43,44 . These metrics are essential for evaluating the quality and reliability of protein models. QMEAN delivers a composite score evaluating various structural features, aiding in the identification of potential errors and ensuring the model's overall quality 42 . ProSA-web server Z-score shows how well the model matches typical structures of a similar size, pointing out any major deviations from expected norms 43,44 . Combined, these tools offer a thorough assessment of the protein model's accuracy and reliability, which is vital for its use in drug discovery and structural biology. The Global_score was determined using the VOROMQA server (Voronoi-based Model Quality Assessment), which evaluates protein models by analyzing the spatial arrangement of atoms through Voronoi tessellation. This global score is obtained by summing the local scores calculated for each residue in the protein structure 45 . The optimal 3D structure was identified and selected based on average metrics using a Python script. This script imported and converted metric columns to numeric values, calculated the average score for each model while ignoring NaN (Not a Number) values, and determined the model with the highest average score as the best. The dataset included structures from AlphaFold, Swiss-Model, I-Tasser, Phyre2, C-Quark, and Robetta. 2. 4. PTM ( Post Translation Modification ) Analysis Extensive analysis of the protein sequence to identify potential post-translational modifications (PTMs) and evaluate various physicochemical properties was conducted. To facilitate this process, the computational environment was set up, and essential libraries such as Biopython 46 , Matplotlib 47 , and NumPy 48 were used. primary objective was to identify potential phosphorylation sites within the protein sequence. Phosphorylation plays a crucial role in regulating protein functions by modifying activity and interactions. Using regular expressions, the sequence was systematically scanned to locate serine (S), threonine (T), and tyrosine (Y) residues. These residues were marked as potential sites for phosphorylation, highlighting key regions that may play pivotal roles in the protein's regulatory mechanisms. Next, the sequence was assessed for N-terminal acetylation, a common modification if it starts with methionine (M). N-glycosylation motifs were also scanned for in the sequence. These motifs indicate sites for glycan attachment, which can impact protein folding and stability. Then, various physicochemical properties of the cleaned and modified protein sequence were calculated. Hydrophobicity was assessed using the Kyte-Doolittle scale 49 , the instability index was calculated to predict protein stability, and the molecular weight was determined. Additionally, the isoelectric point was estimated to understand the protein’s solubility under different pH levels. The aliphatic index was calculated to evaluate thermostability, and aromaticity was assessed by counting aromatic residues (F, Y, and W). These analyses provided a holistic understanding of the protein’s structural and functional characteristics. They offered valuable insights into the protein's behavior and potential interactions within a biological context, which could be critical for further research and therapeutic development. 2. 5. Active Site Prediction A precise prediction for the active site was obtained using Molegro Virtual Docker version 6 (Figure 1). This software enabled the precise identification of potential binding sites 50 . A minimum cavity volume of 10 cubic angstroms was established to ensure relevant cavities were included, and a probe size of 2 angstroms was used to accurately map the binding site landscape. These settings helped pinpoint the most promising active site candidates, enhancing the reliability of our predictions and providing a solid foundation for future drug discovery efforts. Figure 1. 2. 6. Ligand Preparation D-Ribose (CID 10975657) was used as the specific ligand and control compound, making it the benchmark for comparison. One hundred compounds were retrieved from the PubChem database to identify similar interactions. A Tanimoto similarity threshold of 90% was employed, ensuring that these compounds had at least 90% structural similarity to D-Ribose, thereby guaranteeing comparable binding properties. Once the compounds were obtained, they were converted from their initial SDF format to PDBQT format for further computational analysis. During this conversion process, MMFF energy minimization 51 was applied to each compound. This step refined the molecular structure, ensuring each compound was in its most stable conformation, which was crucial for accurate docking simulations. OpenBabel 2.4.1 software was used for this process 52 . 2. 7. Molecular Docking Docking simulations of the protein structure were conducted with 100 compounds retrieved from the PubChem database. AutoDock Vina through PYRx - Python Prescription 0.8 software was used for the docking analysis 53 . This high-throughput approach allowed for efficient screening of potential ligands. Following the docking simulations, the results were meticulously analyzed and sorted based on binding affinity. The compounds with the strongest potential interactions with the protein were identified. This process was crucial for pinpointing candidates with the highest likelihood of effective binding, providing a solid foundation for subsequent experimental validation and drug discovery efforts 54 . 2. 8. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) Analysis ADMET analysis, which encompasses Absorption, Distribution, Metabolism, Excretion, and Toxicity, is a fundamental aspect of drug development. This evaluation allows us to examine the pharmacokinetic and toxicological profiles of potential drug candidates, ensuring their safety and efficacy before they reach the clinical trial stage 55 . Evaluating both promising compounds and those with significant drawbacks at an early stage helps focus resources on the most viable candidates, thereby reducing high attrition rates in drug development. In our analysis, we utilized the ADMETlab 2 webserver to gain comprehensive insights into the pharmacokinetic and toxicological profiles of our drug candidates, providing valuable data for making informed decisions about their development 56 . 2. 9. Molecular Dynamics Simulation GROMACS-2021.4 was used to conduct molecular dynamic simulations of the target protein and its best protein-ligand complexes 57 . The initial structure of the protein was prepared using the CHARMM27 force field and the TIP3P water model 58 . The protein-ligand complex was then enclosed in a triclinic box with a 1.0 nm buffer distance from the edges and solvated using the SPC216 water model. The system was ionized by adding Na + and Cl − ions to reach a concentration of 0.1 M, ensuring overall neutrality 59,60 . Subsequently, energy minimization was conducted to eliminate bad contacts within the system. Equilibration steps were performed in two phases: NVT (constant Number of particles, Volume, and Temperature) followed by NPT (constant Number of particles, Pressure, and Temperature) using predefined molecular dynamics parameters. The production molecular dynamics run was executed for an extended simulation time to obtain stable conformations and reliable interaction data 61-64 . Once the simulation was completed, we re-centered and re-wrapped the trajectory to ensure accurate visualization and analysis, including RMSD, RMSF, hydrogen bond calculations, radius of gyration, and energy calculations, were then performed to evaluate the stability and interactions within the protein-ligand complexes. The results were visualized using Xmgrace, which allows to examine and confirm the binding affinities and interactions at the molecular level in great detail 65,66 . 3. Results And Discussion 3. 1. Post Translation Modification The PTM (Post-Translational Modifications) analysis identified several key modifications in the protein sequence. There are potential phosphorylation sites at numerous positions, including 5, 13, 22, 28, 34, 45, 47, 49, 54, 57, 60, 73, 106, 117, 122, 129, 137, 143, 145, 148, 150, 153, 155, 171, 192, 222, 236, 237, 246, 259, 262, 277, 284, and 286. Additionally, there's a potential N-terminal acetylation and an N-glycosylation site at position 58. The analysis also provided hydrophobicity (Kyte-Doolittle scale) at -0.397, an instability index of 54.49, a molecular weight of approximately 34,233.28 Da, an isoelectric point of 5.60, an aliphatic index of 31.90, and an aromaticity value of 0.046. Overall, these modifications and metrics provide a detailed insight into the stability, structure, and potential functional sites within the protein. 3. 2. 3D structure Preparation and Validation The prepared 3d structures was evaluated by several ways, including Ramachandran Plot, ERRAT, verify 3d, overall G-Factors, Planar Groups, Qmean, Global-Score, and Z-Score (table 1). The optimal 3D structure was selected based on average metrics calculated using a Python script. This script imported data, converted metric columns into numeric values, and computed the average score for each model, ignoring NaN values. The model with the highest average score was chosen as the best. The dataset included models from AlphaFold, Swiss-model, I-Tasser, Phyre2, C-Quark, and Robetta. Table 1. Table 1 presents the evaluation metrics for 3D protein structures produced by AlphaFold, Swiss-Model, I-TASSER, Phyre2, C-QUARK, and Robetta. The assessments include Ramachandran favored region percentages, ERRAT scores, VERIFY 3D scores, overall G-factors, planar group assessments, QMEAN scores, Global-Scores, and Z-scores. This comparative analysis offers a comprehensive understanding of the accuracy and reliability of each protein structure prediction tool 29,37,45,67,68 . Ensuring that the residues meet the expected structural criteria boosts confidence in the model's predictions and its potential use in scientific research and development: For ROBEETA, 71.23% of the residues have an average 3D-1D score greater than or equal to 0.1. In AlphaFold, 69.86% of the residues meet this threshold. For SWISS-MODEL, 67.40% of the residues achieve an average 3D-1D score of 0.1 or higher. I-TASSER shows 68.49% of residues with scores meeting or exceeding 0.1. For Phyre2, 64.86% of the residues have a 3D-1D score of at least 0.1. Lastly, in C-QUARK, 65.75% of residues attain an average 3D-1D score of 0.1 or greater. These results indicate the proportion of residues in each model that reach the threshold for acceptable 3D-1D scores, showcasing the overall quality and reliability of the protein structures generated by these different modeling tools 40,41 . AlphaFold stands out as the top choice for the best protein structure model. Based on the average of key quality metrics, AlphaFold achieved the highest overall score of 34.17, demonstrating a reliable and well-rounded model across multiple aspects of structural quality. Its scores reflect excellence in several aspects: a Ramachandran favored percentage of 93.7% (figure 2) indicates good stereochemistry 68 , and a high ERRAT score of 98.9 highlights low error rates in the structure 69 . Additionally, AlphaFold shows strong VERIFY 3D compatibility (69.86) and an overall G-Factor of 0.22, further underscoring the reliability of its stereochemistry 37,40 . Moreover, AlphaFold maintains planar group accuracy at 93.9%, supporting high-quality bond geometry 37 . Although the QMEAN score was not available in this dataset, prior analysis suggests that AlphaFold typically scores well in QMEAN (approximately 0.457). Figure 2. Other models, such as Phyre2 and Robetta, also perform exceptionally well. Phyre2 boasts the highest ERRAT score (99.6) among all models, indicating outstanding structural quality, coupled with a QMEAN score of 0.445 and a moderate Z-score of -7.98. However, its VERIFY 3D score (64.86) is slightly lower compared to AlphaFold. Robetta, on the other hand, excels with the highest VERIFY 3D score (71.23), indicating strong sequence-structure compatibility, and also achieves favorable G-factor (0.24) and QMEAN (0.453) scores. 3. 3. Predicted Lddt and Aligned Error Heatmap of AlphaFold structure The Predicted Aligned Error (PAE) chart shows the alignment errors between pairs of residues. Darker shades on the heat map indicate lower error values, signifying better alignment. The extensive dark green areas imply that the model anticipates low alignment errors for most residue pairs, bolstering the predicted structure's accuracy 70 . The Predicted Local Distance Difference Test (pLDDT) scores illustrate the confidence levels in the predicted positions of residues throughout the protein sequence, with higher scores denoting greater accuracy 70 . The result indicates that the majority of residues exhibit high pLDDT values, implying a strong confidence in the predicted structure of these regions (figure 3) 71 . Together, these visualizations offer a detailed evaluation of the protein structure's reliability, showcasing regions of high confidence and minimal alignment error, which are crucial for precise protein function analysis and experimental validation. This high confidence is vital for the reliability of the protein model, which is essential for comprehending its function and directing further experimental validation. Figure 3 3. 4. Molecular Docking Molecular docking was carried out using AutoDock Vina integrated into the PYRx - Python Prescription 8.0 software. This powerful tool efficiently predicts the binding interactions between ligands and receptor sites. During the docking process, various possible conformations of the compounds were examined, and the most favorable binding conformation, which exhibited the highest binding affinity, was selected for visualization and further analysis. This method ensures that the optimal interactions are identified, providing a solid basis for subsequent experimental validation and functional studies. The compounds were ranked by their binding affinities in descending order. CID 441035 has the highest binding affinity at -5.8 kcal/mol, followed by CID 439357 with -5.7 kcal/mol, and CID 840 with -5.6 kcal/mol. Compounds CID 6323336, 6560213, and 81696 each have a binding affinity of -5.5 kcal/mol. Several other compounds, including CIDs 12285879, 206, 247323, 439508, 439710, 441033, 441480, 444914, 448702, 5793, 642638, 64689, and 6971016, show a binding affinity of -5.4 kcal/mol. Lastly, compounds with CIDs 11869260, 18950, 19466, 229, 25310, 3034742, 439353, 439509, 439680, 445773, 445770, 445887, 448388, 6713579, 79025, 96241, and 10975657 each have a binding affinity of -5.3 kcal/mol. D-Ribose with CID 5311110 , used as a control, also has a binding affinity of -5.3 kcal/mol. Based on these results, CID 441035 related to D-Talopyranose, was selected for further analyses due to its strongest binding affinity. D-Talopyranose, also known as D-Talose, is a hexose sugar with the molecular formula C6H12O6 (Figure 4). This monosaccharide has a molecular weight of approximately 180.16 g/mol and is characterized by its cyclic pyranose form. It features multiple hydroxyl groups attached to its carbon atoms. D-Talose plays a role in various biological processes, including metabolism in certain organisms like bacterias and can be biosynthesized 72 . Based on these findings, CID 441035 was selected as the best candidate for further analyses due to its strongest binding affinity. Figure 4 3. 5. Visualization Following the docking simulations, D-Talose interactions with the AlphaFold structure were visualized using Mol* server 73 . This powerful molecular visualization tool allowed for a detailed examination of the binding poses, offering critical insights into how the ligand interacts with the protein's active site (Figure 5). These visualizations confirmed the predicted binding affinities and highlighted key molecular interactions, supporting the docking simulation results and aiding in the drug discovery process. Figure 5 The visualization reveals that amino acids SER137, ALA68, ASP66 establish hydrogen bonds with the ligand at a distance of roughly 5 Ångström. This indicates specific interactions between these residues and the ligand, which are vital for the binding affinity and stability of the protein-ligand complex. These interactions offer insights into the binding mechanism and help elucidate the functional significance of the complex 73 . 3. 6. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) Analysis To evaluate the ADMET characteristics of D-Talose, we utilized the related isomeric SMILES formula "C([C@@H]1[C@@H]([C@@H](C@@HO)O)O)O". The result provides a comprehensive analysis of various pharmacokinetic, pharmacodynamic, and toxicity parameters for the compound, highlighting its potential as a drug-like molecule (Figure6). The physicochemical properties of the compound include a molecular weight of 180.06, which is within the optimal range for drug candidates. Key features such as the number of hydrogen bond donors (5) and acceptors (6), rotatable bonds (1), and a topological polar surface area (TPSA) of 110.38 are within favorable ranges, suggesting potential stability and compatibility with biological systems 55 . Its aqueous solubility (logS = −0.046) and partition coefficient (logP = −2.19) are also within optimal values, indicating balanced solubility and permeability, which are crucial for effective bioavailability. In medicinal chemistry assessments, the compound's drug-likeness score (QED = 0.29) is on the lower end of desirability; however, it scores well in synthetic accessibility (SA score = 3.595), indicating it is relatively easy to synthesize. The compound meets significant drug-likeness criteria such as Lipinski, Pfizer, and GSK, which evaluate attributes like molecular weight, logP, and hydrogen bond characteristics. However, it does not meet the "Golden Triangle" criteria, which are often associated with favorable ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties 74 . The absorption properties show mixed results, with low Caco-2 permeability (−5.384), indicating limited intestinal absorption. However, there is a moderate probability of human intestinal absorption (HIA = 0.848), suggesting a reasonable chance of bioavailability. Additionally, the compound has low probabilities of being a P-glycoprotein (Pgp) inhibitor (0.002) or substrate (0.054), potentially reducing the risk of efflux-related absorption issues. The distribution profile appears favorable, with plasma protein binding (PPB) being relatively low at 12.88%. Additionally, the compound exhibits a high unbound fraction of 78.7%, indicating that it will be readily available in the plasma for active distribution. The compound also shows a low probability of crossing the blood-brain barrier (BBB penetration = 0.319), suggesting it may not be effective for central nervous system (CNS) targets but could be suitable for peripheral applications. The metabolism data indicates that this compound does not appear to intervene with key cytochrome P450 (CYP) enzymes like CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4. This is a superb sign as it approaches the compound is not likely to cause serious drug-drug interactions, that is truely beneficial for its use as a remedy. In terms of excretion, it has a mild clearance fee of 1.492 mL/min/kg and a quick half of-lifestyles of 0.816 hours. Basically, this indicates the compound is quick cleared from the body, reducing the danger of it constructing up to harmful tiers. However, as it leaves the body so fast, it would be better to take it more regularly to keep the medicine powerful. Toxicity assessments indicate generally low toxicity risks for the compound. It has a low probability of being a hERG inhibitor (0.068), reducing the risk of cardiotoxicity. Additionally, the compound shows low scores for human hepatotoxicity (0.044) and drug-induced liver injury, as well as other toxicity measures such as skin sensitization and acute toxicity, suggesting it is potentially safe for clinical use. However, environmental toxicity assessments raise some concerns. The compound shows alerts for aquatic toxicity and non-biodegradability, indicating potential adverse environmental impacts if introduced in significant amounts into ecosystems. Overall, the compound exhibits several promising drug-like qualities, particularly in terms of ease of synthesis, distribution, and low toxicity. Figure 6 Additionally, the chart displays TPSA (topological polar surface area), impacting absorption and permeability, and nRot (number of rotatable bonds), indicating molecular flexibility. Other properties, such as nRing (number of rings), MaxRing (maximum ring size), and nHet (number of heteroatoms), provide further insights into the compound's structural features. Formal charge (fChar) and the number of rigid bonds (nRig) are also shown, contributing to an understanding of the compound's stability and interaction potential. The radar chart features three types of data points: the upper limit (yellow circles), the lower limit (pink circles), and the compound properties (blue line). This visual representation helps compare the compound's properties against predefined limits, aiding in assessing its suitability for further development or study . The radar chart visually represents the compound's properties against drug-likeness thresholds, offering an overview of its alignment with ideal pharmacokinetic and structural criteria. Starting with molecular weight (MW), the compound comfortably fits within the upper and lower limits, indicating compliance with typical drug standards. The LogP and LogS values, reflecting the compound's lipophilicity and solubility, are also within the desired range. However, the LogS value is near the lower boundary, suggesting that although the compound is somewhat soluble, it is close to the limit of acceptable solubility. Similarly, the LogD value, which measures lipophilicity at physiological pH, lies within drug-like boundaries, indicating that the compound effectively balances solubility and permeability. Both the number of hydrogen bond acceptors (nHA) and donors (nHD) for the compound fall within favorable ranges, which are essential for interactions with biological targets and contribute to its potential bioactivity. The topological polar surface area (TPSA), impacting absorption and permeability, also falls well within the desired range, suggesting the compound's suitability for oral bioavailability. The number of rotatable bonds (nRot), which affects molecular flexibility, is within acceptable limits, indicating good bioavailability and ease of absorption. Additionally, other structural features, such as formal charge (fChar), number of heteroatoms (nHet), maximum ring size (MaxRing), and number of rings (nRing), meet the drug-likeness criteria, highlighting that the compound’s molecular framework is well-suited for biological activity. Lastly, the number of rigid bonds (nRig) is within the acceptable range, contributing to a stable molecular structure that supports favorable pharmacokinetics. Overall, the blue line depicting the compound’s properties mostly remains within the orange boundary (upper limit) and outside the red area (lower limit). This suggests that the compound has a well-balanced set of drug-like properties. However, certain aspects, such as solubility, might need further optimization based on its intended application. 3. 7. Molecular Dynamics Molecular dynamics simulations for the target protein and its top protein-ligand complexes were performed using GROMACS 2021.4. The initial protein structure was prepared using the CHARMM27 force field and the TIP3P water model. The complex was placed in a triclinic box with a 1.0 nm buffer and solvated with the SPC216 water model. To ensure neutrality, Na + and Cl − ions were added to reach a 0.1 M concentration. Energy minimization was conducted to remove unfavorable contacts, followed by equilibration in two phases: NVT (constant Number, Volume, Temperature) and NPT (constant Number, Pressure, Temperature). After the simulation, the trajectory data was re-centered and re-wrapped to ensure precise analysis. To evaluate the stability of the protein-ligand complexes, several stability and interaction analyses were performed, including RMSD, RMSF, hydrogen bond count, radius of gyration, and energy calculations. Xmgrace was used for visualization, enabling a detailed assessment of binding affinities and molecular interactions. The Root Mean Square Deviation (RMSD) of the protein's backbone increased throughout the 5-nanosecond simulation, indicating that the protein's structure deviates from its initial conformation over time. This suggests alterations in the protein's stability and conformational dynamics during the molecular dynamics simulation, which is crucial for understanding the protein's behavior in a simulated environment and evaluating the stability and interactions of protein-ligand complexes. the RMSD increases and fluctuates but does not show a large, continuous increase, suggesting that the protein backbone has reached a relatively stable state, though with some conformational flexibility. The Root Mean Square Fluctuation (RMSF) assesses the flexibility of each atom in the protein structure. Initially, RMSF values are around 1 nm, stabilizing at approximately 0.2 nm for most atom indices as the simulation progresses, with a slight increase towards the end. This suggests that while most protein atoms remain stable throughout the simulation, some regions exhibit higher flexibility that can be in active site. The overall Radius of Gyration (Rg) values remain relatively stable throughout the simulation, indicating that the protein retains its compactness over time. This suggests that the protein's structure does not significantly expand or contract, reflecting its consistency during the molecular dynamics simulation. However, when examining the Rg along specific axes (x, y, and z), some fluctuations are observed, highlighting minor structural changes and reorientations within the protein that provide insights into how different parts of the protein move and adjust during the simulation. The decreasing trend in the number of hydrogen bonds indicates that the stability and interactions within the protein-ligand complex lessen as the simulation progresses. This suggests a potential loss of structural integrity or changes in bonding patterns, which are crucial for understanding the dynamics and stability of the complex. Further analysis of specific regions and residues involved in these hydrogen bonds could provide deeper insights into the structural changes occurring within the complex. The potential energy of the system, ranging from 0 to 5,000 picoseconds, shows fluctuations between approximately −618,000 and −612,000 kJ/mol, reflecting the dynamic nature of molecular interactions within the system throughout the simulation period. The consistent range of potential energy values suggests that the system maintains a relatively stable state over the course of the simulation. This stability is essential for understanding the energetic behavior and structural integrity of the molecular system during the GROMACS simulation run, providing crucial insights into the system's molecular dynamics and overall stability. 4. Conclusion This study underscores the potential of targeting the DBP in Brucella melitensis as an innovative therapeutic strategy for brucellosis. By disrupting this protein, essential for nutrient uptake and bacterial metabolism, we can inhibit Brucella’s growth and pathogenicity, presenting an alternative to conventional antibiotic treatments. Through structural modeling, molecular docking, and molecular dynamics simulations, D-Talopyranose emerged as a promising ligand with strong binding affinity and favorable ADMET properties, suggesting its suitability as a drug candidate. Given the rising challenge of antibiotic resistance, this approach highlights a novel pathway for brucellosis treatment, emphasizing the importance of developing specific inhibitors for bacterial proteins integral to survival and infection. Further experimental validation and optimization of these inhibitors could pave the way for effective, targeted therapies against Brucella, contributing to improved disease management and public health outcomes. 5. Suggestions To further develop D-Talopyranose as a therapeutic candidate against Brucella melitensis , several important steps are proposed. First, experimental validation through in vitro and in vivo studies is crucial to confirm the compound's binding efficacy with the DBP. These studies will provide essential insights into D-Talopyranose's inhibitory potential and effectiveness as a therapeutic agent. Additionally, optimizing the ligand structure through modifications aimed at enhancing D-Talopyranose's binding affinity, stability, and selectivity could improve its therapeutic properties. Structure-activity relationship (SAR) studies would be valuable in refining the molecule’s characteristics for increased drug efficacy. To advance the drug discovery process, further validation of binding affinity, ADMET profiling, and molecular dynamics simulations should be performed on the compound with highest binding affinities, namely CID 439357 and 840. Additionally, optimizing the structures of these compounds could enhance their binding interactions and specificity, potentially leading to new and effective therapeutic agents for brucellosis. Declarations Author Contribution OM: Developing Idea; Formal data analysis; Visualization; Primary manuscript writing.AM: Developing Idea; Primary manuscript writing; Review the final version of the manuscriptAAM: Review the final version of the manuscriptRVT: Review the final version of the manuscript Data Availability Data is provided within the manuscript or supplementary information files. Funding Declaration The authors confirm that this study has no funding declarations. References Taylor, L. H., Latham, S. M. & Woolhouse, M. E. 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Comprehensive Evaluation Metrics for 3D Protein Structures Generated by Different Tools Ramachandran Favored ERRAT Verify 3D Overall G-factors Planar Groups Qmean Global_Score z-score AlphaFold 93.70% 98.9091 69.86 0.22 93.90% 0.26 0.456626 −8.56 Swiss-Model 92.80% 97.2441 67.40 0.19 96.60% −0.16 0.442839 −8.11 I-Tasser 90.60% 96.0854 68.49 0.09 93.90% −1.94 0.415498 −7.47 Phyre2 92.10% 99.6154 64.86 0.22 92.00% −0.52 0.444604 −7.98 C-Quark 88.20% 97.8495 65.75 0.04 93.90% −2.33 0.401066 −8.01 Robetta 92.50% 95.4064 71.23 0.24 93.90% 0.68 0.452642 −8.20 Additional Declarations No competing interests reported. Supplementary Files energy.png hydrogenbonds.png rmsd.png rmsf.png dribosepapermovie.mp4 gyrate.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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18:15:44","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":250117,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/70ca89f3a4b1fa32065dcc01.jpeg"},{"id":100951693,"identity":"a94cdd73-c42a-48dd-b56a-638b7be855e4","added_by":"auto","created_at":"2026-01-23 07:11:06","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109267,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/83cebd40494b23688c39e726.png"},{"id":100951590,"identity":"0f067481-0263-4be1-bc2f-315ea26a41bd","added_by":"auto","created_at":"2026-01-23 07:10:55","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128956,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/29b3fd554941e824601fcde0.png"},{"id":100950678,"identity":"68df4f3a-38a0-47de-ab7e-f7297a02cbc8","added_by":"auto","created_at":"2026-01-23 07:08:53","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102032,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/0f8ab1aefc43faae96d7bf1b.png"},{"id":100915176,"identity":"1933df1c-59ac-4e37-8414-ac253c052a12","added_by":"auto","created_at":"2026-01-22 18:15:44","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25391,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/19a50066ae91ca52b4415cf5.png"},{"id":100915178,"identity":"4cb1a127-1a04-40d6-8e47-98f52ad5d718","added_by":"auto","created_at":"2026-01-22 18:15:44","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105894,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/046fee2f423fee89ea7f61be.png"},{"id":100915177,"identity":"6d76f8f6-5b37-419a-95be-17f449fb5e92","added_by":"auto","created_at":"2026-01-22 18:15:44","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102247,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/726908c168ea8c201683a42d.png"},{"id":100915179,"identity":"35fd2d53-8f25-4a11-a42d-7ea6701d02f5","added_by":"auto","created_at":"2026-01-22 18:15:44","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151888,"visible":true,"origin":"","legend":"","description":"","filename":"61091ad6e1374b8db82361f6cf7664631structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/d84dbe40edcab789393b1042.xml"},{"id":100915194,"identity":"202b0f98-0236-456c-8ffb-402be6d7d321","added_by":"auto","created_at":"2026-01-22 18:15:53","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167972,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/98932bcff919a3acf0bd1cb0.html"},{"id":100915152,"identity":"637c88d5-0ba7-43c0-90ac-705801151122","added_by":"auto","created_at":"2026-01-22 18:15:43","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184043,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of the predicted active site in a AlphaFold structure, highlighted by a green sphere. Provided by Molegro 6 software.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/f83a97392ece8e630b5339e7.jpeg"},{"id":100915154,"identity":"a4021678-4a5e-441e-bf63-70f642cba684","added_by":"auto","created_at":"2026-01-22 18:15:43","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":333504,"visible":true,"origin":"","legend":"\u003cp\u003eRamachandran plots of (a) AlphaFold, (b) Swiss-Model, (c) I-Tasser, (d) Phyre2, (e) C-QUARK, and (f) Robetta that was obtained by SAVES server.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/f15deda7ed512160b84f93e0.jpeg"},{"id":100952105,"identity":"6403535e-f389-4605-95c5-c7ca86807f36","added_by":"auto","created_at":"2026-01-23 07:11:54","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":315566,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Local Distance Difference Test (pLDDT) and Predicted Aligned Error (PAE) for AlphaFold structure\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/77b4f699d3c52c7f5b86cb2d.jpeg"},{"id":100951659,"identity":"8a48b473-6cc8-4f2a-a679-0e26a7c124b1","added_by":"auto","created_at":"2026-01-23 07:11:03","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71731,"visible":true,"origin":"","legend":"\u003cp\u003e3D structures of a) D-Ribose and b) D-Talose without hydrogens\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/3307574cbb421bc722495cc8.jpeg"},{"id":100915162,"identity":"f4b89247-7f37-4bfb-87d3-c2a71c3763d7","added_by":"auto","created_at":"2026-01-22 18:15:43","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":383655,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of the protein-ligand complex showing key hydrogen bonds between D-Talose and DBP in 5 Angstroms distance. visualization was performed in Mol* server which a, b, and c refers to hydrogen bonds with SER137, ALA68, ASP66\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/18b60a767cc745fb02961066.jpeg"},{"id":100915163,"identity":"f89e14dc-80e3-4c3b-ac99-de8a56b2219f","added_by":"auto","created_at":"2026-01-22 18:15:43","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":250117,"visible":true,"origin":"","legend":"\u003cp\u003eThe radar chart from ADMETlab2 visualizes various properties of the compound. Each axis represents a different property, offering a comprehensive overview of the compound's characteristics. These properties include LogP, which measures lipophilicity, and LogS, which indicates solubility in water. LogD reflects lipophilicity at a specific pH, while nHA (number of hydrogen bond acceptors) and nHD (number of hydrogen bond donors) are crucial for interactions with biological targets.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/e50917bf06744c5e6d813c8a.jpeg"},{"id":101752587,"identity":"64a6abcb-5bab-452d-b7b6-7fd897896f45","added_by":"auto","created_at":"2026-02-03 10:28:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2495860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/d32d3623-aef0-4c28-a36a-4865e14787b0.pdf"},{"id":100915150,"identity":"3e1c72df-e65a-4337-9069-a6ec5ec779fb","added_by":"auto","created_at":"2026-01-22 18:15:43","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19681,"visible":true,"origin":"","legend":"","description":"","filename":"energy.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/802bee631ee1aad5d35156c6.png"},{"id":100950911,"identity":"fb83e317-0581-410d-a24d-3fb994a3284d","added_by":"auto","created_at":"2026-01-23 07:09:32","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12716,"visible":true,"origin":"","legend":"","description":"","filename":"hydrogenbonds.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/8bbadb1dcbc963b2dfebcc91.png"},{"id":100951350,"identity":"55dbab49-d553-4edf-b9a3-47a380f43372","added_by":"auto","created_at":"2026-01-23 07:10:31","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16195,"visible":true,"origin":"","legend":"","description":"","filename":"rmsd.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/94a55bc2970e2ec9ddff2e00.png"},{"id":100951006,"identity":"5bf34b50-285c-4123-9298-e9084f53199c","added_by":"auto","created_at":"2026-01-23 07:09:49","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13380,"visible":true,"origin":"","legend":"","description":"","filename":"rmsf.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/e3d192e22d75efb74c8a9efb.png"},{"id":100950836,"identity":"e890624a-d27e-42f0-a290-d916e8895f90","added_by":"auto","created_at":"2026-01-23 07:09:21","extension":"mp4","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":502520,"visible":true,"origin":"","legend":"","description":"","filename":"dribosepapermovie.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/fa1aca031e59fb17c74fbeed.mp4"},{"id":100915169,"identity":"55e4a7eb-bd1d-4c13-a253-4a85032c657f","added_by":"auto","created_at":"2026-01-22 18:15:44","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15303,"visible":true,"origin":"","legend":"","description":"","filename":"gyrate.png","url":"https://assets-eu.researchsquare.com/files/rs-7618159/v1/25a5c5be0030de9069a59ef9.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Targeting D-Ribose-Binding Proteins in Brucella melitensis: A Novel Frontier Against Antibiotic Resistance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eZoonoses are diseases that can be transmitted from animals to humans. They can be caused by various pathogens, including bacteria, viruses, parasites, and fungi. These diseases can spread through direct contact with animals, consumption of contaminated food/water, or through vectors like mosquitoes and parasites. Some common examples of zoonotic diseases are including Rabies, Lyme disease, and Avian Influenza and Brucellosis. Brucellosis is a specific type of zoonosis caused by bacteria of the genus Brucella. It primarily affects livestock such as cattle, goat, and sheep, but can also infects humans \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The Brucella bacteria cause this disease, which remains one of the most common zoonotic infections \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The World Organization for Animal Health (WOAH) views brucellosis as a significant concern due to its impacts on public health, economic losses, and disruptions to international trade \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis disease burdens livestock with substantial costs, as different Brucella species infect various animals: cows (\u003cem\u003eBrucella abortus\u003c/em\u003e), goats (\u003cem\u003eB. melitensis\u003c/em\u003e), dogs (\u003cem\u003eB. canis\u003c/em\u003e), sheep (\u003cem\u003eB. ovis\u003c/em\u003e), pigs (\u003cem\u003eB. suis\u003c/em\u003e), and rodents (\u003cem\u003eB. neotomae\u003c/em\u003e). The broad range of affected species underscores the widespread impact of brucellosis \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Brucellosis is prevalent among people working closely with animals, like farmers and veterinarians, due to exposure to infected animal secretions \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Individuals can contract the disease by handling infected animals, consuming contaminated livestock products, or inhaling the bacteria \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Human symptoms of brucellosis include fever, sweats, malaise, anorexia, headache, and muscle pain. However, chronic brucellosis can result in severe complications such as arthritis, endocarditis, and neurological disorders \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The disease starts as an acute infection and can progress to a chronic condition with various complications. Annually, around 500,000 new cases are estimated, but this number may be underestimated due to diagnostic challenges, especially in areas with limited healthcare \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ATP-binding cassette (ABC) transport system is vital for transporting various molecules across cellular membranes in all species, including bacteria, archaea, and eukaryotes, using energy from ATP hydrolysis. The transport process involves substrate binding, ATP hydrolysis at nucleotide binding domains, conformational changes, substrate translocation, release into the cytoplasm, resetting for another cycle. ABC transporters handle substrates like nutrients, ions, drugs, toxins, and lipids. In bacteria, these transporters are crucial for nutrient uptake and can dismiss toxic compounds, contributing to antibiotic resistance. They also play roles in pathogen virulence by secreting virulence factors and in metabolic regulation by maintaining nutrient levels, highlighting their importance in cellular processes across diverse organisms \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe DBP is essential in the bacterial ABC transport system for D-Ribose uptake, a vital sugar for bacterial metabolism. It binds and transports D-Ribose across the bacterial cell membrane into the cytoplasm. This protein specifically interacts with D-Ribose in the periplasmic space to facilitate its membrane transport \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This process is vital for bacterial energy metabolism, converting D-Ribose to ATP necessary for cellular activities. Binding D-Ribose causes the protein to change shape, aiding interaction with the ABC transport system for efficient transport. Transporting and metabolizing D-Ribose is crucial for bacterial survival and growth, especially in sugar-rich environments. This nutrient absorption illustrates how bacteria adapt and optimize their metabolism for energy generation \u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBrucellosis is usually treated with a combination of antibiotics, such as doxycycline and rifampicin, often alongside an aminoglycoside, over several weeks to months. However, prolonged treatment presents challenges, including difficulties in patient adherence, increased side effects like gastrointestinal issues and liver toxicity, and the emergence of antibiotic-resistant strains, complicating future treatments \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The financial burden on patients and healthcare systems also rises due to the long-term medication and managing side effects. Thus, while lengthy treatment is essential for completely eradicating Brucella bacteria, it significantly affects patients' quality of life and presents several challenges that need addressing. The DBP in Brucella bacteria is critical for drug discovery and understanding bacterial metabolism. \u003cem\u003eBrucella melitensis\u003c/em\u003e, which is intracellular pathogen, utilizes sugars like D-Ribose for survival and replication within host macrophages, key for energy metabolism and pathogenicity. Targeting this protein with specific drugs could reduce off-target effects and side effects, disrupt bacterial metabolism, and potentially lead to bacterial cell death, providing a new mechanism of action against infections, especially amidst rising antibiotic resistance \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWith traditional antibiotics losing their effectiveness, targeting novel proteins like the DBP presents promising alternatives. By studying its structure and function, new drugs can be developed to inhibit its activity, offering effective treatments for antibiotic-resistant bacterial infections. For instance, research on Epigallocatechin Gallate (EGCG) from green tea shows it targets key bacterial metabolism proteins, providing insights into potential drug mechanisms against resistant strains like \u003cem\u003eMycobacterium Tuberculosis\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The discovery of small molecules that bind to viral proteins emphasizes the significance of targeting specific binding sites in drug discovery, similar to targeting DBPs. The creation of inhibitors for Poly Glycohydrolase showcases the value of exploring new mechanisms in drug development, paralleling the approach of targeting DBPs in bacteria. These studies highlight the crucial role of specificity and innovative mechanisms in developing new therapeutics, demonstrating how targeting specific bacterial proteins can result in effective treatments for infections \u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The capability of Brucella to obtain essential nutrients, such as iron, during macrophage infection is vital for its survival, indicating that nutrient transport systems, including those for sugars like D-Ribose, are crucial to its pathogenic strategy \u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This study aims to concentrating on the DBP to identify new therapeutic strategies to hinder Brucella's virulence and pathogenicity. Inhibiting the DBP could disrupt essential bacterial metabolic pathways, thus diminishing their infectious capabilities. This method also presents an alternative to traditional antibiotics, tackling the increasing issue of antibiotic resistance and offering a novel approach for effective brucellosis treatments.\u003c/p\u003e"},{"header":"2. Material And Methods","content":"\u003cp\u003e\u003cstrong\u003e2. 1. Data retrieval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protein sequence of the DBP was retrieved from \u003cem\u003eBrucella melitensis\u003c/em\u003e biotype 1, as documented in UniProt entry Q8YCU3 (Q8YCU3_BRUME), corresponding to strain ATCC 23456 / CCUG 17765 / NCTC 10094 / 16M.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. 2. Gene Ontology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DBP precursor from \u003cem\u003eBrucella melitensis\u003c/em\u003e biotype 1, identified in UniProt under entry Q8YCU3, is a notable molecular structure. This protein, which consists of 292 amino acids, is situated in the periplasmic space of the bacterium. It plays an essential role in nutrient absorption and cellular metabolism. Its functional properties include carbohydrate binding and amino acid transport, which are critical for the organism\u0026apos;s survival and operation. Additionally Detailed structural and functional annotations from the Conserved Domain Database (CDD) reveal the protein\u0026apos;s similarity to the Thermus maltogenic ribose-binding protein (cd19967) \u003csup\u003e28\u003c/sup\u003e. This similarity underscores its potential binding affinity and specificity for ribose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. 3. 3D Structure Prediction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix 3D Structure models of the sequence using various computational tools, including Swiss-Model server \u003csup\u003e29\u003c/sup\u003e, AlphaFold Colab server \u003csup\u003e30\u003c/sup\u003e, Robetta server \u003csup\u003e31\u003c/sup\u003e, I-Tasser server \u003csup\u003e32\u003c/sup\u003e, Phyre2 server \u003csup\u003e33\u003c/sup\u003e, and C-Quark server \u003csup\u003e34\u003c/sup\u003e was generated. Subsequently, all 3D structures were subjected to energy minimization using the Yasara web server \u003csup\u003e35\u003c/sup\u003e. Following this, polar hydrogens were added, and Kollman charges were assigned using AutoDock Tools software \u003csup\u003e36\u003c/sup\u003e. The Saves server offers several tools for validating and analyzing protein structures, The analysis includes Procheck, which evaluates several key factors. The Ramachandran Plot ensures the torsional angles of amino acid residues fall within permitted regions, indicating stable and functional conformations. The overall G-factors evaluation confirms the structural integrity of the protein model, leading to more accurate and reliable predictions. Additionally, checking planar groups to ensure they are within limits shows that most planar groups meet expected geometric standards, thereby enhancing the structural integrity, reliability of predictions, confidence in results, and overall optimization \u003csup\u003e37,38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo identify potential errors within the protein structure, ERRAT analyzes the data of non-bonded interactions between various atom types. This analysis is crucial for ensuring the accuracy of the protein model, which is essential in drug discovery \u003csup\u003e39\u003c/sup\u003e. The Verify 3D evaluation produced the following outcomes for the averaged 3D-1D scores of the residues, demonstrating how well the residues align with the predicted three-dimensional structure of the protein. This evaluation is vital for confirming the protein model\u0026apos;s accuracy and reliability, which is crucial for its application in drug discovery and other fields \u003csup\u003e40,41\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQMEAN was calculated by the SWISS-MODEL webserver \u003csup\u003e42\u003c/sup\u003e, and the Z-score was calculated by the ProSA-web server \u003csup\u003e43,44\u003c/sup\u003e. These metrics are essential for evaluating the quality and reliability of protein models. QMEAN delivers a composite score evaluating various structural features, aiding in the identification of potential errors and ensuring the model\u0026apos;s overall quality \u003csup\u003e42\u003c/sup\u003e. ProSA-web server Z-score shows how well the model matches typical structures of a similar size, pointing out any major deviations from expected norms \u003csup\u003e43,44\u003c/sup\u003e. Combined, these tools offer a thorough assessment of the protein model\u0026apos;s accuracy and reliability, which is vital for its use in drug discovery and structural biology. The Global_score was determined using the VOROMQA server (Voronoi-based Model Quality Assessment), which evaluates protein models by analyzing the spatial arrangement of atoms through Voronoi tessellation. This global score is obtained by summing the local scores calculated for each residue in the protein structure \u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe optimal 3D structure was identified and selected based on average metrics using a Python script. This script imported and converted metric columns to numeric values, calculated the average score for each model while ignoring NaN (Not a Number) values, and determined the model with the highest average score as the best. The dataset included structures from AlphaFold, Swiss-Model, I-Tasser, Phyre2, C-Quark, and Robetta.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. 4. PTM (\u003c/strong\u003e\u003cstrong\u003ePost Translation Modification\u003c/strong\u003e\u003cstrong\u003e)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtensive analysis of the protein sequence to identify potential post-translational modifications (PTMs) and evaluate various physicochemical properties was conducted. To facilitate this process, the computational environment was set up, and essential libraries such as Biopython \u003csup\u003e46\u003c/sup\u003e, Matplotlib \u003csup\u003e47\u003c/sup\u003e, and NumPy \u003csup\u003e48\u003c/sup\u003e were used. primary objective was to identify potential phosphorylation sites within the protein sequence. Phosphorylation plays a crucial role in regulating protein functions by modifying activity and interactions. Using regular expressions, the sequence was systematically scanned to locate serine (S), threonine (T), and tyrosine (Y) residues. These residues were marked as potential sites for phosphorylation, highlighting key regions that may play pivotal roles in the protein\u0026apos;s regulatory mechanisms. Next, the sequence was assessed for N-terminal acetylation, a common modification if it starts with methionine (M). N-glycosylation motifs were also scanned for in the sequence. These motifs indicate sites for glycan attachment, which can impact protein folding and stability. Then, various physicochemical properties of the cleaned and modified protein sequence were calculated. Hydrophobicity was assessed using the Kyte-Doolittle scale \u003csup\u003e49\u003c/sup\u003e, the instability index was calculated to predict protein stability, and the molecular weight was determined. Additionally, the isoelectric point was estimated to understand the protein\u0026rsquo;s solubility under different pH levels. The aliphatic index was calculated to evaluate thermostability, and aromaticity was assessed by counting aromatic residues (F, Y, and W).\u003c/p\u003e\n\u003cp\u003eThese analyses provided a holistic understanding of the protein\u0026rsquo;s structural and functional characteristics. They offered valuable insights into the protein\u0026apos;s behavior and potential interactions within a biological context, which could be critical for further research and therapeutic development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. 5.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eActive Site Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA precise prediction for the active site was obtained using Molegro Virtual Docker version 6 (Figure 1). This software enabled the precise identification of potential binding sites \u003csup\u003e50\u003c/sup\u003e. A minimum cavity volume of 10 cubic angstroms was established to ensure relevant cavities were included, and a probe size of 2 angstroms was used to accurately map the binding site landscape. These settings helped pinpoint the most promising active site candidates, enhancing the reliability of our predictions and providing a solid foundation for future drug discovery efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. 6. Ligand Preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD-Ribose (CID 10975657) was used as the specific ligand and control compound, making it the benchmark for comparison. One hundred compounds were retrieved from the PubChem database to identify similar interactions. A Tanimoto similarity threshold of 90% was employed, ensuring that these compounds had at least 90% structural similarity to D-Ribose, thereby guaranteeing comparable binding properties. Once the compounds were obtained, they were converted from their initial SDF format to PDBQT format for further computational analysis. During this conversion process, MMFF energy minimization \u003csup\u003e51\u003c/sup\u003e was applied to each compound. This step refined the molecular structure, ensuring each compound was in its most stable conformation, which was crucial for accurate docking simulations. OpenBabel 2.4.1 software was used for this process \u003csup\u003e52\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. 7. Molecular Docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDocking simulations of the protein structure were conducted with 100 compounds retrieved from the PubChem database. AutoDock Vina through PYRx - Python Prescription 0.8 software was used for the docking analysis \u003csup\u003e53\u003c/sup\u003e. This high-throughput approach allowed for efficient screening of potential ligands. Following the docking simulations, the results were meticulously analyzed and sorted based on binding affinity. The compounds with the strongest potential interactions with the protein were identified. This process was crucial for pinpointing candidates with the highest likelihood of effective binding, providing a solid foundation for subsequent experimental validation and drug discovery efforts \u003csup\u003e54\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. 8. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eADMET analysis, which encompasses Absorption, Distribution, Metabolism, Excretion, and Toxicity, is a fundamental aspect of drug development. This evaluation allows us to examine the pharmacokinetic and toxicological profiles of potential drug candidates, ensuring their safety and efficacy before they reach the clinical trial stage \u003csup\u003e55\u003c/sup\u003e. Evaluating both promising compounds and those with significant drawbacks at an early stage helps focus resources on the most viable candidates, thereby reducing high attrition rates in drug development. In our analysis, we utilized the ADMETlab 2 webserver to gain comprehensive insights into the pharmacokinetic and toxicological profiles of our drug candidates, providing valuable data for making informed decisions about their development \u003csup\u003e56\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. 9. Molecular Dynamics Simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGROMACS-2021.4 was used to conduct molecular dynamic simulations of the target protein and its best protein-ligand complexes \u003csup\u003e57\u003c/sup\u003e. The initial structure of the protein was prepared using the CHARMM27 force field and the TIP3P water model \u003csup\u003e58\u003c/sup\u003e. The protein-ligand complex was then enclosed in a triclinic box with a 1.0 nm buffer distance from the edges and solvated using the SPC216 water model. The system was ionized by adding Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e ions to reach a concentration of 0.1 M, ensuring overall neutrality \u003csup\u003e59,60\u003c/sup\u003e. Subsequently, energy minimization was conducted to eliminate bad contacts within the system. Equilibration steps were performed in two phases: NVT (constant Number of particles, Volume, and Temperature) followed by NPT (constant Number of particles, Pressure, and Temperature) using predefined molecular dynamics parameters. The production molecular dynamics run was executed for an extended simulation time to obtain stable conformations and reliable interaction data \u003csup\u003e61-64\u003c/sup\u003e. Once the simulation was completed, we re-centered and re-wrapped the trajectory to ensure accurate visualization and analysis, including RMSD, RMSF, hydrogen bond calculations, radius of gyration, and energy calculations, were then performed to evaluate the stability and interactions within the protein-ligand complexes. The results were visualized using Xmgrace, which allows to examine and confirm the binding affinities and interactions at the molecular level in great detail \u003csup\u003e65,66\u003c/sup\u003e.\u003c/p\u003e"},{"header":"3. Results And Discussion","content":"\u003cp\u003e\u003cstrong\u003e3. 1. Post Translation Modification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PTM (Post-Translational Modifications) analysis identified several key modifications in the protein sequence. There are potential phosphorylation sites at numerous positions, including 5, 13, 22, 28, 34, 45, 47, 49, 54, 57, 60, 73, 106, 117, 122, 129, 137, 143, 145, 148, 150, 153, 155, 171, 192, 222, 236, 237, 246, 259, 262, 277, 284, and 286. Additionally, there\u0026apos;s a potential N-terminal acetylation and an N-glycosylation site at position 58. The analysis also provided hydrophobicity (Kyte-Doolittle scale) at -0.397, an instability index of 54.49, a molecular weight of approximately 34,233.28 Da, an isoelectric point of 5.60, an aliphatic index of 31.90, and an aromaticity value of 0.046. Overall, these modifications and metrics provide a detailed insight into the stability, structure, and potential functional sites within the protein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. 2. 3D structure Preparation and Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prepared 3d structures was evaluated by several ways, including Ramachandran Plot, ERRAT, verify 3d, overall G-Factors, Planar Groups, Qmean, Global-Score, and Z-Score (table 1). The optimal 3D structure was selected based on average metrics calculated using a Python script. This script imported data, converted metric columns into numeric values, and computed the average score for each model, ignoring NaN values. The model with the highest average score was chosen as the best. The dataset included models from AlphaFold, Swiss-model, I-Tasser, Phyre2, C-Quark, and Robetta.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 presents the evaluation metrics for 3D protein structures produced by AlphaFold, Swiss-Model, I-TASSER, Phyre2, C-QUARK, and Robetta. The assessments include Ramachandran favored region percentages, ERRAT scores, VERIFY 3D scores, overall G-factors, planar group assessments, QMEAN scores, Global-Scores, and Z-scores. This comparative analysis offers a comprehensive understanding of the accuracy and reliability of each protein structure prediction tool \u003csup\u003e29,37,45,67,68\u003c/sup\u003e. Ensuring that the residues meet the expected structural criteria boosts confidence in the model\u0026apos;s predictions and its potential use in scientific research and development: For ROBEETA, 71.23% of the residues have an average 3D-1D score greater than or equal to 0.1. In AlphaFold, 69.86% of the residues meet this threshold. For SWISS-MODEL, 67.40% of the residues achieve an average 3D-1D score of 0.1 or higher. I-TASSER shows 68.49% of residues with scores meeting or exceeding 0.1. For Phyre2, 64.86% of the residues have a 3D-1D score of at least 0.1. Lastly, in C-QUARK, 65.75% of residues attain an average 3D-1D score of 0.1 or greater. These results indicate the proportion of residues in each model that reach the threshold for acceptable 3D-1D scores, showcasing the overall quality and reliability of the protein structures generated by these different modeling tools \u003csup\u003e40,41\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAlphaFold stands out as the top choice for the best protein structure model. Based on the average of key quality metrics, AlphaFold achieved the highest overall score of 34.17, demonstrating a reliable and well-rounded model across multiple aspects of structural quality. Its scores reflect excellence in several aspects: a Ramachandran favored percentage of 93.7% (figure 2) indicates good stereochemistry \u003csup\u003e68\u003c/sup\u003e, and a high ERRAT score of 98.9 highlights low error rates in the structure \u003csup\u003e69\u003c/sup\u003e. Additionally, AlphaFold shows strong VERIFY 3D compatibility (69.86) and an overall G-Factor of 0.22, further underscoring the reliability of its stereochemistry \u003csup\u003e37,40\u003c/sup\u003e. Moreover, AlphaFold maintains planar group accuracy at 93.9%, supporting high-quality bond geometry \u003csup\u003e37\u003c/sup\u003e. Although the QMEAN score was not available in this dataset, prior analysis suggests that AlphaFold typically scores well in QMEAN (approximately 0.457).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOther models, such as Phyre2 and Robetta, also perform exceptionally well. Phyre2 boasts the highest ERRAT score (99.6) among all models, indicating outstanding structural quality, coupled with a QMEAN score of 0.445 and a moderate Z-score of -7.98. However, its VERIFY 3D score (64.86) is slightly lower compared to AlphaFold. Robetta, on the other hand, excels with the highest VERIFY 3D score (71.23), indicating strong sequence-structure compatibility, and also achieves favorable G-factor (0.24) and QMEAN (0.453) scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. 3. Predicted Lddt and Aligned Error Heatmap of AlphaFold structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Predicted Aligned Error (PAE) chart shows the alignment errors between pairs of residues. Darker shades on the heat map indicate lower error values, signifying better alignment. The extensive dark green areas imply that the model anticipates low alignment errors for most residue pairs, bolstering the predicted structure\u0026apos;s accuracy \u003csup\u003e70\u003c/sup\u003e. The Predicted Local Distance Difference Test (pLDDT) scores illustrate the confidence levels in the predicted positions of residues throughout the protein sequence, with higher scores denoting greater accuracy \u003csup\u003e70\u003c/sup\u003e. The result indicates that the majority of residues exhibit high pLDDT values, implying a strong confidence in the predicted structure of these regions (figure 3) \u003csup\u003e71\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTogether, these visualizations offer a detailed evaluation of the protein structure\u0026apos;s reliability, showcasing regions of high confidence and minimal alignment error, which are crucial for precise protein function analysis and experimental validation. This high confidence is vital for the reliability of the protein model, which is essential for comprehending its function and directing further experimental validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. 4. Molecular Docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular docking was carried out using AutoDock Vina integrated into the PYRx - Python Prescription 8.0 software. This powerful tool efficiently predicts the binding interactions between ligands and receptor sites. During the docking process, various possible conformations of the compounds were examined, and the most favorable binding conformation, which exhibited the highest binding affinity, was selected for visualization and further analysis. This method ensures that the optimal interactions are identified, providing a solid basis for subsequent experimental validation and functional studies.\u0026nbsp;The compounds were ranked by their binding affinities in descending order. CID 441035 has the highest binding affinity at -5.8 kcal/mol, followed by CID 439357 with -5.7 kcal/mol, and CID 840 with -5.6 kcal/mol. Compounds CID 6323336, 6560213, and 81696 each have a binding affinity of -5.5 kcal/mol. Several other compounds, including CIDs 12285879, 206, 247323, 439508, 439710, 441033, 441480, 444914, 448702, 5793, 642638, 64689, and 6971016, show a binding affinity of -5.4 kcal/mol. Lastly, compounds with CIDs 11869260, 18950, 19466, 229, 25310, 3034742, 439353, 439509, 439680, 445773, 445770, 445887, 448388, 6713579, 79025, 96241, and 10975657 each have a binding affinity of -5.3 kcal/mol. D-Ribose with CID 5311110 , used as a control, also has a binding affinity of -5.3 kcal/mol.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Based on these results, CID 441035 related to D-Talopyranose, was selected for further analyses due to its strongest binding affinity. \u0026nbsp;D-Talopyranose, also known as D-Talose, is a hexose sugar with the molecular formula C6H12O6 (Figure 4). This monosaccharide has a molecular weight of approximately 180.16 g/mol and is characterized by its cyclic pyranose form. It features multiple hydroxyl groups attached to its carbon atoms. D-Talose plays a role in various biological processes, including metabolism in certain organisms like bacterias and can be biosynthesized \u003csup\u003e72\u003c/sup\u003e. Based on these findings, CID 441035 was selected as the best candidate for further analyses due to its strongest binding affinity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. 5. Visualization\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the docking simulations, D-Talose interactions with the AlphaFold structure were visualized using Mol* server \u003csup\u003e73\u003c/sup\u003e. This powerful molecular visualization tool allowed for a detailed examination of the binding poses, offering critical insights into how the ligand interacts with the protein\u0026apos;s active site (Figure 5). These visualizations confirmed the predicted binding affinities and highlighted key molecular interactions, supporting the docking simulation results and aiding in the drug discovery process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe visualization reveals that amino acids SER137, ALA68, ASP66 establish hydrogen bonds with the ligand at a distance of roughly 5 \u0026Aring;ngstr\u0026ouml;m. This indicates specific interactions between these residues and the ligand, which are vital for the binding affinity and stability of the protein-ligand complex. These interactions offer insights into the binding mechanism and help elucidate the functional significance of the complex \u003csup\u003e73\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. 6. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the ADMET characteristics of D-Talose, we utilized the related isomeric SMILES formula \u0026quot;C([C@@H]1[C@@H]([C@@H](C@@HO)O)O)O\u0026quot;. The result provides a comprehensive analysis of various pharmacokinetic, pharmacodynamic, and toxicity parameters for the compound, highlighting its potential as a drug-like molecule (Figure6). The physicochemical properties of the compound include a molecular weight of 180.06, which is within the optimal range for drug candidates. Key features such as the number of hydrogen bond donors (5) and acceptors (6), rotatable bonds (1), and a topological polar surface area (TPSA) of 110.38 are within favorable ranges, suggesting potential stability and compatibility with biological systems \u003csup\u003e55\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIts aqueous solubility (logS =\u0026nbsp;\u0026minus;0.046) and partition coefficient (logP =\u0026nbsp;\u0026minus;2.19) are also within optimal values, indicating balanced solubility and permeability, which are crucial for effective bioavailability. In medicinal chemistry assessments, the compound\u0026apos;s drug-likeness score (QED = 0.29) is on the lower end of desirability; however, it scores well in synthetic accessibility (SA score = 3.595), indicating it is relatively easy to synthesize. The compound meets significant drug-likeness criteria such as Lipinski, Pfizer, and GSK, which evaluate attributes like molecular weight, logP, and hydrogen bond characteristics. However, it does not meet the \u0026quot;Golden Triangle\u0026quot; criteria, which are often associated with favorable ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties \u003csup\u003e74\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe absorption properties show mixed results, with low Caco-2 permeability (\u0026minus;5.384), indicating limited intestinal absorption. However, there is a moderate probability of human intestinal absorption (HIA = 0.848), suggesting a reasonable chance of bioavailability. Additionally, the compound has low probabilities of being a P-glycoprotein (Pgp) inhibitor (0.002) or substrate (0.054), potentially reducing the risk of efflux-related absorption issues. The distribution profile appears favorable, with plasma protein binding (PPB) being relatively low at 12.88%. Additionally, the compound exhibits a high unbound fraction of 78.7%, indicating that it will be readily available in the plasma for active distribution. The compound also shows a low probability of crossing the blood-brain barrier (BBB penetration = 0.319), suggesting it may not be effective for central nervous system (CNS) targets but could be suitable for peripheral applications.\u003c/p\u003e\n\u003cp\u003eThe metabolism data indicates that this compound does not appear to intervene with key cytochrome P450 (CYP) enzymes like CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4. This is a superb sign as it approaches the compound is not likely to cause serious drug-drug interactions, that is truely beneficial for its use as a remedy. In terms of excretion, it has a mild clearance fee of 1.492 mL/min/kg and a quick half of-lifestyles of 0.816 hours. Basically, this indicates the compound is quick cleared from the body, reducing the danger of it constructing up to harmful tiers. However, as it leaves the body so fast, it would be better to take it more regularly to keep the medicine powerful.\u003c/p\u003e\n\u003cp\u003eToxicity assessments indicate generally low toxicity risks for the compound. It has a low probability of being a hERG inhibitor (0.068), reducing the risk of cardiotoxicity. Additionally, the compound shows low scores for human hepatotoxicity (0.044) and drug-induced liver injury, as well as other toxicity measures such as skin sensitization and acute toxicity, suggesting it is potentially safe for clinical use. However, environmental toxicity assessments raise some concerns. The compound shows alerts for aquatic toxicity and non-biodegradability, indicating potential adverse environmental impacts if introduced in significant amounts into ecosystems. Overall, the compound exhibits several promising drug-like qualities, particularly in terms of ease of synthesis, distribution, and low toxicity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, the chart displays TPSA (topological polar surface area), impacting absorption and permeability, and nRot (number of rotatable bonds), indicating molecular flexibility. Other properties, such as nRing (number of rings), MaxRing (maximum ring size), and nHet (number of heteroatoms), provide further insights into the compound\u0026apos;s structural features. Formal charge (fChar) and the number of rigid bonds (nRig) are also shown, contributing to an understanding of the compound\u0026apos;s stability and interaction potential. The radar chart features three types of data points: the upper limit (yellow circles), the lower limit (pink circles), and the compound properties (blue line). This visual representation helps compare the compound\u0026apos;s properties against predefined limits, aiding in assessing its suitability for further development or study\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe radar chart visually represents the compound\u0026apos;s properties against drug-likeness thresholds, offering an overview of its alignment with ideal pharmacokinetic and structural criteria. Starting with molecular weight (MW), the compound comfortably fits within the upper and lower limits, indicating compliance with typical drug standards. The LogP and LogS values, reflecting the compound\u0026apos;s lipophilicity and solubility, are also within the desired range. However, the LogS value is near the lower boundary, suggesting that although the compound is somewhat soluble, it is close to the limit of acceptable solubility. Similarly, the LogD value, which measures lipophilicity at physiological pH, lies within drug-like boundaries, indicating that the compound effectively balances solubility and permeability.\u003c/p\u003e\n\u003cp\u003eBoth the number of hydrogen bond acceptors (nHA) and donors (nHD) for the compound fall within favorable ranges, which are essential for interactions with biological targets and contribute to its potential bioactivity. The topological polar surface area (TPSA), impacting absorption and permeability, also falls well within the desired range, suggesting the compound\u0026apos;s suitability for oral bioavailability. The number of rotatable bonds (nRot), which affects molecular flexibility, is within acceptable limits, indicating good bioavailability and ease of absorption. Additionally, other structural features, such as formal charge (fChar), number of heteroatoms (nHet), maximum ring size (MaxRing), and number of rings (nRing), meet the drug-likeness criteria, highlighting that the compound\u0026rsquo;s molecular framework is well-suited for biological activity. Lastly, the number of rigid bonds (nRig) is within the acceptable range, contributing to a stable molecular structure that supports favorable pharmacokinetics.\u003c/p\u003e\n\u003cp\u003eOverall, the blue line depicting the compound\u0026rsquo;s properties mostly remains within the orange boundary (upper limit) and outside the red area (lower limit). This suggests that the compound has a well-balanced set of drug-like properties. However, certain aspects, such as solubility, might need further optimization based on its intended application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. 7. Molecular Dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular dynamics simulations for the target protein and its top protein-ligand complexes were performed using GROMACS 2021.4. The initial protein structure was prepared using the CHARMM27 force field and the TIP3P water model. The complex was placed in a triclinic box with a 1.0 nm buffer and solvated with the SPC216 water model. To ensure neutrality, Na\u003csup\u003e+\u003c/sup\u003e and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e ions were added to reach a 0.1 M concentration. Energy minimization was conducted to remove unfavorable contacts, followed by equilibration in two phases: NVT (constant Number, Volume, Temperature) and NPT (constant Number, Pressure, Temperature).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter the simulation, the trajectory data was re-centered and re-wrapped to ensure precise analysis. To evaluate the stability of the protein-ligand complexes, several stability and interaction analyses were performed, including RMSD, RMSF, hydrogen bond count, radius of gyration, and energy calculations. Xmgrace was used for visualization, enabling a detailed assessment of binding affinities and molecular interactions. The Root Mean Square Deviation (RMSD) of the protein\u0026apos;s backbone increased throughout the 5-nanosecond simulation, indicating that the protein\u0026apos;s structure deviates from its initial conformation over time. This suggests alterations in the protein\u0026apos;s stability and conformational dynamics during the molecular dynamics simulation, which is crucial for understanding the protein\u0026apos;s behavior in a simulated environment and evaluating the stability and interactions of protein-ligand complexes. the RMSD increases and fluctuates but does not show a large, continuous increase, suggesting that the protein backbone has reached a relatively stable state, though with some conformational flexibility.\u003c/p\u003e\n\u003cp\u003eThe Root Mean Square Fluctuation (RMSF) assesses the flexibility of each atom in the protein structure. Initially, RMSF values are around 1 nm, stabilizing at approximately 0.2 nm for most atom indices as the simulation progresses, with a slight increase towards the end. This suggests that while most protein atoms remain stable throughout the simulation, some regions exhibit higher flexibility that can be in active site. The overall Radius of Gyration (Rg) values remain relatively stable throughout the simulation, indicating that the protein retains its compactness over time. This suggests that the protein\u0026apos;s structure does not significantly expand or contract, reflecting its consistency during the molecular dynamics simulation. However, when examining the Rg along specific axes (x, y, and z), some fluctuations are observed, highlighting minor structural changes and reorientations within the protein that provide insights into how different parts of the protein move and adjust during the simulation.\u003c/p\u003e\n\u003cp\u003eThe decreasing trend in the number of hydrogen bonds indicates that the stability and interactions within the protein-ligand complex lessen as the simulation progresses. This suggests a potential loss of structural integrity or changes in bonding patterns, which are crucial for understanding the dynamics and stability of the complex. Further analysis of specific regions and residues involved in these hydrogen bonds could provide deeper insights into the structural changes occurring within the complex. The potential energy of the system, ranging from 0 to 5,000 picoseconds, shows fluctuations between approximately \u0026minus;618,000 and \u0026minus;612,000 kJ/mol, reflecting the dynamic nature of molecular interactions within the system throughout the simulation period. The consistent range of potential energy values suggests that the system maintains a relatively stable state over the course of the simulation. This stability is essential for understanding the energetic behavior and structural integrity of the molecular system during the GROMACS simulation run, providing crucial insights into the system\u0026apos;s molecular dynamics and overall stability.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study underscores the potential of targeting the DBP in \u003cem\u003eBrucella melitensis\u003c/em\u003e as an innovative therapeutic strategy for brucellosis. By disrupting this protein, essential for nutrient uptake and bacterial metabolism, we can inhibit Brucella\u0026rsquo;s growth and pathogenicity, presenting an alternative to conventional antibiotic treatments. Through structural modeling, molecular docking, and molecular dynamics simulations, D-Talopyranose emerged as a promising ligand with strong binding affinity and favorable ADMET properties, suggesting its suitability as a drug candidate. Given the rising challenge of antibiotic resistance, this approach highlights a novel pathway for brucellosis treatment, emphasizing the importance of developing specific inhibitors for bacterial proteins integral to survival and infection. Further experimental validation and optimization of these inhibitors could pave the way for effective, targeted therapies against Brucella, contributing to improved disease management and public health outcomes.\u003c/p\u003e"},{"header":"5. Suggestions","content":"\u003cp\u003eTo further develop D-Talopyranose as a therapeutic candidate against \u003cem\u003eBrucella melitensis\u003c/em\u003e, several important steps are proposed. First, experimental validation through in vitro and in vivo studies is crucial to confirm the compound's binding efficacy with the DBP. These studies will provide essential insights into D-Talopyranose's inhibitory potential and effectiveness as a therapeutic agent. Additionally, optimizing the ligand structure through modifications aimed at enhancing D-Talopyranose's binding affinity, stability, and selectivity could improve its therapeutic properties. Structure-activity relationship (SAR) studies would be valuable in refining the molecule\u0026rsquo;s characteristics for increased drug efficacy. To advance the drug discovery process, further validation of binding affinity, ADMET profiling, and molecular dynamics simulations should be performed on the compound with highest binding affinities, namely CID 439357 and 840. Additionally, optimizing the structures of these compounds could enhance their binding interactions and specificity, potentially leading to new and effective therapeutic agents for brucellosis.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eOM: Developing Idea; Formal data analysis; Visualization; Primary manuscript writing.AM: Developing Idea; Primary manuscript writing; Review the final version of the manuscriptAAM: Review the final version of the manuscriptRVT: Review the final version of the manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eThe authors confirm that this study has no funding declarations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTaylor, L. H., Latham, S. 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Phi/psi-chology: Ramachandran revisited. \u003cem\u003eStructure\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 1395-1400 (1996). \u003c/li\u003e\n\u003cli\u003eDym, O., Eisenberg, D. \u0026amp; Yeates, T. ERRAT. (2012). \u003c/li\u003e\n\u003cli\u003eTunyasuvunakool, K.\u003cem\u003e et al.\u003c/em\u003e Highly accurate protein structure prediction for the human proteome. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e596\u003c/strong\u003e, 590-596 (2021). https://doi.org/10.1038/s41586-021-03828-1\u003c/li\u003e\n\u003cli\u003eVaradi, M.\u003cem\u003e et al.\u003c/em\u003e AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. \u003cem\u003eNucleic acids research\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, D439-D444 (2022). \u003c/li\u003e\n\u003cli\u003ePatel, S. N., Sharma, S., Singh, A. K. \u0026amp; Singh, S. P. Potential Microbial Bioresources for Functional Sugar Molecules. \u003cem\u003eMicrobial Bioreactors for Industrial Molecules\u003c/em\u003e, 211-236 (2023). \u003c/li\u003e\n\u003cli\u003eSehnal, D.\u003cem\u003e et al.\u003c/em\u003e Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures. \u003cem\u003eNucleic acids research\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, W431-W437 (2021). \u003c/li\u003e\n\u003cli\u003eCheng, F., Li, W., Liu, G. \u0026amp; Tang, Y. In silico ADMET prediction: recent advances, current challenges and future trends. \u003cem\u003eCurrent topics in medicinal chemistry\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1273-1289 (2013). \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"720\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 100%;\"\u003e\n \u003cp\u003eTable 1. \u0026nbsp;Comprehensive Evaluation Metrics for 3D Protein Structures Generated by Different Tools\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003eRamachandran Favored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30556%;\"\u003e\n \u003cp\u003eERRAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61111%;\"\u003e\n \u003cp\u003eVerify 3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.25%;\"\u003e\n \u003cp\u003eOverall \u0026nbsp; \u0026nbsp; \u0026nbsp;G-factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003ePlanar Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.86111%;\"\u003e\n \u003cp\u003eQmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8889%;\"\u003e\n \u003cp\u003eGlobal_Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.88889%;\"\u003e\n \u003cp\u003ez-score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlphaFold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e93.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30556%;\"\u003e\n \u003cp\u003e98.9091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61111%;\"\u003e\n \u003cp\u003e69.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.25%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e93.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.86111%;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8889%;\"\u003e\n \u003cp\u003e0.456626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.88889%;\"\u003e\n \u003cp\u003e\u0026minus;8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSwiss-Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e92.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30556%;\"\u003e\n \u003cp\u003e97.2441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61111%;\"\u003e\n \u003cp\u003e67.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.25%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e96.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.86111%;\"\u003e\n \u003cp\u003e\u0026minus;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8889%;\"\u003e\n \u003cp\u003e0.442839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.88889%;\"\u003e\n \u003cp\u003e\u0026minus;8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI-Tasser\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e90.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30556%;\"\u003e\n \u003cp\u003e96.0854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61111%;\"\u003e\n \u003cp\u003e68.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.25%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e93.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.86111%;\"\u003e\n \u003cp\u003e\u0026minus;1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8889%;\"\u003e\n \u003cp\u003e0.415498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.88889%;\"\u003e\n \u003cp\u003e\u0026minus;7.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhyre2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e92.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30556%;\"\u003e\n \u003cp\u003e99.6154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61111%;\"\u003e\n \u003cp\u003e64.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.25%;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e92.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.86111%;\"\u003e\n \u003cp\u003e\u0026minus;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8889%;\"\u003e\n \u003cp\u003e0.444604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.88889%;\"\u003e\n \u003cp\u003e\u0026minus;7.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-Quark\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e88.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30556%;\"\u003e\n \u003cp\u003e97.8495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61111%;\"\u003e\n \u003cp\u003e65.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.25%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e93.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.86111%;\"\u003e\n \u003cp\u003e\u0026minus;2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8889%;\"\u003e\n \u003cp\u003e0.401066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.88889%;\"\u003e\n \u003cp\u003e\u0026minus;8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRobetta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.5833%;\"\u003e\n \u003cp\u003e92.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.30556%;\"\u003e\n \u003cp\u003e95.4064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.61111%;\"\u003e\n \u003cp\u003e71.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.25%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e93.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.86111%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8889%;\"\u003e\n \u003cp\u003e0.452642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.88889%;\"\u003e\n \u003cp\u003e\u0026minus;8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"D-Ribose-binding periplasmic, Molecular Docking, Molecular Dynamics, Drug Discovery, D-Talopyranose","lastPublishedDoi":"10.21203/rs.3.rs-7618159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7618159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntibiotic resistance among pathogens common to human beings and animals, which include \u003cem\u003eBrucella melitensis\u003c/em\u003e, 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. This 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 carried out the 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. Our outcomes reveal that targeting DBP could offer a unique mechanism to combat antibiotic-resistant lines of \u003cem\u003eBrucella melitensis\u003c/em\u003e by using disrupting essential metabolic pathways. This study affords a promising street for revolutionary brucellosis treatments by way of addressing the challenges posed by means of 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.\u003c/p\u003e","manuscriptTitle":"Targeting D-Ribose-Binding Proteins in Brucella melitensis: A Novel Frontier Against Antibiotic Resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 18:15:38","doi":"10.21203/rs.3.rs-7618159/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d0dc119-def7-447c-abf9-4539305ccc52","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61479120,"name":"Biological sciences/Biochemistry"},{"id":61479121,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":61479122,"name":"Biological sciences/Drug discovery"},{"id":61479123,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-01-31T15:39:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 18:15:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7618159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7618159","identity":"rs-7618159","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}