Computational Docking and Virtual Screening of Thymus vulgaris as Potential Inhibitors for Multi-Drug-Resistant Tuberculosis (MDR-TB) Target Proteins

Computational Docking and Virtual Screening of Thymus vulgaris as Potential Inhibitors for Multi-Drug-Resistant Tuberculosis (MDR-TB) Target Proteins | 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 Computational Docking and Virtual Screening of Thymus vulgaris as Potential Inhibitors for Multi-Drug-Resistant Tuberculosis (MDR-TB) Target Proteins Kashif Raza SIDDIQUE, Arli Aditya PARIKESIT, Yustinus MALADAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7803630/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 MDR-TB is a worldwide problem; according to the World Health Organisation (WHO), TB is the second most infectious killer after COVID-19, even above HIV and AIDS. With rising resistance to current antibiotics and limited solutions, the urgent discovery of new, effective, and affordable antibacterials with low toxicity is imperative to combat MDR-TB strains. Multidrug-Resistant tuberculosis (MDR-TB), caused by mycobacterium tuberculosis, is resistant to ethambutol (EMB), which has been widely ported worldwide. EMB resistance is caused by mutations in the embB gene, which encodes the arabinosylindoylacetylinositol synthase enzyme. The mutations are found in M306L, M3306L + E378A, M306V, and D1024N. Caryophyllene oxide, Bisabolene, and Trans-caryophyllene are essential components of the medicinal plant Thymus vulgaris. Hence, this study will introduce an in silico phytochemical-based approach for discovering novel bacterial agents, exploring the potential of a computational approach in therapeutic discovery. This study focuses on screening all these phytochemicals, Caryophyllene oxide, Bisabolene, and Trans-Caryophyllene, as a potential drug candidate to combat MDR-TB infection through a molecular docking approach. Moreover, the interaction of amino acid analysis, in silico pharmacokinetics, compound target prediction, pathway enrichment analysis, and Molecular Dynamics (MD) simulations were conducted for further investigation. Caryophyllene oxide, Bisabolene, and Trans-Caryophyllene also showed a strong binding affinity against these mutations. in silico pharmacokinetic analysis highlights the potency as a drug candidate, showing strong Adsorption, Distribution, Metabolism, and Excretion (ADME) properties in combination with low toxicity. MDR-TB Bioactive Compounds Molecular Docking Molecular Dynamics Thymus vulgaris Figures Figure 1 Figure 2 Figure 3 Introduction Tuberculosis (TB) is an infectious disease that mostly affects the lungs and is caused by Mycobacterium Tuberculosis, which spreads through the air when infected people cough, sneeze, or spit. According to WHO, an estimated 10.6 million people were ill with TB worldwide, including 5.8 million men, 3.5 million women, and 1.3 million children; TB is present in every country, and all age groups and a total of 1.3 million people died from TB in 2022 including 167 000 people with HIV. MDR-TB remains a public health security threat. Only about 2 in 5 people with drug-resistant TB accessed treatment in 2022. Ending the TB epidemic by 2030 is among the United Nations Sustainable Development Goals (SDGs) aims. Case reports of Mycobacterium Tuberculosis being resistant against the first line drugs such as rifampicin, isoniazid, ethambutol (EMB), Streptomycin, and other drugs have been reported widely. About 3% of all newly diagnosed patients have MDR-TB, and the proportion is higher in the patients who have previously received Anti-tuberculosis treatment, which reflects the failures of the programs that were designed to ensure the complete cure of TB. Host genetic factors can also contribute; inadequate and incomplete treatment is the most important factor leading to the development of MDR-TB (Sharma & Mohan, 2004). Therefore, finding new, effective, and affordable antibacterials with low toxicity is crucial to prevent MDR-TB. A recent research paper from (Maladan et al ., 2023) has successfully shown the mutations in the embB gene that encodes for arabinosylindoylacetylinositol synthase. M306L, M306L + E378A, M306V, and D1024N are the protein mutations of the embB gene; also, their paper suggested the amino acid changes that have led to these mutations. Studies have reported different results regarding the mutations at codon 306 on the embB gene (Ruesen et al ., 2018; Hazbón et al ., 2005); some studies suggest M306L and M306V mutants do not play any role in the TB resistance towards ethambutol (Bakuła et al .2013 and Li et al .2020) while many studies predict that M306V mutant is present in both EMB-susceptible and EMB-resistant strains. Other studies suggest that M306L and M306V mutants resist EMB (Sekiguchi et al ., 2007; Lee et al., 2004 ). Molecular docking is the ability to assign ligand sites on the receptor easily. Computer-based techniques can assist and accelerate the drug discovery process. The binding affinity suggests how strongly the ligand interacts with the binding site of the macromolecules (Mehmood et al ., 2014). The lower the binding affinity, the stronger the interaction between the ligand and macromolecule. Caryophyllene oxide, bisabolene, and Trans-Caryophyllene are essential phytochemicals derived from Thymus vulgaris that have shown some potential in combating MDR-TB, often rendering conventional antibiotic treatments (S.Gibbons, 2004 ). The antimicrobial properties of these compounds serve as valuable for therapies and enhancing their efficacy against resistant strains of Mycobacterium Tuberculosis. Caryophyllene oxide, in particular, has shown a strong capacity to inhibit various bacterial strains and disrupt the biofilm formation associated with TB (Joanna et al ., 2021). Biofilms can protect the bacteria from both host immune response and antibiotic treatments. By interpreting these protective layers, caryophyllene oxide could improve the effectiveness of traditional treatments. Bisabolene and Trans-Caryophyllene further contribute to the antimicrobial actions. Tb triggers a robust inflammatory response; these compounds could help mitigate associated symptoms and improve patient comfort. The anti-inflammatory also aids in reducing tissue damage and promoting recovery. Moreover, the effects of compounds could enhance the overall therapeutic profile of thymus vulgaris as a medicinal herb. Integrating these natural compounds into current medicinal practices offers a promising avenue for addressing challenges in infectious disease management today. (Sharifi et al .2017; Rivas et al .2012) This research uses computational methods to evaluate the properties of Caryophyllene oxide, bisabolene, and trans-caryophyllene from Thymus vulgaris against MDR-TB. Methodology We have followed the mutation that is shown by (Maladan et al .2023) and shows the amino acid replacement in a different position of protein structure of arabinosylindoylacetylinositol synthase in MTB shown in Table.1. MTB mutant embB construction: The 3D structure of arabinosylindoylacetylinositol synthase in MTB was obtained from the RCSB PDB with PDB ID: 7BVF and only chain A was considered RCSB PDB. Discovery studio 2024 was used for the removal of other chains, removing the water molecules and other unwanted molecules (Biovia et al .2024). Used SWISS-MODEL for the mutant’s structure prediction for the Homology Modelling which was accessed on 19 August 2024. (Waterhouse A et al .2018) SWISS-MODEL Ligand preparation: First the ligand was obtained from PubChem in SDF format we have used and in PyRx and minimized and made it ready for the docking. Molecular Docking: Molecular docking was performed using PyRx Software for blind docking to sort Thymus vulgaris essential compounds on the basis of Binding affinity (Trott et al .2010) . Later cross checked the autodocking results at CB-Dock2 which has an integrated tool to identify the binding cavities on the proteins. The Grid box size was X: 168.34, Y:146.70, and Z: 175.11 which was determined on the basis of the EMB grid on PDB ID 7BVF as shown in Table 2 (Yang et al .2022) Molecular Dynamics : We have used online molecular dynamics server CABSflex2.0 CABS-flex 2.0. It was designed to determine analyse the protein flexibility and provide data in RMSF value. It measures the amplitude of structures during the dynamic simulation (Bornot et al . 2011). All the parameters were of additional distance restrain, distance restrain generator and advanced simulation options were set as default values. (Kuriata et al ., 2018) Results Docking: After completing the HMM for the mutation and validation of the structures using Ramachandran plotting using VADAR and shortlisted the structures on the basis of Z Score that is generated by the ProSA web server that both websites were accessed on 20 August of 2024 (Wiederstein and Sippl 2007). First, we analysed the blind docking results and found out the best ligands for the MDR-TB mutant proteins as represented in Table 3 and 3D in Figure 1. Binding affinity refers to the strength of the interaction between a molecule, such as a ligand, and its target, typically a protein or receptor. A high binding affinity means that the ligand binds tightly to the target, while a low binding affinity indicates weaker interactions. Validation of Docking: After getting the docking structures form Auto docking software we need to validate the binding sites and to check that we have use BIOVIA Visual Studio to get the 3D and 2D interaction of the ligands with the mutant proteins with the types of bonds and its distances in Figure 2. ADMET: For effective and safe drugs exhibit a finely tuned combination of pharmacokinetics, and pharmacodynamics, including the high affinity, potency and selectivity against the molecular target, along with the adequate Adsorption, Distribution, Metabolism, and Excretion (ADMET) (Ferreira et al .2019) We have used SwissADME to know the toxicity of the ligands and test it under different parameters of a drug. Lipinski's Rule of Five is one the most important rules to pass for a drug, SwissADME was accessed 21 August 2024 and shown the results in Table.4 (Diana et al .2017). As the Lipinski's rule of states that an optimum drug should have molecular weight of less than 500 g/mol, the no. of hydrogen bond donor should be less than 5, the No. of hydrogen acceptor bond should be less than 5, the value of MLogP should be less than 5 and the last is the no. of rotatable bonds should be less than 10. Results obtained from SwissADME shows that none of the components violates the 5 laws of Lipinski’s, which makes these 3 drug components considerable for this disease (Adebayo et al .2023) Toxicology Test: It is a test to predict the potential toxicity of chemicals before they are tested in the labs, it aids to meet regulatory requirements by providing the predictions about how the chemicals might interact with biological systems. Tox tree was accessed on 30 august 2024 and the level of Toxicology is shown in Table 5 (Patlewicz et al .2008). In this study, I have classified the toxicology levels of the ligands based on their degree of toxicity. Level I represents the lowest toxicity, indicating that these substances pose minimal risk and are generally considered safe at certain concentrations. These compounds typically have little to no harmful effects. Level III , on the other hand, indicates a higher toxicity level, where the substances can cause significant harm, even in relatively small amounts. Therefore, Level III corresponds to the highest toxicity, while Level I corresponds to the lowest toxicity. This classification system allows for a clear understanding of the relative risk associated with each ligand, with higher levels signifying more dangerous substances. Molecular Dynamics Simulation: MD simulation is used to get an RMSF graph which displays the fluctuation of individual residues of a protein complex. The lower the RMSF values the more stable the structure. We have used CABSflex2.0 for the molecular dynamic simulation of the proteins which was accessed on 16 September 2024 (Kuriata et al .2018; WicaksonoA., & ParikesitA. A. 2023) After getting the results from CABSflex2.0 as shown in Figure 3 we have analysed the output generated as a result. We have checked the RMSF graphs generated by CABSflex2.0 and got to know about the stability of different proteins. In general, each structure has 1024 amino acid residues in it. After analysing the excel sheet generated by CABSflex2.0, In 7BVF there are only 33 residues with more than 3.0 RMSF value that makes it stable, 306M_V has 41 residues over RMSF value of 3.0, D1024N has 40 residues over RMSF value 3.0, M306_L has 41 residues above RMSF value of 3.0, M306L_378A has 38 residues over the value of 3.0, and M378A has only 22 residues with value more than RMSF 3.0. After analysing the results, we can interpret that the M378A has the lowest varying residues which means it is the most stable protein out of all of them (Fatima et al .2023). DISCUSSION This study demonstrates the potential of Thymus vulgaris Phytochemicals, mainly Caryophyllene oxide, bisabolene, and Trans-Caryophyllene, as promising inhibitors of the MDR-TB proteins. The docking result suggests that trans-caryophyllene and caryophyllene oxide are the most potent inhibitors based on binding affinity from docking results. It was considered to take only the 3 best ligands from the components of Thymus vulgaris as there are more than 10 components found in thymus vulgaris , as shown in this paper (Shabnum et al ., M. G. (2011). After docking between all the compounds and the mutant proteins, we learn about the binding affinity of each compound with the mutant proteins, which creates resistance to the ethambutol drug. The binding affinity is promising, which makes these 3 components considerable for future research. These components, bisabolene bisabolene (Jin et al .2022), Trans-Caryophyllene (Hu et al ., 2022), and Caryophyllene oxide (Kanokmedhakul S et al .2007), have already shown antimicrobial properties against S.aureus, E.coli, and Monilia albicanswhich makes us curious to explore about them. After ADMETox results, we have found out that there is only one compound out of 3 that is showing a high level of hazard to the body because of two membered rings, which can be changed using some structural change in the component or by the experimental work in the labs (Boniface et al ., 2017). These compounds are supported by their pharmacokinetic properties evaluated by SwissADME, showing none violate Lipinski’s Rule of five. In contrast, Caryophyllene oxide shows some level of toxicity while the binding affinity shows as a lead component and for further refinement. The MD simulation result also highlights the stability of the ligand-protein complex, particularly the M378A mutant protein, which has the lowest RMSF value, indicating stable interaction. This study builds on the understanding of the embB mutations and suggests that specific compounds from Thymus vulgaris could be explored as new avenues for treatment. CONCLUSION This study has shown that these components, Caryophyllene oxide, Bisabolene, and Trans-Caryophyllene from Thymus vulgaris , have the potential to inhibit 7BVF and its mutant protein of MDR-TB. The gastrointestinal tract, soluble in water and bioavailable, readily absorbs all ligands. All of the ligands have passed the ADME test. Although Caryophyllene oxide has shown toxicology, other drugs, such as Trans-Caryophyllene and Bisabolene, have no side effects. The drug can be optimized in the wet lab to remove these effects. However, since this is in silico research, further wet lab validations, such as in vivo or in vitro experiments, are needed to approve these results. Abbreviations MDR Multi Drug Resistant TB Tuberculosis WHO World Health Organization MD Molecular Dynamics ADMET Absorption, Digestion, Metabolism, Toxicity RMSF Root Mean Square Fluctuation RMSD Root-Mean-Square-Deviation Declarations Author Contribution KRS worked on the methodological and technical details, while AAP and YM supervise and directing the research. Funding acquisition was done by AAP. The prediction of protein structure was procured from YM, validated by AAP, and analyzed by KRS. ACKNOWLEDGEMENT The authors would like to thank Brijesh K. Soni, President of GoldSmith Institute of Advanced Research (GIAR), India, for his heartfelt support of this project. Thanks also go to the Department of Research and Community Services (LPPM) of Indonesia International Institute for Life Sciences (i3L) for providing the essential facilities. The grammar and vocabulary of the manuscript were improved with Grammarly Premium software. References Becker, S. P., Sidol, C. A., & Burns, G. 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Computers in Human Behavior Reports, 3, 100073. https://doi.org/10.1016/j.chbr.2021.100073 Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press. Shochat, T., Cohen-Zion, M., & Tzischinsky, O. (2014). Functional consequences of inadequate sleep in adolescents: A systematic review. Sleep Medicine Reviews, 18(1), 75–87. https://doi.org/10.1016/j.smrv.2013.03.005 World Health Organization. (2023). Adolescent health and screen time: Global guidelines for healthy digital engagement. WHO Press. Tables Table 1. Mutations found in 7BVF protein structure. Amino Acid Positions Amino acid Amino Acid change Reference 306 Methionine Leucine, valine, isoleucine Maladan et al .2021 328 Aspartic Acid Tyrosine Ali et al .2015 378 Glutamic acid Alanine Ali et al .2015 354 Aspartic acid Alanine Ruesen et al .2018 406 Glycine Alanine, Aspartic acid Ruesen et al .2018 497 Glutamine Arginine Lee et al .2020 1024 Aspartic acid Asparagine, Threonine Maladan et al .2021 Table.2. Site of cavities on mutant proteins and its coordinates Protein Name Cavity volume Center (x, y, z) Cavity size (x, y, z) 7BVF 6380 169, 144, 172 22, 30, 30 306M_V 6590 169, 144, 173 22, 30, 30 D1024N 6610 169, 144, 173 22, 30, 30 M306L 6355 169, 144, 173 22, 30, 30 M306L_378A 6355 169, 144, 173 22, 30, 30 M378A 6531 169, 144, 173 22, 30, 30 Table.3. Binding affinities of mutant proteins with thymus vulgaris components Protein Name Binding Affinity Ligands ID Ligand Name 7BVF -7.3 5281515 Trans-Caryophyllene 306M_V -7.2 1742210 Caryophyllene oxide D1024N -7.1 3033866 Bisabolene M306L -7.2 5281515 Trans-Caryophyllene M306L_378A -7.6 5281515 Trans-Caryophyllene M378A -7.6 5281515 Trans-Caryophyllene Table 4. Analysis of Drug on Lipinski’s Rule of Five Ligand ID M. weight (g/mol) No. Hydrogen acceptors No. Hydrogen donors MlogP Rotatable Bonds 5281515 204.35 0 0 4.63 0 1742210 220.35 1 0 3.63 0 3033866 204.35 0 0 4.53 3 Table 5. Levels of toxicology of different ligands. Ligand ID Level of Toxicology 5281515 Level I 1742210 Level III 3033866 Level I Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7803630","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528207692,"identity":"24c42feb-f176-4767-8d2b-3648d6c57fd8","order_by":0,"name":"Kashif Raza SIDDIQUE","email":"","orcid":"","institution":"GoldSmith Institute of Advanced Research (GIAR)","correspondingAuthor":false,"prefix":"","firstName":"Kashif","middleName":"Raza","lastName":"SIDDIQUE","suffix":""},{"id":528207693,"identity":"76ccef17-d13c-4e7b-abe3-f5fc8e288781","order_by":1,"name":"Arli Aditya 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Trans-Caryophyllene\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7803630/v1/8157b5d7dda0b3b23f1b103d.png"},{"id":93472515,"identity":"b58d0987-a0fe-46a4-997b-f6e3315d29a5","added_by":"auto","created_at":"2025-10-14 08:29:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":498364,"visible":true,"origin":"","legend":"\u003cp\u003eShowing RMSF Values of interaction between ligand and mutant proteins (A) 7BVF with Trans-Caryophyllene (B) 306M_V with Bisabolene (C) D1024N with Caryophyllene oxide (D) M306L with Trans-Caryophyllene (E) M306L_378A with Trans-Caryophyllene (F) M378A with Trans-Caryophyllene\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7803630/v1/6dd2a295b0f9c8046d3b1308.png"},{"id":93472683,"identity":"6eb6b4a6-dba4-4122-9022-a194f94cfca5","added_by":"auto","created_at":"2025-10-14 08:37:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1975590,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7803630/v1/1264fb52-6bfc-43dd-b434-dd3e6dfbb3dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational Docking and Virtual Screening of Thymus vulgaris as Potential Inhibitors for Multi-Drug-Resistant Tuberculosis (MDR-TB) Target Proteins","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) is an infectious disease that mostly affects the lungs and is caused by Mycobacterium Tuberculosis, which spreads through the air when infected people cough, sneeze, or spit. According to WHO, an estimated 10.6\u0026nbsp;million people were ill with TB worldwide, including 5.8\u0026nbsp;million men, 3.5\u0026nbsp;million women, and 1.3\u0026nbsp;million children; TB is present in every country, and all age groups and a total of 1.3\u0026nbsp;million people died from TB in 2022 including 167 000 people with HIV. MDR-TB remains a public health security threat. Only about 2 in 5 people with drug-resistant TB accessed treatment in 2022. Ending the TB epidemic by 2030 is among the United Nations Sustainable Development Goals (SDGs) aims. Case reports of Mycobacterium Tuberculosis being resistant against the first line drugs such as rifampicin, isoniazid, ethambutol (EMB), Streptomycin, and other drugs have been reported widely. About 3% of all newly diagnosed patients have MDR-TB, and the proportion is higher in the patients who have previously received Anti-tuberculosis treatment, which reflects the failures of the programs that were designed to ensure the complete cure of TB. Host genetic factors can also contribute; inadequate and incomplete treatment is the most important factor leading to the development of MDR-TB (Sharma \u0026amp; Mohan, 2004).\u003c/p\u003e\u003cp\u003eTherefore, finding new, effective, and affordable antibacterials with low toxicity is crucial to prevent MDR-TB. A recent research paper from (Maladan \u003cem\u003eet al\u003c/em\u003e., 2023) has successfully shown the mutations in the embB gene that encodes for arabinosylindoylacetylinositol synthase. M306L, M306L\u0026thinsp;+\u0026thinsp;E378A, M306V, and D1024N are the protein mutations of the embB gene; also, their paper suggested the amino acid changes that have led to these mutations. Studies have reported different results regarding the mutations at codon 306 on the embB gene (Ruesen \u003cem\u003eet al\u003c/em\u003e., 2018; Hazb\u0026oacute;n \u003cem\u003eet al\u003c/em\u003e., 2005); some studies suggest M306L and M306V mutants do not play any role in the TB resistance towards ethambutol (Bakuła \u003cem\u003eet al\u003c/em\u003e.2013 and Li \u003cem\u003eet al\u003c/em\u003e.2020) while many studies predict that M306V mutant is present in both EMB-susceptible and EMB-resistant strains. Other studies suggest that M306L and M306V mutants resist EMB (Sekiguchi \u003cem\u003eet al\u003c/em\u003e., 2007; Lee et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Molecular docking is the ability to assign ligand sites on the receptor easily. Computer-based techniques can assist and accelerate the drug discovery process. The binding affinity suggests how strongly the ligand interacts with the binding site of the macromolecules (Mehmood \u003cem\u003eet al\u003c/em\u003e., 2014). The lower the binding affinity, the stronger the interaction between the ligand and macromolecule.\u003c/p\u003e\u003cp\u003eCaryophyllene oxide, bisabolene, and Trans-Caryophyllene are essential phytochemicals derived from \u003cem\u003eThymus vulgaris\u003c/em\u003e that have shown some potential in combating MDR-TB, often rendering conventional antibiotic treatments (S.Gibbons, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The antimicrobial properties of these compounds serve as valuable for therapies and enhancing their efficacy against resistant strains of \u003cem\u003eMycobacterium\u003c/em\u003e Tuberculosis. Caryophyllene oxide, in particular, has shown a strong capacity to inhibit various bacterial strains and disrupt the biofilm formation associated with TB (Joanna \u003cem\u003eet al\u003c/em\u003e., 2021). Biofilms can protect the bacteria from both host immune response and antibiotic treatments. By interpreting these protective layers, caryophyllene oxide could improve the effectiveness of traditional treatments. Bisabolene and Trans-Caryophyllene further contribute to the antimicrobial actions. Tb triggers a robust inflammatory response; these compounds could help mitigate associated symptoms and improve patient comfort. The anti-inflammatory also aids in reducing tissue damage and promoting recovery. Moreover, the effects of compounds could enhance the overall therapeutic profile of \u003cem\u003ethymus vulgaris\u003c/em\u003e as a medicinal herb. Integrating these natural compounds into current medicinal practices offers a promising avenue for addressing challenges in infectious disease management today. (Sharifi \u003cem\u003eet al\u003c/em\u003e.2017; Rivas \u003cem\u003eet al\u003c/em\u003e.2012)\u003c/p\u003e\u003cp\u003eThis research uses computational methods to evaluate the properties of Caryophyllene oxide, bisabolene, and trans-caryophyllene from \u003cem\u003eThymus vulgaris\u003c/em\u003e against MDR-TB.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eWe have followed the mutation that is shown by (Maladan \u003cem\u003eet al\u003c/em\u003e.2023) and shows the amino acid replacement in a different position of protein structure of arabinosylindoylacetylinositol synthase in MTB shown in Table.1.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMTB mutant embB construction:\u003c/h2\u003e\u003cp\u003eThe 3D structure of arabinosylindoylacetylinositol synthase in MTB was obtained from the RCSB PDB with PDB ID: 7BVF and only chain A was considered RCSB PDB. Discovery studio 2024 was used for the removal of other chains, removing the water molecules and other unwanted molecules (Biovia \u003cem\u003eet al\u003c/em\u003e.2024). Used SWISS-MODEL for the mutant\u0026rsquo;s structure prediction for the Homology Modelling which was accessed on 19 August 2024. (Waterhouse A \u003cem\u003eet al\u003c/em\u003e.2018) SWISS-MODEL\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLigand preparation:\u003c/h3\u003e\n\u003cp\u003eFirst the ligand was obtained from PubChem in SDF format we have used and in PyRx and minimized and made it ready for the docking.\u003c/p\u003e\n\u003ch3\u003eMolecular Docking:\u003c/h3\u003e\n\u003cp\u003eMolecular docking was performed using PyRx Software for blind docking to sort \u003cem\u003eThymus vulgaris\u003c/em\u003e essential compounds on the basis of Binding affinity (Trott \u003cem\u003eet al\u003c/em\u003e.2010) .\u003c/p\u003e\u003cp\u003eLater cross checked the autodocking results at CB-Dock2 which has an integrated tool to identify the binding cavities on the proteins. The Grid box size was X: 168.34, Y:146.70, and Z: 175.11 which was determined on the basis of the EMB grid on PDB ID 7BVF as shown in Table\u0026nbsp;2 (Yang \u003cem\u003eet al\u003c/em\u003e.2022)\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003eMolecular Dynamics\u003c/b\u003e:\u003c/div\u003e\u003cp\u003eWe have used online molecular dynamics server CABSflex2.0 CABS-flex 2.0. It was designed to determine analyse the protein flexibility and provide data in RMSF value. It measures the amplitude of structures during the dynamic simulation (Bornot \u003cem\u003eet al\u003c/em\u003e. 2011). All the parameters were of additional distance restrain, distance restrain generator and advanced simulation options were set as default values. (Kuriata \u003cem\u003eet al\u003c/em\u003e., 2018)\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDocking:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter completing the HMM for the mutation and validation of the structures using Ramachandran plotting using VADAR and shortlisted the structures on the basis of Z Score that is generated by the ProSA web server that both websites were accessed on 20 August of 2024 (Wiederstein and Sippl 2007). First, we analysed the blind docking results and found out the best ligands for the MDR-TB mutant proteins as represented in Table 3 and 3D in Figure 1. Binding affinity refers to the strength of the interaction between a molecule, such as a ligand, and its target, typically a protein or receptor. A high binding affinity means that the ligand binds tightly to the target, while a low binding affinity indicates weaker interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Docking:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter getting the docking structures form Auto docking software we need to validate the binding sites and to check that we have use BIOVIA Visual Studio to get the 3D and 2D interaction of the ligands with the mutant proteins with the types of bonds and its distances in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADMET:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor effective and safe drugs exhibit a finely tuned combination of pharmacokinetics, and pharmacodynamics, including the high affinity, potency and selectivity against the molecular target, along with the adequate Adsorption, Distribution, Metabolism, and Excretion (ADMET) (Ferreira \u003cem\u003eet al\u003c/em\u003e.2019) We have used SwissADME to know the toxicity of the ligands and test it under different parameters of a drug. Lipinski's Rule of Five is one the most important rules to pass for a drug, SwissADME was accessed 21 August 2024 and shown the results in Table.4 (Diana \u003cem\u003eet al\u003c/em\u003e.2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs the Lipinski's rule of states that an optimum drug should have molecular weight of less than 500 g/mol, the no. of hydrogen bond donor should be less than 5, the No. of hydrogen acceptor bond should be less than 5, the value of MLogP should be less than 5 and the last is the no. of rotatable bonds should be less than 10. Results obtained from SwissADME shows that none of the components violates the 5 laws of Lipinski’s, which makes these 3 drug components considerable for this disease (Adebayo \u003cem\u003eet al\u003c/em\u003e.2023)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eToxicology Test:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is a test to predict the potential toxicity of chemicals before they are tested in the labs, it aids to meet regulatory requirements by providing the predictions about how the chemicals might interact with biological systems. Tox tree was accessed on 30 august 2024 and the level of Toxicology is shown in Table 5 (Patlewicz \u003cem\u003eet al\u003c/em\u003e.2008). In this study, I have classified the toxicology levels of the ligands based on their degree of toxicity. \u003cstrong\u003eLevel I\u003c/strong\u003e represents the lowest toxicity, indicating that these substances pose minimal risk and are generally considered safe at certain concentrations. These compounds typically have little to no harmful effects. \u003cstrong\u003eLevel III\u003c/strong\u003e, on the other hand, indicates a higher toxicity level, where the substances can cause significant harm, even in relatively small amounts. Therefore, \u003cstrong\u003eLevel III\u003c/strong\u003e corresponds to the highest toxicity, while \u003cstrong\u003eLevel I\u003c/strong\u003e corresponds to the lowest toxicity. This classification system allows for a clear understanding of the relative risk associated with each ligand, with higher levels signifying more dangerous substances.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Dynamics Simulation:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMD simulation is used to get an RMSF graph which displays the fluctuation of individual residues of a protein complex. The lower the RMSF values the more stable the structure. We have used CABSflex2.0 for the molecular dynamic simulation of the proteins which was accessed on 16 September 2024 (Kuriata \u003cem\u003eet al\u003c/em\u003e.2018; WicaksonoA., \u0026amp; ParikesitA. A. 2023)\u003c/p\u003e\n\u003cp\u003eAfter getting the results from CABSflex2.0 as shown in Figure 3 we have analysed the output generated as a result. We have checked the RMSF graphs generated by CABSflex2.0 and got to know about the stability of different proteins. In general, each structure has 1024 amino acid residues in it. After analysing the excel sheet generated by CABSflex2.0, In 7BVF there are only 33 residues with more than 3.0 RMSF value that makes it stable, 306M_V has 41 residues over RMSF value of 3.0, D1024N has 40 residues over RMSF value 3.0, M306_L has 41 residues above RMSF value of 3.0, M306L_378A has 38 residues over the value of 3.0, and M378A has only 22 residues with value more than RMSF 3.0. \u0026nbsp; After analysing the results, we can interpret that the M378A has the lowest varying residues which means it is the most stable protein out of all of them (Fatima \u003cem\u003eet al\u003c/em\u003e.2023).\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study demonstrates the potential of \u003cem\u003eThymus vulgaris\u003c/em\u003e Phytochemicals, mainly Caryophyllene oxide, bisabolene, and Trans-Caryophyllene, as promising inhibitors of the MDR-TB proteins. The docking result suggests that trans-caryophyllene and caryophyllene oxide are the most potent inhibitors based on binding affinity from docking results. It was considered to take only the 3 best ligands from the components of \u003cem\u003eThymus vulgaris\u003c/em\u003e as there are more than 10 components found in \u003cem\u003ethymus vulgaris\u003c/em\u003e, as shown in this paper (Shabnum \u003cem\u003eet al\u003c/em\u003e., M. G. (2011). After docking between all the compounds and the mutant proteins, we learn about the binding affinity of each compound with the mutant proteins, which creates resistance to the ethambutol drug. The binding affinity is promising, which makes these 3 components considerable for future research. These components, bisabolene bisabolene (Jin \u003cem\u003eet al\u003c/em\u003e.2022), Trans-Caryophyllene (Hu \u003cem\u003eet al\u003c/em\u003e., 2022), and Caryophyllene oxide (Kanokmedhakul S \u003cem\u003eet al\u003c/em\u003e.2007), have already shown antimicrobial properties against S.aureus, E.coli, and Monilia albicanswhich makes us curious to explore about them. After ADMETox results, we have found out that there is only one compound out of 3 that is showing a high level of hazard to the body because of two membered rings, which can be changed using some structural change in the component or by the experimental work in the labs (Boniface \u003cem\u003eet al\u003c/em\u003e., 2017).\u003c/p\u003e\u003cp\u003eThese compounds are supported by their pharmacokinetic properties evaluated by SwissADME, showing none violate Lipinski\u0026rsquo;s Rule of five. In contrast, Caryophyllene oxide shows some level of toxicity while the binding affinity shows as a lead component and for further refinement. The MD simulation result also highlights the stability of the ligand-protein complex, particularly the M378A mutant protein, which has the lowest RMSF value, indicating stable interaction. This study builds on the understanding of the embB mutations and suggests that specific compounds from \u003cem\u003eThymus vulgaris\u003c/em\u003e could be explored as new avenues for treatment.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study has shown that these components, Caryophyllene oxide, Bisabolene, and Trans-Caryophyllene from \u003cem\u003eThymus vulgaris\u003c/em\u003e, have the potential to inhibit 7BVF and its mutant protein of MDR-TB. The gastrointestinal tract, soluble in water and bioavailable, readily absorbs all ligands. All of the ligands have passed the ADME test. Although Caryophyllene oxide has shown toxicology, other drugs, such as Trans-Caryophyllene and Bisabolene, have no side effects. The drug can be optimized in the wet lab to remove these effects. However, since this is\u003cem\u003ein silico\u003c/em\u003e research, further wet lab validations, such as \u003cem\u003ein vivo\u003c/em\u003e or in vitro experiments, are needed to approve these results.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMulti Drug Resistant\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTuberculosis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMolecular Dynamics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eADMET\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAbsorption, Digestion, Metabolism, Toxicity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRMSF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRoot Mean Square Fluctuation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRMSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRoot-Mean-Square-Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKRS worked on the methodological and technical details, while AAP and YM supervise and directing the research. Funding acquisition was done by AAP. The prediction of protein structure was procured from YM, validated by AAP, and analyzed by KRS.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENT\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Brijesh K. Soni, President of GoldSmith Institute of Advanced Research (GIAR), India, for his heartfelt support of this project. Thanks also go to the Department of Research and Community Services (LPPM) of Indonesia International Institute for Life Sciences (i3L) for providing the essential facilities. The grammar and vocabulary of the manuscript were improved with Grammarly Premium software.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBecker, S. P., Sidol, C. A., \u0026amp; Burns, G. L. (2022). Adolescent sleep and academic functioning: The role of emotional regulation and fatigue. Journal of Adolescence, 95, 47\u0026ndash;58. https://doi.org/10.1016/j.adolescence.2022.04.007\u003c/li\u003e\n\u003cli\u003eBenjelloun, A. (2022). Digital lifestyles and student performance: A Moroccan perspective on technology and schooling. International Journal of Education and Social Science Research, 5(3), 112\u0026ndash;125.\u003c/li\u003e\n\u003cli\u003eBoksem, M. A. S., \u0026amp; Tops, M. (2008). Mental fatigue: Costs and benefits. Brain Research Reviews, 59(1), 125\u0026ndash;139. https://doi.org/10.1016/j.brainresrev.2008.07.001\u003c/li\u003e\n\u003cli\u003eBouslaham, N. (2020). Smartphone use and sleep disturbance among Moroccan adolescents. North African Journal of Behavioral Science, 2(1), 29\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eBoyd, D. (2014). It\u0026rsquo;s complicated: The social lives of networked teens. Yale University Press.\u003c/li\u003e\n\u003cli\u003eBraun, V., \u0026amp; Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589\u0026ndash;597. https://doi.org/10.1080/2159676X.2019.1628806\u003c/li\u003e\n\u003cli\u003eBuysse, D. J. (2014). Sleep health: Can we define it? Does it matter? Sleep, 37(1), 9\u0026ndash;17. https://doi.org/10.5665/sleep.3298\u003c/li\u003e\n\u003cli\u003eCajochen, C., Frey, S., Anders, D., Sp\u0026auml;ti, J., Bues, M., Pross, A., Mager, R., Wirz-Justice, A., \u0026amp; Stefani, O. (2011). Evening exposure to a light-emitting diodes (LED)-backlit computer screen affects circadian physiology and cognitive performance. Journal of Applied Physiology, 110(5), 1432\u0026ndash;1438. https://doi.org/10.1152/japplphysiol.00165.2011\u003c/li\u003e\n\u003cli\u003eCarskadon, M. A. (2011). Sleep in adolescents: The perfect storm. Pediatric Clinics of North America, 58(3), 637\u0026ndash;647. https://doi.org/10.1016/j.pcl.2011.03.003\u003c/li\u003e\n\u003cli\u003eCastells, M. (2010). The rise of the network society (2nd ed.). Wiley-Blackwell.\u003c/li\u003e\n\u003cli\u003eHale, L., \u0026amp; Guan, S. (2019). Screen time and sleep among school-aged children and adolescents: A systematic literature review. Sleep Medicine Reviews, 48, 101\u0026ndash;113. https://doi.org/10.1016/j.smrv.2019.101226\u003c/li\u003e\n\u003cli\u003eHayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.\u003c/li\u003e\n\u003cli\u003eKardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351\u0026ndash;354. https://doi.org/10.1016/j.chb.2013.10.059\u003c/li\u003e\n\u003cli\u003eLevenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., \u0026amp; Primack, B. A. (2017). The association between social media use and sleep disturbance among young adults. Preventive Medicine, 85, 36\u0026ndash;41. https://doi.org/10.1016/j.ypmed.2016.11.017\u003c/li\u003e\n\u003cli\u003eMark, G., Gudith, D., \u0026amp; Klocke, U. (2016). The cost of interrupted work: More speed and stress. Journal of Experimental Psychology: Applied, 22(1), 1\u0026ndash;15. https://doi.org/10.1037/xap0000073\u003c/li\u003e\n\u003cli\u003eMontag, C., Sindermann, C., \u0026amp; Lachmann, B. (2021). Digital overload and mental fatigue: A biopsychological perspective. Computers in Human Behavior Reports, 3, 100073. https://doi.org/10.1016/j.chbr.2021.100073\u003c/li\u003e\n\u003cli\u003eRyan, R. M., \u0026amp; Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.\u003c/li\u003e\n\u003cli\u003eShochat, T., Cohen-Zion, M., \u0026amp; Tzischinsky, O. (2014). Functional consequences of inadequate sleep in adolescents: A systematic review. Sleep Medicine Reviews, 18(1), 75\u0026ndash;87. https://doi.org/10.1016/j.smrv.2013.03.005\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2023). Adolescent health and screen time: Global guidelines for healthy digital engagement. WHO Press.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Mutations found in 7BVF protein structure.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"627\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0223%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino Acid Positions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8373%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino acid\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5789%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmino Acid change\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5614%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0223%;\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8373%;\"\u003e\n \u003cp\u003eMethionine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5789%;\"\u003e\n \u003cp\u003eLeucine, valine, isoleucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5614%;\"\u003e\n \u003cp\u003eMaladan \u003cem\u003eet al\u003c/em\u003e.2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0223%;\"\u003e\n \u003cp\u003e328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8373%;\"\u003e\n \u003cp\u003eAspartic Acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5789%;\"\u003e\n \u003cp\u003eTyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5614%;\"\u003e\n \u003cp\u003eAli \u003cem\u003eet al\u003c/em\u003e.2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0223%;\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8373%;\"\u003e\n \u003cp\u003eGlutamic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5789%;\"\u003e\n \u003cp\u003eAlanine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5614%;\"\u003e\n \u003cp\u003eAli \u003cem\u003eet al\u003c/em\u003e.2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0223%;\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8373%;\"\u003e\n \u003cp\u003eAspartic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5789%;\"\u003e\n \u003cp\u003eAlanine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5614%;\"\u003e\n \u003cp\u003eRuesen \u003cem\u003eet al\u003c/em\u003e.2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0223%;\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8373%;\"\u003e\n \u003cp\u003eGlycine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5789%;\"\u003e\n \u003cp\u003eAlanine, Aspartic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5614%;\"\u003e\n \u003cp\u003eRuesen \u003cem\u003eet al\u003c/em\u003e.2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0223%;\"\u003e\n \u003cp\u003e497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8373%;\"\u003e\n \u003cp\u003eGlutamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5789%;\"\u003e\n \u003cp\u003eArginine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5614%;\"\u003e\n \u003cp\u003eLee \u003cem\u003eet al\u003c/em\u003e.2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0223%;\"\u003e\n \u003cp\u003e1024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8373%;\"\u003e\n \u003cp\u003eAspartic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.5789%;\"\u003e\n \u003cp\u003eAsparagine, Threonine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5614%;\"\u003e\n \u003cp\u003eMaladan \u003cem\u003eet al\u003c/em\u003e.2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable.2. Site of cavities on mutant proteins and its coordinates\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCavity volume\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg width=\"31\" height=\"23\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCenter\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(x, y, z)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCavity size\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(x, y, z)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003e7BVF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e6380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e169, 144, 172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e22, 30, 30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003e306M_V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e6590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e169, 144, 173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e22, 30, 30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eD1024N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e6610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e169, 144, 173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e22, 30, 30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eM306L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e6355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e169, 144, 173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e22, 30, 30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eM306L_378A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e6355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e169, 144, 173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e22, 30, 30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.1248%;\"\u003e\n \u003cp\u003eM378A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e6531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e169, 144, 173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9584%;\"\u003e\n \u003cp\u003e22, 30, 30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable.3. Binding affinities of mutant proteins with \u003cem\u003ethymus vulgaris\u003c/em\u003e components\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.651%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBinding Affinity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9732%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigands ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3758%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigand Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e7BVF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.651%;\"\u003e\n \u003cp\u003e-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9732%;\"\u003e\n \u003cp\u003e5281515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3758%;\"\u003e\n \u003cp\u003eTrans-Caryophyllene\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e306M_V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.651%;\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9732%;\"\u003e\n \u003cp\u003e1742210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3758%;\"\u003e\n \u003cp\u003eCaryophyllene oxide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eD1024N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.651%;\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9732%;\"\u003e\n \u003cp\u003e3033866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3758%;\"\u003e\n \u003cp\u003eBisabolene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eM306L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.651%;\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9732%;\"\u003e\n \u003cp\u003e5281515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3758%;\"\u003e\n \u003cp\u003eTrans-Caryophyllene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eM306L_378A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.651%;\"\u003e\n \u003cp\u003e-7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9732%;\"\u003e\n \u003cp\u003e5281515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3758%;\"\u003e\n \u003cp\u003eTrans-Caryophyllene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eM378A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.651%;\"\u003e\n \u003cp\u003e-7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.9732%;\"\u003e\n \u003cp\u003e5281515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3758%;\"\u003e\n \u003cp\u003eTrans-Caryophyllene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4. Analysis of Drug on Lipinski\u0026rsquo;s Rule of Five\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eLigand ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eM. weight\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(g/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eNo. Hydrogen acceptors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eNo. Hydrogen donors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eMlogP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eRotatable Bonds\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e5281515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e204.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1742210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e220.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e3033866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e204.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5. Levels of toxicology of different ligands.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eLigand ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eLevel of Toxicology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e5281515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eLevel I\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e1742210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eLevel III\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003e3033866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eLevel I\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":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":"MDR-TB, Bioactive Compounds, Molecular Docking, Molecular Dynamics, Thymus vulgaris","lastPublishedDoi":"10.21203/rs.3.rs-7803630/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7803630/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMDR-TB is a worldwide problem; according to the World Health Organisation (WHO), TB is the second most infectious killer after COVID-19, even above HIV and AIDS. With rising resistance to current antibiotics and limited solutions, the urgent discovery of new, effective, and affordable antibacterials with low toxicity is imperative to combat MDR-TB strains. Multidrug-Resistant tuberculosis (MDR-TB), caused by mycobacterium tuberculosis, is resistant to ethambutol (EMB), which has been widely ported worldwide. EMB resistance is caused by mutations in the embB gene, which encodes the arabinosylindoylacetylinositol synthase enzyme. The mutations are found in M306L, M3306L\u0026thinsp;+\u0026thinsp;E378A, M306V, and D1024N. Caryophyllene oxide, Bisabolene, and Trans-caryophyllene are essential components of the medicinal plant \u003cem\u003eThymus vulgaris.\u003c/em\u003e Hence, this study will introduce an \u003cem\u003ein silico\u003c/em\u003e phytochemical-based approach for discovering novel bacterial agents, exploring the potential of a computational approach in therapeutic discovery. This study focuses on screening all these phytochemicals, Caryophyllene oxide, Bisabolene, and Trans-Caryophyllene, as a potential drug candidate to combat MDR-TB infection through a molecular docking approach.\u003c/p\u003e\u003cp\u003eMoreover, the interaction of amino acid analysis, \u003cem\u003ein silico\u003c/em\u003e pharmacokinetics, compound target prediction, pathway enrichment analysis, and Molecular Dynamics (MD) simulations were conducted for further investigation. Caryophyllene oxide, Bisabolene, and Trans-Caryophyllene also showed a strong binding affinity against these mutations. \u003cem\u003ein silico\u003c/em\u003e pharmacokinetic analysis highlights the potency as a drug candidate, showing strong Adsorption, Distribution, Metabolism, and Excretion (ADME) properties in combination with low toxicity.\u003c/p\u003e","manuscriptTitle":"Computational Docking and Virtual Screening of Thymus vulgaris as Potential Inhibitors for Multi-Drug-Resistant Tuberculosis (MDR-TB) Target Proteins","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 08:29:26","doi":"10.21203/rs.3.rs-7803630/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":"56792ca5-b65e-46a4-8454-2084641f9741","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T08:29:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 08:29:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7803630","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7803630","identity":"rs-7803630","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}