In-silico Insights into Protein Targets: New Avenues for Treating E. faecalis in Endodontic Infections – A Systematic Review

In-silico Insights into Protein Targets: New Avenues for Treating E. faecalis in Endodontic Infections – A Systematic Review | 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 Insights into Protein Targets: New Avenues for Treating E. faecalis in Endodontic Infections – A Systematic Review Dr Swetha Geervani V, Dr Kiran Kumar Neelakantappa, Dr Seema Merwade, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5768874/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 Enterococcus faecalis is a key pathogen in persistent endodontic infections, known for its resilience and resistance to conventional treatments. This systematic review (PROSPERO CRD42024610795) explores in silico methodologies targeting E. faecalis proteins to identify novel therapeutic approaches. A comprehensive literature search identified 11 relevant studies employing molecular docking tools such as AutoDock Vina, Glide XP, and SwissDock to investigate interactions between ligands and critical protein targets, including Sortase A, MurA, c-di-AMP synthetase, and quorum sensing regulators. Promising inhibitors, such as pinocembrin, 24-propylcholesterol, and embelin, exhibited potential to disrupt biofilm formation, quorum sensing, and bacterial metabolism. These findings highlight the potential of plant-derived compounds and novel antibacterial agents in addressing E. faecalis -associated infections. While most studies validated computational results with in vitro assays, variability in ligand preparation, protein optimization, and docking methodologies indicates the need for standardization. This review underscores the significant promise of computer-aided drug design (CADD) in developing effective treatments for E. faecalis infections. Integrating in silico and in vitro approaches can accelerate drug discovery, but further in vivo studies are essential to confirm therapeutic potential and facilitate clinical application. Biological sciences/Drug discovery/Drug screening Biological sciences/Drug discovery/Target identification Biological sciences/Drug discovery/Target validation Figures Figure 1 Figure 2 Introduction Enterococcus faecalis is a Gram-positive facultative anaerobe commonly implicated in endodontic infections 1 . It is present in approximately 40% of primary endodontic infections, but its persistence is particularly concerning, as it is more likely to be found in persistent endodontic infections 2 . This persistence is attributed to E. faecalis' survival strategies, including its ability to alter the host's immune response, making it a formidable challenge in endodontic treatment 3 . The difficulty in eradicating this bacterium highlights the critical importance of effective endodontic therapy, which depends heavily on the successful elimination of E. faecalis 4 . The growing resistance of pathogenic bacteria, including multidrug-resistant enterococci, to antibiotics poses a significant threat to future treatment strategies 5 . As a result, alternative approaches are urgently needed, such as the discovery of new modes of action for novel antibiotics 6 . One promising method in this context is molecular docking, a computer-based technique that assesses the interaction between medications and their molecular targets 7 . This preliminary process is time-saving and effective, providing detailed insights into atomic-level molecular interactions and binding mechanisms 8 . Molecular docking facilitates the efficient screening of a wide range of chemical compounds for potential inhibitors, offering a cost-effective and expedient approach to drug discovery 9 . Moreover, computational methods can explore a broader spectrum of chemical diversity, thereby aiding in the identification of new lead compounds with therapeutic potential in various fields, including endodontics. This systematic review (Prospero registration number CRD42024610795) focuses on Molecular dynamic simulations of various virulent protein components of E.faecalis and their implications for endodontic therapy. Materials and methods Focus question(s) 1. What are the trends and types of computer-aided drug design and discovery methods based on virtual screening to explore the protein targets of E.faecalis to treat Endodontic infections 2.What is the therapeutic potential of target protein of E.Faecalis elucidated by Computer Aided Drug Design Methods to treat Endodontic infections Information sources A comprehensive search of PubMed/Medline and EMBASE databases was undertaken. The language was restricted to English, and no restrictions were placed on the year of publication. The results were limited to In-silico and In-Vitro . A manual search of reference lists of all included articles was performed Table 1: Search strategy, database search, and selection criteria Details Database Electronic PubMed, Google scholar and Cochrane library Selection cr Criteria Inclusion criteria 1. Problem- Studies with clear descriptions of CADD methods used in virtual screening as primary tool based on E.Faecalis protein to treat endodontic infections will be included. 2. Exposure- Studies investigating the Anti-bacterial action of ligands towards E.faecalis will be included. 3. Context- Only original studies utilizing CADD methods for the purpose of either target protein prediction/validation, hit identification, hit-to-lead and lead optimization will be included. Exclusion criteria Rev Review articles. 1.Problem- Studies without details of the CADD methods, not involving E.faecalis protein for endodontic therapy will be excluded. 2. Exposure- Studies investigating diseases other than Endodontic therapy and involving non-human targets will be excluded 3.Context- Studies exclusively in-vivo, in-vitro, and other types of in-silico methods that don't serve the purpose of either target prediction/validation, hit identification, hit-to-lead or lead optimization will be excluded. Articles in languages other than English Study selection Studies retrieved from the search databases were imported into a citation manager and screened for duplicates using an automated system. Two authors Dr S.G and Dr.M.M, independently screened the title and abstract for eligibility. The full text was consulted if sufficient information could not be extracted from titles and abstracts. Items deemed irrelevant by both reviewers were excluded. A third author Dr A.M monitored screening and data extraction and supported resolving queries, ensuring protocol adherence. The full texts of potentially relevant articles were obtained and reviewed in detail by both reviewers Dr.S.G and Dr.M.M. The final list of articles was selected for further analysis after the second screening. Whole articles were inspected for data extraction and quality assessment. A database for information retrieval was created in Microsoft Excel 365 v.17 for Microsoft spreadsheets (Microsoft, Redmond, WA, USA). The data extraction was pilot-tested before final data retrieval. Quality assessment Due to the lack of a standardized tool for this type of study, the quality and risk of bias of the selected papers which involve molecular docking will be assessed by adopting a checklist previously developed and applied by Taldaev et al 2022. The assessment will be carried out separately by two independent reviewers. Any discrepancies will be resolved by a third reviewer. Risk of bias assessment Table 2 Table depicting the criteria for risk of bias Bias Domain Issue Low Risk of Bias High Risk of Bias Unclear Risk of Bias Ligand selection Ligand filtering Should be performed Did not applied No data Ligands optimization Ionization assessment Generation of energetically possible conformations The ligands were ionized according to pKa and pH values of media Should be performed The research was performed without reference to pKa values of ligands and pH values of media Generation was performed without reference to potential energy calculation No data No data Target selection Resolution of protein structure Method of protein target structure obtaining Not more than 2.5 Å NMR spectroscopy More than 2.5 Å X-ray crystallography or cryogenic electron microscopy No data No data Target optimization Control of histidine Protonation Protonation of amino acids after X-ray crystallography or cryogenic electron microscopy Addition of missing residues and side chains after X-ray crystallography or cryogenic electron microscopy Addition of metals Should be performed Should be performed Should be performed Should be performed The structure of target did not reference biological conditions The structure of target did not reference biological conditions Was performed without special tools The structure of target did not reference biological conditions No data No data No data No data Docking Molecular docking software Glide, GOLD AutoDock, DOCK, FlexX No data Results assessment Visual control Re-docking Verification of docking results by in vitro study Should be performed Should be performed Binding constant should be determined Structure defects were observed The RMSD value is too high compared with the initial structure The quantitative calculations were not performed No data No data No data Two review authors (S.M and K.K) made systematic and independent assessments of the risk of bias in each research using the methodological domains presented in Table Disagreements in judgments about the risk of bias were resolved by discussion or, when necessary, arbitrated by an independent third review author(S.G.) Results The studies analyzed in this review are summarized in the table below, providing an overview of their methodologies, tools, and outcomes. Table 3 table representing the studies included in the review Sl no Study Year of publication Target Ligand Software used Invitro verification 1. Abhishek Parolia 10 2021 SortaseA β-Galactosidase Pinocembrin kaemferol Quercetin Glide(XP) Propolis nanoparticles 300µg/mL was equally as effective as 6% NaOCl and 2% CHX in reducing the E. faecalis biofilms. Molecules 2. Lulu Chen 1 2018 c-di-AMP synthetase DisA ST056083 UCSF Chimera bromophenol-TH compound (ST056083) Inhibits E. faecalis growth and biofilms by targeting c-di-AMP synthetase DisA 3 Devi Windaryanti 7 2022 MurA enzyme Esp GBAP and Gelatinase 24-propylcholesterol Positive control Ambuic acid Fosfomycin Taxifolin Quercetin Auto dock Vina 24-propylcholesterol has the potential to act as a noncompetitive inhibitor of MurA, Esp, GBAP, and competitive to gelatinase. 4 Dikdik Kurnia 6 2021 MurA, GBAP, Gelatinase,and Serine proteases Catechin Fosfomycin Ambuic acid Quercetin Taxifolin Autodock Vina Catechin from Uncaria gambir Roxb. Fruit has good activity to inhibit E. faecalis and consequently presented as an anti-QS and antibacterial agent. 5 Dikdik Kurnia 11 2020 PrgQ prgX PrgZ CcfA dibenzo-p-dioxin-2,8-dicarboxylic Acid Positive control amoxicillin ampicillin enalapril esomeprazole Autodock Vina The in vitro study showed that compound 1 has the lowest of inhibition zone (8.8 mm) while in silico study suggested it has the highest binding affinity against PrgX and PrgZ (− 9.2 kcal.mol − 1 and − 7.4 kcal.mol − 1). 6. IB Geeta 12 2019 Dihydrofolate reductase Ledermix Liquorice The combination of licorice + Ca(OH)2 completely inhibited the growth of E. faecalis . Licorice + Ca(OH)2 compounds show best results both in Microbial inhibition concentration (6 ± 1 cm) and in docking mechanism (− 7 K cal/mol). 7. S. Jayalakshmi 2 2021 Agmatine Catabolism of Enterococcus Faecalis Embelin, vilangin, and phenyl vilangin Auto Dock suite embelin as a very good antimicrobial agent and phenyl vilangin as a good cytocompatible agent. 8 Jayavarsha V 5 2023 VanA Coumarin, Xanthotoxol, Imperatorin, Aegeline, Marmeline, and Erythromycin. AutoDock vina. the leaves of A. marmelos are effective against E. faecalis VRE strains. Among the five biocompounds selected for analysis, imperatorin exhibits good binding energy, 9 Nezar Boreak 4 2024 SortaseA Pinocembrin Glabridin Ampicillin Autodock Vina No invitro studies 10 S. Radha 8 2024 Esp Thymoquinone ledene oxide Swissdock suite A molecular docking study showed that blackseed had more affinity towards the enterococcus surface protein when compared to brown seaweed. 11 Umer Daood 13 2021 Sortase A k21/E Glide(XP) k21/E irrigant has ability to reduce and disrupts E. fae-calis biofilm, key sources of bias in molecular docking, covering the following domains: Ligand Selection and Optimization : Proper ligand filtering excludes molecules with unsuitable properties such as excessive volume, low safety, or poor pharmacokinetics, optimizing computational efficiency. Ionization, a critical parameter, must be accurately assessed based on the ligand's pKa and the pH of the medium, as it influences complex formation with the target. Generating conformers with the lowest potential energy and optimal geometry (bond lengths, angles, and dihedrals) is essential, as variations in conformer affinity can introduce significant bias. Protein Target Selection and Preparation : Protein target resolution must be below 2.5 Å for accurate atomic positioning; structures with lower resolution should be avoided. NMR spectroscopy is preferred for generating 3D models under biologically relevant conditions. However, correcting protonation states, adding missing residues, side chains, and metals, especially in X-ray crystallography and cryo-EM models, significantly reduces bias and enhances structural accuracy. Molecular Docking Process : The choice of docking software and its algorithms (e.g., Monte-Carlo) and scoring functions (e.g., empirical methods) plays a crucial role, as these approaches have shown greater reliability when validated with experimental in vitro methods. Results Assessment : Re-docking ensures modeling parameters are appropriate for the system being studied, while visual inspection helps identify and exclude artifacts generated by computational methods. Verification through in vitro experiments is essential for confirming theoretical docking results. Additionally, potential conflicts of interest are mitigated by evaluating funding sources to ensure unbiased outcomes. This comprehensive approach minimizes bias across ligand selection, target preparation, docking, and result validation, improving the reliability of molecular docking studies Evaluation of Risk in Ligand Filtering, Optimization, and Docking Studies In ligand filtering, 91% of studies 1 , 2 , 4 , 5 , 6 , 7 , 8 , 10 , 11 , 13 were classified as low risk, whereas only 9% achieved a low-risk classification 5 in ligand optimization. Ligand optimization, in particular, was frequently associated with high-risk parameters or unclear methodologies. Similarly, only 9% of studies 5 demonstrated clarity in target selection, and 36% in target optimization 1 , 2 , 4 , 13 . Docking studies revealed significant variability in reliability, with 73% of studies 2 , 4 , 5 , 6 , 7 , 8 , 11 , 12 categorized as having a high risk of bias due to the widespread use of AutoDock Vina, which is generally considered less reliable compared to Glide XP 14 . Despite these limitations, nearly all studies verified their in silico findings through corresponding in vivo studies or visual controls. However, a notable gap was observed, as many studies lacked explicit mention or proper execution of protein and ligand preparation steps. Software used AutoDock Vina, employed in approximately 55% of studies 2 , 4 , 5 , 6 , 7 , 11 , emerged as the most frequently utilized software for molecular docking research. A smaller proportion of studies utilized Glide XP (9–18%) 10,13 , while others incorporated tools such as SwissDock 8 and Chimera 2 . For protein structure acquisition, nearly 64% of the studies 2 , 5 , 6 , 7 , 8 , 10 relied on the Protein Data Bank (PDB) as the primary source for 3D protein structures. Some studies, however, opted to model protein structures based on known sequences using software like Modeller 4 and Maestro 13 . In terms of ligand preparation, the majority of studies sourced 3D ligand structures from PubChem 6 , 7 , 8 , 11 , while others employed tools such as ChemSketch 4 , 5 and LigPrep 12 to generate 3D structures of specific ligands. Discussion Multiple therapeutic targets have been extensively studied to address the challenges posed by E. faecalis , a resilient bacterium implicated in various infections, particularly in the context of endodontics. These studies focus on disrupting key bacterial mechanisms, including cell wall synthesis, metabolic pathways, quorum sensing (QS), and biofilm formation, to counteract its growth, virulence, and resistance to conventional treatments. The bacterial cell wall is a vital structure composed of polysaccharides, polypeptides, and peptidoglycan, which collectively shield bacteria from environmental stress. Peptidoglycan, the principal structural component, not only provides mechanical stability but also serves as a primary target for antibacterial strategies due to its absence in mammalian cells. Its biosynthesis involves a multistep process starting with the formation of N-acetylglucosamine (NAG) and N-acetylmuramic acid (NAM) precursors 7 . A key enzyme in this process is MurA, which catalyzes the initial step in the synthesis of peptidoglycan precursors. Since this reaction does not occur in mammals, MurA represents an attractive target for antibacterial drug development. Inhibition or inactivation of MurA compromises bacterial cell wall integrity, making cells susceptible to osmotic stress and lysis. Targeting MurA has been highlighted as a potential strategy for combating a wide range of Gram-positive and Gram-negative bacterial infections 11 . Intracellular pathways critical for bacterial survival and virulence also present valuable therapeutic opportunities. One such pathway involves cyclic-di-AMP (c-di-AMP), a second messenger essential for bacterial growth, biofilm formation, and adaptation to environmental stressors. Bacteria deficient in c-di-AMP exhibit impaired survival, indicating its role in various cellular processes such as morphology regulation, fatty acid synthesis, and immune evasion. Recent studies in Enterococci have identified a novel phosphodiesterase from the GdpP family that regulates c-di-AMP in response to cell membrane stress induced by antibiotics 2 . These findings open avenues for targeting c-di-AMP signaling pathways as a novel antibacterial strategy. Similarly, dihydrofolate reductase (DHFR), an enzyme crucial for reducing dihydrofolate to tetrahydrofolate—a cofactor necessary for DNA synthesis—has emerged as another promising target. Inhibiting DHFR disrupts nucleotide biosynthesis, effectively hindering bacterial proliferation 12 . Additionally, glutathione (GSH), a molecule found in certain Gram-positive bacteria like Enterococcus and Streptococcus species, contributes to antibiotic resistance by regulating intracellular potassium levels and maintaining cellular turgor and pH homeostasis. By targeting glutathione biosynthesis or its associated pathways, researchers can potentially enhance the efficacy of existing antibiotics. Another promising target is the metabolic pathway of E. faecalis that involves the catabolism of agmatine. This pathway, mediated by a three-enzyme system—Agmatine deiminase (AgDI), Putrescine transcarbamylase (PTC), and Carbamate kinase (CK)—enables ATP generation, making it an essential component of bacterial energy metabolism and a viable therapeutic target 2 . Quorum sensing (QS), a mechanism that allows bacteria to coordinate behaviors such as biofilm formation, virulence expression, and environmental adaptation, is another critical area for therapeutic intervention. In E. faecalis , biofilm formation represents a significant challenge due to its inherent resistance to antibacterial agents. Disruption of QS can inhibit biofilm formation and reduce bacterial virulence. Sortase A (SrtA), a transpeptidase located on the bacterial membrane, plays a vital role in anchoring surface proteins that mediate adhesion, biofilm formation, and host colonization 4 . Targeting SrtA with specific inhibitors can reduce bacterial pathogenicity by impairing these processes without exerting selective pressure for resistance 10 . Autoinducers such as GBAP further regulate QS by facilitating bacterial communication 7 . Blocking QS at the autoinducer level or interfering with peptide pheromones like pCF10 can disrupt bacterial coordination, impair biofilm formation, and mitigate infection 6 . These innovative strategies highlight the potential of QS-targeting therapies in managing bacterial infections. The growing prevalence of antibiotic resistance, exemplified by vancomycin-resistant Enterococcus (VRE), underscores the urgency for novel therapeutic approaches 5 . Resistance in VRE is mediated by the vanA and vanB gene clusters, which alter peptidoglycan synthesis to evade the effects of vancomycin and teicoplanin. Addressing this resistance requires the development of innovative agents capable of overcoming these genetic adaptations. In recent years, numerous studies have explored novel antibacterial agents and endodontic irrigants targeting E. faecalis . Propolis-based irrigants have shown significant promise due to their bioactive compounds—Pinocembrin, Kaempferol, and Quercetin—which exhibit strong interactions with bacterial proteins such as Sortase A and β-galactosidase. Propolis nanoparticles have demonstrated comparable efficacy to conventional agents like NaOCl and CHX in reducing E. faecalis populations in vitro 10 . Similarly, the bromophenol thiohydantoin compound ST056083 has been identified as an inhibitor of c-di-AMP synthesis by targeting DisA, a c-di-AMP synthetase. This compound significantly impairs bacterial growth and biofilm formation, underscoring its potential as a novel antimicrobial agent 1 . Ethnobotanical studies have highlighted the antibacterial potential of compounds derived from plants such as Piper betel 7 , Uncaria gambir 6 , and Myrmecodia pendans 6 . For example, 24-propylcholesterol from Piper betel exhibits strong affinity for QS-related proteins and enzymes like MurA and gelatinase, positioning it as a promising QS inhibitor. Similarly, compounds from Uncaria gambir and Myrmecodia pendans show high binding affinities to QS regulatory proteins, suggesting their utility in disrupting bacterial communication. Licorice extract, enriched with Glycyrrhizin, has demonstrated potent antibacterial activity against E. faecalis when combined with calcium hydroxide 12 . Embelin and its combinations with NaOCl have shown notable effects on E. faecalis biofilms, with molecular docking studies confirming strong interactions with agmatine catabolism-related proteins 2 . Quaternary ammonium silane (k21/E) has emerged as a dual-action irrigant, targeting Sortase A and compromising bacterial membrane integrity 13 , while phytochemicals from Aegle marmelos have shown potential against vanA-related targets associated with vancomycin resistance 5 . Other compounds, such as pinocembrin and glabridin, identified as potential Sortase A inhibitors, demonstrate strong binding affinities but require further experimental validation 4 . In silico studies have significantly advanced the understanding of antibacterial agents molecular interactions, complementing in vitro findings. These computational approaches enable visualization of precise mechanisms of action, reduce experimental efforts, and identify synergistic compounds capable of targeting multiple bacterial components. This integration of in silico and in vitro methodologies has accelerated the development of innovative therapies, offering hope in the fight against resistant E. faecalis strains and enhancing the efficacy of existing treatments. Conclusion The reliance on protein structures from databases like PDB poses a risk of inaccuracies in molecular docking studies due to suboptimal protein preparations, which may affect the reliability of in silico findings. While many studies provide in vitro verification, the significant variation between in vitro and in vivo conditions underscores the need for further experimental validation to confirm therapeutic potential. Despite these limitations, the in silico approach demonstrates substantial promise in effectively targeting E. faecalis and addressing the challenge of antibiotic resistance, paving the way for innovative and precise strategies in endodontic therapy. Declarations Author Contribution Concepts, Design and Manuscript preparation were done by Dr S.G.VThe definition of intellectual Content was conceptualized by Dr K.K.NData analysis and Manuscript editing were done by Dr S.MLiterature search was done by Dr M.MData Acquisition was done by Dr A.Mall the authors have reviewed the manuscript References Chen L, et al. Inhibition of Enterococcus faecalis growth and biofilm formation by molecule targeting cyclic di-AMP synthetase activity. Journal of endodontics. 2018;44(9):1381–8. 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Newly discovered clouting interplay between matrix metalloproteinases structures and novel quaternary Ammonium K21: computational and in-vivo testing. BMC Oral Health. 2024;24(1):382. Parolia A, et al. Effect of propolis nanoparticles against Enterococcus faecalis biofilm in the root canal. Molecules. 2021;26(3):715. Kurnia D, Ramadhanty ZF, Ardani AM, Zainuddin A, Dharsono HD, Satari MH. Bio-mechanism of catechin as pheromone signal inhibitor: prediction of antibacterial agent action mode by in vitro and in silico study. Molecules. 2021;26(21):6381. Geeta IB, Husna AA, Sha SS, Muhammed AM, Moses J. In silico evaluation of the efficacies of two different medicaments against Enterococcus faecalis. Endodontology. 2019;31(1):9–12. Daood U, et al. Antibacterial and antibiofilm efficacy of k21-E in root canal disinfection. Dental Materials. 2021;37(10):1511–28. Taldaev A, Terekhov R, Nikitin I, Zhevlakova A, Selivanova I. Insights into the pharmacological effects of flavonoids: the systematic review of computer modeling. International Journal of Molecular Sciences. 2022;23(11):6023. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5768874","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399551917,"identity":"5597d08e-83c6-4a38-86d4-9f1ba5f78240","order_by":0,"name":"Dr Swetha Geervani V","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBADHjZm5oMPQAw+orXwsbclG4D1Em2NHM8ZMwkQg6AWeffmY595au7IsEmkpVV+zbGTYWNgfvjoBh4thmeOJc/mOfaMh00i+dht2W3JQIexGRvn4NMyI8eYcQbbYR6QLbcltzEDtfCwSePVMv/9Z8YZ/0BacsyKJbfVE9YiL8HDzPCxDagF6H3Gj9sOE9ZiwJNmzPCxD6gSGMjSjNuOAyOIgF/k2w8/Zkj4dthevpn54Mef26rt+dmbHz7Ga8sBJA4zD5jEoxxsSwMSh/EHAdWjYBSMglEwMgEAORdBjfOGaiMAAAAASUVORK5CYII=","orcid":"","institution":"Government Dental College \u0026 Research Institute","correspondingAuthor":true,"prefix":"Dr","firstName":"Swetha","middleName":"Geervani","lastName":"V","suffix":""},{"id":399551918,"identity":"20733e97-1b2e-4f12-aeda-bebca7994c20","order_by":1,"name":"Dr Kiran Kumar Neelakantappa","email":"","orcid":"","institution":"Government Dental College \u0026 Research Institute","correspondingAuthor":false,"prefix":"Dr","firstName":"Kiran","middleName":"Kumar","lastName":"Neelakantappa","suffix":""},{"id":399551919,"identity":"b26dbfa8-8d3c-4adf-8305-490a015e5a1a","order_by":2,"name":"Dr Seema Merwade","email":"","orcid":"","institution":"Government Dental College \u0026 Research Institute","correspondingAuthor":false,"prefix":"Dr","firstName":"Seema","middleName":"","lastName":"Merwade","suffix":""},{"id":399551920,"identity":"d94e58f9-c174-4e93-bae1-b90387946160","order_by":3,"name":"Dr Abhishek M","email":"","orcid":"","institution":"Government Dental College \u0026 Research Institute","correspondingAuthor":false,"prefix":"Dr","firstName":"Abhishek","middleName":"","lastName":"M","suffix":""},{"id":399551921,"identity":"2276e473-3722-4b70-948d-63f7c8cdb396","order_by":4,"name":"Dr Manimozhi M","email":"","orcid":"","institution":"Government Dental College \u0026 Research Institute","correspondingAuthor":false,"prefix":"Dr","firstName":"Manimozhi","middleName":"","lastName":"M","suffix":""}],"badges":[],"createdAt":"2025-01-05 17:08:06","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5768874/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5768874/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73512085,"identity":"41665804-38ed-4706-ba10-0e92f47df240","added_by":"auto","created_at":"2025-01-10 16:59:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136059,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart depicting risk of bias\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5768874/v1/7c6527fcd5a8d12eef0423eb.jpg"},{"id":73512585,"identity":"ac71d818-560e-40e1-a86c-c17928c8f8de","added_by":"auto","created_at":"2025-01-10 17:07:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85984,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the \u003cstrong\u003eMaterials and methods \u003c/strong\u003esection.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5768874/v1/bc507ef57d364c0ae6863adf.jpg"},{"id":73513748,"identity":"af755974-cbfe-47d1-ad21-10bb02667be2","added_by":"auto","created_at":"2025-01-10 17:23:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1119536,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5768874/v1/d103b119-9e2f-489a-bda4-70e16de6393e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"In-silico Insights into Protein Targets: New Avenues for Treating E. faecalis in Endodontic Infections – A Systematic Review","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEnterococcus faecalis is a Gram-positive facultative anaerobe commonly implicated in endodontic infections\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It is present in approximately 40% of primary endodontic infections, but its persistence is particularly concerning, as it is more likely to be found in persistent endodontic infections\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This persistence is attributed to E. faecalis' survival strategies, including its ability to alter the host's immune response, making it a formidable challenge in endodontic treatment\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The difficulty in eradicating this bacterium highlights the critical importance of effective endodontic therapy, which depends heavily on the successful elimination of E. faecalis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The growing resistance of pathogenic bacteria, including multidrug-resistant enterococci, to antibiotics poses a significant threat to future treatment strategies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As a result, alternative approaches are urgently needed, such as the discovery of new modes of action for novel antibiotics\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne promising method in this context is molecular docking, a computer-based technique that assesses the interaction between medications and their molecular targets\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This preliminary process is time-saving and effective, providing detailed insights into atomic-level molecular interactions and binding mechanisms\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Molecular docking facilitates the efficient screening of a wide range of chemical compounds for potential inhibitors, offering a cost-effective and expedient approach to drug discovery\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Moreover, computational methods can explore a broader spectrum of chemical diversity, thereby aiding in the identification of new lead compounds with therapeutic potential in various fields, including endodontics.\u003c/p\u003e \u003cp\u003eThis systematic review (Prospero registration number CRD42024610795) focuses on Molecular dynamic simulations of various virulent protein components of E.faecalis and their implications for endodontic therapy.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eFocus question(s)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. What are the trends and types of computer-aided drug design and discovery methods based on virtual screening to explore the protein targets of E.faecalis to treat Endodontic infections\u003c/p\u003e\n\u003cp\u003e2.What is the therapeutic potential of target protein of E.Faecalis elucidated by Computer Aided Drug Design Methods to treat Endodontic infections\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInformation sources\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive search of PubMed/Medline and EMBASE databases was undertaken. The language was restricted to English, and no restrictions were placed on the year of publication. The results were limited to In-silico and In-Vitro . A manual search of reference lists of all included articles was performed\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Search strategy, database search, and selection criteria\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"695\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDetails\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDatabase\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eElectronic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePubMed, \u0026nbsp;Google scholar and Cochrane library\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelection \u0026nbsp;cr \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Criteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInclusion \u0026nbsp;criteria\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1. Problem- Studies with clear descriptions of CADD methods used in virtual screening as primary tool based on E.Faecalis protein to treat endodontic infections will be included.\u003c/p\u003e\n \u003cp\u003e2. Exposure- Studies investigating the Anti-bacterial action of ligands towards E.faecalis will be included.\u003c/p\u003e\n \u003cp\u003e3. Context- Only original studies utilizing CADD methods for the purpose of either target protein\u003c/p\u003e\n \u003cp\u003eprediction/validation, hit identification, hit-to-lead and lead optimization will be included.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExclusion criteria \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 415px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eRev \u0026nbsp; \u0026nbsp; \u0026nbsp;Review articles.\u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e1.Problem- Studies without details of the CADD methods, not involving E.faecalis protein for endodontic\u003c/p\u003e\n \u003cp\u003etherapy will be excluded.\u003c/p\u003e\n \u003cp\u003e2. Exposure- Studies investigating diseases other than Endodontic therapy and involving non-human targets\u003c/p\u003e\n \u003cp\u003ewill be excluded\u003c/p\u003e\n \u003cp\u003e3.Context- Studies exclusively in-vivo, in-vitro, and other types of in-silico methods that don\u0026apos;t serve the\u003c/p\u003e\n \u003cp\u003epurpose of either target prediction/validation, hit identification, hit-to-lead or lead optimization will be\u003c/p\u003e\n \u003cp\u003eexcluded.\u003c/p\u003e\n \u003cp\u003eArticles in languages other than English\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy selection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudies retrieved from the search databases were imported into a citation manager and screened for duplicates using an automated system. Two authors Dr S.G \u0026nbsp;and Dr.M.M, \u0026nbsp; independently screened the title and abstract for eligibility. The full text was consulted if sufficient information could not be extracted from titles and abstracts. Items deemed irrelevant by both reviewers were excluded. A third author Dr A.M monitored screening and data extraction and supported resolving queries, ensuring protocol adherence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe full texts of potentially relevant articles were obtained and reviewed in detail by both reviewers Dr.S.G and Dr.M.M. The final list of articles was selected for further analysis after the second screening. Whole articles were inspected for data extraction and quality assessment. A database for information retrieval was created in Microsoft Excel 365 v.17 for Microsoft spreadsheets (Microsoft, Redmond, WA, USA). The data extraction was pilot-tested before final data retrieval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuality assessment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the lack of a standardized tool for this type of study, the quality and risk of bias of the selected papers which involve molecular docking will be assessed by adopting a checklist previously developed and applied by Taldaev et al 2022. The assessment will be carried out separately by two independent reviewers. Any discrepancies will be resolved by a third reviewer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk of bias assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eTable depicting the criteria for risk of bias\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBias Domain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIssue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow Risk of Bias\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh Risk of Bias\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnclear Risk of Bias\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand selection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eLigand filtering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eShould be performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eDid not applied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigands optimization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIonization assessment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGeneration of\u003c/p\u003e\n \u003cp\u003eenergetically possible\u003c/p\u003e\n \u003cp\u003econformations\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eThe ligands were\u003c/p\u003e\n \u003cp\u003eionized according to\u003c/p\u003e\n \u003cp\u003epKa and pH values\u003c/p\u003e\n \u003cp\u003eof media\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eShould be performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eThe research was\u003c/p\u003e\n \u003cp\u003eperformed without\u003c/p\u003e\n \u003cp\u003ereference to pKa values\u003c/p\u003e\n \u003cp\u003eof ligands and pH\u003c/p\u003e\n \u003cp\u003evalues of media\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGeneration was\u003c/p\u003e\n \u003cp\u003eperformed without\u003c/p\u003e\n \u003cp\u003ereference to potential\u003c/p\u003e\n \u003cp\u003eenergy calculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget selection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eResolution of\u003c/p\u003e\n \u003cp\u003eprotein structure\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMethod of protein\u003c/p\u003e\n \u003cp\u003etarget structure\u003c/p\u003e\n \u003cp\u003eobtaining\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNot more than 2.5 \u0026Aring;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNMR spectroscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMore than 2.5 \u0026Aring;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eX-ray crystallography\u003c/p\u003e\n \u003cp\u003eor cryogenic\u003c/p\u003e\n \u003cp\u003eelectron microscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget optimization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eControl of histidine\u003c/p\u003e\n \u003cp\u003eProtonation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eProtonation of amino\u003c/p\u003e\n \u003cp\u003eacids after X-ray\u003c/p\u003e\n \u003cp\u003ecrystallography or\u003c/p\u003e\n \u003cp\u003ecryogenic electron\u003c/p\u003e\n \u003cp\u003emicroscopy\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAddition of missing\u003c/p\u003e\n \u003cp\u003eresidues and side\u003c/p\u003e\n \u003cp\u003echains after X-ray\u003c/p\u003e\n \u003cp\u003ecrystallography or\u003c/p\u003e\n \u003cp\u003ecryogenic electron\u003c/p\u003e\n \u003cp\u003emicroscopy\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAddition of metals\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eShould be performed\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eShould be performed\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eShould be performed\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eShould be performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eThe structure of target\u003c/p\u003e\n \u003cp\u003edid not reference\u003c/p\u003e\n \u003cp\u003ebiological conditions\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe structure of target\u003c/p\u003e\n \u003cp\u003edid not reference\u003c/p\u003e\n \u003cp\u003ebiological conditions\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWas performed\u003c/p\u003e\n \u003cp\u003ewithout special tools\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe structure of target\u003c/p\u003e\n \u003cp\u003edid not reference\u003c/p\u003e\n \u003cp\u003ebiological conditions\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDocking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eMolecular docking\u003c/p\u003e\n \u003cp\u003esoftware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGlide, GOLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAutoDock, DOCK,\u003c/p\u003e\n \u003cp\u003eFlexX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eVisual control\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRe-docking\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVerification of docking\u003c/p\u003e\n \u003cp\u003eresults by in vitro study\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eShould be performed\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eShould be performed\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBinding constant\u003c/p\u003e\n \u003cp\u003eshould be determined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eStructure defects\u003c/p\u003e\n \u003cp\u003ewere observed\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe RMSD value is too\u003c/p\u003e\n \u003cp\u003ehigh compared with\u003c/p\u003e\n \u003cp\u003ethe initial structure\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe quantitative\u003c/p\u003e\n \u003cp\u003ecalculations were\u003c/p\u003e\n \u003cp\u003enot performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNo data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTwo review authors (S.M and K.K) made systematic and independent assessments of\u0026nbsp; the risk of bias in each research using the methodological domains presented in Table\u003c/p\u003e\n\u003cp\u003eDisagreements in judgments about the risk of bias were resolved by discussion or, when necessary, arbitrated by an independent third review author(S.G.)\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe studies analyzed in this review are summarized in the table below, providing an overview of their methodologies, tools, and outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003etable representing the studies included in the review\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=\"char\" char=\".\" 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\u003eSl no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear of\u003c/p\u003e \u003cp\u003epublication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLigand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSoftware\u003c/p\u003e \u003cp\u003eused\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInvitro\u003c/p\u003e \u003cp\u003everification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbhishek Parolia\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSortaseA\u003c/p\u003e \u003cp\u003eβ-Galactosidase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePinocembrin \u003c/p\u003e \u003cp\u003ekaemferol\u003c/p\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGlide(XP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePropolis nanoparticles 300\u0026micro;g/mL was equally\u003c/p\u003e \u003cp\u003eas effective as 6% NaOCl and 2% CHX in reducing the E. faecalis biofilms.\u003c/p\u003e \u003cp\u003eMolecules\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLulu Chen\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec-di-AMP synthetase DisA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST056083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUCSF Chimera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ebromophenol-TH compound\u003c/p\u003e \u003cp\u003e(ST056083)\u003c/p\u003e \u003cp\u003eInhibits E. faecalis growth and biofilms by targeting\u003c/p\u003e \u003cp\u003ec-di-AMP synthetase DisA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevi Windaryanti\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMurA enzyme\u003c/p\u003e \u003cp\u003eEsp\u003c/p\u003e \u003cp\u003eGBAP and\u003c/p\u003e \u003cp\u003eGelatinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24-propylcholesterol\u003c/p\u003e \u003cp\u003ePositive control\u003c/p\u003e \u003cp\u003eAmbuic acid\u003c/p\u003e \u003cp\u003eFosfomycin\u003c/p\u003e \u003cp\u003eTaxifolin\u003c/p\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAuto dock Vina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24-propylcholesterol has the potential to act as a noncompetitive inhibitor of MurA, Esp, GBAP, and competitive to gelatinase.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDikdik Kurnia\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMurA,\u003c/p\u003e \u003cp\u003eGBAP, Gelatinase,and Serine proteases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCatechin\u003c/p\u003e \u003cp\u003eFosfomycin\u003c/p\u003e \u003cp\u003eAmbuic acid\u003c/p\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003cp\u003eTaxifolin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAutodock\u003c/p\u003e \u003cp\u003eVina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCatechin from Uncaria gambir Roxb. Fruit\u003c/p\u003e \u003cp\u003ehas good activity\u003c/p\u003e \u003cp\u003eto inhibit E. faecalis and\u003c/p\u003e \u003cp\u003econsequently presented as an anti-QS and antibacterial agent.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDikdik Kurnia\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrgQ\u003c/p\u003e \u003cp\u003eprgX\u003c/p\u003e \u003cp\u003ePrgZ\u003c/p\u003e \u003cp\u003eCcfA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edibenzo-p-dioxin-2,8-dicarboxylic Acid\u003c/p\u003e \u003cp\u003ePositive control\u003c/p\u003e \u003cp\u003eamoxicillin\u003c/p\u003e \u003cp\u003eampicillin\u003c/p\u003e \u003cp\u003eenalapril\u003c/p\u003e \u003cp\u003eesomeprazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAutodock Vina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe in vitro study showed that compound 1 has the lowest\u003c/p\u003e \u003cp\u003eof inhibition zone (8.8 mm) while in silico study suggested\u003c/p\u003e \u003cp\u003eit has the highest binding affinity against PrgX and PrgZ\u003c/p\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;9.2 kcal.mol\u0026thinsp;\u0026minus;\u0026thinsp;1 and \u0026minus;\u0026thinsp;7.4 kcal.mol\u0026thinsp;\u0026minus;\u0026thinsp;1).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIB Geeta\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDihydrofolate\u003c/p\u003e \u003cp\u003ereductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLedermix\u003c/p\u003e \u003cp\u003eLiquorice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe combination\u003c/p\u003e \u003cp\u003eof licorice\u0026thinsp;+\u0026thinsp;Ca(OH)2 completely inhibited the growth of\u003c/p\u003e \u003cp\u003e\u003cem\u003eE. faecalis\u003c/em\u003e. Licorice\u0026thinsp;+\u0026thinsp;Ca(OH)2 compounds show best results\u003c/p\u003e \u003cp\u003eboth in Microbial inhibition concentration (6\u0026thinsp;\u0026plusmn;\u0026thinsp;1 cm) and\u003c/p\u003e \u003cp\u003ein docking mechanism (\u0026minus;\u0026thinsp;7 K cal/mol).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS. Jayalakshmi\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgmatine Catabolism of Enterococcus Faecalis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEmbelin,\u003c/p\u003e \u003cp\u003evilangin, and phenyl vilangin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAuto Dock suite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eembelin as a very good antimicrobial\u003c/p\u003e \u003cp\u003eagent and phenyl vilangin as a good cytocompatible agent.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJayavarsha V\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVanA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoumarin, Xanthotoxol, Imperatorin, Aegeline, Marmeline, and Erythromycin.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAutoDock vina.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ethe leaves of \u003cem\u003eA. marmelos\u003c/em\u003e are effective against \u003cem\u003eE. faecalis\u003c/em\u003e VRE strains. Among the five biocompounds selected for analysis, imperatorin exhibits good binding energy,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNezar Boreak\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSortaseA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePinocembrin Glabridin\u003c/p\u003e \u003cp\u003eAmpicillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAutodock Vina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo invitro studies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS. Radha\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEsp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThymoquinone\u003c/p\u003e \u003cp\u003eledene oxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSwissdock suite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA molecular docking study showed that blackseed had more affinity towards the enterococcus surface protein when compared to brown seaweed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUmer Daood\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSortase A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ek21/E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGlide(XP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek21/E irrigant has ability to reduce and disrupts \u003cem\u003eE. fae-calis\u003c/em\u003e biofilm,\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\u003ekey sources of bias in molecular docking, covering the following domains:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLigand Selection and Optimization\u003c/b\u003e: Proper ligand filtering excludes molecules with unsuitable properties such as excessive volume, low safety, or poor pharmacokinetics, optimizing computational efficiency. Ionization, a critical parameter, must be accurately assessed based on the ligand's pKa and the pH of the medium, as it influences complex formation with the target. Generating conformers with the lowest potential energy and optimal geometry (bond lengths, angles, and dihedrals) is essential, as variations in conformer affinity can introduce significant bias.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eProtein Target Selection and Preparation\u003c/b\u003e: Protein target resolution must be below 2.5 \u0026Aring; for accurate atomic positioning; structures with lower resolution should be avoided. NMR spectroscopy is preferred for generating 3D models under biologically relevant conditions. However, correcting protonation states, adding missing residues, side chains, and metals, especially in X-ray crystallography and cryo-EM models, significantly reduces bias and enhances structural accuracy.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMolecular Docking Process\u003c/b\u003e: The choice of docking software and its algorithms (e.g., Monte-Carlo) and scoring functions (e.g., empirical methods) plays a crucial role, as these approaches have shown greater reliability when validated with experimental in vitro methods.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eResults Assessment\u003c/b\u003e: Re-docking ensures modeling parameters are appropriate for the system being studied, while visual inspection helps identify and exclude artifacts generated by computational methods. Verification through in vitro experiments is essential for confirming theoretical docking results. Additionally, potential conflicts of interest are mitigated by evaluating funding sources to ensure unbiased outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis comprehensive approach minimizes bias across ligand selection, target preparation, docking, and result validation, improving the reliability of molecular docking studies\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of Risk in Ligand Filtering, Optimization, and Docking Studies\u003c/h2\u003e \u003cp\u003eIn ligand filtering, 91% of studies\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e were classified as low risk, whereas only 9% achieved a low-risk classification\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e in ligand optimization. Ligand optimization, in particular, was frequently associated with high-risk parameters or unclear methodologies. Similarly, only 9% of studies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e demonstrated clarity in target selection, and 36% in target optimization\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDocking studies revealed significant variability in reliability, with 73% of studies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e categorized as having a high risk of bias due to the widespread use of AutoDock Vina, which is generally considered less reliable compared to Glide XP\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite these limitations, nearly all studies verified their in silico findings through corresponding in vivo studies or visual controls. However, a notable gap was observed, as many studies lacked explicit mention or proper execution of protein and ligand preparation steps.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoftware used\u003c/h3\u003e\n\u003cp\u003eAutoDock Vina, employed in approximately 55% of studies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, emerged as the most frequently utilized software for molecular docking research. A smaller proportion of studies utilized Glide XP (9\u0026ndash;18%)\u003csup\u003e10,13\u003c/sup\u003e, while others incorporated tools such as SwissDock\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and Chimera\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For protein structure acquisition, nearly 64% of the studies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e relied on the Protein Data Bank (PDB) as the primary source for 3D protein structures. Some studies, however, opted to model protein structures based on known sequences using software like Modeller\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and Maestro\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In terms of ligand preparation, the majority of studies sourced 3D ligand structures from PubChem\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, while others employed tools such as ChemSketch\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and LigPrep\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e to generate 3D structures of specific ligands.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMultiple therapeutic targets have been extensively studied to address the challenges posed by \u003cem\u003eE. faecalis\u003c/em\u003e, a resilient bacterium implicated in various infections, particularly in the context of endodontics. These studies focus on disrupting key bacterial mechanisms, including cell wall synthesis, metabolic pathways, quorum sensing (QS), and biofilm formation, to counteract its growth, virulence, and resistance to conventional treatments.\u003c/p\u003e \u003cp\u003eThe bacterial cell wall is a vital structure composed of polysaccharides, polypeptides, and peptidoglycan, which collectively shield bacteria from environmental stress. Peptidoglycan, the principal structural component, not only provides mechanical stability but also serves as a primary target for antibacterial strategies due to its absence in mammalian cells. Its biosynthesis involves a multistep process starting with the formation of N-acetylglucosamine (NAG) and N-acetylmuramic acid (NAM) precursors\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. A key enzyme in this process is MurA, which catalyzes the initial step in the synthesis of peptidoglycan precursors. Since this reaction does not occur in mammals, MurA represents an attractive target for antibacterial drug development. Inhibition or inactivation of MurA compromises bacterial cell wall integrity, making cells susceptible to osmotic stress and lysis. Targeting MurA has been highlighted as a potential strategy for combating a wide range of Gram-positive and Gram-negative bacterial infections\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIntracellular pathways critical for bacterial survival and virulence also present valuable therapeutic opportunities. One such pathway involves cyclic-di-AMP (c-di-AMP), a second messenger essential for bacterial growth, biofilm formation, and adaptation to environmental stressors. Bacteria deficient in c-di-AMP exhibit impaired survival, indicating its role in various cellular processes such as morphology regulation, fatty acid synthesis, and immune evasion. Recent studies in Enterococci have identified a novel phosphodiesterase from the GdpP family that regulates c-di-AMP in response to cell membrane stress induced by antibiotics\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These findings open avenues for targeting c-di-AMP signaling pathways as a novel antibacterial strategy. Similarly, dihydrofolate reductase (DHFR), an enzyme crucial for reducing dihydrofolate to tetrahydrofolate\u0026mdash;a cofactor necessary for DNA synthesis\u0026mdash;has emerged as another promising target. Inhibiting DHFR disrupts nucleotide biosynthesis, effectively hindering bacterial proliferation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, glutathione (GSH), a molecule found in certain Gram-positive bacteria like Enterococcus and Streptococcus species, contributes to antibiotic resistance by regulating intracellular potassium levels and maintaining cellular turgor and pH homeostasis. By targeting glutathione biosynthesis or its associated pathways, researchers can potentially enhance the efficacy of existing antibiotics. Another promising target is the metabolic pathway of \u003cem\u003eE. faecalis\u003c/em\u003e that involves the catabolism of agmatine. This pathway, mediated by a three-enzyme system\u0026mdash;Agmatine deiminase (AgDI), Putrescine transcarbamylase (PTC), and Carbamate kinase (CK)\u0026mdash;enables ATP generation, making it an essential component of bacterial energy metabolism and a viable therapeutic target\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eQuorum sensing (QS), a mechanism that allows bacteria to coordinate behaviors such as biofilm formation, virulence expression, and environmental adaptation, is another critical area for therapeutic intervention. In \u003cem\u003eE. faecalis\u003c/em\u003e, biofilm formation represents a significant challenge due to its inherent resistance to antibacterial agents. Disruption of QS can inhibit biofilm formation and reduce bacterial virulence. Sortase A (SrtA), a transpeptidase located on the bacterial membrane, plays a vital role in anchoring surface proteins that mediate adhesion, biofilm formation, and host colonization\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Targeting SrtA with specific inhibitors can reduce bacterial pathogenicity by impairing these processes without exerting selective pressure for resistance\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Autoinducers such as GBAP further regulate QS by facilitating bacterial communication\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Blocking QS at the autoinducer level or interfering with peptide pheromones like pCF10 can disrupt bacterial coordination, impair biofilm formation, and mitigate infection\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These innovative strategies highlight the potential of QS-targeting therapies in managing bacterial infections.\u003c/p\u003e \u003cp\u003eThe growing prevalence of antibiotic resistance, exemplified by vancomycin-resistant \u003cem\u003eEnterococcus\u003c/em\u003e (VRE), underscores the urgency for novel therapeutic approaches\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Resistance in VRE is mediated by the vanA and vanB gene clusters, which alter peptidoglycan synthesis to evade the effects of vancomycin and teicoplanin. Addressing this resistance requires the development of innovative agents capable of overcoming these genetic adaptations.\u003c/p\u003e \u003cp\u003eIn recent years, numerous studies have explored novel antibacterial agents and endodontic irrigants targeting \u003cem\u003eE. faecalis\u003c/em\u003e. Propolis-based irrigants have shown significant promise due to their bioactive compounds\u0026mdash;Pinocembrin, Kaempferol, and Quercetin\u0026mdash;which exhibit strong interactions with bacterial proteins such as Sortase A and β-galactosidase. Propolis nanoparticles have demonstrated comparable efficacy to conventional agents like NaOCl and CHX in reducing \u003cem\u003eE. faecalis\u003c/em\u003e populations in vitro\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Similarly, the bromophenol thiohydantoin compound ST056083 has been identified as an inhibitor of c-di-AMP synthesis by targeting DisA, a c-di-AMP synthetase. This compound significantly impairs bacterial growth and biofilm formation, underscoring its potential as a novel antimicrobial agent\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEthnobotanical studies have highlighted the antibacterial potential of compounds derived from plants such as \u003cem\u003ePiper betel\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e, \u003cem\u003eUncaria gambir\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e, and \u003cem\u003eMyrmecodia pendans\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. For example, 24-propylcholesterol from \u003cem\u003ePiper betel\u003c/em\u003e exhibits strong affinity for QS-related proteins and enzymes like MurA and gelatinase, positioning it as a promising QS inhibitor. Similarly, compounds from \u003cem\u003eUncaria gambir\u003c/em\u003e and \u003cem\u003eMyrmecodia pendans\u003c/em\u003e show high binding affinities to QS regulatory proteins, suggesting their utility in disrupting bacterial communication. Licorice extract, enriched with Glycyrrhizin, has demonstrated potent antibacterial activity against \u003cem\u003eE. faecalis\u003c/em\u003e when combined with calcium hydroxide\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Embelin and its combinations with NaOCl have shown notable effects on \u003cem\u003eE. faecalis\u003c/em\u003e biofilms, with molecular docking studies confirming strong interactions with agmatine catabolism-related proteins\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eQuaternary ammonium silane (k21/E) has emerged as a dual-action irrigant, targeting Sortase A and compromising bacterial membrane integrity\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, while phytochemicals from \u003cem\u003eAegle marmelos\u003c/em\u003e have shown potential against vanA-related targets associated with vancomycin resistance\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Other compounds, such as pinocembrin and glabridin, identified as potential Sortase A inhibitors, demonstrate strong binding affinities but require further experimental validation\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn silico studies have significantly advanced the understanding of antibacterial agents molecular interactions, complementing in vitro findings. These computational approaches enable visualization of precise mechanisms of action, reduce experimental efforts, and identify synergistic compounds capable of targeting multiple bacterial components. This integration of in silico and in vitro methodologies has accelerated the development of innovative therapies, offering hope in the fight against resistant \u003cem\u003eE. faecalis\u003c/em\u003e strains and enhancing the efficacy of existing treatments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe reliance on protein structures from databases like PDB poses a risk of inaccuracies in molecular docking studies due to suboptimal protein preparations, which may affect the reliability of in silico findings. While many studies provide in vitro verification, the significant variation between in vitro and in vivo conditions underscores the need for further experimental validation to confirm therapeutic potential. Despite these limitations, the in silico approach demonstrates substantial promise in effectively targeting \u003cem\u003eE. faecalis\u003c/em\u003e and addressing the challenge of antibiotic resistance, paving the way for innovative and precise strategies in endodontic therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConcepts, Design and Manuscript preparation were done by Dr S.G.VThe definition of intellectual Content was conceptualized by Dr K.K.NData analysis and Manuscript editing were done by Dr S.MLiterature search was done by Dr M.MData Acquisition was done by Dr A.Mall the authors have reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen L, et al. Inhibition of Enterococcus faecalis growth and biofilm formation by molecule targeting cyclic di-AMP synthetase activity. Journal of endodontics. 2018;44(9):1381\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayalakshmi S, Narendran K, Sukumar E, Irshad Ahamed J, Nivedhitha MS, Rajesh Kumar S. Molecular docking studies, in vitro-brine shrimp lethal assay and antibacterial assessment of embelin, vilangin and phenyl vilangin against endodontic pathogen, Enterococcus faecalis. Research on Chemical Intermediates. 2021;47(11):4855\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang S, et al. Strategies and mechanisms targeting Enterococcus faecalis biofilms associated with endodontic infections: A comprehensive review. Frontiers in Cellular and Infection Microbiology. 2024;14:1433313.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoreak, N., et al, 2024. Exploring Plant-Based Compounds as Alternatives for Targeting Enterococcus faecalis in Endodontic Therapy: A Molecular Docking Approach. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(14), p.7727.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayavarsha V, AS SG, Gunasekaran S. Characterization of Vancomycin Resistant Enterococci and Drug Ligand Interaction between vanA of E. faecalis with the Bio-Compounds from Aegles marmelos. Journal of pharmacopuncture. 2023;26(3):247.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurnia, D., Rachmawati, P. and Satari, M.H., 2020. Antibacterial of dibenzo-p-dioxi-2, 8-dicarboxylic acid against pathogenic oral bacteria E. faecalis ATCC 29212 peptide pheromones: quorum sensing of in vitro and in silico study. \u003cem\u003eDrug Design, Development and Therapy\u003c/em\u003e, pp.3079\u0026ndash;3086.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWindaryanti D, et al. The Potential of 24-Propylcholestrol as Antibacterial Oral Bacteria of Enterococcus faecalis ATCC 29212 and Inhibitor Biofilms Formation: in vitro and in silico Study. Advances and Applications in Bioinformatics and Chemistry. 2022 Dec 31:99\u0026ndash;111.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadha S, Ahamed AS, Gutmann JL, Bhavani S, Rajaraman G, Chittrarasu M. Comparative Evaluation of Antibacterial Efficacy, Molecular Docking of Ethanolic Extract of Blackseed, Seaweed and Calcium Hydroxide Intracanal Medicament with Enterococcus Faecalis Antigens. Journal of Pharmacy and Bioallied Sciences. 2024;16(Suppl 2):S1731-5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBapat RA, et al. Newly discovered clouting interplay between matrix metalloproteinases structures and novel quaternary Ammonium K21: computational and in-vivo testing. BMC Oral Health. 2024;24(1):382.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParolia A, et al. Effect of propolis nanoparticles against Enterococcus faecalis biofilm in the root canal. Molecules. 2021;26(3):715.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurnia D, Ramadhanty ZF, Ardani AM, Zainuddin A, Dharsono HD, Satari MH. Bio-mechanism of catechin as pheromone signal inhibitor: prediction of antibacterial agent action mode by in vitro and in silico study. Molecules. 2021;26(21):6381.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeeta IB, Husna AA, Sha SS, Muhammed AM, Moses J. In silico evaluation of the efficacies of two different medicaments against Enterococcus faecalis. Endodontology. 2019;31(1):9\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaood U, et al. Antibacterial and antibiofilm efficacy of k21-E in root canal disinfection. Dental Materials. 2021;37(10):1511\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaldaev A, Terekhov R, Nikitin I, Zhevlakova A, Selivanova I. Insights into the pharmacological effects of flavonoids: the systematic review of computer modeling. International Journal of Molecular Sciences. 2022;23(11):6023. \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":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-5768874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5768874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eEnterococcus faecalis\u003c/em\u003e is a key pathogen in persistent endodontic infections, known for its resilience and resistance to conventional treatments. This systematic review (PROSPERO CRD42024610795) explores in silico methodologies targeting \u003cem\u003eE. faecalis\u003c/em\u003e proteins to identify novel therapeutic approaches. A comprehensive literature search identified 11 relevant studies employing molecular docking tools such as AutoDock Vina, Glide XP, and SwissDock to investigate interactions between ligands and critical protein targets, including Sortase A, MurA, c-di-AMP synthetase, and quorum sensing regulators.\u003c/p\u003e \u003cp\u003ePromising inhibitors, such as pinocembrin, 24-propylcholesterol, and embelin, exhibited potential to disrupt biofilm formation, quorum sensing, and bacterial metabolism. These findings highlight the potential of plant-derived compounds and novel antibacterial agents in addressing \u003cem\u003eE. faecalis\u003c/em\u003e-associated infections. While most studies validated computational results with in vitro assays, variability in ligand preparation, protein optimization, and docking methodologies indicates the need for standardization.\u003c/p\u003e \u003cp\u003eThis review underscores the significant promise of computer-aided drug design (CADD) in developing effective treatments for \u003cem\u003eE. faecalis\u003c/em\u003e infections. Integrating in silico and in vitro approaches can accelerate drug discovery, but further in vivo studies are essential to confirm therapeutic potential and facilitate clinical application.\u003c/p\u003e","manuscriptTitle":"In-silico Insights into Protein Targets: New Avenues for Treating E. faecalis in Endodontic Infections – A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 16:58:55","doi":"10.21203/rs.3.rs-5768874/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":"3850d64c-e85b-4049-b5e4-0b274ef8af91","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42574999,"name":"Biological sciences/Drug discovery/Drug screening"},{"id":42575000,"name":"Biological sciences/Drug discovery/Target identification"},{"id":42575001,"name":"Biological sciences/Drug discovery/Target validation"}],"tags":[],"updatedAt":"2025-01-10T16:58:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 16:58:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5768874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5768874","identity":"rs-5768874","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}