In silico Discovery of Novel Antibacterial Compounds against Staphylococcus aureus Targeting Extracellular Domain of Lipoteichoic Acid Synthase, Quantum mechanical quantification, and in vitro experimental validation

In silico Discovery of Novel Antibacterial Compounds against Staphylococcus aureus Targeting Extracellular Domain of Lipoteichoic Acid Synthase, Quantum mechanical quantification, and in vitro experimental validation | 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 Research Article In silico Discovery of Novel Antibacterial Compounds against Staphylococcus aureus Targeting Extracellular Domain of Lipoteichoic Acid Synthase, Quantum mechanical quantification, and in vitro experimental validation Seiya Morita, Mikuri Yokota, Kotomi Saiki, Subaru Shioi, Shunsuke Aoki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8922369/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract Staphylococcus aureus is a major causative agent of serious hospital-acquired infections, including pneumonia and sepsis. The emergence of drug-resistant strains such as methicillin-resistant and vancomycin-resistant S. aureus poses a global threat. Infections caused by these multidrug-resistant bacteria are predicted to become a leading cause of human mortality in the future. However, the development pipeline for new antibiotics is declining, raising concerns about a future shortage of effective treatments. Therefore, the discovery of novel antimicrobial agents, particularly those with new mechanisms of action, is urgently needed. In this study, we aimed to discover novel antimicrobial compounds by targeting Lipoteichoic acid synthase (LtaS). LtaS is a membrane protein essential for the synthesis of lipoteichoic acid (LTA), a key cell wall component in S. aureus . We performed a hierarchical in silico screening of the ChemBridge chemical 3D structure library, which contains about 150 thousand compounds. The screening identified a compound (7195703) as a promising lead candidate. Quantum mechanical calculation of DG using the FMO method yielded a value of -93.64 kcal/mol. To evaluate its antimicrobial activity, an in vitro growth inhibition assay was performed using Staphylococcus epidermidis , a closely related model organism for S. aureus . 7195703 exhibited potent and dose-dependent growth inhibition against S. epidermidis , with a half-maximal inhibitory concentration (IC 50 ) of 16.61 µM. Conversely, 7195703 showed no significant inhibitory activity against the Gram-negative bacterium Escherichia coli , suggesting its selective activity against certain Gram-positive bacteria. Furthermore, a search for structural analogues of 7195703 identified five derivatives that also displayed antimicrobial activity. staphylococcus aureus in silico screening lipoteichoic acid synthase molecular dynamics simulation Ab initio fragment molecular orbital method Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Staphylococcus aureus ( S. aureus ), a Gram-positive commensal bacterium found on human skin and in the nasal passages, is an opportunistic pathogen that causes severe infections, including pneumonia and sepsis, particularly in immunocompromised hosts[ 1 , 2 ]. The treatment of S. aureus infectious diseases is further complicated by the emergence of multidrug-resistant strains, notably methicillin-resistant S. aureus (MRSA) and vancomycin-resistant S. aureus (VRSA)[ 3 ]. In addition to conventional hospital-acquired MRSA (HA-MRSA), community-acquired MRSA (CA-MRSA) has now emerged, posing a new clinical threat due to its ability to infect even healthy young populations. CA-MRSA produces Panton-Valentine leucocidin (PVL), a cytotoxin that causes severe necrotizing pneumonia associated with a high mortality rate[ 4 , 5 ]. Despite the emergence of highly threatening drug-resistant bacteria, the number of newly approved antimicrobial agents continues to decline, raising concerns about the future depletion of effective treatments. To address the future challenge of antimicrobial depletion, large-scale in silico screening is considered an effective strategy for the discovery of new antimicrobial drugs[ 6 ]. LtaS is a polytopic membrane protein involved in the cell wall synthesis of Gram-positive bacteria, such as S. aureus . LtaS possesses five transmembrane domains and a large extracellular catalytic domain (eLtaS) that functions on the outer surface of the bacterial membrane. LtaS synthesizes polyglycerol-phosphate (type I) lipoteichoic acid (LTA) by using phosphatidylglycerol (PG) as a substrate and sequentially adds glycerol-phosphate units to the Glc₂-DAG anchor. Type I LTA is a crucial component of the bacterial cell wall. LtaS plays an essential role in the survival and proliferation of S. aureus . It mediates the adsorption of magnesium ions (Mg²⁺) due to the negative charge of LTA and the localization of cell wall degradation enzymes triggered by pH changes. LtaS-deficient mutants exhibit growth abnormalities, leading to the identification of LtaS as a potential drug target in S. aureus [ 7 , 8 ]. Inhibitory compounds targeting eLtaS offer a significant advantage in drug discovery because they do not need to cross the cell membrane [ 9 ]. The X-ray crystal structure of eLtaS has been determined (Fig. 1 ). Structure-based drug screening (SBDS) is a computational method for discovering drugs that bind to the active site structure of target proteins. SBDS rapidly screens vast numbers of compounds, reducing time and cost in drug discovery. Several cases of novel antimicrobial compound identification via SBDS have been reported [ 11 , 12 ]. Docking simulation tools such as DOCK [ 13 ], GOLD [ 14 ], AutoDock Vina (ADV) [ 15 ], and GNINA [ 16 ]are used in SBDS. Hierarchical in silico SBDS combining multiple tools is a powerful approach for efficiently identifying drugs. In this study, a novel antimicrobial lead compound, 7195703, was identified through a hierarchical in silico SBDS approach combining GNINA docking and molecular dynamics simulations targeting S. aureus eLtaS. 7195703 exhibited antimicrobial activity (IC 50 = 16.61 µM) against Staphylococcus epidermidis ( S. epidermidis ). However, it showed no antimicrobial effect against Gram-negative bacteria. These data suggest that 7195703 possesses high potential as a lead compound for future anti-bacterial drug development. 2. Methods 2.1 Compound Data Library This study utilized a three-dimensional chemical structure library containing 154,118 compounds provided by ChemBridge. This library was obtained from the web-based database of the Ressource Parisienne en Bioinformatique Structurale (https://chembridge.com/). The library has been pre-filtered using ADME/Tox criteria to exclude compounds with properties unsuitable for drug development. For the analogue search of the hit compound, a library comprising 1,638,575 compounds from ChemBridge was utilized (https://chembridge.com/). This library incorporates the company's "CORE Library Stock," which consists of small molecules exhibiting lead-like properties. These compounds were virtually assessed before synthesis to ensure that only those with favorable physicochemical profiles and free from undesirable chemical functionalities were selected. 2.2 GNINA GNINA is an AI-based molecular docking software that employs a scoring function based on a convolutional neural network (CNN), which has been trained and tested on the PDBbind database. It generates features by mapping 14 distinct ligand atom types and 14 receptor atom types onto a 0.5 Å resolution cubic grid. These features are then used to predict both the binding affinity and the quality of the binding pose (CNN score) for a given protein-ligand complex. GNINA was used for the first step screening using the three-dimensional chemical structure library of 154,118 compounds from ChemBridge. 2.3 Preparation of Target Protein The X-ray crystal structure of S. aureus eLtaS (PDB ID: 2W5Q) was obtained from the Protein Data Bank. The structure, resolved at 1.20 Å, provided a resolution sufficient for the subsequent hierarchical in silico SBDS. The protein structure was preprocessed using the Molecular Operating Environment (MOE). This preparation included the addition of hydrogen atoms using the Protonate 3D module (MMFF94x force field), calculation of partial charges with the Partial Charge module, and energy minimization of the structure with the Energy Minimize module. 2.4 Molecular Dynamics simulation (MDS) We simulated the dynamic behavior, intermolecular interactions, and structural changes of the docked protein-ligand complex using MDS and evaluated the binding stability of the complex classically. The simulation system was prepared using the Solution Builder within CHARMM-GUI[17, 18] with the CHARMM36m force field. Each complex was placed in a cubic water box using the TIP3P water model and buffered in 0.15 M NaCl. Short-range electrostatic and van der Waals interactions were simulated using the Verlet method with a 12 Å cutoff, while long-range electrostatic interactions were handled by the particle mesh Ewald (PME) method. All bonds involving hydrogen atoms were constrained using the LINCS algorithm[19]. The systems, each containing approximately 30,000 atoms, were first energy-minimized for up to 5,000 steps with the steepest descent method. Subsequently, the systems were equilibrated in two 100 ps stages: first in the NVT ensemble (310 K) and then in the NPT ensemble (310 K, 1 bar). All simulations were conducted using GROMACS 2022.4. 2.5 Trajectory Analysis The stability of the protein-ligand complex was evaluated by calculating the root mean square deviation of the ligand (ligand RMSD) relative to backbone atoms in the protein structure. Ligand RMSD quantifies the deviation of the ligand's binding pose from the initial docking structure throughout the simulation and can be used to evaluate binding stability. 2.6 MM-GBSA method To estimate the binding free energy of the protein-ligand complex, we employed the molecular mechanics generalized Born surface area (MM-GBSA) method. The calculations were performed using the gmx_MMPBSA tool on the trajectories obtained from the GROMACS 2022.4 simulations. 2.7 Ab initio fragment molecular orbital (FMO) method The FMO method is a technique for performing first-principles quantum mechanical calculations at high speed by dividing large molecular systems into fragments and solving the Schrödinger equation in parallel for each fragment[20, 21]. It was implemented using the ABINIT-MP software package[22]. The electronic states of each fragment were calculated using the MP2/6-31G basis set to determine the total electron density of the entire system[23]. The binding free energy of the protein-ligand complex was quantitatively analyzed using IFIE total , obtained by summing the inter-fragment interaction energy (IFIE) between the ligand and all amino acid residues of the protein[24]. 2.8 Growth inhibitory assay against S. epidermidis An antimicrobial activity test was conducted using Staphylococcus epidermidis to verify the antibacterial activity of the selected compound. S. epidermidis was used as a surrogate for S. aureus because S. aureus is a biosafety level 2 pathogen. The amino acid sequence identity between S. aureus eLtaS and S. epidermidis eLtaS was 88 % as analyzed by the BLAST server. 7195703 was purchased from ChemBridge and dissolved in dimethyl sulfoxide (DMSO, Sigma) to a concentration of 33 mM. S. epidermidis was obtained from the RIKEN BioResource Research Center (Saitama, Japan). An aliquot of the S. epidermidis stock culture was added to the culture medium [1 % peptone (BD), 1 % meat extract (BD), 0.5 % NaCl (Wako, Japan), pH 7.0] to a final concentration of 25 % (v/v), and cultured at 37 °C and 240 rpm for 2 h. The culture was then diluted 20-fold with the medium. We added 7195703, or ampicillin (positive control), to a final concentration of 100 mM, and DMSO (negative control) to a final concentration of 0.3 %. This mixture was dispensed at 200 mL per well into a 96-well plate, incubated at 37 °C and 240 rpm for 3.5 h, and the turbidity (OD 590 ) was measured using an MPR-A100 microplate reader (AS ONE). 2.9 Growth inhibitory assay against Escherichia coli ( E. coli ) To investigate the antibacterial spectrum of the selected compound, we performed growth inhibitory assays using E. coli . The E. coli BL21 strain stock solution was added to Luria-Bertani (LB) medium [1 % Bacto Tryptone (BD), 0.5 % Yeast Extract (BD), 1 % NaCl (Wako, Japan), pH 7.0] to a final concentration of 25 % (v/v) and cultured at 37 °C and 204 rpm for 3 h. The culture was then diluted 20-fold with the medium. We added 7195703, or ampicillin (positive control), to a final concentration of 100 mM, and DMSO (negative control) to a final concentration of 0.3 %. This mixture was dispensed at 200 mL per well into a 96-well plate, incubated at 37 °C and 204 rpm for 8 h, and the turbidity (OD 590 ) was measured using an MPR-A100 microplate reader (AS ONE). 2.10 Toxicity Assay against Mammalian Cells For toxicity testing of 7195703, we used African green monkey kidney-derived COS-7 cells and human liver-derived HepG2 cells. The number of viable cells was measured using the Cell Counting Kit-8. Each cell line was seeded in 100 mm dishes and cultured in D-MEM medium [10 % FBS, 1 % non-essential amino acids, 1 % L-Glutamine, 1 % Penicillin-Streptomycin, pH 7.0] at 37 °C under 5 % CO2 conditions. The cells were then seeded into 96-well plates at a density of 1.0 × 10 4 cells/well. After culturing for 6 h, the medium was replaced with D-MEM starvation medium [0.25 % FBS, 1 % L-Glutamine, 1 % Penicillin-Streptomycin, pH 7.0]. The cells were further cultured overnight at 37 °C under 5 % CO2 conditions. The medium was replaced with starvation medium containing 7195703 and cultured overnight. Subsequently, Cell Counting Kit-8 solution was added to each well, and after 2 h, the absorbance was measured at 405 nm using an MPR-A100 microplate reader (AS ONE). 2.11 Drug-Likeness Analysis and Toxicity Prediction The toxicity profiles of the selected compounds were predicted in silico using two web-based tools: SwissADME and ProTox-3.0. The SwissADME server was utilized to estimate absorption, distribution, metabolism, and excretion (ADME) parameters, as well as other pharmacokinetic properties[25]. The ProTox-3.0 server predicted various toxicological endpoints, including cytotoxicity and carcinogenicity[26, 27]. 2.12 Statistical Analysis All data were statistically analyzed using GraphPad Prism version 4 (GraphPad Software, Inc., San Diego, CA, USA). 2.13 Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the authors used DeepL and Gemini in order to improve the readability and ensure the grammatical accuracy of the manuscript. After using these tools/services, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. 3. Results 3.1 Hierarchical in silico SBDS Hierarchical in silico SBDS targeting S. aureus eLtaS was performed using the ChemBridge 3D structure library of 154,118 compounds ( Fig . 2 ). In the primary screening, docking simulations with GNINA were conducted, selecting the top 10 compounds based on CNN affinity and CNN score, respectively, for a total of 20 compounds. In the secondary screening, MDS was performed for 10 ns using their docking poses as initial structures, resulting in the selection of 8 compounds with a maximum ligand RMSD value of less than 0.55 nm ( Fig. S1 in Online Resource 1). In the tertiary screening, MDS was applied to these 8 compounds for 50 ns, and the 6 compounds with average ligand RMSD values below 0.55 nm were then analyzed with MM-GBSA ( Fig. S2 in Online Resource 1). From this analysis, two compounds with negative maximum DG values were selected ( Table S1 in Online Resource 1). Finally, these two compounds underwent two additional 50 ns MDS runs, and the compound that maintained an average ligand RMSD value below 0.55 nm in all three simulations was designated as the final hit compound ( Table S2 , Fig. S3 in Online Resource 1). The structure of the final hit compound is shown in Table S3 and Fig. S4 in Online Resource 1. 3.2 Growth inhibitory assay against S. epidermidis The growth inhibition assay against S. epidermidis showed that 7195703 significantly inhibited the growth of S. epidermidis ( Fig . 3 ). The growth inhibition rate was 83.52%. The dose-dependent effect of 7195703 on S. epidermidis was also analyzed, and the 50% growth inhibition concentration (IC 50 value) was found to be 16.61 mM ( Fig . 4 ). 3.3 Analysis of the Binding Mode and Interaction Energy of S. aureus eLtaS-7195703 The binding mode of S. aureus eLtaS and 7195703 was analyzed using the Protein-Ligand Interaction Profiler (PLIP). 7195703 was predicted to form hydrophobic interactions with TRP354, LEU384, and LEU413, and hydrogen bonds with TRP354 and ARG356 ( Fig . 5 ). Inter-fragment interaction energy analysis was performed using ab initio FMO calculations, and the total IFIE between S. aureus eLtaS and 7195703 was -93.64 kcal/mol. The top three residues for IFIE, as calculated by the FMO analysis, were ASP349, ARG356, and TRP354 ( Table 1 ). Among these, ARG356 is predicted to form a hydrogen bond with the previously identified inhibitor Compound 1771[28], reaffirming its importance in inhibiting the LTA production of LtaS. Table 1 : Top 3 residues with the highest FMO IEIE values amino acid residue IFIE (kcal/mol) ASP349 -35.66 ARG356 -24.84 TRP354 -12.48 3.4 Growth inhibitory assay against E. coli A growth inhibition assay of 7195703 against E. coli was performed. The results showed that 7195703 did not inhibit the growth of E. coli ( Fig . 6 ). This result suggests that 7195703 selectively inhibits the growth of S. epidermidis . 3.5 Toxicity Assay against Mammalian Cells Cytotoxicity assays for 7195703 were conducted using human liver-derived HepG2 cells and African green monkey kidney-derived COS-7 cells. 7195703 exhibited a 19.71% growth inhibitory effect against HepG2 cells ( Fig . 7A ), whereas it did not show a significant inhibitory effect against COS-7 cells ( Fig . 7B ). 3.6 Drug-Likeness Analysis and Toxicity Prediction The drug-likeness of 7195703 was evaluated using SwissADME. The compound fell within the optimal ranges for five physicochemical parameters (Lipophilicity, Size, Polarity, Insolubility, and Flexibility) depicted in the bioavailability radar, whereas it exceeded the acceptable range for Insaturation. 7195703 satisfied all tested drug-likeness rules, including Lipinski, Ghose, Veber, Egan, and Muegge. Furthermore, the compound was predicted to have high gastrointestinal (GI) absorption ( Fig. S5 in Online Resource 1). Regarding toxicity prediction using ProTox-3.0, the compound exhibited probabilities ranging from 0.50 to 1.0 for several toxicity endpoints (e.g., hepatotoxicity and immunotoxicity). Consequently, the predicted toxicity class was determined to be Class 4 (300 < LD 50 [mg/kg] ≤ 2000) ( Fig. S6 in Online Resource 1). 3.7 Growth Inhibitory Assay against S. epidermidis using Analogues We searched for analogs of 7195703 in the ChemBridge library of approximately 1.6 million compounds to identify compounds that exhibit even stronger growth inhibitory effects, using 7195703 as a lead compound. From the SMILES in the library, we extracted compounds with a Tanimoto coefficient of 0.6 or higher, based on the Morgan fingerprint(Bajusz et al., 2015). We selected five compounds with characteristics different from those of 7195703 ( Table S4 ). Then, growth inhibition assays against S. epidermidis were performed. However, although all five compounds showed significant growth inhibition, the maximum growth inhibition rate was 71.34% for 2274941, which did not exceed the 83.52% inhibition observed for 7195703 ( Fi g. 8 ). Furthermore, drug-likeness analysis and toxicity prediction were conducted for each compound using SwissADME ( Fig. S7–S11 in Online Resource 1) and ProTox-3.0 ( Fig. S12–S16 in Online Resource 1), respectively. Discussion The threat of drug-resistant S. aureus remains severe, making the development of antimicrobial agents with novel mechanisms of action an urgent priority. In this study, a hierarchical in silico screening combining docking simulations and MDS targeting LtaS was performed on the library of 154,118 compounds, identifying the promising candidate compound 7195703. This compound exhibited an IC 50 value of 16.61 µM in an in vitro growth inhibition assay against S. epidermidis . This finding, combined with the 88% amino acid sequence homology between S. epidermidis LtaS and S. aureus LtaS, suggests that 7195703 is a promising drug discovery seed. Since 7195703 did not show significant growth inhibitory effects in an in vitro growth inhibition assay against E. coli , it was demonstrated that 7195703 is a compound with a narrow antimicrobial spectrum. However, in the search for analogues of 7195703 within the library of 1,638,575 compounds, all compounds exhibited significant inhibitory effects against S. epidermidis, although no compounds surpassing the growth-inhibitory effect of 7195703 were discovered. The analog compounds possess an acetamide group at their core, enabling hydrogen bonding with ARG356. This is considered the reason why the inhibitory effect was not completely lost. Conversely, the growth inhibition rates of 2166318 (where the methoxy group of 7195703 changed to a carboxyl group) and 2302155 (where it changed to an acetamide group) decreased to 11.36% and 14.71%, respectively. This suggests that the hydrophobic interactions predicted by PLIP analysis between LEU384 and LEU413 predicted by PLIP analysis might be important for antibacterial activity. This is supported by the fact that 2274941, where the 3-chloro-4-methoxyphenyl group changed to hydrophobic naphthalene, showed the highest growth inhibition effect among the analogues. Furthermore, the activity of 2987305 and 135476455, where the 5-methoxybenzimidazole ring was altered, indicates that strict bond angles and positions are required for hydrogen bonding with TRP354. Combined with the results of binding free energy analysis using the FMO method, this suggests the importance of the hydrogen bonding. Drug suitability analysis using SwissADME predicted that 7195703 satisfies empirical rules, including Lipinski's five rules, and possesses high GI absorption. Although weak toxicity was observed in mammalian cell assays, this compound demonstrated lower toxicity compared to the common antimicrobial agent triclosan (TCS). Moreover, it was classified as Class 4 in ProTox-3.0 predictions, suggesting its potential as a lead compound for oral administration. Notably, a correlation was observed between the Consensus Log P o/w (Log P) and predicted carcinogenicity among the analogues. Compounds with a Log P > 2.8 (7195703, 2274941, 2987305, 135476455) were predicted to be carcinogenic, whereas those with a Log P < 2.8 (2166318, 2302155) were not. Therefore, future drug design based on 7195703 structure should balance the maintenance of activity with safety by appropriately controlling Log P. In future work, verifying direct antimicrobial activity through in vitro growth inhibition assays using clinical S. aureus strains, including MRSA and VRSA, is essential. Additionally, more advanced safety evaluations through in vivo experiments are required. Conclusion In this study, we conducted a hierarchical in silico screening targeting S. aureus LtaS and identified compound 7195703 as a promising hit from a library of 154,118 compounds. This compound exhibited significant growth-inhibitory activity against the model bacterium S. epidermidis, with an IC 50 value of 16.61 mM. Furthermore, it showed no growth-inhibitory activity against E. coli , confirming its selectivity for Gram-positive bacteria. These results demonstrate that 7195703 represents a viable lead compound for the development of novel antimicrobial agents against S. epidermidis and S. aureus . Statement and declaration Acknowledgement The authors would like to thank Mr. K. Moriyama, Mr. R. Namiguchi, and Mr. Y. Shibahara for their helpful discussions and technical assistance. Declaration of competing interests The authors declare that they have no conflicts of interest. Funding This work was supported by research funds from Kyushu Institute of Technology. Data availability statement The data supporting the findings of this study are available from the corresponding author upon reasonable request. Author Contributions Seiya Morita: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft Mikuri Yokota: Investigation, Formal analysis. Kotomi Saiki: Investigation, Formal analysis. Subaru Shioi: Investigation, Formal analysis. Shunsuke Aoki: Conceptualization, Methodology, Resources, Writing – review and editing, Supervision, Project administration. References J.C. Lam, W. Stokes, The Golden Grapes of Wrath–Staphylococcus aureus bacteremia: A clinical review, Am J Med 136 (2023) 19–26. B.P. Howden, S.G. Giulieri, T. Wong Fok Lung, S.L. Baines, L.K. Sharkey, J.Y.H. Lee, A. Hachani, I.R. Monk, T.P. Stinear, Staphylococcus aureus host interactions and adaptation, Nat Rev Microbiol 21 (2023) 380–395. U. Tasneem, K. Mehmood, M. Majid, S.R. Ullah, S. 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Preissner, ProTox 3.0: a webserver for the prediction of toxicity of chemicals, Nucleic Acids Res 52 (2024) W513–W520. https://doi.org/10.1093/nar/gkae303. P. Banerjee, A.O. Eckert, A.K. Schrey, R. Preissner, ProTox-II: a webserver for the prediction of toxicity of chemicals, Nucleic Acids Res 46 (2018) W257–W263. https://doi.org/10.1093/nar/gky318. X. Chee Wezen, A. Chandran, R.S. Eapen, E. Waters, L. Bricio-Moreno, T. Tosi, S. Dolan, C. Millership, A. Kadioglu, A. Gründling, L.S. Itzhaki, M. Welch, T. Rahman, Structure-Based Discovery of Lipoteichoic Acid Synthase Inhibitors, J Chem Inf Model 62 (2022) 2586–2599. https://doi.org/10.1021/acs.jcim.2c00300. D. Bajusz, A. Rácz, K. Héberger, Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?, J Cheminform 7 (2015) 20. https://doi.org/10.1186/s13321-015-0069-3. D. Lu, M.E. Wörmann, X. Zhang, O. Schneewind, A. Gründling, P.S. Freemont, Structure-based mechanism of lipoteichoic acid synthesis by Staphylococcus aureus LtaS, Proceedings of the National Academy of Sciences 106 (2009) 1584–1589. https://doi.org/10.1073/pnas.0809020106. Additional Declarations No competing interests reported. Supplementary Files ESM1.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Editor assigned by journal 21 Feb, 2026 Submission checks completed at journal 21 Feb, 2026 First submitted to journal 19 Feb, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8922369","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595600311,"identity":"5556b2b0-c3a5-4095-9a88-205742ce66af","order_by":0,"name":"Seiya Morita","email":"","orcid":"","institution":"Kyushu Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Seiya","middleName":"","lastName":"Morita","suffix":""},{"id":595600312,"identity":"789c99ea-90e6-4bf0-ac8d-0b9ed211bc4e","order_by":1,"name":"Mikuri Yokota","email":"","orcid":"","institution":"Kyushu Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mikuri","middleName":"","lastName":"Yokota","suffix":""},{"id":595600313,"identity":"b2372e21-e770-40d5-9669-8309c830943d","order_by":2,"name":"Kotomi Saiki","email":"","orcid":"","institution":"Kyushu Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kotomi","middleName":"","lastName":"Saiki","suffix":""},{"id":595600314,"identity":"118ea726-0389-4a8d-87d4-4392ef938789","order_by":3,"name":"Subaru Shioi","email":"","orcid":"","institution":"Kyushu Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Subaru","middleName":"","lastName":"Shioi","suffix":""},{"id":595600315,"identity":"47889086-a099-4ee4-9816-82fdd52e47e2","order_by":4,"name":"Shunsuke Aoki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACCRBxQIKBn4GBmSGBwYLBgGgtkg1gLRJEa2FgMDgA1MJAjBb52T2GHz6csbA3Pn74sMGDPxLy5gzMDx/dwKPF4M4ZY8kZNyQSt51JS05IbJMw3NnAZmycg0+LRI6BNM8HiQSzAznGBxIbJBg3HOBhk8anRX5GjvFvoBZ74/43xgcS/kjYE9TCcCPHTJrnBtBwiRzjhAQ2iUSCWgxupJVZzjgjkTjjxrNkA6Bfknc2E/CL/IzkzTc+HKuz5+9PPiz544+N7Xb25oeP8ToMEzCTpnwUjIJRMApGARYAABGATHmbsCTqAAAAAElFTkSuQmCC","orcid":"","institution":"Kyushu Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Shunsuke","middleName":"","lastName":"Aoki","suffix":""}],"badges":[],"createdAt":"2026-02-20 04:53:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8922369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8922369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103712539,"identity":"3601a180-5010-4e02-b92f-aaebd5777fa1","added_by":"auto","created_at":"2026-03-02 04:21:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256094,"visible":true,"origin":"","legend":"\u003cp\u003ecrystal structure of \u003cem\u003eS. aureus\u003c/em\u003e eLtaS (2W5Q). Residues critical for S. aureus growth, as demonstrated in \u003cem\u003ein vitro\u003c/em\u003e experiments[10], are highlighted in red. The Mn\u003csup\u003e2+\u003c/sup\u003e ion is represented as a purple sphere\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8922369/v1/e3099c3b860e5c7468cb640f.png"},{"id":103712532,"identity":"277333ef-08b3-4c0b-a82a-9758ca768ae8","added_by":"auto","created_at":"2026-03-02 04:21:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161979,"visible":true,"origin":"","legend":"\u003cp\u003epathway of hierarchical \u003cem\u003ein silico\u003c/em\u003e SBDS\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8922369/v1/869e4907ddd0ab178bacc1ac.png"},{"id":104399757,"identity":"85d41ae9-af70-4854-8166-94765f66f262","added_by":"auto","created_at":"2026-03-11 12:07:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119548,"visible":true,"origin":"","legend":"\u003cp\u003eInhibitory effect of 7195703 on the growth of \u003cem\u003eS. epidermidis\u003c/em\u003e. Data are presented as the mean ± SEM derived from four independent experiments (n = 4). Statistical analysis was performed using Dunnett's test (****p \u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8922369/v1/1b0a196e1cc800c2170e5312.png"},{"id":104399701,"identity":"afc86541-a919-420b-bb32-712de78329ae","added_by":"auto","created_at":"2026-03-11 12:07:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165237,"visible":true,"origin":"","legend":"\u003cp\u003eDose-dependent effect of 7195703 on \u003cem\u003eS. epidermidis\u003c/em\u003e. The vertical axis represents the bacterial growth rate (%) of \u003cem\u003eS. epidermidis\u003c/em\u003e, and the horizontal axis represents the concentration of 7195703 on a logarithmic scale (log mM). Each plot represents the mean ± SEM of four independent experiments (n = 4). The IC\u003csub\u003e50\u003c/sub\u003e value was determined using non-linear regression analysis\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8922369/v1/ad7ea2f8eedfaea5df341398.png"},{"id":103712536,"identity":"455700c9-a05e-4859-83c4-5f671ab15757","added_by":"auto","created_at":"2026-03-02 04:21:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171292,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction analysis of \u003cem\u003eS. aureus\u003c/em\u003e eLtaS-7195703 by the PLIP tool\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8922369/v1/54e9b03651e55c5043456f12.png"},{"id":103712538,"identity":"19176e3f-dcd8-4d9a-9c24-1aa7fb006855","added_by":"auto","created_at":"2026-03-02 04:21:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":59177,"visible":true,"origin":"","legend":"\u003cp\u003eInhibitory effect of 7195703 on the growth of \u003cem\u003eE. coli\u003c/em\u003e. Data are presented as the mean ± SEM derived from four independent experiments (n = 4). Statistical analysis was performed using Dunnett's test (****p \u0026lt; 0.0001, n.s. = not significant)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8922369/v1/393aeb7de0456ded0768a9d2.png"},{"id":104399691,"identity":"cd8b1fa4-a042-4e36-aadd-687aee27ed0d","added_by":"auto","created_at":"2026-03-11 12:07:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":71503,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of 7195703 on the growth of HepG2 cells (A) and COS-7 cells (B). Data are presented as the mean ± SEM derived from four independent experiments (n = 4). Statistical analysis was performed using Dunnett's test (****p \u0026lt; 0.0001, *p = 0.0332, n.s. = not significant)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8922369/v1/d070eb19c50f67ea85404948.png"},{"id":103712535,"identity":"986a0f72-9785-4215-997f-ed3dd62fdf55","added_by":"auto","created_at":"2026-03-02 04:21:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":189144,"visible":true,"origin":"","legend":"\u003cp\u003eInhibitory effect of analog compounds on the growth of \u003cem\u003eS. epidermidis\u003c/em\u003e. Data are presented as the mean ± SEM derived from four independent experiments (n = 4). Statistical analysis was performed using Dunnett's test (****p \u0026lt; 0.0001, ***p = 0.0002)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8922369/v1/1a074881a1291a43005c52fb.png"},{"id":104407759,"identity":"f19e9999-44aa-4fe2-8dcc-4e366134bcd1","added_by":"auto","created_at":"2026-03-11 12:39:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1709924,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8922369/v1/9b614f27-0a8c-40ac-90fe-e77852f076f7.pdf"},{"id":103712540,"identity":"b50eb84b-b475-4346-9079-70ac0cff6411","added_by":"auto","created_at":"2026-03-02 04:21:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8387768,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8922369/v1/6e320b4c4f8043a13d9fd19b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"In silico Discovery of Novel Antibacterial Compounds against Staphylococcus aureus Targeting Extracellular Domain of Lipoteichoic Acid Synthase, Quantum mechanical quantification, and in vitro experimental validation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (\u003cem\u003eS. aureus\u003c/em\u003e), a Gram-positive commensal bacterium found on human skin and in the nasal passages, is an opportunistic pathogen that causes severe infections, including pneumonia and sepsis, particularly in immunocompromised hosts[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The treatment of \u003cem\u003eS. aureus\u003c/em\u003e infectious diseases is further complicated by the emergence of multidrug-resistant strains, notably methicillin-resistant \u003cem\u003eS. aureus\u003c/em\u003e (MRSA) and vancomycin-resistant \u003cem\u003eS. aureus\u003c/em\u003e (VRSA)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition to conventional hospital-acquired MRSA (HA-MRSA), community-acquired MRSA (CA-MRSA) has now emerged, posing a new clinical threat due to its ability to infect even healthy young populations. CA-MRSA produces Panton-Valentine leucocidin (PVL), a cytotoxin that causes severe necrotizing pneumonia associated with a high mortality rate[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite the emergence of highly threatening drug-resistant bacteria, the number of newly approved antimicrobial agents continues to decline, raising concerns about the future depletion of effective treatments. To address the future challenge of antimicrobial depletion, large-scale \u003cem\u003ein silico\u003c/em\u003e screening is considered an effective strategy for the discovery of new antimicrobial drugs[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLtaS is a polytopic membrane protein involved in the cell wall synthesis of Gram-positive bacteria, such as \u003cem\u003eS. aureus\u003c/em\u003e. LtaS possesses five transmembrane domains and a large extracellular catalytic domain (eLtaS) that functions on the outer surface of the bacterial membrane. LtaS synthesizes polyglycerol-phosphate (type I) lipoteichoic acid (LTA) by using phosphatidylglycerol (PG) as a substrate and sequentially adds glycerol-phosphate units to the Glc₂-DAG anchor. Type I LTA is a crucial component of the bacterial cell wall. LtaS plays an essential role in the survival and proliferation of \u003cem\u003eS. aureus\u003c/em\u003e. It mediates the adsorption of magnesium ions (Mg\u0026sup2;⁺) due to the negative charge of LTA and the localization of cell wall degradation enzymes triggered by pH changes. LtaS-deficient mutants exhibit growth abnormalities, leading to the identification of LtaS as a potential drug target in \u003cem\u003eS. aureus\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Inhibitory compounds targeting eLtaS offer a significant advantage in drug discovery because they do not need to cross the cell membrane [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The X-ray crystal structure of eLtaS has been determined (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStructure-based drug screening (SBDS) is a computational method for discovering drugs that bind to the active site structure of target proteins. SBDS rapidly screens vast numbers of compounds, reducing time and cost in drug discovery. Several cases of novel antimicrobial compound identification via SBDS have been reported [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Docking simulation tools such as DOCK [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], GOLD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], AutoDock Vina (ADV) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and GNINA [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]are used in SBDS. Hierarchical \u003cem\u003ein silico\u003c/em\u003e SBDS combining multiple tools is a powerful approach for efficiently identifying drugs.\u003c/p\u003e \u003cp\u003eIn this study, a novel antimicrobial lead compound, 7195703, was identified through a hierarchical \u003cem\u003ein silico\u003c/em\u003e SBDS approach combining GNINA docking and molecular dynamics simulations targeting \u003cem\u003eS. aureus\u003c/em\u003e eLtaS. 7195703 exhibited antimicrobial activity (IC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;16.61 \u0026micro;M) against \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e (\u003cem\u003eS. epidermidis\u003c/em\u003e). However, it showed no antimicrobial effect against Gram-negative bacteria. These data suggest that 7195703 possesses high potential as a lead compound for future anti-bacterial drug development.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e2.1 Compound Data Library\u003c/p\u003e\n\u003cp\u003eThis study utilized a three-dimensional chemical structure library containing 154,118 compounds provided by ChemBridge. This library was obtained from the web-based database of the Ressource Parisienne en Bioinformatique Structurale (https://chembridge.com/). The library has been pre-filtered using ADME/Tox criteria to exclude compounds with properties unsuitable for drug development. For the analogue search of the hit compound, a library comprising 1,638,575 compounds from ChemBridge was utilized (https://chembridge.com/). This library incorporates the company\u0026apos;s \u0026quot;CORE Library Stock,\u0026quot; which consists of small molecules exhibiting lead-like properties. These compounds were virtually assessed before synthesis to ensure that only those with favorable physicochemical profiles and free from undesirable chemical functionalities were selected.\u003c/p\u003e\n\u003cp id=\"_Toc210565281\"\u003e2.2\u0026nbsp;GNINA\u003c/p\u003e\n\u003cp\u003eGNINA is an AI-based molecular docking software that employs a scoring function based on a convolutional neural network (CNN), which has been trained and tested on the PDBbind database. It generates features by mapping 14 distinct ligand atom types and 14 receptor atom types onto a 0.5 \u0026Aring; resolution cubic grid. These features are then used to predict both the binding affinity and the quality of the binding pose (CNN score) for a given protein-ligand complex. GNINA was used for the first step screening using the three-dimensional chemical structure library of 154,118 compounds from ChemBridge.\u003c/p\u003e\n\u003cp\u003e2.3 Preparation of Target Protein\u003c/p\u003e\n\u003cp\u003eThe X-ray crystal structure of \u003cem\u003eS. aureus\u003c/em\u003e eLtaS (PDB ID: 2W5Q) was obtained from the Protein Data Bank. The structure, resolved at 1.20 \u0026Aring;, provided a resolution sufficient for the subsequent hierarchical \u003cem\u003ein silico\u003c/em\u003e SBDS. The protein structure was preprocessed using the Molecular Operating Environment (MOE). This preparation included the addition of hydrogen atoms using the Protonate 3D module (MMFF94x force field), calculation of partial charges with the Partial Charge module, and energy minimization of the structure with the Energy Minimize module.\u003c/p\u003e\n\u003cp id=\"_Toc210565283\"\u003e2.4\u0026nbsp;Molecular Dynamics simulation\u0026nbsp;(MDS)\u003c/p\u003e\n\u003cp\u003eWe simulated the dynamic behavior, intermolecular interactions, and structural changes of the docked protein-ligand complex using MDS and evaluated the binding stability of the complex classically. The simulation system was prepared using the Solution Builder within CHARMM-GUI[17, 18] with the CHARMM36m force field. Each complex was placed in a cubic water box using the TIP3P water model and buffered in 0.15 M NaCl. Short-range electrostatic and van der Waals interactions were simulated using the Verlet method with a 12 \u0026Aring; cutoff, while long-range electrostatic interactions were handled by the particle mesh Ewald (PME) method. All bonds involving hydrogen atoms were constrained using the LINCS algorithm[19]. The systems, each containing approximately 30,000 atoms, were first energy-minimized for up to 5,000 steps with the steepest descent method. Subsequently, the systems were equilibrated in two 100 ps stages: first in the NVT ensemble (310 K) and then in the NPT ensemble (310 K, 1 bar). All simulations were conducted using GROMACS 2022.4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.5 Trajectory Analysis\u003c/p\u003e\n\u003cp\u003eThe stability of the protein-ligand complex was evaluated by calculating the root mean square deviation of the ligand (ligand RMSD) relative to backbone atoms in the protein structure. Ligand RMSD quantifies the deviation of the ligand\u0026apos;s binding pose from the initial docking structure throughout the simulation and can be used to evaluate binding stability.\u003c/p\u003e\n\u003cp id=\"_Toc210565285\"\u003e2.6\u0026nbsp;MM-GBSA\u0026nbsp;method\u003c/p\u003e\n\u003cp\u003eTo estimate the binding free energy of the protein-ligand complex, we employed the molecular mechanics generalized Born surface area (MM-GBSA) method. The calculations were performed using the gmx_MMPBSA tool on the trajectories obtained from the GROMACS 2022.4 simulations.\u003c/p\u003e\n\u003cp id=\"_Toc210565286\"\u003e2.7\u0026nbsp;\u003cem\u003eAb initio\u003c/em\u003e fragment molecular orbital (FMO)\u0026nbsp;method\u003c/p\u003e\n\u003cp\u003eThe FMO method is a technique for performing first-principles quantum mechanical calculations at high speed by dividing large molecular systems into fragments and solving the Schr\u0026ouml;dinger equation in parallel for each fragment[20, 21]. It was implemented using the ABINIT-MP software package[22]. The electronic states of each fragment were calculated using the MP2/6-31G basis set to determine the total electron density of the entire system[23]. The binding free energy of the protein-ligand complex was quantitatively analyzed using IFIE\u003csub\u003etotal\u003c/sub\u003e, obtained by summing the inter-fragment interaction energy (IFIE) between the ligand and all amino acid residues of the protein[24].\u003c/p\u003e\n\u003cp\u003e2.8 Growth inhibitory assay against \u003cem\u003eS. epidermidis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAn antimicrobial activity test was conducted using \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e to verify the antibacterial activity of the selected compound. \u003cem\u003eS. epidermidis\u003c/em\u003e was used as a surrogate for \u003cem\u003eS. aureus\u003c/em\u003e because \u003cem\u003eS. aureus\u003c/em\u003e is a biosafety level 2 pathogen. The amino acid sequence identity between \u003cem\u003eS. aureus\u003c/em\u003e eLtaS and \u003cem\u003eS. epidermidis\u003c/em\u003e eLtaS was 88 % as analyzed by the BLAST server. 7195703 was purchased from ChemBridge and dissolved in dimethyl sulfoxide (DMSO, Sigma) to a concentration of 33 mM. \u003cem\u003eS. epidermidis\u003c/em\u003e was obtained from the RIKEN BioResource Research Center (Saitama, Japan). An aliquot of the \u003cem\u003eS. epidermidis\u003c/em\u003e stock culture was added to the culture medium [1 % peptone (BD), 1 % meat extract (BD), 0.5 % NaCl (Wako, Japan), pH 7.0] to a final concentration of 25 % (v/v), and cultured at 37 \u0026deg;C and 240 rpm for 2 h. The culture was then diluted 20-fold with the medium. We added 7195703, or ampicillin (positive control), to a final concentration of 100 mM, and DMSO (negative control) to a final concentration of 0.3 %. This mixture was dispensed at 200 mL per well into a 96-well plate, incubated at 37 \u0026deg;C and 240 rpm for 3.5 h, and the turbidity (OD\u003csup\u003e590\u003c/sup\u003e) was measured using an MPR-A100 microplate reader (AS ONE).\u003c/p\u003e\n\u003cp\u003e2.9 Growth inhibitory assay against \u003cem\u003eEscherichia coli\u0026nbsp;\u003c/em\u003e(\u003cem\u003eE. coli\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eTo investigate the antibacterial spectrum of the selected compound, we performed growth inhibitory assays using \u003cem\u003eE. coli\u003c/em\u003e. The \u003cem\u003eE. coli\u003c/em\u003e BL21 strain stock solution was added to Luria-Bertani (LB) medium [1 % Bacto Tryptone (BD), 0.5 % Yeast Extract (BD), 1 % NaCl (Wako, Japan), pH 7.0] to a final concentration of 25 % (v/v) and cultured at 37 \u0026deg;C and 204 rpm for 3 h. The culture was then diluted 20-fold with the medium. We added 7195703, or ampicillin (positive control), to a final concentration of 100\u0026nbsp;mM, and DMSO (negative control) to a final concentration of 0.3 %. This mixture was dispensed at 200 mL per well into a 96-well plate, incubated at 37 \u0026deg;C and 204 rpm for 8 h, and the turbidity (OD\u003csup\u003e590\u003c/sup\u003e) was measured using an MPR-A100 microplate reader (AS ONE).\u003c/p\u003e\n\u003cp\u003e2.10 Toxicity Assay against Mammalian Cells\u003c/p\u003e\n\u003cp\u003eFor toxicity testing of 7195703, we used African green monkey kidney-derived COS-7 cells and human liver-derived HepG2 cells. The number of viable cells was measured using the Cell Counting Kit-8. Each cell line was seeded in 100 mm dishes and cultured in D-MEM medium [10 % FBS, 1 % non-essential amino acids, 1 % L-Glutamine, 1 % Penicillin-Streptomycin, pH 7.0] at 37 \u0026deg;C under 5 % CO2 conditions. The cells were then seeded into 96-well plates at a density of 1.0 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells/well. After culturing for 6 h, the medium was replaced with D-MEM starvation medium [0.25 % FBS, 1 % L-Glutamine, 1 % Penicillin-Streptomycin, pH 7.0]. The cells were further cultured overnight at 37 \u0026deg;C under 5 % CO2 conditions. The medium was replaced with starvation medium containing 7195703 and cultured overnight. Subsequently, Cell Counting Kit-8 solution was added to each well, and after 2 h, the absorbance was measured at 405 nm using an MPR-A100 microplate reader (AS ONE).\u003c/p\u003e\n\u003cp\u003e2.11 Drug-Likeness Analysis and Toxicity Prediction\u003c/p\u003e\n\u003cp\u003eThe toxicity profiles of the selected compounds were predicted \u003cem\u003ein silico\u003c/em\u003e using two web-based tools: SwissADME and ProTox-3.0. The SwissADME server was utilized to estimate absorption, distribution, metabolism, and excretion (ADME) parameters, as well as other pharmacokinetic properties[25]. The ProTox-3.0 server predicted various toxicological endpoints, including cytotoxicity and carcinogenicity[26, 27].\u003c/p\u003e\n\u003cp\u003e2.12 Statistical Analysis\u003c/p\u003e\n\u003cp\u003eAll data were statistically analyzed using GraphPad Prism version 4 (GraphPad Software, Inc., San Diego, CA, USA).\u003c/p\u003e\n\u003cp\u003e2.13\u0026nbsp;Declaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used DeepL and Gemini in order to improve the readability and ensure the grammatical accuracy of the manuscript. After using these tools/services, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Hierarchical \u003cem\u003ein silico\u003c/em\u003e SBDS\u003c/h2\u003e\n\u003cp\u003eHierarchical \u003cem\u003ein silico\u003c/em\u003e SBDS targeting \u003cem\u003eS. aureus\u003c/em\u003e eLtaS was performed using the ChemBridge 3D structure library of 154,118 compounds (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2\u003c/strong\u003e). In the primary screening, docking simulations with GNINA were conducted, selecting the top 10 compounds based on CNN affinity and CNN score, respectively, for a total of 20 compounds. In the secondary screening, MDS was performed for 10 ns using their docking poses as initial structures, resulting in the selection of 8 compounds with a maximum ligand RMSD value of less than 0.55 nm (\u003cstrong\u003eFig. S1\u003c/strong\u003e in Online Resource 1). In the tertiary screening, MDS was applied to these 8 compounds for 50 ns, and the 6 compounds with average ligand RMSD values below 0.55 nm were then analyzed with MM-GBSA (\u003cstrong\u003eFig. S2\u003c/strong\u003e in Online Resource 1). From this analysis, two compounds with negative maximum DG values were selected (\u003cstrong\u003eTable S1\u003c/strong\u003e in Online Resource 1). Finally, these two compounds underwent two additional 50 ns MDS runs, and the compound that maintained an average ligand RMSD value below 0.55 nm in all three simulations was designated as the final hit compound (\u003cstrong\u003eTable S2\u003c/strong\u003e, \u003cstrong\u003eFig. S3\u003c/strong\u003e in Online Resource 1). The structure of the final hit compound is shown in \u003cstrong\u003eTable S3\u003c/strong\u003e and \u003cstrong\u003eFig. S4\u003c/strong\u003e in Online Resource 1.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.2\u0026nbsp;\u003c/em\u003eGrowth inhibitory assay against \u003cem\u003eS. epidermidis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe growth inhibition assay against \u003cem\u003eS. epidermidis\u003c/em\u003e showed that 7195703 significantly inhibited the growth of \u003cem\u003eS. epidermidis\u003c/em\u003e (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;3\u003c/strong\u003e). The growth inhibition rate was 83.52%. The dose-dependent effect of 7195703 on \u003cem\u003eS. epidermidis\u003c/em\u003e was also analyzed, and the 50% growth inhibition concentration (IC\u003csub\u003e50\u003c/sub\u003e value) was found to be 16.61 mM (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;4\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003e3.3 Analysis of the Binding Mode and Interaction Energy of \u003cem\u003eS. aureus\u003c/em\u003e eLtaS-7195703\u003c/h2\u003e\n\u003cp\u003eThe binding mode of \u003cem\u003eS. aureus\u003c/em\u003e eLtaS and 7195703 was analyzed using the Protein-Ligand Interaction Profiler (PLIP). 7195703 was predicted to form hydrophobic interactions with TRP354, LEU384, and LEU413, and hydrogen bonds with TRP354 and ARG356 (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;5\u003c/strong\u003e). Inter-fragment interaction energy analysis was performed using \u003cem\u003eab initio\u003c/em\u003e FMO calculations, and the total IFIE between \u003cem\u003eS. aureus\u003c/em\u003e eLtaS and 7195703 was -93.64 kcal/mol. The top three residues for IFIE, as calculated by the FMO analysis, were ASP349, ARG356, and TRP354 (\u003cstrong\u003eTable 1\u003c/strong\u003e). Among these, ARG356 is predicted to form a hydrogen bond with the previously identified inhibitor Compound 1771[28], reaffirming its importance in inhibiting the LTA production of LtaS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Top 3 residues with the highest FMO IEIE values\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eamino acid residue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eIFIE (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eASP349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e-35.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eARG356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e-24.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTRP354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e-12.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.4 Growth inhibitory assay against \u003cem\u003eE. coli\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eA growth inhibition assay of 7195703 against \u003cem\u003eE. coli\u003c/em\u003e was performed. The results showed that 7195703 did not inhibit the growth of \u003cem\u003eE. coli\u003c/em\u003e (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e). This result suggests that 7195703 selectively inhibits the growth of \u003cem\u003eS. epidermidis\u003c/em\u003e.\u003c/p\u003e\n\u003ch2\u003e3.5 Toxicity Assay against Mammalian Cells\u003c/h2\u003e\n\u003cp\u003eCytotoxicity assays for 7195703 were conducted using human liver-derived HepG2 cells and African green monkey kidney-derived COS-7 cells. 7195703 exhibited a 19.71% growth inhibitory effect against HepG2 cells (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;7A\u003c/strong\u003e), whereas it did not show a significant inhibitory effect against COS-7 cells (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;7B\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003e3.6 Drug-Likeness Analysis and Toxicity Prediction\u003c/h2\u003e\n\u003cp\u003eThe drug-likeness of 7195703 was evaluated using SwissADME. The compound fell within the optimal ranges for five physicochemical parameters (Lipophilicity, Size, Polarity, Insolubility, and Flexibility) depicted in the bioavailability radar, whereas it exceeded the acceptable range for Insaturation. 7195703 satisfied all tested drug-likeness rules, including Lipinski, Ghose, Veber, Egan, and Muegge. Furthermore, the compound was predicted to have high gastrointestinal (GI) absorption (\u003cstrong\u003eFig. S5\u003c/strong\u003e in Online Resource 1). Regarding toxicity prediction using ProTox-3.0, the compound exhibited probabilities ranging from 0.50 to 1.0 for several toxicity endpoints (e.g., hepatotoxicity and immunotoxicity). Consequently, the predicted toxicity class was determined to be Class 4 (300 \u0026lt; LD\u003csub\u003e50\u003c/sub\u003e [mg/kg] \u0026le; 2000) (\u003cstrong\u003eFig. S6\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ein Online Resource 1).\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.7 Growth Inhibitory Assay against \u003cem\u003eS. epidermidis\u003c/em\u003e using Analogues\u003c/h2\u003e\n\u003cp\u003eWe searched for analogs of 7195703 in the ChemBridge library of approximately 1.6 million compounds to identify compounds that exhibit even stronger growth inhibitory effects, using 7195703 as a lead compound. From the SMILES in the library, we extracted compounds with a Tanimoto coefficient of 0.6 or higher, based on the Morgan fingerprint(Bajusz et al., 2015). We selected five compounds with characteristics different from those of 7195703 (\u003cstrong\u003eTable S4\u003c/strong\u003e). Then, growth inhibition assays against \u003cem\u003eS. epidermidis\u003c/em\u003e were performed. However, although all five compounds showed significant growth inhibition, the maximum growth inhibition rate was 71.34% for 2274941, which did not exceed the 83.52% inhibition observed for 7195703 (\u003cstrong\u003eFi\u003c/strong\u003e\u003cstrong\u003eg.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;8\u003c/strong\u003e). Furthermore, drug-likeness analysis and toxicity prediction were conducted for each compound using SwissADME (\u003cstrong\u003eFig. S7\u0026ndash;S11\u003c/strong\u003e in Online Resource 1) and ProTox-3.0 (\u003cstrong\u003eFig. S12\u0026ndash;S16\u003c/strong\u003e in Online Resource 1), respectively.\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThe threat of drug-resistant \u003cem\u003eS. aureus\u003c/em\u003e remains severe, making the development of antimicrobial agents with novel mechanisms of action an urgent priority. In this study, a hierarchical \u003cem\u003ein silico\u003c/em\u003e screening combining docking simulations and MDS targeting LtaS was performed on the library of 154,118 compounds, identifying the promising candidate compound 7195703. This compound exhibited an IC\u003csub\u003e50\u003c/sub\u003e value of 16.61 \u0026micro;M in an \u003cem\u003ein vitro\u003c/em\u003e growth inhibition assay against \u003cem\u003eS. epidermidis\u003c/em\u003e. This finding, combined with the 88% amino acid sequence homology between \u003cem\u003eS. epidermidis\u003c/em\u003e LtaS and \u003cem\u003eS. aureus\u003c/em\u003e LtaS, suggests that 7195703 is a promising drug discovery seed. Since 7195703 did not show significant growth inhibitory effects in an \u003cem\u003ein vitro\u003c/em\u003e growth inhibition assay against \u003cem\u003eE. coli\u003c/em\u003e, it was demonstrated that 7195703 is a compound with a narrow antimicrobial spectrum.\u003c/p\u003e \u003cp\u003eHowever, in the search for analogues of 7195703 within the library of 1,638,575 compounds, all compounds exhibited significant inhibitory effects against S. epidermidis, although no compounds surpassing the growth-inhibitory effect of 7195703 were discovered. The analog compounds possess an acetamide group at their core, enabling hydrogen bonding with ARG356. This is considered the reason why the inhibitory effect was not completely lost. Conversely, the growth inhibition rates of 2166318 (where the methoxy group of 7195703 changed to a carboxyl group) and 2302155 (where it changed to an acetamide group) decreased to 11.36% and 14.71%, respectively. This suggests that the hydrophobic interactions predicted by PLIP analysis between LEU384 and LEU413 predicted by PLIP analysis might be important for antibacterial activity. This is supported by the fact that 2274941, where the 3-chloro-4-methoxyphenyl group changed to hydrophobic naphthalene, showed the highest growth inhibition effect among the analogues. Furthermore, the activity of 2987305 and 135476455, where the 5-methoxybenzimidazole ring was altered, indicates that strict bond angles and positions are required for hydrogen bonding with TRP354. Combined with the results of binding free energy analysis using the FMO method, this suggests the importance of the hydrogen bonding.\u003c/p\u003e \u003cp\u003eDrug suitability analysis using SwissADME predicted that 7195703 satisfies empirical rules, including Lipinski's five rules, and possesses high GI absorption. Although weak toxicity was observed in mammalian cell assays, this compound demonstrated lower toxicity compared to the common antimicrobial agent triclosan (TCS). Moreover, it was classified as Class 4 in ProTox-3.0 predictions, suggesting its potential as a lead compound for oral administration. Notably, a correlation was observed between the Consensus Log P\u003csub\u003eo/w\u003c/sub\u003e (Log P) and predicted carcinogenicity among the analogues. Compounds with a Log P\u0026thinsp;\u0026gt;\u0026thinsp;2.8 (7195703, 2274941, 2987305, 135476455) were predicted to be carcinogenic, whereas those with a Log P\u0026thinsp;\u0026lt;\u0026thinsp;2.8 (2166318, 2302155) were not. Therefore, future drug design based on 7195703 structure should balance the maintenance of activity with safety by appropriately controlling Log P.\u003c/p\u003e \u003cp\u003eIn future work, verifying direct antimicrobial activity through \u003cem\u003ein vitro\u003c/em\u003e growth inhibition assays using clinical \u003cem\u003eS. aureus\u003c/em\u003e strains, including MRSA and VRSA, is essential. Additionally, more advanced safety evaluations through \u003cem\u003ein vivo\u003c/em\u003e experiments are required.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we conducted a hierarchical \u003cem\u003ein silico\u003c/em\u003e screening targeting \u003cem\u003eS. aureus\u003c/em\u003e LtaS and identified compound 7195703 as a promising hit from a library of 154,118 compounds. This compound exhibited significant growth-inhibitory activity against the model bacterium S. epidermidis, with an IC\u003csub\u003e50\u003c/sub\u003e value of 16.61\u0026nbsp;mM. Furthermore, it showed no growth-inhibitory activity against \u003cem\u003eE. coli\u003c/em\u003e, confirming its selectivity for Gram-positive bacteria. These results demonstrate that 7195703 represents a viable lead compound for the development of novel antimicrobial agents against \u003cem\u003eS. epidermidis\u0026nbsp;\u003c/em\u003eand \u003cem\u003eS. aureus\u003c/em\u003e.\u003c/p\u003e"},{"header":"Statement and declaration","content":"\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Mr. K. Moriyama, Mr. R. Namiguchi, and Mr. Y. Shibahara\u0026nbsp;for their helpful discussions and technical assistance.\u003c/p\u003e\n\u003cp\u003eDeclaration of competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by research funds from Kyushu Institute of Technology.\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eSeiya Morita: Conceptualization, Methodology, Formal analysis, Investigation, Writing \u0026ndash; original draft\u003c/p\u003e\n\u003cp\u003eMikuri Yokota: Investigation, Formal analysis.\u003c/p\u003e\n\u003cp\u003eKotomi Saiki: Investigation, Formal analysis.\u003c/p\u003e\n\u003cp\u003eSubaru Shioi: Investigation, Formal analysis.\u003c/p\u003e\n\u003cp\u003eShunsuke Aoki: Conceptualization, Methodology, Resources, Writing \u0026ndash; review and editing, Supervision, Project administration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJ.C. Lam, W. Stokes, The Golden Grapes of Wrath\u0026ndash;Staphylococcus aureus bacteremia: A clinical review, Am J Med 136 (2023) 19\u0026ndash;26.\u003c/li\u003e\n \u003cli\u003eB.P. Howden, S.G. Giulieri, T. Wong Fok Lung, S.L. Baines, L.K. Sharkey, J.Y.H. Lee, A. Hachani, I.R. Monk, T.P. Stinear, Staphylococcus aureus host interactions and adaptation, Nat Rev Microbiol 21 (2023) 380\u0026ndash;395.\u003c/li\u003e\n \u003cli\u003eU. Tasneem, K. Mehmood, M. Majid, S.R. Ullah, S. Andleeb, Methicillin resistant Staphylococcus aureus: A brief review of virulence and resistance., J Pak Med Assoc 72 (2022) 509\u0026ndash;515.\u003c/li\u003e\n \u003cli\u003eG. Balakirski, G. Hischebeth, J. Altengarten, D. Exner, T. Bieber, J. Dohmen, S. 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Chandran, R.S. Eapen, E. Waters, L. Bricio-Moreno, T. Tosi, S. Dolan, C. Millership, A. Kadioglu, A. Gr\u0026uuml;ndling, L.S. Itzhaki, M. Welch, T. Rahman, Structure-Based Discovery of Lipoteichoic Acid Synthase Inhibitors, J Chem Inf Model 62 (2022) 2586\u0026ndash;2599. https://doi.org/10.1021/acs.jcim.2c00300.\u003c/li\u003e\n \u003cli\u003eD. Bajusz, A. R\u0026aacute;cz, K. H\u0026eacute;berger, Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?, J Cheminform 7 (2015) 20. https://doi.org/10.1186/s13321-015-0069-3.\u003c/li\u003e\n \u003cli\u003eD. Lu, M.E. W\u0026ouml;rmann, X. Zhang, O. Schneewind, A. Gr\u0026uuml;ndling, P.S. Freemont, Structure-based mechanism of lipoteichoic acid synthesis by Staphylococcus aureus LtaS, Proceedings of the National Academy of Sciences 106 (2009) 1584\u0026ndash;1589. https://doi.org/10.1073/pnas.0809020106.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"staphylococcus aureus, in silico screening, lipoteichoic acid synthase, molecular dynamics simulation, Ab initio fragment molecular orbital method","lastPublishedDoi":"10.21203/rs.3.rs-8922369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8922369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eStaphylococcus aureus\u003c/em\u003e is a major causative agent of serious hospital-acquired infections, including pneumonia and sepsis. The emergence of drug-resistant strains such as methicillin-resistant and vancomycin-resistant \u003cem\u003eS. aureus\u003c/em\u003e poses a global threat. Infections caused by these multidrug-resistant bacteria are predicted to become a leading cause of human mortality in the future. However, the development pipeline for new antibiotics is declining, raising concerns about a future shortage of effective treatments. Therefore, the discovery of novel antimicrobial agents, particularly those with new mechanisms of action, is urgently needed. In this study, we aimed to discover novel antimicrobial compounds by targeting Lipoteichoic acid synthase (LtaS). LtaS is a membrane protein essential for the synthesis of lipoteichoic acid (LTA), a key cell wall component in \u003cem\u003eS. aureus\u003c/em\u003e. We performed a hierarchical \u003cem\u003ein silico\u003c/em\u003e screening of the ChemBridge chemical 3D structure library, which contains about 150 thousand compounds. The screening identified a compound (7195703) as a promising lead candidate. Quantum mechanical calculation of DG using the FMO method yielded a value of -93.64 kcal/mol. To evaluate its antimicrobial activity, an \u003cem\u003ein vitro\u003c/em\u003e growth inhibition assay was performed using \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e, a closely related model organism for \u003cem\u003eS. aureus\u003c/em\u003e. 7195703 exhibited potent and dose-dependent growth inhibition against \u003cem\u003eS. epidermidis\u003c/em\u003e, with a half-maximal inhibitory concentration (IC\u003csub\u003e50\u003c/sub\u003e) of 16.61 \u0026micro;M. Conversely, 7195703 showed no significant inhibitory activity against the Gram-negative bacterium \u003cem\u003eEscherichia coli\u003c/em\u003e, suggesting its selective activity against certain Gram-positive bacteria. Furthermore, a search for structural analogues of 7195703 identified five derivatives that also displayed antimicrobial activity.\u003c/p\u003e","manuscriptTitle":"In silico Discovery of Novel Antibacterial Compounds against Staphylococcus aureus Targeting Extracellular Domain of Lipoteichoic Acid Synthase, Quantum mechanical quantification, and in vitro experimental validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 04:21:04","doi":"10.21203/rs.3.rs-8922369/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T11:10:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-21T15:23:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-21T14:53:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Diversity","date":"2026-02-20T04:48:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bf1dd687-2c96-47f2-8fd1-3964bfafa524","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T11:10:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 04:21:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8922369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8922369","identity":"rs-8922369","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}