Discovery of novel FtsZ inhibitors with antimicrobial activity by virtual screening and in vitro biological evaluation

Discovery of novel FtsZ inhibitors with antimicrobial activity by virtual screening and in vitro biological evaluation | 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 Discovery of novel FtsZ inhibitors with antimicrobial activity by virtual screening and in vitro biological evaluation Aoqi Luo, Chenliang Qian, Zhengyu Zhang, Jie Xia, Hongwei Jin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4781484/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 Filamentous temperature-sensitive protein Z (FtsZ) plays an important role in bacterial division, and the inhibition of normal physiological function of FtsZ can make a devastating effect on bacterial growth and proliferation, which making it an important antibacterial target. The inhibitor activity targeting the cleft between the H7 helix and the C-terminal substructural domain exhibited superior binding compared to the GTP binding site. Therefore, the discovery of inhibitors targeting the cleft as a binding site holds promise for further research. By performing virtual screening with the workflow mainly composed of pharmacophore modeling and molecular docking as well as the following FtsZ inhibition assay, we identified four compounds B6 , B21 , B26 and B31 . Futher experiment showed that compound B6 and B26 possessed antimicrobial activity with MIC values of 8 µg-mL-1 and 32 µg-mL-1. In conclusion, our study successfully identified novel FtsZ inhibitors with antimicrobial activity through virtual screening and in vitro biological evaluation, demonstrating their potential for further investigation. FtsZ virtual screening pharmacophore model molecular docking antimicrobial activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Bacterial infections and drug resistance pose significant global public health challenges, necessitating the development of novel inhibitors to tackle these problems [ 1 ]. The overuse of antibiotics in clinical, agricultural, and animal husbandry settings has resulted in the emergence of drug-resistant bacteria, leading to a global health problem of antibiotic resistance. Currently, it causes approximately 700,000 deaths annually. It is projected that by 2050, drug resistance will cause an estimated annual death toll of 10 million people [ 2 – 5 ]. In addition, the health problems caused by drug resistance will cause a huge economic burden worldwide [ 6 ]. Therefore, there is an urgent need to identify new antimicrobial drug targets and develop novel inhibitors to combat drug-resistant bacteria. The process of bacterial cell division is accomplished by the regulation of a variety of proteins, among which Filamentous temperature-sensitive protein Z (FtsZ) plays an important role [ 7 ]. During the initial stage of cell division, FtsZ assembles at the center of the bacterial inner membrane, forming a ring-like structure known as the Z-ring. It then recruits downstream division proteins, leading to Z-ring contraction and cell membrane invagination, ultimately resulting in the division of the cell into two daughter cells [ 8 ]. It’s function is similar to that of microtubule proteins in eukaryotic cells [ 9 ]. Due to the critical role of FtsZ in bacterial division, inhibiting its normal physiological function can significantly impair bacterial growth and proliferation. Recent years have witnessed extensive research on the structure and function of FtsZ [ 3 ], which serves as a prerequisite for the development of antimicrobial drugs targeting FtsZ. Ftsz consists of two subregions at C-terminal and N-terminal (Fig. 1 a), which are connected by an H7 helix. The N-terminal portion of the protein consists of a six-stranded β-folded sheet and contains the GDPase structural domain, and the active site of GTPase is between two FtsZ monomers [ 10 ]. The polymerization process of FtsZ proteins relies on the energy provided by GTP hydrolysis [ 11 ]. The C-terminal structural domain consists of a four-stranded β-fold in contact with the H7 helix. The reported FtsZ inhibitors have been shown to exist in two active sites (Fig. 1 b): (1) The GTP-binding pocket in the N-terminal subdomain, e.g., the polyphenol derivatives UCM05, UCM44 [ 10 ] and the natural product berberine [ 12 ] (IC 50 : 10.0 µM); (2) The cleft located between the H7 helix and the C-terminal substructural domain, such as the best FtsZ inhibitor to date, benzamide PC190723 (IC 50 : 0.1 µM, MIC anti−S. aureus : 1 µg-mL-1), as well as its derivatives TXY436 and TXY709 [ 13 ]. However, the compounds with active sites located in the GTP-binding pocket are currently the most studied but most of them have less optimal activity, so the binding site located in the cleft between the H7 helix and the C-terminal substructural domain deserves further investigation. In this study, we aim to identify FtsZ inhibitors with antimicrobial activity and which binding sites located in the cleft between the H7 helix and the C-terminal sub-structural domains by VS and in vitro biological evaluation. A computational workflow was rationally designed, which was composed of pharmacophore modeling, molecular docking, visualization of binding modes and structural clustering. Based on this workflow, we screened the ChemDiv chemical library, identified 38 potential compounds, and tested them for FtsZ inhibition and against Staphylococcus aureus (S. aureus (ATCC29213)). As a result, we identified two novel FtsZ inhibitors, B6 and B26 . Results and Discussions 2.1 Virtual Screening 2.1.1 Pharmacophore Model and shape model The crystal structure 5XDT was first downloaded from the PDB ( https://www.rcsb.org ) for the construction of the pharmacophore and molecular docking because of its good resolution and the binding of the active ligand, TAX707, to the T7 site (Fig. 2 A). Ten pharmacophore models were then generated using the “Receptor-Ligand Pharmacophore Generation” module in Discovery Studio (v16.1.0, Dassault Systèmes Biovia Corp) (Fig. 2 D). Table 1 listed the parameters of these models. All models had a selectivity score of 7.4177 and consisted of four features. Next, the “DruglikeDiverse [5384mol]” chemical library in Discovery Studio was through these models. From Table 1, it can be seen that the number of small molecules hit by the four models, Pharm_01, Pharm_02, Pharm_07 and Pharm_08, is greater than 20%, which indicates that their specificity is weaker than that of the other six models. Observation of the interaction of TAX707 with FtsZ revealed that hydrogen bonding interactions on the amide may play an important role, the hydrogen bond acceptor and hydrogen bond donor features at the amide position were retained. As shown in Fig. 2 B, there are raw hydrogen bonds between amide groups and protein, and the hydrogen bonding feature at this position should not be ignored, we excluded Pharm_04, Pharm_05, and Pharm_06 Finally, three pharmacophores, Pharm_03, Pharm_09 and Pharm_10, were retained for the next screening. They all consist of a hydrogen bond acceptor feature, a hydrogen bond donor feature and two hydrophobic features. The shape model generated by ROCS is shown in Fig. 2 C. Table.1 The parameters of 10 receptor-ligand pharmacophore models generated Discovery Studio and the result of DruglikeDiverse database screened by the models. Model Features a Selectivity Score Hit molecule Pharm_01 ADHH 7.4177 1487 Pharm_02 ADHH 7.4177 1359 Pharm_03 ADHH 7.4177 575 Pharm_04 DHHH 7.4177 519 Pharm_05 DHHH 7.4177 577 Pharm_06 DHHH 7.4177 336 Pharm_07 DHHH 7.4177 1387 Pharm_08 ADHH 7.4177 2610 Pharm_09 ADHH 7.4177 557 Pharm_10 ADHH 7.4177 515 a H, general hydrophobic feature; A, hydrogen bond acceptor; D, hydrogen bond donor. 2.1.2 The workflow of VS and potential hits The workflow of VS included pharmacophore filtering with the aforementioned models ( Pharm_03 , Pharm_09 and Pharm_10 ), molecular docking by DRED, visual inspection of binding modes, and molecular clustering based on FCFP_6 fingerprints (cf. Figure 3 ). As the pharmacophore models contained four features, the FitValue score for a perfect match of the pharmacophore was 4. Accordingly, we used the FitValue of 2 (50% match) as the cutoff. We retained a total of 190,674 compounds for which more than two pharmacophore models had hits. For the shape screening, we defined the ShapeTanimoto score of 0.65 as the critical value and obtained 13,678 compounds. The above two score lines were determined by taking into account the cost of the calculation. By docking with the selected protein structure (PDB code: 5XDT), we selected the top 4,493 compounds (FRED Chemgauss4 score less than − 13). Based on key amino acid residues, we further screened 550 compounds. Finally, the compounds were clustered into 38 clusters based on FCFP_6 fingerprints. The chemical structures, FitValue, ShapeTanimoto, FRED Chemgauss4 scores, inhibition of FtsZ at concentration of 50 µM, and MIC (anti-S. aureus (ATCC29213)) values of these compounds were listed in Table 2 and Fig.S1. By searching the PubChem database ( https://pubchem.ncbi.nlm.nih.gov ), we confirmed that the FtsZ inhibitory activity of these compounds has never been reported before. 2.2 Experimentally validated hits We tested the inhibitory activity of 38 potential compounds against FtsZ at the concentration of 50 µM. As shown in Table 2 , 4 compounds were experimentally confirmed to have FtsZ inhibitory activity (≥ 50% inhibition), indicating the hit rate of the computational workflow was 10.53%. In further experiments we determined the IC 50 values of these 4 compounds and we found that compound B26 had FtsZ inhibitory activity (IC 50 : 17.97 µM). The IC 50 values of the other three compounds ( B6 , B21 and B31 ) were 50.25 µM, 41.40 µM and 41.29 µM, respectively. As shown in Fig. 4 , we tested the antimicrobial activity of four compounds, and found that B6 (3-(1-nonyl-1H-benzo[d]imidazol-2-yl)propan-1-ol) and B26 (5-((5-(2,3- dichlorophenyl)furan-2-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione) showed MIC values of 8 µg-mL-1 and 32 µg-mL-1. The results indicated that the VS we used was indeed effective and that compounds B6 and B26 were the compounds with the antimicrobial activity as new structural types of FtsZ inhibitors that deserve further investigation. Table 2 38 purchased compounds: ChemDiv IDNUMBER, FitValue, ShapeTanimoto (ROCS), FRED Chemgauss4 score, FtsZ inhibition rate (%) at 50 µM and S.aureus (ATCC29213) MIC ID ChemDIV IDNUMBER FitValue ShapeTanimoto (ROCS) FRED Chemgauss4 score (FRED) Ftsz inhibition rate (%) at 50µM S.aureus (ATCC29213) MIC B1 V003-3576 2.18994 0.655 -15.7367 25.00 > 64 B2 4534 − 3024 2.14494 0.821 -15.871 27.65 > 64 B3 L414-1912 2.48708 0.657 -16.338 15.36 > 64 B4 G877-0083 2.52924 0.735 -15.6636 41.63 > 64 B5 8012 − 3374 2.39074 0.683 -15.7013 16.33 > 64 B6 2211 − 0151 2.7238 0.688 -14.2675 50.62 32 B7 8020 − 2855 2.29506 0.658 -15.9521 10.57 > 64 B8 F267-0049 2.46962 0.719 -16.2827 27.41 > 64 B9 G346-0366 2.27053 0.663 -17.0044 16.29 > 64 B10 8539 − 0736 2.14637 0.758 -16.1113 21.23 > 64 B11 G357-2005 3.13923 0.667 -15.773 12.85 > 64 B12 V029-0807 2.67146 0.681 -16.8294 40.07 > 64 B13 E957-1768 3.1309 0.681 -15.6994 25.24 > 64 B14 Y501-6527 2.06592 0.703 -15.825 11.36 > 64 B15 F065-4298 2.04599 0.687 -15.5649 11.74 > 64 B16 V029-4817 2.39143 0.653 -16.2226 27.36 > 64 B17 G017-4213 2.56588 0.744 -15.8409 38.50 > 64 B18 J077-0882 2.67884 0.698 -15.4992 38.00 > 64 B19 L390-2192 2.09343 0.736 -14.227 32.57 > 64 B20 E707-0116 2.91217 0.673 -16.6605 12.30 > 64 B21 C999-0137 2.54798 0.686 -15.506 58.78 > 64 B22 Y513-2252 3.44628 0.651 -14.7058 33.74 > 64 B23 Y501-0276 2.01544 0.738 -16.0013 4.23 > 64 B24 F465-0125 3.60217 0.653 -15.2639 20.02 > 64 B25 G423-0129 3.0962 0.685 -13.5263 31.72 > 64 B26 8006 − 4773 2.32571 0.657 -15.8307 68.71 8 B27 F267-0155 2.53426 0.689 -16.2238 12.31 > 64 B28 G346-0470 2.4866 0.661 -15.9334 27.76 > 64 B29 L655-0041 2.77585 0.7 -15.8616 11.42 > 64 B30 C890-0927 2.88424 0.694 -15.327 24.35 > 64 B31 V029-0830 2.75181 0.658 -16.2969 60.08 > 64 B32 G821-0211 2.56342 0.714 -15.1141 0.00 > 64 B33 K786-9580 2.27656 0.714 -15.5573 41.22 > 64 B34 V030-1269 2.82424 0.653 -16.1527 0.00 > 64 B35 8539 − 0547 2.36252 0.746 -15.7137 25.72 > 64 B36 G325-0437 2.49852 0.707 -14.8775 13.66 > 64 B37 P708-0872 2.09043 0.675 -13.8092 2.03 > 64 B38 8016 − 0453 3.4073 0.772 -16.4401 19.56 > 64 2.3 Structure-activity relationships analysis To further analyze the interactions between compound B6 , B26 and the FtsZ protein, we analyzed the results of molecular docking. As shown in the Fig. 5 , compound B6 formed hydrogen bonds with VAL207 and LEU209, compound B26 forms hydrogen bonding interactions with GLY196 and ASN263. By comparing their binding patterns and the results of biological experiments, we speculate that (1) Hydrogen bonding with ASN263 may be a critical interaction affecting the inhibitory activity of the FtsZ enzyme; (2) Hydrogen bonding with VAL207, LEU209 may play a key role in influencing the antimicrobial activity. In subsequent structural modifications, we can try to retain several key hydrogen bonding interactions to enhance enzyme inhibitory and antimicrobial activities. 2.4 Plausible binding modes from MD simulation We further performed 100-ns MD simulations to explore the binding of compound B6 and B26 to the Ftsz protein. As for the FtsZ-B6 complex, it reached the stable state after about 95 ns (cf. Figure 6 A). The protein-ligand binding mode was extracted from the trajectory after equilibration. As shown in the figure, the core scaffold of compound B6 formed hydrogen bonds with LEU209 and ANS263. Figure 6 B shows that the binding mode between compound B26 and FtsZ was different from that between compound B6 and FtsZ. Though it also kept the hydrogen bond with ANS263, the difference was that the dihydropyrazolo-pyridazine fragment formed hydrogen bonds with GLN195 and THR265. Materials and Methods 3.1 Computational modeling VS was used to screen potential FtsZ inhibitor molecules from the ChemDiv compound library (~ 1.6 million molecules). To make the whole VS process fast and accurate, before molecular docking, pharmacophore models and structural model were used for screening. 3.1.1 Construction and screening of pharmacophore models A pharmacophore model based on receptor-ligand interactions was constructed and validated using the "Receptor-Ligand Pharmacophore Generation" module in Discovery Studio 2016. In this study, the best crystal structure complex of FtsZ was selected. First, 10 pharmacophores were generated using the "Receptor-Ligand Pharmacophore Generation" module of Discovery Studio 2016, and each model could contain 3–4 features. The specificity of the pharmacophore was then tested using Discovery Studio's compound library by selecting "DruglikeDiverse [5384mol]" in the "Search 3D Database" module. " in the "Search 3D Database" module, and poorly specific models were excluded by comparing the percentage of results screened for each model. Finally, the compliant pharmacophore models were selected based on the key hydrogen bonding interactions in the FtsZ eutectic complex. A 3D conformational database of ChemDiv compounds was constructed using the "Build 3D Database" module of Discovery Studio. All conformations in the database were mapped to pharmacophore models using a rigid fitting algorithm ("FAST" search) in the "Build 3D Database" module. The FitValue score is a measure of the similarity of each conformation to the pharmacophore model. Compounds with a score greater than 2 were retained in order of FitValue score. 3.1.2 Construction and screening of shape model Shape models were constructed using ROCS (3.3.1.2 Inc., OpenEye Scientific Software Inc.) based on the binding conformation of the homologous ligand RJ5 in the eutectic structure. The shape models were used for the next step of screening, where shape similarity was measured by the ShapeTanimoto score. ShapeTanimoto score values greater than 0.65 compounds were saved during the screening process. 3.1.3 Molecular docking Molecular docking was performed using FRED. First, a maximum of 200 conformations per compound were generated using OMEGA, placed at the binding site to the receptor, and scored using the Chemgauss4 scoring function. Compounds with scores below − 13 were retained and visually inspected for binding modes. Compounds that formed hydrogen bonding interactions with VAL207, LEU209 or Asn263 were retained. Structural clustering based on FCFP_6 fingerprints was performed using Discovery Studio's “Cluster Ligands” module. One or two compounds from each cluster were selected by visual inspection, prioritizing compounds with higher FitValue, ShapeTanimoto score and Chemgauss4 score and better synthetic feasibility. 3.1.4 Molecular dynamics simulation Molecular dynamics (MD) simulation was performed according to the published protocol[ 14 , 15 ]. The FtsZ protein topology file was constructed with the GROMOS96 43A1 force field [ 16 ]. The ligand topology file was constructed by the LigParGen server ( http://zarbi.chem.yale.edu/ligpargen/ ) [ 17 ], with the PDB coordinates as the input. Then, the GROMACS software (version 2019.4) was used to perform MD simulations of the FtsZ-ligand complex [ 18 ]. The system consisted of single point charge water to solvate the entire system. The water box was then extended by 10 Å from the periphery of the system in each dimension. Than, 16 Na + were added to the system to make the total charge become zero. The MD simulation included energy minimization, equilibration, and production phases. The simulation started with 5000 steps of energy minimization based on steepest descent algorithm. In the equilibrium phase, 500 ps simulation for NVT and 500 ps simulation for NPT were included. The system was maintained at a pressure of 1 atm using Parrinello Rahman and a constant temperature of 300 K using V-rescale. Lastly, 100-ns MD simulation without restraint was performed at NPT. The coordinates of the system were saved every 100 ps during the simulation. 3.2 In vitro biological evaluation 3.2.1 FtsZ protein expression and purification The gene for FtsZ of S. aureus origin was synthesised in vitro, and the FtsZ gene was cloned into the pET-28b (+) vector using BamHI and HindIII as cleavage sites, and a 6×histidine tag (His-Tag) was pre-inserted at the N-terminal end of the expressed one for purification by affinity chromatography. The pET-28b-FtsZ exprssion plasmid was transformed into BL21 (DE3) competent cells, and FtsZ protein expression was induced using 0.5 mmol/L of Isopropyl b-D-1-thiogalactopyranoside (IPTG) for 16 h at 16℃. The bacteria are collected after fermentation, and resuspended and lysed using sonication in lysis buffer (50 mM HEPES, 500 mM NaCl, 1 mM EDTA, Ph 7.4). The lysate was centrifuged at 4°C, 12500 rpm for 1 h, and the resulting supernatant was subjected to affinity chromatography using a Ni 2+ chelated affinity chromatography column with nickel binding for 1 h. Elution was performed with elution buffer (50 mM HEPES, 500 mM NaCl, 250 mM imidazole, 1 mM EDTA, pH = 7.4). The purified proteins were subjected to concentration determination and SDS-PAGE running gel for purity determination. FtsZ protein was aliquoted, flash frozen, and stored at -80℃. 3.2.2 FtsZ activity and inhibition assay Principle of FtsZ activity assay: GTPase can catalyse the decomposition of GTP into GDP and phosphate ions, which can form green complexes with malachite green and molybdate. The GTPase activity of FtsZ was calculated by detecting the amount of free phosphate generated from hydrolysed GTP per unit time of FtsZ protein at 620 nm[ 19 ]. To determine the activity of FtsZ, add 5 µM FtsZ, buffer (250 mM HEPES, 250 mM KCl, 5 mM EDTA), GTP, water and MgCl 2 to a 96-well plate (100 µL), mix well and react for 20 min at 37 ℃, then add the acidic solution (5 µL) and react for 10 min at room temperature (25 ℃), protected from light. Add blue solution (15 µL and react for 20 min at 25°C, protected from light. The absorbance was measured at 620 nm. Using the above established enzyme activity assay, 38 compound samples were initially screened at a final concentration of 50 µM. The compounds with enzyme inhibition greater than 50% obtained from the screening were subjected to multiplicative dilution to obtain the inhibition rate at different concentrations, the logarithm of the inhibitor concentration was taken as the horizontal coordinate and the enzyme activity as the vertical coordinate, and then the IC 50 value could be obtained by fitting the curve using the software Graphpad Prism 5. 3.2.3 Antimicrobial Testing MIC is an important index for evaluating the in vitro antimicrobial effect of compounds, and the twofold dilution method is usually used to determine the MIC values of the target compounds and control drugs[ 20 , 21 ]. The concentration of the sample storage solution to be tested was dissolved in DMSO to 6.4 mg/mL. A bacterial suspension of S. aureus strain (equivalent to a bacterial suspension of 0.5 McFarland turbidity standard), was diluted with liquid LB medium to obtain a final inoculum of 10 5 CFU/mL. 196 µL of the inoculum was added to A1-H1 of a 96-well plate, and the rest of 100 µL of liquid LB medium was added to each well. Add 4 µL of sample storage solution to A1-H1 of the 96-well plate and mix well. Then 100 µL was pipetted from well A1-H1 and added to A2-H2 and mixed well. Another 100 µL was pipetted from A2-H2 and added to A3-H3 and mixed well, and so on until it was added to wells A10-H10, and 100 µL of liquid was pipetted and discarded from the tenth column of wells. The above 96-well plate was placed at 37℃ for 17–20 h. After incubation, bacterial growth was observed and the minimum inhibitory concentration (MIC) value was determined by visual inspection as the lowest dilution of the compound without turbidity. Conclusion Bacterial division is an important process in bacterial life activity and an important target in antibiotic development. FtsZ is an essential protein for bacterial division and is present in almost all bacteria. FtsZ is the first protein recruited to the division site, which polymerizes to form the Z-loop and acts as a scaffold for the recruitment of downstream proteins together with FtsA. If the polymerization of the FtsZ protein is blocked, the formation of the Z-ring is impeded and bacterial cell division is inhibited, which in this case leads to cell death, leading to antimicrobial activity. Therefore, constructing a screening model using FtsZ as a target has the potential to screen for lead compounds with antibacterial activity. In this study, FtsZ from Staphylococcus aureus was cloned, expressed, isolated and purified, and a highly pure target protein was obtained, and the protein was later proved to be active by enzyme activity assay. A screening model was established with FtsZ as the target, and a compound library was screened to obtain 38 candidate compounds by molecular docking and molecular clustering, which were subjected to enzyme inhibitory activity assay to screen out the compounds with better activity (compound B6 and B26 ). Molecular docking and molecular dynamics simulations were used to demonstrate the possible binding patterns between the compounds and FtsZ proteins, providing assistance for subsequent structural optimization The compounds obtained from the screening based on this workflow were assayed for enzyme activity inhibition and antimicrobial activity, and were found to have good inhibitory activity in both enzyme activity and antimicrobial activity, which indicates that the model is reliable and stable, and provides a new technique and methodology for searching for and discovering FtsZ inhibitors. Declarations AUTHOR INFORMATION Corresponding Author *E-mail: Xinxin Si (sixx@ jou.edu.cn) Shaojie Ma ( [email protected] ) Address: Xinxin Si, School of Pharmacy, Jiangsu Ocean University, Lianyungang, Jiangsu 222005, China Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This research received support from a project that was funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Author Contributions Chenliang Qian: perform virtual screening and MD simulation; write the computational part. Aoqi Luo: perform in vitro experiment; write the in vitro experiment part. Shaojie Ma: assist with the computational simulation. Zhengyu Zhang: assist with the in vitro experiment. 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FEBS Lett 585(24):3880–3883. https://doi.org/10.1016/j.febslet.2011.10.046 Andrews JM (2001) Determination of minimum inhibitory concentrations. J Antimicrob Chemother 48 Suppl 1:5–16. https://doi.org/10.1093/jac/48.suppl_1.5 Lambert RJ, Pearson J (2000) Susceptibility testing: accurate and reproducible minimum inhibitory concentration (MIC) and non-inhibitory concentration (NIC) values. J Appl Microbiol 88(5):784–790. https://doi.org/10.1046/j.1365-2672.2000.01017.x Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4781484","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334381923,"identity":"b9cb2c85-6a61-4f16-84e7-7faabe162cfc","order_by":0,"name":"Aoqi Luo","email":"","orcid":"","institution":"Jiangsu Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Aoqi","middleName":"","lastName":"Luo","suffix":""},{"id":334381924,"identity":"5f565b93-aa07-4bef-a025-567bc4368796","order_by":1,"name":"Chenliang Qian","email":"","orcid":"","institution":"Jiangsu Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Chenliang","middleName":"","lastName":"Qian","suffix":""},{"id":334381929,"identity":"6aa6e863-562d-46b9-b72a-92eea8328f51","order_by":2,"name":"Zhengyu Zhang","email":"","orcid":"","institution":"Jiangsu Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Zhengyu","middleName":"","lastName":"Zhang","suffix":""},{"id":334381931,"identity":"cfbae3ca-9773-4514-809e-041401b0106f","order_by":3,"name":"Jie Xia","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Xia","suffix":""},{"id":334381932,"identity":"e9dc5af8-1537-44c5-8303-b8dc2704ae48","order_by":4,"name":"Hongwei Jin","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Jin","suffix":""},{"id":334381934,"identity":"75aaeb9f-be3e-4b70-a1bb-90b2dd4a94a6","order_by":5,"name":"Xinxin Si","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDACZiD+wANmGhCvhXEGaVpAuiA6iNUi385jJm0jY5PYwN68TYKh5g5hLQaH2ZKNc3jSEht4jpVJMBx7RoQWZuaDj3N4Dic2SOSYSTA2HCbCYc1AZRYgLfJviNTCcBhoCwPYFh4itYD8YtjDk2bcxpNWbJFwjBiH9Z8xk/jZYyPbz354440PNcQ4DAQYexgY2ECMBCI1AMEP4pWOglEwCkbBCAQAPqQw/JgZp38AAAAASUVORK5CYII=","orcid":"","institution":"Jiangsu Ocean University","correspondingAuthor":true,"prefix":"","firstName":"Xinxin","middleName":"","lastName":"Si","suffix":""},{"id":334381937,"identity":"4aa10248-6cbe-4d72-8cb1-2473bacb4f54","order_by":6,"name":"Shaojie Ma","email":"","orcid":"","institution":"Jiangsu Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Shaojie","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-07-22 11:29:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4781484/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4781484/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62837623,"identity":"765347a7-420a-4949-a282-f282a59aad32","added_by":"auto","created_at":"2024-08-20 05:42:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":197647,"visible":true,"origin":"","legend":"\u003cp\u003eThe structures of the FtsZ protein (A) and common inhibitors (B).PDB code: 3VOB.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4781484/v1/4bb9584944005390debc0a62.jpg"},{"id":62837625,"identity":"7e348cb5-54d2-49c9-ad02-87f70f4928cf","added_by":"auto","created_at":"2024-08-20 05:42:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":354706,"visible":true,"origin":"","legend":"\u003cp\u003eModels used for virtual screening. (A) The cocrystal structure used for computational modeling (PDB code: 5XDT). (B) The shape model. (C) 10 pharmacophore models generated by the “Receptor-Ligand Pharmacophore Generation” module implemented in Discovery Studio. Color code: general hydrophobic feature, blue; hydrogen bond acceptor, green; hydrogen bond donor, purple .\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4781484/v1/5fad7e4456003fc06ae5b1c4.jpg"},{"id":62837624,"identity":"d715308e-9a61-4f52-b840-c6b97da07a1c","added_by":"auto","created_at":"2024-08-20 05:42:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247704,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart for novel FtsZ inhibitor discovery.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4781484/v1/6ee3dd5f03a203b2c0f7997c.jpg"},{"id":62837629,"identity":"1fd1766d-2620-47d5-8021-861c40755384","added_by":"auto","created_at":"2024-08-20 05:42:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":392426,"visible":true,"origin":"","legend":"\u003cp\u003eBiological activity of 4 compounds: \u003cstrong\u003eB6 (A)、B21(B)、B26(C) \u003c/strong\u003eand\u003cstrong\u003e B31(D)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4781484/v1/02f3cceb1a602628320203c3.jpg"},{"id":62838078,"identity":"a329657f-72bd-4572-8791-cde8a9c2e0f1","added_by":"auto","created_at":"2024-08-20 05:50:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":251383,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of molecular docking\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4781484/v1/500fb6cbfe4b79e33ed1102a.jpg"},{"id":62837628,"identity":"26317cb1-97c5-44dd-844f-a92a35eb068e","added_by":"auto","created_at":"2024-08-20 05:42:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":188936,"visible":true,"origin":"","legend":"\u003cp\u003eThe C-alpha RMSDs of the FtsZ protein and heavy-atom RMSDs of three compounds, (A) \u003cstrong\u003eB6\u003c/strong\u003e and (B) \u003cstrong\u003eB26\u003c/strong\u003e during 100-ns MD simulation, and the plausible binding modes. Images were created by Discovery Studio 2016. The RMSDs were calculated with the starting protein conformation or ligand pose as the reference.\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4781484/v1/3eb3b1119836f47d5b68155e.jpg"},{"id":67744758,"identity":"8d02e6b4-993e-4c3d-9b85-b85179ab0bd1","added_by":"auto","created_at":"2024-10-29 09:24:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2371679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4781484/v1/26d7863c-4150-4e26-8e9a-603fa400a583.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discovery of novel FtsZ inhibitors with antimicrobial activity by virtual screening and in vitro biological evaluation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBacterial infections and drug resistance pose significant global public health challenges, necessitating the development of novel inhibitors to tackle these problems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The overuse of antibiotics in clinical, agricultural, and animal husbandry settings has resulted in the emergence of drug-resistant bacteria, leading to a global health problem of antibiotic resistance. Currently, it causes approximately 700,000 deaths annually. It is projected that by 2050, drug resistance will cause an estimated annual death toll of 10\u0026nbsp;million people [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, the health problems caused by drug resistance will cause a huge economic burden worldwide [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, there is an urgent need to identify new antimicrobial drug targets and develop novel inhibitors to combat drug-resistant bacteria.\u003c/p\u003e \u003cp\u003eThe process of bacterial cell division is accomplished by the regulation of a variety of proteins, among which Filamentous temperature-sensitive protein Z (FtsZ) plays an important role [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. During the initial stage of cell division, FtsZ assembles at the center of the bacterial inner membrane, forming a ring-like structure known as the Z-ring. It then recruits downstream division proteins, leading to Z-ring contraction and cell membrane invagination, ultimately resulting in the division of the cell into two daughter cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It\u0026rsquo;s function is similar to that of microtubule proteins in eukaryotic cells [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Due to the critical role of FtsZ in bacterial division, inhibiting its normal physiological function can significantly impair bacterial growth and proliferation. Recent years have witnessed extensive research on the structure and function of FtsZ [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which serves as a prerequisite for the development of antimicrobial drugs targeting FtsZ.\u003c/p\u003e \u003cp\u003eFtsz consists of two subregions at C-terminal and N-terminal (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), which are connected by an H7 helix. The N-terminal portion of the protein consists of a six-stranded β-folded sheet and contains the GDPase structural domain, and the active site of GTPase is between two FtsZ monomers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The polymerization process of FtsZ proteins relies on the energy provided by GTP hydrolysis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The C-terminal structural domain consists of a four-stranded β-fold in contact with the H7 helix. The reported FtsZ inhibitors have been shown to exist in two active sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb): (1) The GTP-binding pocket in the N-terminal subdomain, e.g., the polyphenol derivatives UCM05, UCM44 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and the natural product berberine [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] (IC\u003csub\u003e50\u003c/sub\u003e: 10.0 \u0026micro;M); (2) The cleft located between the H7 helix and the C-terminal substructural domain, such as the best FtsZ inhibitor to date, benzamide PC190723 (IC\u003csub\u003e50\u003c/sub\u003e : 0.1 \u0026micro;M, MIC\u003csub\u003eanti\u0026minus;S. aureus\u003c/sub\u003e : 1 \u0026micro;g-mL-1), as well as its derivatives TXY436 and TXY709 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the compounds with active sites located in the GTP-binding pocket are currently the most studied but most of them have less optimal activity, so the binding site located in the cleft between the H7 helix and the C-terminal substructural domain deserves further investigation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, we aim to identify FtsZ inhibitors with antimicrobial activity and which binding sites located in the cleft between the H7 helix and the C-terminal sub-structural domains by VS and \u003cem\u003ein vitro\u003c/em\u003e biological evaluation. A computational workflow was rationally designed, which was composed of pharmacophore modeling, molecular docking, visualization of binding modes and structural clustering. Based on this workflow, we screened the ChemDiv chemical library, identified 38 potential compounds, and tested them for FtsZ inhibition and against Staphylococcus aureus (S. aureus (ATCC29213)). As a result, we identified two novel FtsZ inhibitors, \u003cb\u003eB6\u003c/b\u003e and \u003cb\u003eB26\u003c/b\u003e.\u003c/p\u003e"},{"header":"Results and Discussions","content":"\u003cdiv class=\"Heading\"\u003e\u003cb\u003e\u003c/b\u003e\u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Virtual Screening\u003c/h2\u003e \u003cp\u003e \u003cb\u003e2.1.1\u003c/b\u003e \u003cb\u003ePharmacophore Model and shape model\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe crystal structure 5XDT was first downloaded from the PDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the construction of the pharmacophore and molecular docking because of its good resolution and the binding of the active ligand, TAX707, to the T7 site (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Ten pharmacophore models were then generated using the \u0026ldquo;Receptor-Ligand Pharmacophore Generation\u0026rdquo; module in Discovery Studio (v16.1.0, Dassault Syst\u0026egrave;mes Biovia Corp) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Table\u0026nbsp;1 listed the parameters of these models. All models had a selectivity score of 7.4177 and consisted of four features. Next, the \u0026ldquo;DruglikeDiverse [5384mol]\u0026rdquo; chemical library in Discovery Studio was through these models. From Table\u0026nbsp;1, it can be seen that the number of small molecules hit by the four models, Pharm_01, Pharm_02, Pharm_07 and Pharm_08, is greater than 20%, which indicates that their specificity is weaker than that of the other six models. Observation of the interaction of TAX707 with FtsZ revealed that hydrogen bonding interactions on the amide may play an important role, the hydrogen bond acceptor and hydrogen bond donor features at the amide position were retained. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, there are raw hydrogen bonds between amide groups and protein, and the hydrogen bonding feature at this position should not be ignored, we excluded Pharm_04, Pharm_05, and Pharm_06 Finally, three pharmacophores, Pharm_03, Pharm_09 and Pharm_10, were retained for the next screening. They all consist of a hydrogen bond acceptor feature, a hydrogen bond donor feature and two hydrophobic features. The shape model generated by ROCS is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1\u003c/b\u003e The parameters of 10 receptor-ligand pharmacophore models generated Discovery Studio and the result of DruglikeDiverse database screened by the models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeatures \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelectivity Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHit molecule\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharm_01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharm_02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharm_03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharm_04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharm_05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharm_06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharm_07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharm_08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharm_09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharm_10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e H, general hydrophobic feature; A, hydrogen bond acceptor; D, hydrogen bond donor.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1.2 The workflow of VS and potential hits\u003c/h2\u003e \u003cp\u003eThe workflow of VS included pharmacophore filtering with the aforementioned models (\u003cb\u003ePharm_03\u003c/b\u003e, \u003cb\u003ePharm_09\u003c/b\u003e and \u003cb\u003ePharm_10\u003c/b\u003e), molecular docking by DRED, visual inspection of binding modes, and molecular clustering based on FCFP_6 fingerprints (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As the pharmacophore models contained four features, the FitValue score for a perfect match of the pharmacophore was 4. Accordingly, we used the FitValue of 2 (50% match) as the cutoff. We retained a total of 190,674 compounds for which more than two pharmacophore models had hits. For the shape screening, we defined the ShapeTanimoto score of 0.65 as the critical value and obtained 13,678 compounds. The above two score lines were determined by taking into account the cost of the calculation. By docking with the selected protein structure (PDB code: 5XDT), we selected the top 4,493 compounds (FRED Chemgauss4 score less than \u0026minus;\u0026thinsp;13). Based on key amino acid residues, we further screened 550 compounds. Finally, the compounds were clustered into 38 clusters based on FCFP_6 fingerprints. The chemical structures, FitValue, ShapeTanimoto, FRED Chemgauss4 scores, inhibition of FtsZ at concentration of 50 \u0026micro;M, and MIC (anti-S. aureus (ATCC29213)) values of these compounds were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.S1. By searching the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we confirmed that the FtsZ inhibitory activity of these compounds has never been reported before.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Experimentally validated hits\u003c/h3\u003e\n\u003cp\u003eWe tested the inhibitory activity of 38 potential compounds against FtsZ at the concentration of 50 \u0026micro;M. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 4 compounds were experimentally confirmed to have FtsZ inhibitory activity (\u0026ge;\u0026thinsp;50% inhibition), indicating the hit rate of the computational workflow was 10.53%. In further experiments we determined the IC\u003csub\u003e50\u003c/sub\u003e values of these 4 compounds and we found that compound \u003cb\u003eB26\u003c/b\u003e had FtsZ inhibitory activity (IC\u003csub\u003e50\u003c/sub\u003e: 17.97 \u0026micro;M). The IC\u003csub\u003e50\u003c/sub\u003e values of the other three compounds (\u003cb\u003eB6\u003c/b\u003e, \u003cb\u003eB21\u003c/b\u003e and \u003cb\u003eB31\u003c/b\u003e) were 50.25 \u0026micro;M, 41.40 \u0026micro;M and 41.29 \u0026micro;M, respectively.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we tested the antimicrobial activity of four compounds, and found that \u003cb\u003eB6\u003c/b\u003e (3-(1-nonyl-1H-benzo[d]imidazol-2-yl)propan-1-ol) and \u003cb\u003eB26\u003c/b\u003e (5-((5-(2,3- dichlorophenyl)furan-2-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione) showed MIC values of 8 \u0026micro;g-mL-1 and 32 \u0026micro;g-mL-1. The results indicated that the VS we used was indeed effective and that compounds B6 and B26 were the compounds with the antimicrobial activity as new structural types of FtsZ inhibitors that deserve further investigation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e38 purchased compounds: ChemDiv IDNUMBER, FitValue, ShapeTanimoto (ROCS), FRED Chemgauss4 score, FtsZ inhibition rate (%) at 50 \u0026micro;M and S.aureus (ATCC29213) MIC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemDIV\u003c/p\u003e \u003cp\u003eIDNUMBER\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFitValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShapeTanimoto\u003c/p\u003e \u003cp\u003e(ROCS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFRED Chemgauss4 score\u003c/p\u003e \u003cp\u003e(FRED)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFtsz inhibition rate (%) at 50\u0026micro;M\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS.aureus (ATCC29213) MIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV003-3576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.7367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4534\u0026thinsp;\u0026minus;\u0026thinsp;3024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL414-1912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.48708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG877-0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.52924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.6636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8012\u0026thinsp;\u0026minus;\u0026thinsp;3374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.7013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2211\u0026thinsp;\u0026minus;\u0026thinsp;0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.2675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8020\u0026thinsp;\u0026minus;\u0026thinsp;2855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.9521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF267-0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16.2827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG346-0366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-17.0044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8539\u0026thinsp;\u0026minus;\u0026thinsp;0736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16.1113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e 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colname=\"c3\"\u003e \u003cp\u003e2.4866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.9334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL655-0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.77585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.8616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC890-0927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV029-0830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16.2969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG821-0211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.56342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.1141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK786-9580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.5573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV030-1269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.82424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16.1527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8539\u0026thinsp;\u0026minus;\u0026thinsp;0547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.7137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG325-0437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.8775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP708-0872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.8092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8016\u0026thinsp;\u0026minus;\u0026thinsp;0453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-16.4401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Structure-activity relationships analysis\u003c/h2\u003e \u003cp\u003eTo further analyze the interactions between compound \u003cb\u003eB6\u003c/b\u003e, \u003cb\u003eB26\u003c/b\u003e and the FtsZ protein, we analyzed the results of molecular docking. As shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, compound \u003cb\u003eB6\u003c/b\u003e formed hydrogen bonds with VAL207 and LEU209, compound \u003cb\u003eB26\u003c/b\u003e forms hydrogen bonding interactions with GLY196 and ASN263. By comparing their binding patterns and the results of biological experiments, we speculate that (1) Hydrogen bonding with ASN263 may be a critical interaction affecting the inhibitory activity of the FtsZ enzyme; (2) Hydrogen bonding with VAL207, LEU209 may play a key role in influencing the antimicrobial activity. In subsequent structural modifications, we can try to retain several key hydrogen bonding interactions to enhance enzyme inhibitory and antimicrobial activities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.4 Plausible binding modes from MD simulation\u003c/h3\u003e\n\u003cp\u003eWe further performed 100-ns MD simulations to explore the binding of compound \u003cb\u003eB6\u003c/b\u003e and \u003cb\u003eB26\u003c/b\u003e to the Ftsz protein. As for the FtsZ-B6 complex, it reached the stable state after about 95 ns (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The protein-ligand binding mode was extracted from the trajectory after equilibration. As shown in the figure, the core scaffold of compound \u003cb\u003eB6\u003c/b\u003e formed hydrogen bonds with LEU209 and ANS263. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB shows that the binding mode between compound \u003cb\u003eB26\u003c/b\u003e and FtsZ was different from that between compound \u003cb\u003eB6\u003c/b\u003e and FtsZ. Though it also kept the hydrogen bond with ANS263, the difference was that the dihydropyrazolo-pyridazine fragment formed hydrogen bonds with GLN195 and THR265.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Computational modeling\u003c/h2\u003e \u003cp\u003eVS was used to screen potential FtsZ inhibitor molecules from the ChemDiv compound library (~\u0026thinsp;1.6\u0026nbsp;million molecules). To make the whole VS process fast and accurate, before molecular docking, pharmacophore models and structural model were used for screening.\u003c/p\u003e \u003cp\u003e3.1.1 Construction and screening of pharmacophore models\u003c/p\u003e \u003cp\u003eA pharmacophore model based on receptor-ligand interactions was constructed and validated using the \"Receptor-Ligand Pharmacophore Generation\" module in Discovery Studio 2016. In this study, the best crystal structure complex of FtsZ was selected. First, 10 pharmacophores were generated using the \"Receptor-Ligand Pharmacophore Generation\" module of Discovery Studio 2016, and each model could contain 3\u0026ndash;4 features. The specificity of the pharmacophore was then tested using Discovery Studio's compound library by selecting \"DruglikeDiverse [5384mol]\" in the \"Search 3D Database\" module. \" in the \"Search 3D Database\" module, and poorly specific models were excluded by comparing the percentage of results screened for each model. Finally, the compliant pharmacophore models were selected based on the key hydrogen bonding interactions in the FtsZ eutectic complex.\u003c/p\u003e \u003cp\u003eA 3D conformational database of ChemDiv compounds was constructed using the \"Build 3D Database\" module of Discovery Studio. All conformations in the database were mapped to pharmacophore models using a rigid fitting algorithm (\"FAST\" search) in the \"Build 3D Database\" module. The FitValue score is a measure of the similarity of each conformation to the pharmacophore model. Compounds with a score greater than 2 were retained in order of FitValue score.\u003c/p\u003e \u003cp\u003e3.1.2 Construction and screening of shape model\u003c/p\u003e \u003cp\u003eShape models were constructed using ROCS (3.3.1.2 Inc., OpenEye Scientific Software Inc.) based on the binding conformation of the homologous ligand RJ5 in the eutectic structure. The shape models were used for the next step of screening, where shape similarity was measured by the ShapeTanimoto score. ShapeTanimoto score values greater than 0.65 compounds were saved during the screening process.\u003c/p\u003e \u003cp\u003e3.1.3 Molecular docking\u003c/p\u003e \u003cp\u003eMolecular docking was performed using FRED. First, a maximum of 200 conformations per compound were generated using OMEGA, placed at the binding site to the receptor, and scored using the Chemgauss4 scoring function. Compounds with scores below \u0026minus;\u0026thinsp;13 were retained and visually inspected for binding modes. Compounds that formed hydrogen bonding interactions with VAL207, LEU209 or Asn263 were retained.\u003c/p\u003e \u003cp\u003eStructural clustering based on FCFP_6 fingerprints was performed using Discovery Studio's \u0026ldquo;Cluster Ligands\u0026rdquo; module. One or two compounds from each cluster were selected by visual inspection, prioritizing compounds with higher FitValue, ShapeTanimoto score and Chemgauss4 score and better synthetic feasibility.\u003c/p\u003e \u003cp\u003e3.1.4 Molecular dynamics simulation\u003c/p\u003e \u003cp\u003eMolecular dynamics (MD) simulation was performed according to the published protocol[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The FtsZ protein topology file was constructed with the GROMOS96 43A1 force field [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The ligand topology file was constructed by the LigParGen server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://zarbi.chem.yale.edu/ligpargen/\u003c/span\u003e\u003cspan address=\"http://zarbi.chem.yale.edu/ligpargen/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], with the PDB coordinates as the input. Then, the GROMACS software (version 2019.4) was used to perform MD simulations of the FtsZ-ligand complex [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The system consisted of single point charge water to solvate the entire system. The water box was then extended by 10 \u0026Aring; from the periphery of the system in each dimension. Than, 16 Na\u0026thinsp;+\u0026thinsp;were added to the system to make the total charge become zero. The MD simulation included energy minimization, equilibration, and production phases. The simulation started with 5000 steps of energy minimization based on steepest descent algorithm. In the equilibrium phase, 500 ps simulation for NVT and 500 ps simulation for NPT were included. The system was maintained at a pressure of 1 atm using Parrinello Rahman and a constant temperature of 300 K using V-rescale. Lastly, 100-ns MD simulation without restraint was performed at NPT. The coordinates of the system were saved every 100 ps during the simulation.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2\u003c/b\u003e \u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003ebiological evaluation\u003c/b\u003e\u003c/p\u003e \u003cp\u003e3.2.1 FtsZ protein expression and purification\u003c/p\u003e \u003cp\u003eThe gene for FtsZ of S. aureus origin was synthesised in vitro, and the FtsZ gene was cloned into the pET-28b (+) vector using BamHI and HindIII as cleavage sites, and a 6\u0026times;histidine tag (His-Tag) was pre-inserted at the N-terminal end of the expressed one for purification by affinity chromatography. The pET-28b-FtsZ exprssion plasmid was transformed into BL21 (DE3) competent cells, and FtsZ protein expression was induced using 0.5 mmol/L of Isopropyl b-D-1-thiogalactopyranoside (IPTG) for 16 h at 16℃. The bacteria are collected after fermentation, and resuspended and lysed using sonication in lysis buffer (50 mM HEPES, 500 mM NaCl, 1 mM EDTA, Ph 7.4). The lysate was centrifuged at 4\u0026deg;C, 12500 rpm for 1 h, and the resulting supernatant was subjected to affinity chromatography using a Ni\u003csup\u003e2+\u003c/sup\u003e chelated affinity chromatography column with nickel binding for 1 h. Elution was performed with elution buffer (50 mM HEPES, 500 mM NaCl, 250 mM imidazole, 1 mM EDTA, pH\u0026thinsp;=\u0026thinsp;7.4). The purified proteins were subjected to concentration determination and SDS-PAGE running gel for purity determination. FtsZ protein was aliquoted, flash frozen, and stored at -80℃.\u003c/p\u003e \u003cp\u003e3.2.2 FtsZ activity and inhibition assay\u003c/p\u003e \u003cp\u003ePrinciple of FtsZ activity assay: GTPase can catalyse the decomposition of GTP into GDP and phosphate ions, which can form green complexes with malachite green and molybdate. The GTPase activity of FtsZ was calculated by detecting the amount of free phosphate generated from hydrolysed GTP per unit time of FtsZ protein at 620 nm[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To determine the activity of FtsZ, add 5 \u0026micro;M FtsZ, buffer (250 mM HEPES, 250 mM KCl, 5 mM EDTA), GTP, water and MgCl\u003csub\u003e2\u003c/sub\u003e to a 96-well plate (100 \u0026micro;L), mix well and react for 20 min at 37 ℃, then add the acidic solution (5 \u0026micro;L) and react for 10 min at room temperature (25 ℃), protected from light. Add blue solution (15 \u0026micro;L and react for 20 min at 25\u0026deg;C, protected from light. The absorbance was measured at 620 nm.\u003c/p\u003e \u003cp\u003eUsing the above established enzyme activity assay, 38 compound samples were initially screened at a final concentration of 50 \u0026micro;M. The compounds with enzyme inhibition greater than 50% obtained from the screening were subjected to multiplicative dilution to obtain the inhibition rate at different concentrations, the logarithm of the inhibitor concentration was taken as the horizontal coordinate and the enzyme activity as the vertical coordinate, and then the IC\u003csub\u003e50\u003c/sub\u003e value could be obtained by fitting the curve using the software Graphpad Prism 5.\u003c/p\u003e \u003cp\u003e3.2.3 Antimicrobial Testing\u003c/p\u003e \u003cp\u003eMIC is an important index for evaluating the in vitro antimicrobial effect of compounds, and the twofold dilution method is usually used to determine the MIC values of the target compounds and control drugs[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The concentration of the sample storage solution to be tested was dissolved in DMSO to 6.4 mg/mL. A bacterial suspension of S. aureus strain (equivalent to a bacterial suspension of 0.5 McFarland turbidity standard), was diluted with liquid LB medium to obtain a final inoculum of 10\u003csup\u003e5\u003c/sup\u003e CFU/mL. 196 \u0026micro;L of the inoculum was added to A1-H1 of a 96-well plate, and the rest of 100 \u0026micro;L of liquid LB medium was added to each well. Add 4 \u0026micro;L of sample storage solution to A1-H1 of the 96-well plate and mix well. Then 100 \u0026micro;L was pipetted from well A1-H1 and added to A2-H2 and mixed well. Another 100 \u0026micro;L was pipetted from A2-H2 and added to A3-H3 and mixed well, and so on until it was added to wells A10-H10, and 100 \u0026micro;L of liquid was pipetted and discarded from the tenth column of wells. The above 96-well plate was placed at 37℃ for 17\u0026ndash;20 h. After incubation, bacterial growth was observed and the minimum inhibitory concentration (MIC) value was determined by visual inspection as the lowest dilution of the compound without turbidity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBacterial division is an important process in bacterial life activity and an important target in antibiotic development. FtsZ is an essential protein for bacterial division and is present in almost all bacteria. FtsZ is the first protein recruited to the division site, which polymerizes to form the Z-loop and acts as a scaffold for the recruitment of downstream proteins together with FtsA. If the polymerization of the FtsZ protein is blocked, the formation of the Z-ring is impeded and bacterial cell division is inhibited, which in this case leads to cell death, leading to antimicrobial activity. Therefore, constructing a screening model using FtsZ as a target has the potential to screen for lead compounds with antibacterial activity.\u003c/p\u003e \u003cp\u003eIn this study, FtsZ from Staphylococcus aureus was cloned, expressed, isolated and purified, and a highly pure target protein was obtained, and the protein was later proved to be active by enzyme activity assay. A screening model was established with FtsZ as the target, and a compound library was screened to obtain 38 candidate compounds by molecular docking and molecular clustering, which were subjected to enzyme inhibitory activity assay to screen out the compounds with better activity (compound \u003cb\u003eB6\u003c/b\u003e and \u003cb\u003eB26\u003c/b\u003e). Molecular docking and molecular dynamics simulations were used to demonstrate the possible binding patterns between the compounds and FtsZ proteins, providing assistance for subsequent structural optimization\u003c/p\u003e \u003cp\u003eThe compounds obtained from the screening based on this workflow were assayed for enzyme activity inhibition and antimicrobial activity, and were found to have good inhibitory activity in both enzyme activity and antimicrobial activity, which indicates that the model is reliable and stable, and provides a new technique and methodology for searching for and discovering FtsZ inhibitors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*E-mail: Xinxin Si (sixx@ jou.edu.cn) \u0026nbsp;Shaojie Ma ([email protected])\u003c/p\u003e\n\u003cp\u003eAddress: Xinxin Si, School of Pharmacy, Jiangsu Ocean University, Lianyungang, Jiangsu 222005, China\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received support from a project that was funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChenliang Qian: perform virtual screening and MD simulation; write the computational part. Aoqi Luo: perform \u003cem\u003ein vitro\u003c/em\u003e experiment; write the in vitro experiment part. Shaojie Ma: assist with the computational simulation. Zhengyu Zhang: assist with the \u003cem\u003ein vitro\u003c/em\u003e experiment. Hongwei Jin: supervise the MD simulation; Xinxin Si: design the experiments and provide research idea; supervise the whole project, write and edit.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChristaki E, Marcou M, Tofarides A (2020) Antimicrobial Resistance in Bacteria: Mechanisms, Evolution, and Persistence. 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J Appl Microbiol 88(5):784\u0026ndash;790. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1365-2672.2000.01017.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-2672.2000.01017.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"FtsZ, virtual screening, pharmacophore model, molecular docking, antimicrobial activity","lastPublishedDoi":"10.21203/rs.3.rs-4781484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4781484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFilamentous temperature-sensitive protein Z (FtsZ) plays an important role in bacterial division, and the inhibition of normal physiological function of FtsZ can make a devastating effect on bacterial growth and proliferation, which making it an important antibacterial target. The inhibitor activity targeting the cleft between the H7 helix and the C-terminal substructural domain exhibited superior binding compared to the GTP binding site. Therefore, the discovery of inhibitors targeting the cleft as a binding site holds promise for further research. By performing virtual screening with the workflow mainly composed of pharmacophore modeling and molecular docking as well as the following FtsZ inhibition assay, we identified four compounds \u003cb\u003eB6\u003c/b\u003e, \u003cb\u003eB21\u003c/b\u003e, \u003cb\u003eB26\u003c/b\u003e and \u003cb\u003eB31\u003c/b\u003e. Futher experiment showed that compound \u003cb\u003eB6\u003c/b\u003e and \u003cb\u003eB26\u003c/b\u003e possessed antimicrobial activity with MIC values of 8 \u0026micro;g-mL-1 and 32 \u0026micro;g-mL-1. In conclusion, our study successfully identified novel FtsZ inhibitors with antimicrobial activity through virtual screening and in vitro biological evaluation, demonstrating their potential for further investigation.\u003c/p\u003e","manuscriptTitle":"Discovery of novel FtsZ inhibitors with antimicrobial activity by virtual screening and in vitro biological evaluation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-20 05:42:24","doi":"10.21203/rs.3.rs-4781484/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":"d0962c19-5419-48b3-bcaa-028bc51e6771","owner":[],"postedDate":"August 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-29T09:24:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-20 05:42:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4781484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4781484","identity":"rs-4781484","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}