In Silico Identification and ADME/Toxicity Prediction of Phytochemical Inhibitors Targeting Glucosyltransferases of Streptococcus mutans for Anti-Biofilm Applications

In Silico Identification and ADME/Toxicity Prediction of Phytochemical Inhibitors Targeting Glucosyltransferases of Streptococcus mutans for Anti-Biofilm Applications | 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 Identification and ADME/Toxicity Prediction of Phytochemical Inhibitors Targeting Glucosyltransferases of Streptococcus mutans for Anti-Biofilm Applications Ravikanth Nanduri, E.A.V.V Rambabu Matta, Ramesh Jonnada, Vasu Penumaka, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7591427/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 Dental caries remains a major global health concern, with Streptococcus mutans recognized as a key cariogenic pathogen due to its capacity to form resilient biofilms. Central to this process are the glucosyltransferases (GtfB, GtfC, and GtfD), which synthesize extracellular glucans that promote bacterial adhesion and biofilm stability. Targeting these enzymes represents a promising strategy for anti-caries therapy. In this study, we employed molecular docking and in silico pharmacokinetic profiling to evaluate the inhibitory potential of 21 phytochemicals against S. mutans glucosyltransferases. Docking analysis revealed that several compounds, including sanguinarine, rutin, quercetin, and chelerythrine, exhibited strong binding affinities across all three GTFs (− 7.4 to − 9.5 kcal/mol), often surpassing the binding affinities of known inhibitors. Ligand–protein interaction analysis identified recurrent hotspots such as Gln592 in GtfC and Gln347 in GtfD, underscoring conserved binding pockets. ADME and toxicity predictions further suggested that most top-ranking compounds possessed favorable pharmacokinetic properties with acceptable safety profiles, highlighting their potential as lead molecules. Overall, this integrated computational study identifies multiple plant-derived inhibitors with promising activity against S. mutans glucosyltransferases, supporting their further evaluation as natural anti-biofilm and anti-caries agents. In-silico docking Gtf inhibitor S. mutans dental caries Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Dental caries is among the most widespread infectious conditions globally, affecting billions of individuals and significantly influencing oral and systemic health (Giacaman et al. 2022 ). The global burden of dental caries is exacerbated by the ability of Streptococcus mutans ( S. mutans ), a key cariogenic pathogen, to form resilient biofilms on tooth surfaces (Lemos et al. 2019 ). These biofilms provide a protective environment that promotes plaque accumulation and generates acidic conditions capable of demineralizing the enamel. Due to this, strategies that disrupt S. mutans biofilm development are considered crucial to modern approaches for preventing and managing dental caries. Among the factors contributing to biofilm formation, the glucosyltransferases (Gtfs) produced by S. mutans —namely GtfB, GtfC, and GtfD—are crucial (Lemos et al. 2019 ). These enzymes catalyze the synthesis of glucans, which serve as structural components of the biofilm matrix, enhancing its stability and facilitating microbial adhesion. GtfB primarily produces water-insoluble glucans with α-1,3 linkages, while GtfC synthesizes a mix of soluble and insoluble glucans, and GtfD creates predominantly water-soluble glucans (Bowen and Koo 2011). As key drivers of caries progression, GTFs have emerged as validated targets for the development of anti-caries therapies. Inhibiting these enzymes could effectively disrupt biofilm formation and mitigate the pathogenic effects of S. mutans (Chen et al. 2025 ). In recent years, plant-derived bioactive compounds have garnered attention as potential candidates for the development of safe, multi-target inhibitors (Vaou et al. 2021 ). These compounds offer a promising alternative to conventional antimicrobial agents due to their natural origin, diverse bioactivity, and relatively low toxicity profiles. Furthermore, the broad chemical diversity of plant metabolites provides opportunities for targeting multiple bacterial pathways simultaneously, an approach that may overcome the limitations of single-target treatments. Despite these promising prospects, current research on GTF inhibitors remains limited, particularly in terms of multi-GTF targeting (Atta et al. 2025 ). Most studies focus on inhibiting individual GTF enzymes, leaving a significant gap in understanding how to simultaneously target multiple GTFs or address their combined role in biofilm formation. Despite evidence of phytochemicals against S. mutans , systematic in silico evaluation against multiple GTFs is lacking. This gap presents an opportunity for further exploration in the field of anti-caries drug development. This study aims to employ computational screening to identify phytochemicals with the potential to inhibit GTFs. By leveraging in silico methods, we will assess the binding affinity of various plant-derived compounds to GTFs and perform ADME (absorption, distribution, metabolism, excretion) and toxicity profiling to evaluate their suitability as safe, effective therapeutic agents. This research seeks to contribute to the development of novel, natural-based strategies for combating dental caries through targeted inhibition of S. mutans glucosyltransferases. Materials and Methods Selection of phytochemicals: A total of 21 plant-derived compounds reported in the literature for their antimicrobial or antibiofilm effects against S. mutans were shortlisted. The selected molecules covered diverse chemical classes, including alkaloids, flavonoids, phenolic acids, terpenoids, and coumarins (Table 1). Canonical SMILES and three-dimensional structures were retrieved from the PubChem database (Adil et al. 2014 ; Anjaneyulu et al. 2021; Ankri and Mirelman 1999 ; Cheng et al. 2007 ; Choi et al. 2018 ; Cushnie and Lamb 2005 ; Dwivedi and Singh 2016 ; Eisenberg et al. 1991 ; Kanwal et al. 2025 ; Kim et al. 2025 ; Nair et al. 2016 ; Priya et al. 2021 ; Quek et al. 2021 ; Stermitz et al. 2000 ; Vieira et al. 2014a; Vieira et al. 2014b; Zhang et al. 2023 ). Protein Structures- Three-dimensional coordinates of S. mutans GtfB (PDB ID: 8fg8) and GtfC (PDB ID: 3aic) were downloaded from the Protein Data Bank (Ahirwar et al. 2023 ; Ito et al. 2011 ). Since the crystal structure of GtfD was unavailable, a homology model was generated using SwissModel with chain A of 3aib as the template (Waterhouse et al. 2018 ). The quality of the model was confirmed based on GMQE (0.83), QMEANDisCo (± 0.05), and Ramachandran plot analysis showing 96.29% of residues in favored regions (Fig. 1 ). Prior to docking, proteins were processed in AutoDock Tools by deleting crystallographic waters, adding polar hydrogens, and assigning Gasteiger charges (Morris et al. 2009 ). Ligand Preparation- Ligand structures were optimized and converted from SDF to PDB format using Open Babel. During preparation, torsional flexibility and rotatable bonds were automatically assigned. All ligand structures were prepared using AutoDock tools by adding Kollman charges and detecting torsion tree root (Morris et al. 2009 ). Molecular Docking- Docking was carried out using AutoDock Vina v1.2.0 (Eberhardt et al. 2021 ). Grid boxes were centered on the catalytic domains of each GTF with dimensions of 60 × 60 × 60 Å. Each compound was docked individually, and the conformation with the lowest binding energy was chosen for further inspection. As a validation step, known inhibitors were re-docked into the GtfB and GtfC active sites, and the predicted binding poses were compared with their reference positions. Visualization of docking interactions was performed in UCSF ChimeraX (Meng et al. 2023 ). ADME Prediction-Pharmacokinetic and drug-likeness properties of the top eight ligands were estimated using the SwissADME web server. (Daina et al. 2017 ). The analysis included Lipinski’s rule of five, bioavailability score, gastrointestinal absorption, blood–brain barrier penetration, and cytochrome P450 inhibition potential. Toxicity Prediction- Toxicity profiles of the leading candidates were predicted with ProTox-III (Banerjee et al. 2024 ). Parameters assessed included oral LD₅₀ values, and risks of hepatotoxicity, carcinogenicity, mutagenicity, and immunotoxicity, which were used to classify compounds into toxicity categories (Table 4). Results Docking Analysis We performed manual curation of the literature on medicinal plants that showed antimicrobial activity or antibiofilm activity against S. mutans using databases such as PubMed, Scopus, or Google Scholar. The search resulted in more than 50 medicinal plants, which comprise around 21 phytochemicals/bioactive compounds altogether. The list of medicinal plants and bioactive compounds is listed in Table 1 (Adil et al. 2014; Anjaneyulu et al. 2021; Ankri and Mirelman 1999; Cheng et al. 2007; Choi et al. 2018; Cushnie and Lamb 2005; Dwivedi and Singh 2016; Eisenberg et al. 1991; Kanwal et al. 2025; Nair et al. 2016; Priya et al. 2021; Quek et al. 2021; Stermitz et al. 2000; Vieira et al. 2014a; Vieira et al. 2014b; Zhang et al. 2023). The protein structures of GtfB, Gtf C and GtfD used for docking studies are shown in Figure 1 A, B, and C, respectively. Structural modelling of the glucosyltransferases revealed high sequence identity (62.06%), a GMQE score of 0.83, and robust model validation metrics (Ramachandran favoured: 96.29%), confirming the suitability of the GtfD model for docking analysis (Figure 1C and D). The docking studies revealed that multiple phytochemicals exhibited significant binding affinities towards GtfB, GtfC, and GtfD of S. mutans (Table 2). Among the tested compounds, sanguinarine, rutin, quercetin, chelerythrine, luteolin, berberine, piperine, and chrysin showed the highest docking scores for all three glucosyltransferases, with values ranging from −7.4 to −9.3 for GtfB, -7.9 to -9.1 for GtfC, and -7.9 to -9.5 for GtfD, indicating strong interactions with the enzyme active sites. The chemical structures of the top 8 compounds were displayed in Figure 2. These compounds consistently outperformed other bioactive molecules such as allicin, berberine, caffeic acid, and thymol, which showed moderate binding affinities. The docked poses of the top 4 compounds were displayed in Figure 3. Ligand binding pocket visualizations further highlighted the molecular interactions of top-scoring compounds, particularly the hydrogen bonding with key residues in GtfB, GtfC, and GtfD, supporting the proposed mechanism of inhibition. We analysed key residues involved in lead molecule binding using ChimeraX software. We observed that GtfB interaction with lead molecules involved different residues for each molecule docked. Sanguinarine formed one H-bond with Arg 514, rutin formed H-bond with Asn 511 and His 561. Quercetin formed an H-bond with Asn836, chelerythrine with Asn 566, leuteolin with Asp 883, berberine with Glu 564, piperine with Arg 514, and chrysin with Asn 836. In case of GtfC, sanguinarine interacted with Gln 592, Rutin with Asp 477 and Gln 592, quercetin with Gln 592, Tyr 610, and Asn 862, chelerythrine interacted with Gln 592 and Arg 450, luteolin with Gln 592, Asn 481, and Asp 909, berberine with Gln 592 and Asn 481. Piperine interacted with only Gln 592, while chrysin interacted with Gln 592, Asp 909, and Gln 960. Overall, GtfC residues Gln 592 and Asn 481 have been identified as key residues involved in lead molecule binding. In case of GtfD, Sanguinarine interacted with His 342 and Gln 347, rutin with Asp 224, Asn 228 and Gln 347, quercetin with Asn 228 and Gln 347, chelerythrine with Gln 347, luteolin with Asn 228 and Gln 347, berberine with Gln 327, piperine with His 342 and Gln 347 and chrysin with Gln 347 and Asn 626. Overall, Gtf D residues Gln 347 and Asn 228 have been identified as key residues involved in lead molecule binding. ADMET Profiles ADME profiling of the top eight phytochemicals/lead molecules indicated favourable pharmacokinetic properties, with most compounds showing good absorption, distribution, metabolism, and excretion scores (Table 3). Toxicity predictions suggest that these bioactive molecules have low toxicity risks, thereby enhancing their potential as safe therapeutic agents (Table 4). Comparative analysis with known Gtf inhibitors acarbose and piceatannol demonstrated superior or comparable profiles for several phytochemicals, especially in terms of binding affinity and predicted drug-likeness (Table 3 & 4 & 5) (Newbrun et al. 1983; Nijampatnam et al. 2018). ADME analysis indicated favorable drug-like properties for most top-ranking compounds. Toxicity prediction further differentiated the candidates: rutin, luteolin, and chrysin demonstrated relatively high LD₅₀ values (>3000 mg/kg) and fell into lower toxicity classes (4-5), suggesting a broad safety margin. By contrast, alkaloids such as sanguinarine and chelerythrine, although potent binders (−9.1 to −9.5 kcal/mol), were predicted to belong to lower LD₅₀ values, indicating potential risks at therapeutic doses. Piperine and luteolin showed intermediate profiles, combining moderate to strong docking affinities (−7.4 to −8.3 kcal/mol) with acceptable safety predictions. Taken together, rutin, luteolin, and chrysin emerge as the most promising lead molecules due to their optimal balance of strong binding, favorable pharmacokinetics, and low predicted toxicity, whereas sanguinarine and chelerythrine may serve as valuable structural scaffolds but require careful toxicity evaluation. Discussion This study identified several phytochemicals with strong inhibitory potential against S. mutans glucosyltransferases through molecular docking and in silico pharmacokinetic/toxicity profiling. The docking results highlighted rutin, quercetin, chelerythrine, and sanguinarine as top candidates with binding energies ranging from − 8.0 to − 9.5 kcal/mol, suggesting robust affinity for the catalytic pockets of all three GTFs. These findings support the potential of natural compounds to act as broad-spectrum inhibitors that disrupt biofilm-associated virulence mechanisms in S. mutans . A few of these compounds, including quercetin and luteolin, have previously been reported to inhibit individual Gtf’s activity and reduce biofilm formation, thereby validating our computational pipeline (Nakahara et al. 1993 ; Rudin et al. 2023 ). However, the strong multi-target binding observed for sanguinarine, rutin, piperine, and chelerythrine represents novel insights, as their direct interactions with S. mutans GTFs have not been extensively documented. The interaction analysis further revealed recurrent binding hotspots, particularly Gln592 in GtfC and Gln347 in GtfD, which were consistently involved in stabilizing top-ranking ligands. These conserved residues may represent key structural determinants for future inhibitor design. Integration of ADME and toxicity predictions provided an additional layer of candidate prioritization. Rutin, luteolin, and chrysin emerged as promising leads, combining high binding affinities with favorable pharmacokinetic profiles, high LD₅₀ values, and classification into low-to-moderate toxicity classes (4–5). In contrast, alkaloids such as berberine and quercetin, while exhibiting some of the strongest docking scores, were predicted to fall into higher toxicity classes (2–3) with relatively low LD₅₀ values, suggesting potential risks if developed as direct therapeutic agents. These molecules may instead serve as valuable scaffolds for structural modification aimed at reducing toxicity while retaining potency. Overall, this work provides a systematic in silico evaluation of phytochemicals targeting multiple S. mutans GTFs, offering both validation of known inhibitors and the identification of novel candidates. The combination of high-affinity binding, favorable pharmacokinetics, and low toxicity profiles makes rutin, sanguinarine, chelerythrine, and piperine particularly attractive for further experimental evaluation. At the same time, our results emphasize the importance of balancing potency with safety in natural product–based drug discovery. Future studies should include in vitro enzyme inhibition assays, biofilm disruption models, and cytotoxicity testing in mammalian cell systems to confirm the therapeutic potential of these compounds. Conclusion This study demonstrates that several plant-derived phytochemicals, including rutin, quercetin, sanguinarine, and chelerythrine, exhibit strong and multi-target inhibitory potential against the glucosyltransferases of Streptococcus mutans, as revealed by molecular docking and in silico ADME/toxicity profiling. Being luteolin already reported as a known inhibitor of Gtfs, rutin, and chrysin stand out for their combination of high binding affinity, favorable pharmacokinetics, and low predicted toxicity, supporting their advancement as lead anti-biofilm agents. These findings lay the groundwork for future experimental validation and highlight the promise of natural compounds for novel anti-caries therapy targeting multiple virulence factors in S. mutans. Declarations Competing Interests The Authors declare no competing interests. Funding The authors declare that no funds, external grants were received during the preparation of this manuscript. Author Contribution RN and EAVVRM conceptualized and wrote the manuscriptRN, EAVVRM, RJ, VP, SG conducted researchRN analyzed the results and prepared figures for publicationRN coordinated the project Acknowledgement The authors acknowledge the financial support provided by B V Raju College, Vishnupur, Bhimavaram, in the form of salary support, which facilitated the completion of this research work. We thank Dr. I. R. K. Raju, Principal, B V Raju College, for constant support and motivation. Data Availability Data will be made available on reasonable request. References Adil M, Singh K, Verma PK, Khan AU (2014) Eugenol-induced suppression of biofilm-forming genes in Streptococcus mutans: An approach to inhibit biofilms. J Glob Antimicrob Resist 2(4):286–292 Ahirwar P, Kozlovskaya V, Nijampatnam B, Rojas EM, Pukkanasut P, Inman D et al (2023) Hydrogel-Encapsulated Biofilm Inhibitors Abrogate the Cariogenic Activity of Streptococcus mutans . J Med Chem 66(12):7909–7925 Anjaneyulu B, Sangeeta, Saini N (2021) A Study on Camphor Derivatives and Its Applications: A Review. Curr Org Chem 25(12):1404–1428 Ankri S, Mirelman D (1999) Antimicrobial properties of allicin from garlic. Microbes Infect 1(2):125–129 Atta L, Siddiqui AR, Mushtaq M, Munsif S, Nur-e-Alam M, Ahmed A et al (2025) Molecular insights into antibiofilm inhibitors of Streptococcus mutans glucosyltransferases through in silico approaches. Sci Rep 15(1):14160 Banerjee P, Kemmler E, Dunkel M, Preissner R (2024) ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 52(W1):W513–W520 Bowen WH, Koo H. Biology of Streptococcus mutans- Derived Glucosyltransferases:Role in Extracellular Matrix Formation of Cariogenic Biofilms. Caries Res. 2011;45(1):69–86 Chen Z, Zhao X, Zheng H, Wang Y, Zhang L (2025) Advances and challenges in drug design against dental caries: Application of in silico approaches. J Pharm Anal 15(6):101161 Cheng R-B, Chen X, Liu S-J, Zhang X-F (2007) [Effect of Chelerythrine on cell surface hydrophobicity and adherence of Streptococcus mutans]. Shanghai Kou Qiang Yi Xue 16(1):68–72 Choi HG, Tran PT, Lee J-H, Min BS, Kim JA (2018) Anti-inflammatory activity of caffeic acid derivatives isolated from the roots of Salvia miltiorrhiza Bunge. Arch Pharm Res 41(1):64–70 Cushnie TPT, Lamb AJ (2005) Antimicrobial activity of flavonoids. Int J Antimicrob Agents 26(5):343–356 Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7(1):42717 Dwivedi D, Singh V (2016) Effects of the natural compounds embelin and piperine on the biofilm-producing property of Streptococcus mutans. J Tradit Complement Med 6(1):57–61 Eberhardt J, Santos-Martins D, Tillack AF, Forli S, AutoDock (2021) Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. J Chem Inf Model 61(8):3891–3898 Eisenberg AD, Young DA, Fan-Hsu JF, Spitz LM (1991) Interactions of Sanguinarine and Zinc on Oral Streptococci and Actinomyces Species. Caries Res 25(3):185–190 Giacaman RA, Fernández CE, Muñoz-Sandoval C, León S, García-Manríquez N, Echeverría C et al (2022) Understanding dental caries as a non-communicable and behavioral disease: Management implications. Front Oral Health. ;3 Ito K, Ito S, Shimamura T, Weyand S, Kawarasaki Y, Misaka T et al (2011) Crystal Structure of Glucansucrase from the Dental Caries Pathogen Streptococcus mutans. J Mol Biol 408(2):177–186 Kanwal S, Perveen S, Hameed H, Tahir S, Rashid R, Raish M et al (2025) Evaluating the efficacy of Chenopodium murale plant extract in inhibiting Streptococcus mutans and reducing dental caries risk. Sci Rep 15(1):15406 Kim S, Chen J, Cheng T, Gindulyte A, He J, He S et al (2025) PubChem 2025 update. Nucleic Acids Res 53(D1):D1516–D1525 Lemos JA, Palmer SR, Zeng L, Wen ZT, Kajfasz JK, Freires IA et al (2019) The Biology of Streptococcus mutans . Microbiol Spectr. ;7(1) Meng EC, Goddard TD, Pettersen EF, Couch GS, Pearson ZJ, Morris JH et al (2023) UCSF ChimeraX : Tools for structure building and analysis. Protein Sci. ;32(11) Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS et al (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791 Nair SV, Baranwal G, Chatterjee M, Sachu A, Vasudevan AK, Bose C et al (2016) Antimicrobial activity of plumbagin, a naturally occurring naphthoquinone from Plumbago rosea, against Staphylococcus aureus and Candida albicans. Int J Med Microbiol 306(4):237–248 Nakahara K, Kawabata S, Ono H, Ogura K, Tanaka T, Ooshima T et al (1993) Inhibitory effect of oolong tea polyphenols on glycosyltransferases of mutans Streptococci. Appl Environ Microbiol 59(4):968–973 Newbrun E, Hoover CI, Walker GJ (1983) Inhibition by acarbose, nojirimycin and 1-deoxynojirimycin of glucosyltransferase produced by oral Streptococci. Arch Oral Biol 28(6):531–536 Nijampatnam B, Zhang H, Cai X, Michalek SM, Wu H, Velu SE (2018) Inhibition of Streptococcus mutans Biofilms by the Natural Stilbene Piceatannol Through the Inhibition of Glucosyltransferases. ACS Omega 3(7):8378–8385 Priya A, Selvaraj A, Divya D, Karthik Raja R, Pandian SK (2021) In Vitro and In Vivo Anti-infective Potential of Thymol Against Early Childhood Caries Causing Dual Species Candida albicans and Streptococcus mutans. Front Pharmacol. ;12 Quek A, Mohd Zaini H, Kassim NK, Sulaiman F, Rukayadi Y, Ismail A et al (2021) Oxygen radical antioxidant capacity (ORAC) and antibacterial properties of Melicope glabra bark extracts and isolated compounds. PLoS ONE 16(5):e0251534 Rudin L, Roth N, Kneubühler J, Dubey BN, Bornstein MM, Shyp V (2023) Inhibitory effect of natural flavone luteolin on Streptococcus mutans biofilm formation. Microbiol Spectr. ;11(5) Stermitz FR, Lorenz P, Tawara JN, Zenewicz LA, Lewis K (2000) Synergy in a medicinal plant: Antimicrobial action of berberine potentiated by 5′-methoxyhydnocarpin, a multidrug pump inhibitor. Proceedings of the National Academy of Sciences. ;97(4):1433–7 Vaou N, Stavropoulou E, Voidarou C, Tsigalou C, Bezirtzoglou E (2021) Towards Advances in Medicinal Plant Antimicrobial Activity: A Review Study on Challenges and Future Perspectives. Microorganisms 9(10):2041 Vieira DRP, Amaral FMM, Maciel MCG, Nascimento FRF, Libério SA, Rodrigues VP (2014a Sep) Plant species used in dental diseases: Ethnopharmacology aspects and antimicrobial activity evaluation. J Ethnopharmacol 155(3):1441–1449 Vieira DRP, Amaral FMM, Maciel MCG, Nascimento FRF, Libério SA, Rodrigues VP (2014b Sep) Plant species used in dental diseases: Ethnopharmacology aspects and antimicrobial activity evaluation. J Ethnopharmacol 155(3):1441–1449 Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46(W1):W296–303 Zhang OL, Niu JY, Yin IX, Yu OY, Mei ML, Chu CH (2023) Antibacterial Properties of the Antimicrobial Peptide Gallic Acid-Polyphemusin I (GAPI). Antibiotics 12(9):1350 Tables Table 1 to 5 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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11:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7591427/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7591427/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91172500,"identity":"c5ae5026-7519-4dc3-a528-1e0c83c2444a","added_by":"auto","created_at":"2025-09-12 11:50:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":245397,"visible":true,"origin":"","legend":"\u003cp\u003ea) The structure of GtfB crystal structure 8fg8 is represented using a preset in ChimeraX\u003c/p\u003e\n\u003cp\u003eb) The structure of GtfC crystal structure 3aic is represented using a preset in ChimeraX\u003c/p\u003e\n\u003cp\u003ec) The structure of GtfD model generated using the SWISS-MODELER software is represented using a preset in ChimeraX\u003c/p\u003e","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7591427/v1/fa32583a480e3d20dcfbd1fb.png"},{"id":91172075,"identity":"6710b693-23cb-43df-b419-276993cae0eb","added_by":"auto","created_at":"2025-09-12 11:42:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54221,"visible":true,"origin":"","legend":"\u003cp\u003ea) Sanguinarine, b) Rutin, c) Quercetin, d) Chelerythrine, e) Luteolin, f) Berberine, g) Piperine, h) Chrysin.\u003c/p\u003e","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7591427/v1/3f9698d8a37367dca778cd2b.png"},{"id":91172072,"identity":"0aeed136-7149-44a8-bc4b-3fc7ea5c342a","added_by":"auto","created_at":"2025-09-12 11:42:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":490928,"visible":true,"origin":"","legend":"\u003cp\u003ea) sanguinarine, b) rutin, c) quercetin, and d) chelerythrine\u003cem\u003e.\u003c/em\u003eDotted lines represent hydrogen bonds\u003c/p\u003e","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7591427/v1/8b287a2d102bc6c0ac471ae4.png"},{"id":91172074,"identity":"10b73151-ad83-44e4-ab2b-627eac9a472d","added_by":"auto","created_at":"2025-09-12 11:42:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53629,"visible":true,"origin":"","legend":"\u003cp\u003ea) acarbose, b) piceatannol.\u003c/p\u003e","description":"","filename":"OnlineFig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7591427/v1/9329953bf317477e4c9ac42f.png"},{"id":91176245,"identity":"bf685d70-fa4d-407b-84ce-fc57ac615011","added_by":"auto","created_at":"2025-09-12 12:14:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1450172,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7591427/v1/b9388a7f-0738-4eeb-bf82-d59d587ac9af.pdf"},{"id":91172070,"identity":"aae03954-4329-4695-afb0-dca694cd4700","added_by":"auto","created_at":"2025-09-12 11:42:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39089,"visible":true,"origin":"","legend":"","description":"","filename":"Table15.docx","url":"https://assets-eu.researchsquare.com/files/rs-7591427/v1/5bf0650d023d3de2afa3eba5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"In Silico Identification and ADME/Toxicity Prediction of Phytochemical Inhibitors Targeting Glucosyltransferases of Streptococcus mutans for Anti-Biofilm Applications","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDental caries is among the most widespread infectious conditions globally, affecting billions of individuals and significantly influencing oral and systemic health (Giacaman et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The global burden of dental caries is exacerbated by the ability of \u003cem\u003eStreptococcus mutans\u003c/em\u003e (\u003cem\u003eS. mutans\u003c/em\u003e), a key cariogenic pathogen, to form resilient biofilms on tooth surfaces (Lemos et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These biofilms provide a protective environment that promotes plaque accumulation and generates acidic conditions capable of demineralizing the enamel. Due to this, strategies that disrupt S. mutans biofilm development are considered crucial to modern approaches for preventing and managing dental caries.\u003c/p\u003e\u003cp\u003eAmong the factors contributing to biofilm formation, the glucosyltransferases (Gtfs) produced by \u003cem\u003eS. mutans\u003c/em\u003e\u0026mdash;namely GtfB, GtfC, and GtfD\u0026mdash;are crucial (Lemos et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These enzymes catalyze the synthesis of glucans, which serve as structural components of the biofilm matrix, enhancing its stability and facilitating microbial adhesion. GtfB primarily produces water-insoluble glucans with α-1,3 linkages, while GtfC synthesizes a mix of soluble and insoluble glucans, and GtfD creates predominantly water-soluble glucans (Bowen and Koo 2011). As key drivers of caries progression, GTFs have emerged as validated targets for the development of anti-caries therapies. Inhibiting these enzymes could effectively disrupt biofilm formation and mitigate the pathogenic effects of \u003cem\u003eS. mutans\u003c/em\u003e (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, plant-derived bioactive compounds have garnered attention as potential candidates for the development of safe, multi-target inhibitors (Vaou et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These compounds offer a promising alternative to conventional antimicrobial agents due to their natural origin, diverse bioactivity, and relatively low toxicity profiles. Furthermore, the broad chemical diversity of plant metabolites provides opportunities for targeting multiple bacterial pathways simultaneously, an approach that may overcome the limitations of single-target treatments.\u003c/p\u003e\u003cp\u003eDespite these promising prospects, current research on GTF inhibitors remains limited, particularly in terms of multi-GTF targeting (Atta et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Most studies focus on inhibiting individual GTF enzymes, leaving a significant gap in understanding how to simultaneously target multiple GTFs or address their combined role in biofilm formation. Despite evidence of phytochemicals against \u003cem\u003eS. mutans\u003c/em\u003e, systematic in silico evaluation against multiple GTFs is lacking. This gap presents an opportunity for further exploration in the field of anti-caries drug development.\u003c/p\u003e\u003cp\u003eThis study aims to employ computational screening to identify phytochemicals with the potential to inhibit GTFs. By leveraging in silico methods, we will assess the binding affinity of various plant-derived compounds to GTFs and perform ADME (absorption, distribution, metabolism, excretion) and toxicity profiling to evaluate their suitability as safe, effective therapeutic agents. This research seeks to contribute to the development of novel, natural-based strategies for combating dental caries through targeted inhibition of \u003cem\u003eS. mutans\u003c/em\u003e glucosyltransferases.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eSelection of phytochemicals: A total of 21 plant-derived compounds reported in the literature for their antimicrobial or antibiofilm effects against \u003cem\u003eS. mutans\u003c/em\u003e were shortlisted. The selected molecules covered diverse chemical classes, including alkaloids, flavonoids, phenolic acids, terpenoids, and coumarins (Table\u0026nbsp;1). Canonical SMILES and three-dimensional structures were retrieved from the PubChem database (Adil et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Anjaneyulu et al. 2021; Ankri and Mirelman \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Cheng et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Choi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cushnie and Lamb \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Dwivedi and Singh \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Eisenberg et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Kanwal et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nair et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Priya et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Quek et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Stermitz et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Vieira et al. 2014a; Vieira et al. 2014b; Zhang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProtein Structures- Three-dimensional coordinates of \u003cem\u003eS. mutans\u003c/em\u003e GtfB (PDB ID: 8fg8) and GtfC (PDB ID: 3aic) were downloaded from the Protein Data Bank (Ahirwar et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ito et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Since the crystal structure of GtfD was unavailable, a homology model was generated using SwissModel with chain A of 3aib as the template (Waterhouse et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The quality of the model was confirmed based on GMQE (0.83), QMEANDisCo (\u0026plusmn;\u0026thinsp;0.05), and Ramachandran plot analysis showing 96.29% of residues in favored regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Prior to docking, proteins were processed in AutoDock Tools by deleting crystallographic waters, adding polar hydrogens, and assigning Gasteiger charges (Morris et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLigand Preparation-\u003c/p\u003e\u003cp\u003eLigand structures were optimized and converted from SDF to PDB format using Open Babel. During preparation, torsional flexibility and rotatable bonds were automatically assigned. All ligand structures were prepared using AutoDock tools by adding Kollman charges and detecting torsion tree root (Morris et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMolecular Docking- Docking was carried out using AutoDock Vina v1.2.0 (Eberhardt et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Grid boxes were centered on the catalytic domains of each GTF with dimensions of 60 \u0026times; 60 \u0026times; 60 \u0026Aring;. Each compound was docked individually, and the conformation with the lowest binding energy was chosen for further inspection. As a validation step, known inhibitors were re-docked into the GtfB and GtfC active sites, and the predicted binding poses were compared with their reference positions. Visualization of docking interactions was performed in UCSF ChimeraX (Meng et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eADME Prediction-Pharmacokinetic and drug-likeness properties of the top eight ligands were estimated using the SwissADME web server. (Daina et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The analysis included Lipinski\u0026rsquo;s rule of five, bioavailability score, gastrointestinal absorption, blood\u0026ndash;brain barrier penetration, and cytochrome P450 inhibition potential.\u003c/p\u003e\u003cp\u003eToxicity Prediction- Toxicity profiles of the leading candidates were predicted with ProTox-III (Banerjee et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Parameters assessed included oral LD₅₀ values, and risks of hepatotoxicity, carcinogenicity, mutagenicity, and immunotoxicity, which were used to classify compounds into toxicity categories (Table\u0026nbsp;4).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDocking Analysis\u003c/p\u003e\n\u003cp\u003eWe performed manual curation of the literature on medicinal plants that showed antimicrobial activity or antibiofilm activity against S. mutans using databases such as PubMed, Scopus, or Google Scholar. The search resulted in more than 50 medicinal plants, which comprise around 21 phytochemicals/bioactive compounds altogether. The list of medicinal plants and bioactive compounds is listed in Table 1 (Adil et al. 2014; Anjaneyulu et al. 2021; Ankri and Mirelman 1999; Cheng et al. 2007; Choi et al. 2018; Cushnie and Lamb 2005; Dwivedi and Singh 2016; Eisenberg et al. 1991; Kanwal et al. 2025; Nair et al. 2016; Priya et al. 2021; Quek et al. 2021; Stermitz et al. 2000; Vieira et al. 2014a; Vieira et al. 2014b; Zhang et al. 2023).\u003c/p\u003e\n\u003cp\u003eThe protein structures of GtfB, Gtf C and GtfD used for docking studies are shown in Figure 1 A, B, and C, respectively. Structural modelling of the glucosyltransferases revealed high sequence identity (62.06%), a GMQE score of 0.83, and robust model validation metrics (Ramachandran favoured: 96.29%), confirming the suitability of the GtfD model for docking analysis (Figure 1C and D). The docking studies revealed that multiple phytochemicals exhibited significant binding affinities towards GtfB, GtfC, and GtfD of \u003cem\u003eS. mutans\u003c/em\u003e (Table 2). Among the tested compounds, sanguinarine, rutin, quercetin, chelerythrine, luteolin, berberine, piperine, and chrysin showed the highest docking scores for all three glucosyltransferases, with values ranging from \u0026minus;7.4 to \u0026minus;9.3 for GtfB, -7.9 to -9.1 for GtfC, and -7.9 to -9.5 for GtfD, indicating strong interactions with the enzyme active sites. The chemical structures of the top 8 compounds were displayed in Figure 2. These compounds consistently outperformed other bioactive molecules such as allicin, berberine, caffeic acid, and thymol, which showed moderate binding affinities. The docked poses of the top 4 compounds were displayed in Figure 3. Ligand binding pocket visualizations further highlighted the molecular interactions of top-scoring compounds, particularly the hydrogen bonding with key residues in GtfB, GtfC, and GtfD, supporting the proposed mechanism of inhibition.\u003c/p\u003e\n\u003cp\u003eWe analysed key residues involved in lead molecule binding using ChimeraX software. We observed that GtfB interaction with lead molecules involved different residues for each molecule docked. Sanguinarine formed one H-bond with Arg 514, rutin formed H-bond with Asn 511 and His 561. Quercetin formed an H-bond with Asn836, chelerythrine with Asn 566, leuteolin with Asp 883, berberine with Glu 564, piperine with Arg 514, and chrysin with Asn 836.\u003c/p\u003e\n\u003cp\u003eIn case of GtfC, sanguinarine interacted with Gln 592, Rutin with Asp 477 and Gln 592, quercetin with Gln 592, Tyr 610, and Asn 862, chelerythrine interacted with Gln 592 and Arg 450, luteolin with Gln 592, Asn 481, and Asp 909, berberine with Gln 592 and Asn 481. Piperine interacted with only Gln 592, while chrysin interacted with Gln 592, Asp 909, and Gln 960. Overall, GtfC residues Gln 592 and Asn 481 have been identified as key residues involved in lead molecule binding.\u003c/p\u003e\n\u003cp\u003eIn case of GtfD, Sanguinarine interacted with His 342 and Gln 347, rutin with Asp 224, Asn 228 and Gln 347, quercetin with Asn 228 and Gln 347, chelerythrine with Gln 347, luteolin with Asn 228 and Gln 347, berberine with Gln 327, piperine with His 342 and Gln 347 and chrysin with Gln 347 and Asn 626. Overall, Gtf D residues Gln 347 and Asn 228 have been identified as key residues involved in lead molecule binding.\u003c/p\u003e\n\u003cp\u003eADMET Profiles\u003c/p\u003e\n\u003cp\u003eADME profiling of the top eight phytochemicals/lead molecules indicated favourable pharmacokinetic properties, with most compounds showing good absorption, distribution, metabolism, and excretion scores (Table 3). Toxicity predictions suggest that these bioactive molecules have low toxicity risks, thereby enhancing their potential as safe therapeutic agents (Table 4). Comparative analysis with known Gtf inhibitors acarbose and piceatannol demonstrated superior or comparable profiles for several phytochemicals, especially in terms of binding affinity and predicted drug-likeness (Table 3 \u0026amp; 4 \u0026amp; 5) (Newbrun et al. 1983; Nijampatnam et al. 2018).\u003c/p\u003e\n\u003cp\u003eADME analysis indicated favorable drug-like properties for most top-ranking compounds. Toxicity prediction further differentiated the candidates: rutin, luteolin, and chrysin demonstrated relatively high LD₅₀ values (\u0026gt;3000 mg/kg) and fell into lower toxicity classes (4-5), suggesting a broad safety margin. By contrast, alkaloids such as sanguinarine and chelerythrine, although potent binders (\u0026minus;9.1 to \u0026minus;9.5 kcal/mol), were predicted to belong to lower LD₅₀ values, indicating potential risks at therapeutic doses. Piperine and luteolin showed intermediate profiles, combining moderate to strong docking affinities (\u0026minus;7.4 to \u0026minus;8.3 kcal/mol) with acceptable safety predictions. Taken together, rutin, luteolin, and chrysin emerge as the most promising lead molecules due to their optimal balance of strong binding, favorable pharmacokinetics, and low predicted toxicity, whereas sanguinarine and chelerythrine may serve as valuable structural scaffolds but require careful toxicity evaluation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified several phytochemicals with strong inhibitory potential against \u003cem\u003eS. mutans\u003c/em\u003e glucosyltransferases through molecular docking and in silico pharmacokinetic/toxicity profiling. The docking results highlighted rutin, quercetin, chelerythrine, and sanguinarine as top candidates with binding energies ranging from \u0026minus;\u0026thinsp;8.0 to \u0026minus;\u0026thinsp;9.5 kcal/mol, suggesting robust affinity for the catalytic pockets of all three GTFs. These findings support the potential of natural compounds to act as broad-spectrum inhibitors that disrupt biofilm-associated virulence mechanisms in \u003cem\u003eS. mutans\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eA few of these compounds, including quercetin and luteolin, have previously been reported to inhibit individual Gtf\u0026rsquo;s activity and reduce biofilm formation, thereby validating our computational pipeline (Nakahara et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Rudin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the strong multi-target binding observed for sanguinarine, rutin, piperine, and chelerythrine represents novel insights, as their direct interactions with \u003cem\u003eS. mutans\u003c/em\u003e GTFs have not been extensively documented. The interaction analysis further revealed recurrent binding hotspots, particularly Gln592 in GtfC and Gln347 in GtfD, which were consistently involved in stabilizing top-ranking ligands. These conserved residues may represent key structural determinants for future inhibitor design.\u003c/p\u003e\u003cp\u003eIntegration of ADME and toxicity predictions provided an additional layer of candidate prioritization. Rutin, luteolin, and chrysin emerged as promising leads, combining high binding affinities with favorable pharmacokinetic profiles, high LD₅₀ values, and classification into low-to-moderate toxicity classes (4\u0026ndash;5). In contrast, alkaloids such as berberine and quercetin, while exhibiting some of the strongest docking scores, were predicted to fall into higher toxicity classes (2\u0026ndash;3) with relatively low LD₅₀ values, suggesting potential risks if developed as direct therapeutic agents. These molecules may instead serve as valuable scaffolds for structural modification aimed at reducing toxicity while retaining potency.\u003c/p\u003e\u003cp\u003eOverall, this work provides a systematic in silico evaluation of phytochemicals targeting multiple \u003cem\u003eS. mutans\u003c/em\u003e GTFs, offering both validation of known inhibitors and the identification of novel candidates. The combination of high-affinity binding, favorable pharmacokinetics, and low toxicity profiles makes rutin, sanguinarine, chelerythrine, and piperine particularly attractive for further experimental evaluation. At the same time, our results emphasize the importance of balancing potency with safety in natural product\u0026ndash;based drug discovery. Future studies should include in vitro enzyme inhibition assays, biofilm disruption models, and cytotoxicity testing in mammalian cell systems to confirm the therapeutic potential of these compounds.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that several plant-derived phytochemicals, including rutin, quercetin, sanguinarine, and chelerythrine, exhibit strong and multi-target inhibitory potential against the glucosyltransferases of Streptococcus mutans, as revealed by molecular docking and in silico ADME/toxicity profiling. Being luteolin already reported as a known inhibitor of Gtfs, rutin, and chrysin stand out for their combination of high binding affinity, favorable pharmacokinetics, and low predicted toxicity, supporting their advancement as lead anti-biofilm agents. These findings lay the groundwork for future experimental validation and highlight the promise of natural compounds for novel anti-caries therapy targeting multiple virulence factors in S. mutans.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe Authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe authors declare that no funds, external grants were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRN and EAVVRM conceptualized and wrote the manuscriptRN, EAVVRM, RJ, VP, SG conducted researchRN analyzed the results and prepared figures for publicationRN coordinated the project\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the financial support provided by B V Raju College, Vishnupur, Bhimavaram, in the form of salary support, which facilitated the completion of this research work. We thank Dr. I. R. K. Raju, Principal, B V Raju College, for constant support and motivation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdil M, Singh K, Verma PK, Khan AU (2014) Eugenol-induced suppression of biofilm-forming genes in Streptococcus mutans: An approach to inhibit biofilms. J Glob Antimicrob Resist 2(4):286\u0026ndash;292\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhirwar P, Kozlovskaya V, Nijampatnam B, Rojas EM, Pukkanasut P, Inman D et al (2023) Hydrogel-Encapsulated Biofilm Inhibitors Abrogate the Cariogenic Activity of \u003cem\u003eStreptococcus mutans\u003c/em\u003e. J Med Chem 66(12):7909\u0026ndash;7925\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnjaneyulu B, Sangeeta, Saini N (2021) A Study on Camphor Derivatives and Its Applications: A Review. Curr Org Chem 25(12):1404\u0026ndash;1428\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnkri S, Mirelman D (1999) Antimicrobial properties of allicin from garlic. Microbes Infect 1(2):125\u0026ndash;129\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAtta L, Siddiqui AR, Mushtaq M, Munsif S, Nur-e-Alam M, Ahmed A et al (2025) Molecular insights into antibiofilm inhibitors of Streptococcus mutans glucosyltransferases through in silico approaches. Sci Rep 15(1):14160\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBanerjee P, Kemmler E, Dunkel M, Preissner R (2024) ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 52(W1):W513\u0026ndash;W520\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBowen WH, Koo H. Biology of \u003cem\u003eStreptococcus mutans-\u003c/em\u003eDerived Glucosyltransferases:Role in Extracellular Matrix Formation of Cariogenic Biofilms. Caries Res. 2011;45(1):69\u0026ndash;86\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Z, Zhao X, Zheng H, Wang Y, Zhang L (2025) Advances and challenges in drug design against dental caries: Application of in silico approaches. J Pharm Anal 15(6):101161\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheng R-B, Chen X, Liu S-J, Zhang X-F (2007) [Effect of Chelerythrine on cell surface hydrophobicity and adherence of Streptococcus mutans]. Shanghai Kou Qiang Yi Xue 16(1):68\u0026ndash;72\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoi HG, Tran PT, Lee J-H, Min BS, Kim JA (2018) Anti-inflammatory activity of caffeic acid derivatives isolated from the roots of Salvia miltiorrhiza Bunge. Arch Pharm Res 41(1):64\u0026ndash;70\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCushnie TPT, Lamb AJ (2005) Antimicrobial activity of flavonoids. Int J Antimicrob Agents 26(5):343\u0026ndash;356\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDaina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7(1):42717\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDwivedi D, Singh V (2016) Effects of the natural compounds embelin and piperine on the biofilm-producing property of Streptococcus mutans. J Tradit Complement Med 6(1):57\u0026ndash;61\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEberhardt J, Santos-Martins D, Tillack AF, Forli S, AutoDock (2021) Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. J Chem Inf Model 61(8):3891\u0026ndash;3898\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEisenberg AD, Young DA, Fan-Hsu JF, Spitz LM (1991) Interactions of Sanguinarine and Zinc on Oral Streptococci and Actinomyces Species. Caries Res 25(3):185\u0026ndash;190\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiacaman RA, Fern\u0026aacute;ndez CE, Mu\u0026ntilde;oz-Sandoval C, Le\u0026oacute;n S, Garc\u0026iacute;a-Manr\u0026iacute;quez N, Echeverr\u0026iacute;a C et al (2022) Understanding dental caries as a non-communicable and behavioral disease: Management implications. Front Oral Health. ;3\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIto K, Ito S, Shimamura T, Weyand S, Kawarasaki Y, Misaka T et al (2011) Crystal Structure of Glucansucrase from the Dental Caries Pathogen Streptococcus mutans. J Mol Biol 408(2):177\u0026ndash;186\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanwal S, Perveen S, Hameed H, Tahir S, Rashid R, Raish M et al (2025) Evaluating the efficacy of Chenopodium murale plant extract in inhibiting Streptococcus mutans and reducing dental caries risk. Sci Rep 15(1):15406\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim S, Chen J, Cheng T, Gindulyte A, He J, He S et al (2025) PubChem 2025 update. Nucleic Acids Res 53(D1):D1516\u0026ndash;D1525\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLemos JA, Palmer SR, Zeng L, Wen ZT, Kajfasz JK, Freires IA et al (2019) The Biology of \u003cem\u003eStreptococcus mutans\u003c/em\u003e. Microbiol Spectr. ;7(1)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeng EC, Goddard TD, Pettersen EF, Couch GS, Pearson ZJ, Morris JH et al (2023) \u0026lt;scp\u0026thinsp;\u0026gt;\u0026thinsp;UCSF ChimeraX : Tools for structure building and analysis\u0026lt;/scp\u0026thinsp;\u0026gt;. Protein Sci. ;32(11)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS et al (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30(16):2785\u0026ndash;2791\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNair SV, Baranwal G, Chatterjee M, Sachu A, Vasudevan AK, Bose C et al (2016) Antimicrobial activity of plumbagin, a naturally occurring naphthoquinone from Plumbago rosea, against Staphylococcus aureus and Candida albicans. Int J Med Microbiol 306(4):237\u0026ndash;248\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNakahara K, Kawabata S, Ono H, Ogura K, Tanaka T, Ooshima T et al (1993) Inhibitory effect of oolong tea polyphenols on glycosyltransferases of mutans Streptococci. Appl Environ Microbiol 59(4):968\u0026ndash;973\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewbrun E, Hoover CI, Walker GJ (1983) Inhibition by acarbose, nojirimycin and 1-deoxynojirimycin of glucosyltransferase produced by oral Streptococci. Arch Oral Biol 28(6):531\u0026ndash;536\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNijampatnam B, Zhang H, Cai X, Michalek SM, Wu H, Velu SE (2018) Inhibition of \u003cem\u003eStreptococcus mutans\u003c/em\u003e Biofilms by the Natural Stilbene Piceatannol Through the Inhibition of Glucosyltransferases. ACS Omega 3(7):8378\u0026ndash;8385\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePriya A, Selvaraj A, Divya D, Karthik Raja R, Pandian SK (2021) In Vitro and In Vivo Anti-infective Potential of Thymol Against Early Childhood Caries Causing Dual Species Candida albicans and Streptococcus mutans. Front Pharmacol. ;12\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuek A, Mohd Zaini H, Kassim NK, Sulaiman F, Rukayadi Y, Ismail A et al (2021) Oxygen radical antioxidant capacity (ORAC) and antibacterial properties of Melicope glabra bark extracts and isolated compounds. PLoS ONE 16(5):e0251534\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRudin L, Roth N, Kneub\u0026uuml;hler J, Dubey BN, Bornstein MM, Shyp V (2023) Inhibitory effect of natural flavone luteolin on \u003cem\u003eStreptococcus mutans\u003c/em\u003e biofilm formation. Microbiol Spectr. ;11(5)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStermitz FR, Lorenz P, Tawara JN, Zenewicz LA, Lewis K (2000) Synergy in a medicinal plant: Antimicrobial action of berberine potentiated by 5\u0026prime;-methoxyhydnocarpin, a multidrug pump inhibitor. Proceedings of the National Academy of Sciences. ;97(4):1433\u0026ndash;7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVaou N, Stavropoulou E, Voidarou C, Tsigalou C, Bezirtzoglou E (2021) Towards Advances in Medicinal Plant Antimicrobial Activity: A Review Study on Challenges and Future Perspectives. Microorganisms 9(10):2041\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVieira DRP, Amaral FMM, Maciel MCG, Nascimento FRF, Lib\u0026eacute;rio SA, Rodrigues VP (2014a Sep) Plant species used in dental diseases: Ethnopharmacology aspects and antimicrobial activity evaluation. J Ethnopharmacol 155(3):1441\u0026ndash;1449\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVieira DRP, Amaral FMM, Maciel MCG, Nascimento FRF, Lib\u0026eacute;rio SA, Rodrigues VP (2014b Sep) Plant species used in dental diseases: Ethnopharmacology aspects and antimicrobial activity evaluation. J Ethnopharmacol 155(3):1441\u0026ndash;1449\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWaterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46(W1):W296\u0026ndash;303\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang OL, Niu JY, Yin IX, Yu OY, Mei ML, Chu CH (2023) Antibacterial Properties of the Antimicrobial Peptide Gallic Acid-Polyphemusin I (GAPI). Antibiotics 12(9):1350\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"In-silico, docking, Gtf inhibitor, S. mutans, dental caries","lastPublishedDoi":"10.21203/rs.3.rs-7591427/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7591427/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDental caries remains a major global health concern, with \u003cem\u003eStreptococcus mutans\u003c/em\u003e recognized as a key cariogenic pathogen due to its capacity to form resilient biofilms. Central to this process are the glucosyltransferases (GtfB, GtfC, and GtfD), which synthesize extracellular glucans that promote bacterial adhesion and biofilm stability. Targeting these enzymes represents a promising strategy for anti-caries therapy. In this study, we employed molecular docking and in silico pharmacokinetic profiling to evaluate the inhibitory potential of 21 phytochemicals against S. mutans glucosyltransferases. Docking analysis revealed that several compounds, including sanguinarine, rutin, quercetin, and chelerythrine, exhibited strong binding affinities across all three GTFs (− 7.4 to − 9.5 kcal/mol), often surpassing the binding affinities of known inhibitors. Ligand–protein interaction analysis identified recurrent hotspots such as Gln592 in GtfC and Gln347 in GtfD, underscoring conserved binding pockets. ADME and toxicity predictions further suggested that most top-ranking compounds possessed favorable pharmacokinetic properties with acceptable safety profiles, highlighting their potential as lead molecules. Overall, this integrated computational study identifies multiple plant-derived inhibitors with promising activity against S. mutans glucosyltransferases, supporting their further evaluation as natural anti-biofilm and anti-caries agents.\u003c/p\u003e","manuscriptTitle":"In Silico Identification and ADME/Toxicity Prediction of Phytochemical Inhibitors Targeting Glucosyltransferases of Streptococcus mutans for Anti-Biofilm Applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 11:42:14","doi":"10.21203/rs.3.rs-7591427/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":"45d366f3-f1d0-4b99-aaf8-fc59a2553960","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-12T11:42:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 11:42:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7591427","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7591427","identity":"rs-7591427","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}