In-Silico Identification and Evaluation of Diosgenin as a Promising Phytomedicine Targeting MHC-I for Cancer Therapy

In-Silico Identification and Evaluation of Diosgenin as a Promising Phytomedicine Targeting MHC-I for Cancer Therapy | 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 Evaluation of Diosgenin as a Promising Phytomedicine Targeting MHC-I for Cancer Therapy Shailja Jasrotia, Awadhesh Kumar Verma*, Tanya Singh, Sumit Sheoran, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6024968/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 Immunotherapeutic strategies targeting MHC-I have gained significant attention for combating cancer, a leading global health. However, the emergence of therapeutic resistance and the manifestation of adverse concerneffects associated with conventional treatment modalities underscore the imperative for innovative therapeutic strategies. In the current study, we conducted an “ in silico” investigation to identify potential phytomedicines for cancer treatment targeting MHC-I (3AM8). Using AutodockTools software, 408 natural secondary polyphenols were screened against MHC-I, with Diosgenin exhibiting the highest affinity for binding (−8.93 Kcal/mol). Pharmacokinetic profiling of the highest-ranking ligands elucidated their suitability for subsequent development and optimization. Molecular dynamics simulations, conducted using the Desmond software suite, demonstrated pronounced stability of the Diosgenin-MHC-I complex throughout a 100-ns trajectory. Our findings suggest that Diosgenin holds significant promise as a candidate for cancer therapeutics; however, further verifications by use of extensive “ in vitro” and “ in vivo” research is warranted to substantiate its validity and therapeutic potential. This study highlights the potential of Diosgenin in upcoming trial regimens and advances the choices for treating cancer. Immunology Bioinformatics Computational Biology Toxicology Drug Discovery, Design, & Development Pharmacokinetics MHC-I Diosgenin Phytomedicine In silico Computational analysis Molecular Docking MD Simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction MHC-I molecules have a crucial role in displaying short peptides from intracellular proteins, whether they are alien or native, on the cell surface [ 1 ]. This presentation is tightly regulated and plays a vital role in the recognition of these peptides by via T-cell receptors (TCRs) on CD8 + T cells [ 2 ]. MHC-I is crucial in multiple physiological processes and medical treatments, including as responses of CD8 + cytotoxic T cells, successful immunization, T-cell adoption therapy, hematopoietic stem cell transplantation (HSCT), and rejection following transplantation [ 3 ]. Novel treatment techniques including TCR-mimic antibodies and TCR-based structures are customised to target complexes of MHC and peptide, highlighting the significance of MHC-I in immunotherapy for cancer, infectious illnesses, and autoimmune conditions [ 4 ], [ 5 ]. However, despite the growing success of immunotherapies, the precise mechanisms governing the regulation of antigen presentation on MHC-I remain incompletely understood and are yet to be fully exploited in clinical settings. One significant challenge in cancer treatment lies in the decreased tumour antigen presentation on the cell surface on MHC-I [ 6 ]. This limitation poses a significant obstacle to the effectiveness of various immunotherapeutic modalities [ 7 ], [ 8 ], [ 9 ], including adoptive T-cell transfer, TCR-mimic antibodies, tumour vaccines, and TCR constructions. Overcoming this obstacle is essential for enhancing the efficacy of immunotherapy in combating cancer and other diseases [ 10 ]. The strong association between the use of phytomedicines and a reduced risk of developing cancer has captured the attention of scientists, who are now investigating the potential of phytomedicines as chemotherapy agents [ 11 ]. Natural chemicals that exhibit a high affinity for cancer cells have the potential to transform the way cancer is treated [ 12 ].We hypothesize that targeting the MHC-I can play an important part of cancer treatment because it is responsible for presenting peptides derived from intracellular proteins, including tumour-specific antigens, to CD8 + cytotoxic T cells. If any drug candidate binds effectively to the MHC class I may potentially enhance the presentation of tumor antigens, leading to improved recognition and targeting of cancer cells by the immune system and it may stimulate or modulate the activity of cytotoxic T cells, promoting their ability to recognize and destroy cancer cells. Moreover, if the drug candidate binds with MHC class I molecules could be explored as immunotherapeutic agents for cancer treatment. Molecular docking, a pivotal computational technique, entails a meticulous two-step process commencing with the accommodation of ligands within the active site of receptors, followed by their ranking based on interaction conformational energies [ 13 ], [ 14 ], [ 15 ]. To ascertain the interplay of docking affinities of diverse forms of ligand-receptors, several commonly available instruments like AutoDock, SwissDock, ClickDocking, GLIDE and GOLD have been extensively employed [ 16 ], [ 17 ], [ 18 ]. Among these, GLIDE and GOLD stands out due to its commendable performance, accuracy, adaptability, and comprehensive analytical capabilities [ 11 ]. In our investigation, we adopt GLIDE and autodock for molecular docking owing to the aforementioned attributes. Our study centers on the exploration of phytomedicine as a potential antagonist against cancer initiation and progression. Consequently, we direct our focus towards leveraging phytomedicine against specific targets, including MHC-I. Within the scope of our current research, we elucidate the interaction of the selected target MHC class I (MHC-I) with a particular phytomedicine through an array of in-silico methodologies, including molecular docking, assessment of characteristics of a drug, evaluation of pharmacokinetic properties, and molecular simulation [ 19 ], [ 20 ]. Methodology Curation of Biologically Active Phytochemicals A comprehensive selection of 408 biologically active plant-derived compounds, known as phytochemicals, was curated for our study. These compounds exhibit a diverse array of structural characteristics and belong to various phytochemical classes. Documented medicinal and biological significance, derived from modern phytomedicines as well as traditional healing practices (Ayurvedic and Chinese medicines), guided the selection process [ 21 ]. Structural Optimisation of Drug Ligands For the ligand preparation, first optimisation of the drug candidates was done through Avogadro software [ 18 ], and then their chemical and structural validation was done through structures available at PubChem [ 22 ]. Further, these structures were used in molecular docking analysis against the protein structure of MHC-I. Protein Preparation 3D crystallographic structure of MHC-I was taken from the Protein Data Bank of the RCSB (PDB ID: 3AM8) with a 2.80 Å resolution [ 23 ], having no mutation [ 11 ]. Using AutoDockTools software, Kollmann charges and hydrogens were introduced, and Gasteiger charges were calculated. The type of atom was set to assign type AD4 [ 24 ], [ 25 ]. The protein structure preparation was performed by using the DS 2020 program. During this process, co-crystallized inhibitors, water molecules, and heteroatoms were excluded from the protein molecules to isolate the protein-ligand interactions of interest [ 26 ]. Then, the energetically unfavorable conformations of the protein structures were minimized using the CHARMm minimizer algorithm, involving 200 steps of reduction to achieve RMSD or a root-mean-square deviation 0.1 kcal/mol value [ 27 ]. This optimization step ensured the attainment of stable and energetically favorable protein conformations suitable for subsequent computational analyses. Ligand Preparation By selecting the optimised ligands, torsion tree feature is assigned to the ligand so as to recognize the flexibility of that ligand and determine the smallest of molecules interacting with the protein [ 28 ]. By assigning choose and detect root options, the software analyses the ligand in the form of a tree, where the rigid core is the root and the flexible parts are the branches. This also helps in incorporating the free energy which gets released because of the rotational bonds of the ligand [ 29 ]. Grid Box Generation To enable comprehensive docking analyses, a three-dimensional grid box was strategically positioned to encompass the entirety of the receptor proteins. This grid box was carefully positioned to fit the receptor proteins as well as the ligands (compounds), guaranteeing a thorough investigation of possible binding interactions [ 17 ], [ 30 ]. The dimensions of the grid box were tailored to match the spatial requirements of the ligands and receptors, with adjustments made to the Z, Y, and X dimensions to optimize the fit. The dimensions of grid box are receptor = 3am8.pdbqt with exhaustiveness = 8, center_x = 18.3765651107, center_y = 23.2233903109, center_z = 11.7902580453, size_x = 40.9508026378, size_y = 40.9508026378, and size_z = 40.9508026378. The adjustments in grid box size were guided by the desired spacing between grid points, set at 0.5 Å, which approximately corresponds to one-quarter of the length of a carbon single bonding atom. This spacing was chosen to sufficiently capture the spatial intricacies of the ligand-receptor interactions while maintaining computational efficiency [ 31 ]. Subsequently, the ligand was positioned within the grid box, ensuring its ideal alignment within the receptor proteins' active location. Upon finalizing the grid box configuration, the dimensions were saved for subsequent processing and analysis, facilitating further exploration of ligand-receptor interactions and the determination of docking scores. Pharmacokinetic Profiling and Drug Candidate Optimization The efficacy of a potential drug candidate relies not only on its ability to reach its target within the body but also on its capacity to maintain a bioactive state at sufficient concentrations for the requisite duration to elicit desired biological responses. Drug development involves a thorough assessment of the features of absorption, distribution, metabolism, and excretion (ADME). Among them, pharmacokinetic characteristics are essential for directing the choice and refinement of new therapeutic compounds [ 32 ]. Assessing pharmacokinetic properties is integral to the identification of drug candidates with optimal biological activity and pharmacokinetic profiles. To this end, we employed two widely utilized tools, SwissADME [ 33 ] and ADMElab 2.0 [ 34 ], to analyze the pharmacokinetic properties of candidate compounds. A crucial aspect of pharmacokinetic evaluation involves the assessment of P-glycoprotein (Pgp), a transmembrane efflux pump predominantly expressed in hepatic tissues. Pgp s essential to the development and discovery of new drugs by regulating the efflux of various xenobiotics and therapeutic agents from cells [ 35 ], [ 36 ]. Inhibition of Pgp by a drug molecule can enhance its bioavailability and efficacy by preventing its expulsion from target cells. Conversely, if a drug molecule exhibits potent inhibition of Pgp, it may be considered a promising candidate due to its potential to overcome multidrug resistance mechanisms and improve therapeutic outcomes. Lipinski's Rule of Five for Predicting Drug-like Characteristics Lipinski in 2007, established the "Rule of Five.", a fundamental criterion widely employed to assess drug-likeness, as documented by [ 37 ]. This criterion serves as a pivotal filter for evaluating the potential oral absorption of drug candidates, delineating four key rules to ascertain a molecule's suitability for oral administration. A compound could be deemed orally active if it adheres to these rules; however, violation of two or more rules may indicate diminished oral bioavailability [ 11 ]. We utilized the zinc online server and SwissADME to forecast adherence to Lipinski's rule and drug-like charcteristics including hydrogen bond donors (HBD), acceptors of hydrogen bonds (HBA), partition coefficient (Log P), and molecular weight (MW) and the quantity of rotatable bonds. These computational tools offer robust predictive capabilities, enabling the assessment of molecular characteristics crucial for oral absorption and pharmacokinetic viability [ 38 ]. Through the utilization of these resources, we sought to identify drug candidates exhibiting favorable physicochemical properties aligned with Lipinski's criteria, thereby facilitating in making choices throughout the medication development and discovery process. Molecular Dynamics Simulation The current study utilized a program for simulating molecular dynamics called Schrodinger's Desmond that is widely recognized for its specialized functionalities and capacity to compute binding free energies and analyze protein-ligand interactions [ 39 ]. It was performed on Ubuntu 22.04 on an Acer workstation using Desmond version 2021-4 [ 7 ], [ 40 ]. The force field in OPLS-2005 was used to create the protein-ligand complex's topology. In order to develop the complex, the system builder platform was utilized in conjunction with an orthorhombic simulation box and simple point-charge clear water models. Following that, an adequate amount of Na + /Cl − counterions were introduced to neutralize the solvated complex system. To replicate physiological conditions, a salt concentration of 0.15 M was introduced [ 40 ]. A transparent fluid model with SPC water atoms was used to confine the system in an orthorhombic simulation box. The calculations were carried out at a temperature of 300 K (NPT or NVT) and a pressure of 1.0325 bar. Prior to the commencement of the molecular dynamics simulations, the system underwent an initial minimization phase lasting 100 picoseconds (ps) to alleviate potential steric clashes and unfavorable interactions. Subsequently, the system was allowed to relax using default relaxation techniques before initiating the 100 nanoseconds (ns) trajectory. This all-encompassing strategy made sure that the conformational dynamics and interactions within the receptor-ligand complex system were thoroughly investigated under physiologically relevant circumstances. Results and Discussion Through Molecular Docking analysis, Diosgenin shows a higher binding score of -8.93 Kcal/mol with MHC-I, therefore, it can be deduced that Diosgenin while interacting with MHC-I could help in enhancing the immune response against cancer, since it possesses pharmacokinetic properties. In Fig. 1 (a), the Ligplot visualisation shows hydrophobic interactions along with hydrogen bond interaction of the ligand Diosgenin with TYR 67(D) of MHC-I, showing H-bond distance of 3.11 Å. In Fig. 1 (b), through 2D visualisation by Discovery Studio, van der Waals interaction can be observed in TYR 26(D) and 67(D), SER 52(D), THR 233(B), PHE 241(B) and ASP 30(B); conventional hydrogen bonding with ARG 48(B); and alkyl to pi-alkyl interactions with ALA 211(B), TYR 27(B) and 63(D), PRO 235(B) and LEU 65(D). Figure 1 (c) shows the 3D representation of these interactions, generated using Discovery Studio. These in silico analyses may act as a substantial foundation for subsequent “ in-vitro” and “ in-vivo” tests to investigate the biological activity of the bioactive compound in an effort to identify its potential as a treatment for tumor disease. This compound is presently undergoing in-vitro analysis in our laboratory (Lovely Professional University and Thyme Phyto BioMed Pvt. Ltd.). Table 1 lists the docking outcomes for the topmost five compounds. Comprehensive ADME Profiling and Enzyme Interaction Analysis of Diosgenin for Drug Development ADME prediction serves as a critical component in the process of finding new drugs and development mechanism, aiming to anticipate in vivo pharmacokinetic behavior of prospective therapeutic molecules. ADME evaluations are essential for defining the safety and effectiveness profiles of pharmaceutical drugs when used in tandem with molecular docking studies [ 13 ]. Tables 2 and 3 present a comprehensive overview of the pharmacological characteristics, wherein the evaluated compounds exhibit values within acceptable ranges. Notably, Diosgenin, a natural molecule under scrutiny, demonstrates adherence to Lipinski's Rule of Five (RO5), suggesting favorable oral absorption and desirable pharmacokinetic attributes. Diosgenin exhibits excellent permeability, indicating its potential for increased bioavailability and efficacy as an healing therapeutic agent in contrast to substances that have been screened, sourced from the Zinc database and PubChem. Moreover, Diosgenin's interaction with key enzymes involved in drug metabolism, notably cytochrome P450 (CYP) subtypes, was assessed. Results indicate that Diosgenin is not a substrate for CYP2D6 and CYP3A4, suggesting minimal metabolism via these pathways. While Diosgenin is not predicted to inhibit CYP2C19, it displays inhibitory potential against CYP2C9 and CYP2D6, hinting at potential metabolic processing in the liver. The description for the same regarding the best 5 ligands has been shown in Table 4 . Furthermore, predictions regarding Diosgenin's interaction with P-glycoprotein (P-gp), a key efflux pump implicated in drug transport, were evaluated using SwissADME. P-gp, predominantly expressed in various tissues, plays a crucial role in cellular drug efflux mechanisms [ 41 ]. Notably, Diosgenin's interaction with P-gp suggests potential involvement in drug transport processes, with implications for its pharmacokinetic profile and tissue distribution. These findings underscore Diosgenin's promising pharmacological attributes, suggesting its potential as a viable therapeutic candidate with favorable pharmacokinetic properties and metabolic characteristics. Further elucidation of Diosgenin's pharmacological profile by in vitro and in vivo studies is warranted to confirm its therapeutic potential and inform subsequent drug development efforts. Boiled Egg Plot Apart from ADME forecasting, the effectiveness and security of small molecules are often compromised by unfavorable pharmacokinetic attributes. Notably, adverse pharmacokinetic characteristics can contribute to the failure of minor molecule candidates in drug development efforts. Figure 2 illustrates the pharmacokinetic properties of Diosgenin using a Boiled Egg plot, [ 7 ], [ 8 ]. This plot highlights two favorable attributes of Diosgenin: gastrointestinal absorption and Blood-Brain Barrier (BBB) permeability. Diosgenin exhibits a high rate of BBB, evident by its depiction within the yellow zone of the egg in Fig. 2 . Consequently, Diosgenin is indicated to be gastrointestinal impermeable, suggesting limited absorption across the gastrointestinal. Furthermore, Diosgenin emerged as the paramount efficacious drug candidate in our investigation. However, it is imperative to note that further in vitro and in vivo research on this natural ligand is warranted to validate and corroborate the result of this study. Comprehensive characterization of Diosgenin's pharmacological profile through rigorous experimental studies is essential to ascertain its efficacy, safety, and therapeutic potential in the context of cancer treatment. Molecular Dynamics Evaluation of Diosgenin-3AM8 Complex Stability and Interaction Dynamics Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and hydrogen bonds (H-bonds) were among the molecular dynamics parameters that were analysed in order to assess the stability of the Diosgenin-3AM8 protein complex and identify the type of interactions that take place during their bindingThe RMSD profile of the Diosgenin-3AM8 complex during a 100-ns solvent-environment molecular dynamics simulation is displayed in Fig. 4 . The ligand and protein's respective RMSD values exhibited a consistent range during the simulation. Specifically, the protein values fluctuated between 2.4 and 2.8 Å, while the ligand values fluctuated between 2 and 2.7 Å. Initial fluctuations in the RMSD, particularly observed between 0 and 15 ns, are attributed to movements in the 3AM8 protein's activation loop. However, minimal deviations were observed thereafter, indicating that the 3AM8-Diosgenin interaction remained stable throughout the simulation duration, with no dissociation of Diosgenin from the protein. These findings suggest a consistent RMSD profile with no significant deviations, indicative of a stable protein-ligand complex. The protein and Ligand complex's structural stability, as determined by RMSD study, is shown in Fig. 3 . The stability of the Diosgenin-3AM8 complex is further supported by the state stability of observed at 100 ns, which shows that both the initial and final simulated structures retained stable conformations throughout the simulation period. The stability of the Diosgenin-3AM8 complex is further supported by the state stability of observed at 100 ns, which shows that both the initial and final simulated structures retained stable conformations throughout the simulation period. The state stability observed at 100 ns indicates that both the initial and final simulated structures maintained stable conformations throughout the simulation period, further corroborating the stability of the Diosgenin-3AM8 complex. Furthermore, RMSF analysis was conducted to assess the adaptiblity of the 3AM8 protein's activation loop, as depicted in Fig. 4 . This analysis revealed variations in specific amino acid residues, primarily localized to regions of loop flexibility and disorder. Notably, residues exhibiting RMSF peaks exceeding 0.1 nm were identified, although these fluctuations did not impact the stability of the protein-Diosgenin interaction, as observed in docking studies. Consequently, the trajectory analysis suggests the prospective and active nature of the Diosgenin-3AM8 complex, underscoring its potential as a promising therapeutic candidate requiring further research through in vitro and in vivo studies. In the current study, dynamic simulation analyses were performed to assess the protein’s structural stability over a 100-ns duration. Key parameters, including the radius of gyration (Rg) and Root Mean Square Deviation, were utilized to evaluate protein compactness and structural deviation, respectively. Protein compactness was measured using the radius of gyration (Rg), which was found to be 4.50 Å, as shown in Fig. 6 . This metric provides insights into the overall spatial distribution of atoms within the protein structure, with lower Rg values indicative of greater compactness. Furthermore, trajectory analysis of RMSD values revealed minimal structural deviation throughout the Simulation time of 100 ns, using RMSD values spaning from 0.6 to 0.9 Å, as illustrated in Fig. 5 . RMSD serves as a metric for quantifying the extent of structural fluctuations or deviations from the initial reference structure. The observed low RMSD values suggest that the protein maintained its conformational integrity with negligible distortion or deviation from the starting structure during the simulation duration. Protein Secondary Structure The protein's secondary structural elements (SSE), including beta- and alpha-strands, were monitored throughout the molecular dynamics simulation to elucidate their spread across the protein structure. The plot depicting SSE distribution by residue index provides insight into the temporal evolution of SSE assignments througout the simulation. Specifically, the lower plot tracks the SSE assignment for each residue over time, while the plot below offers an overview of SSE components for every frame of the trajectory throughout the simulation, as depicted in Fig. 6 . It is important to note that while computational studies offer invalable insights, they are subject to certain limitations. To address these limitations and provide comprehensive validation of computational findings, in vitro and in vivo investigations are essential. Conclusion The computational analyses conducted in this study identify Diosgenin as a promising candidate for cancer therapy, demonstrating superior binding affinity to the 3AM8 protein, as evidenced by its favorable binding affinity score of -8.93 kcal/mol. Comprehensive pharmacokinetic profiling and ADME analyses confirm Diosgenin's compliance with Lipinski's Rule of Five and other drug-like criteria, including high BBB permeability and favorable pharmacokinetic attributes. Molecular dynamics simulations further validate the stability of the Diosgenin-3AM8 complex over a 100-ns timeframe, supporting its possible efficacy as a therapeutic agent. The stability of the Diosgenin-3AM8 complex further corroborates its potential as an effective therapeutic agent. These simulations reveal robust binding stability, suggesting that Diosgenin could maintain its bioactive conformation within the target site, thereby enhancing its therapeutic efficacy. These findings provide a strong rationale for advancing Diosgenin to “ in vitro” and “ in vivo” studies, paving way for its potential development as a targeted cancer therapy. The results not only highlight Diosgenin's potential as a lead compound for cancer therapy but also contribute to the larger area of study on cancer by identifying novel therapeutic targets and approaches. Future empirical research is essential to validate these in silico findings and to fully elucidate the therapeutic potential of Diosgenin in clinical settings, eventually playing a part in the development of targeted cancer therapies. Declarations Acknowledgment The authors would like to express their gratitude to Lovely Professional University's School of Bioengineering and Biosciences in Phagwara, Punjab, for all of their help. Author Contribution SJ, TS, SS, SA and AKV performed research and analyzed data, writing - entire original draft preparation; SJ, TS, SS, CC and AKV: Writing - review and editing; AKV, RS and AK: Supervision; AK and AKV designed the research, provided the facility and edited the paper for the final version. All authors have read and agreed to the published version of the manuscript. Conflict of Interest Statement The authors declare no conflict of interest. Data Availability Statement Not applicable Funding No funding was received for this work Ethics Statement Not applicable References M. Wieczorek et al. , “Major Histocompatibility Complex (MHC) Class I and MHC Class II Proteins: Conformational Plasticity in Antigen Presentation,” Front. 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Unlocked , vol. 31, p. 101002, 2022, doi: 10.1016/j.imu.2022.101002. A. Daina, O. Michielin, and V. Zoete, “SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules,” Sci. Rep. , vol. 7, no. 1, p. 42717, Mar. 2017, doi: 10.1038/srep42717. G. Xiong et al. , “ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties,” Nucleic Acids Res. , vol. 49, no. W1, pp. W5–W14, Jul. 2021, doi: 10.1093/nar/gkab255. S. Muthumanickam et al. , “In silico approach of naringin as potent phosphatase and tensin homolog (PTEN) protein agonist against prostate cancer,” J. Biomol. Struct. Dyn. , vol. 40, no. 4, pp. 1629–1638, Mar. 2022, doi: 10.1080/07391102.2020.1830855. S. Muthumanickam et al. , “In silico approach of naringin as potent phosphatase and tensin homolog (PTEN) protein agonist against prostate cancer,” J. Biomol. Struct. Dyn. , vol. 0, no. 0, pp. 1–10, 2020, doi: 10.1080/07391102.2020.1830855. N. di Leo et al. , “A catechin nanoformulation inhibits WM266 melanoma cell proliferation, migration and associated neo-angiogenesis,” Eur. J. Pharm. Biopharm. , vol. 114, pp. 1–10, May 2017, doi: 10.1016/j.ejpb.2016.12.024. S. Kar and J. Leszczynski, “Open access in silico tools to predict the ADMET profiling of drug candidates,” Expert Opin. Drug Discov. , vol. 15, no. 12, pp. 1473–1487, Dec. 2020, doi: 10.1080/17460441.2020.1798926. K. J. Bowers et al. , “Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters,” in ACM/IEEE SC 2006 Conference (SC’06) , IEEE, Nov. 2006, pp. 43–43. doi: 10.1109/SC.2006.54. S. Sheoran et al. , “In silico analysis of Diosmetin as an effective chemopreventive agent against prostate cancer: molecular docking, validation, dynamic simulation and pharmacokinetic prediction-based studies,” J. Biomol. Struct. Dyn. , vol. 42, no. 17, pp. 9105–9117, Nov. 2024, doi: 10.1080/07391102.2023.2250451. M. J. Ruiz Gómez, A. Souviron Rodríguez, and M. Martínez Morillo, “La glicoproteína-P una bomba de membrana que representa una barrera a la quimioterapia de los pacientes con cáncer,” in Anales de Medicina Interna , SciELO Espana, 2002, pp. 49–57. Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files floatimage1.jpeg Graphical Abstract InsilicoMHC1Supplementary.docx Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6024968","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415479285,"identity":"5a88a427-d5da-45bb-8a1f-b8d11f98f827","order_by":0,"name":"Shailja Jasrotia","email":"","orcid":"","institution":"Lovely Professional University, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Shailja","middleName":"","lastName":"Jasrotia","suffix":""},{"id":415479292,"identity":"79b219f1-ff8d-4cd9-9125-f26ee3b25122","order_by":1,"name":"Awadhesh Kumar Verma*","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDCCAwwMEmAGewOQMLAgRQvPAZAWCVK0SCSAScI6+G4ffnibp+JeNP/M51c3/CiQYOBv707Aq0XyXJqxNc+Z4twZt3PKbvYAHSZx5uwGvFoMzjCYSc5sS8htuJ2TdoMHqMVAIpeQFvZvkjP/JeTOv3km7eYf4rTwmEl8bEjI3XCD/dhtomyRPMNTbPHhWELuxjM5bLdlDCR4CPqF7wz7xhsJNQm5844ff3bzzR8bOf72XvxakACPAZgkVjkIsD8gRfUoGAWjYBSMIAAAzahMPORV/eoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1497-6210","institution":"Lovely Professional University, Punjab","correspondingAuthor":true,"prefix":"","firstName":"Awadhesh","middleName":"Kumar","lastName":"Verma*","suffix":""},{"id":415479287,"identity":"4af93d6b-1696-475c-8d12-de74a1013ea9","order_by":2,"name":"Tanya Singh","email":"","orcid":"https://orcid.org/0000-0002-3817-145X","institution":"Lovely Professional University, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Tanya","middleName":"","lastName":"Singh","suffix":""},{"id":415479286,"identity":"e4c367cc-f393-4882-a9aa-e55361d13d9a","order_by":3,"name":"Sumit Sheoran","email":"","orcid":"https://orcid.org/0000-0002-2519-8506","institution":"Lovely Professional University, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Sumit","middleName":"","lastName":"Sheoran","suffix":""},{"id":415479288,"identity":"ec6ca9fc-76cd-41d1-bb4d-858949265b2c","order_by":4,"name":"Swati Arora","email":"","orcid":"https://orcid.org/0000-0003-2301-0239","institution":"Lovely Professional University, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Swati","middleName":"","lastName":"Arora","suffix":""},{"id":415479289,"identity":"db547b3d-941a-4eba-bb40-37e45bfca5fd","order_by":5,"name":"Reena Singh","email":"","orcid":"https://orcid.org/0000-0002-2011-756X","institution":"Lovely Professional University, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Reena","middleName":"","lastName":"Singh","suffix":""},{"id":415479290,"identity":"ac39de50-2b27-4b72-87a7-f264091f5ce4","order_by":6,"name":"Chirag Chopra","email":"","orcid":"https://orcid.org/0000-0002-7239-709X","institution":"Lovely Professional University, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Chirag","middleName":"","lastName":"Chopra","suffix":""},{"id":415479291,"identity":"281b70f8-de9e-40fa-8260-b0a06bfdee60","order_by":7,"name":"Anupam Kumar*","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBADAzCZUPFfDkQfeEC0lgdnmI3BWhKI1cL4sIU5sQFsHR6l/LN7zB78zGEwNrh2+NmHxAa29Plhhx8CbbGT023ArkXizhlzw95tDGYGt9OMZyTu4MndeDvNAKgl2djsAA5rbuSYSfBuY7AxuJ1gzJB4RiJ34+wEkJYDidtwaJEHapH8C9aS/pkhsc0g3XB2+ge8WgyAWqR5wQ7LAdrSlpAgL52D3xbDG2nlxrLbJIwlb+cUMyScOWC4QTqn4ECCAW6/yN1I3vbw7TYbw77b6ZsZf1QckJefnb75w4cKOzmc3mdgYANiCSSnglUa4FQO04IE5Bvwqh4Fo2AUjIIRCAB5E2PuIc0DeAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6608-3860","institution":"Asian International University, Imphal West, Manipur","correspondingAuthor":true,"prefix":"","firstName":"Anupam","middleName":"","lastName":"Kumar*","suffix":""}],"badges":[],"createdAt":"2025-02-13 17:17:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6024968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6024968/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77234464,"identity":"57204199-1736-47aa-bb9b-91f481c7fdfa","added_by":"auto","created_at":"2025-02-26 12:54:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":587333,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated diagram showing the 2D interaction of MHC-I and Diosgenin through (a) Ligplot software, (b) Discovery Studio, and (c) 3D representation of MHC-I-Diosgenin through Discovery Studio, whose observed docking score was -8.93 Kcal/mol.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6024968/v1/1bcdbbd040f11c36e029e246.png"},{"id":77234908,"identity":"6eea2917-c978-49ab-ac5b-e847c2a6addc","added_by":"auto","created_at":"2025-02-26 13:02:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52394,"visible":true,"origin":"","legend":"\u003cp\u003eBoiled egg plot analysis for Diosgenin by employing SwissADME showing the location of the drug for GI absorption and BBB permeability.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6024968/v1/8ac84328c6a678469800e323.png"},{"id":77234487,"identity":"dcb2df7d-471c-4d3c-a8d5-0bc9fc11a37e","added_by":"auto","created_at":"2025-02-26 12:54:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":240936,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical results depicting the RMSD of the ligand-protein complex.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6024968/v1/842d32fd133fe5f1628208b9.png"},{"id":77234482,"identity":"9e894659-8c7f-487c-82a0-344e26e26301","added_by":"auto","created_at":"2025-02-26 12:54:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":176126,"visible":true,"origin":"","legend":"\u003cp\u003ePlot for protein RMSF with green colour lines showing the hydrogen bonds formed during the simulation at 100ns.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6024968/v1/8a3464a43794ee24c14c4293.png"},{"id":77234463,"identity":"fc15a876-13c2-4988-bb6f-81b752ad9bc3","added_by":"auto","created_at":"2025-02-26 12:54:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":358442,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical illustration showing estimation for PSA, RMSD, RGYR, IntraHB, and MolSA for the ligand properties.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6024968/v1/1cbd5fa12d94132a993f5549.png"},{"id":77234485,"identity":"c8d2f4ac-77d6-480b-8590-a41d153a98f2","added_by":"auto","created_at":"2025-02-26 12:54:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":542354,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration showing the supplementary configurations of proteins. The SSE is depicted in the preceding plot, while the SSE configuration for each motion phase during the simulation process is summarized in the below plot.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6024968/v1/9dd5d0280e17da093a0d1c3b.png"},{"id":77235696,"identity":"2450fd16-33cf-45e2-a4f5-b1b7f0cf4e6c","added_by":"auto","created_at":"2025-02-26 13:10:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2980558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6024968/v1/66dd86cb-83b4-4bdc-ae00-029b8908df35.pdf"},{"id":77234470,"identity":"25bd6eff-6d2f-4659-b982-171ecae1a3a2","added_by":"auto","created_at":"2025-02-26 12:54:07","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":885909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6024968/v1/882e4a61aefaca51372cbe1e.jpeg"},{"id":77234909,"identity":"b9424b4a-b1c3-4f3a-9d4a-0a2e3cc8d894","added_by":"auto","created_at":"2025-02-26 13:02:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3986842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"InsilicoMHC1Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6024968/v1/5aa68d59de45d3ff4623ac74.docx"},{"id":77234467,"identity":"a2711268-68ea-468f-8c14-3be6a56aeb95","added_by":"auto","created_at":"2025-02-26 12:54:07","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19588,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6024968/v1/743cd836f85bde72ac5b6191.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn-Silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Identification and Evaluation of Diosgenin as a Promising Phytomedicine Targeting MHC-I for Cancer Therapy\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMHC-I molecules have a crucial role in displaying short peptides from intracellular proteins, whether they are alien or native, on the cell surface [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This presentation is tightly regulated and plays a vital role in the recognition of these peptides by via T-cell receptors (TCRs) on CD8\u0026thinsp;+\u0026thinsp;T cells [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MHC-I is crucial in multiple physiological processes and medical treatments, including as responses of CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells, successful immunization, T-cell adoption therapy, hematopoietic stem cell transplantation (HSCT), and rejection following transplantation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Novel treatment techniques including TCR-mimic antibodies and TCR-based structures are customised to target complexes of MHC and peptide, highlighting the significance of MHC-I in immunotherapy for cancer, infectious illnesses, and autoimmune conditions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, despite the growing success of immunotherapies, the precise mechanisms governing the regulation of antigen presentation on MHC-I remain incompletely understood and are yet to be fully exploited in clinical settings. One significant challenge in cancer treatment lies in the decreased tumour antigen presentation on the cell surface on MHC-I [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This limitation poses a significant obstacle to the effectiveness of various immunotherapeutic modalities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], including adoptive T-cell transfer, TCR-mimic antibodies, tumour vaccines, and TCR constructions. Overcoming this obstacle is essential for enhancing the efficacy of immunotherapy in combating cancer and other diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The strong association between the use of phytomedicines and a reduced risk of developing cancer has captured the attention of scientists, who are now investigating the potential of phytomedicines as chemotherapy agents [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Natural chemicals that exhibit a high affinity for cancer cells have the potential to transform the way cancer is treated [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].We hypothesize that targeting the MHC-I can play an important part of cancer treatment because it is responsible for presenting peptides derived from intracellular proteins, including tumour-specific antigens, to CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells. If any drug candidate binds effectively to the MHC class I may potentially enhance the presentation of tumor antigens, leading to improved recognition and targeting of cancer cells by the immune system and it may stimulate or modulate the activity of cytotoxic T cells, promoting their ability to recognize and destroy cancer cells. Moreover, if the drug candidate binds with MHC class I molecules could be explored as immunotherapeutic agents for cancer treatment. Molecular docking, a pivotal computational technique, entails a meticulous two-step process commencing with the accommodation of ligands within the active site of receptors, followed by their ranking based on interaction conformational energies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To ascertain the interplay of docking affinities of diverse forms of ligand-receptors, several commonly available instruments like AutoDock, SwissDock, ClickDocking, GLIDE and GOLD have been extensively employed [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Among these, GLIDE and GOLD stands out due to its commendable performance, accuracy, adaptability, and comprehensive analytical capabilities [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In our investigation, we adopt GLIDE and autodock for molecular docking owing to the aforementioned attributes. Our study centers on the exploration of phytomedicine as a potential antagonist against cancer initiation and progression. Consequently, we direct our focus towards leveraging phytomedicine against specific targets, including MHC-I. Within the scope of our current research, we elucidate the interaction of the selected target MHC class I (MHC-I) with a particular phytomedicine through an array of in-silico methodologies, including molecular docking, assessment of characteristics of a drug, evaluation of pharmacokinetic properties, and molecular simulation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCuration of Biologically Active Phytochemicals\u003c/h2\u003e \u003cp\u003eA comprehensive selection of 408 biologically active plant-derived compounds, known as phytochemicals, was curated for our study. These compounds exhibit a diverse array of structural characteristics and belong to various phytochemical classes. Documented medicinal and biological significance, derived from modern phytomedicines as well as traditional healing practices (Ayurvedic and Chinese medicines), guided the selection process [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStructural Optimisation of Drug Ligands\u003c/h3\u003e\n\u003cp\u003eFor the ligand preparation, first optimisation of the drug candidates was done through Avogadro software [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and then their chemical and structural validation was done through structures available at PubChem [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Further, these structures were used in molecular docking analysis against the protein structure of MHC-I.\u003c/p\u003e\n\u003ch3\u003eProtein Preparation\u003c/h3\u003e\n\u003cp\u003e3D crystallographic structure of MHC-I was taken from the Protein Data Bank of the RCSB (PDB ID: 3AM8) with a 2.80 \u0026Aring; resolution [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], having no mutation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Using AutoDockTools software, Kollmann charges and hydrogens were introduced, and Gasteiger charges were calculated. The type of atom was set to assign type AD4 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe protein structure preparation was performed by using the DS 2020 program. During this process, co-crystallized inhibitors, water molecules, and heteroatoms were excluded from the protein molecules to isolate the protein-ligand interactions of interest [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Then, the energetically unfavorable conformations of the protein structures were minimized using the CHARMm minimizer algorithm, involving 200 steps of reduction to achieve RMSD or a root-mean-square deviation 0.1 kcal/mol value [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This optimization step ensured the attainment of stable and energetically favorable protein conformations suitable for subsequent computational analyses.\u003c/p\u003e\n\u003ch3\u003eLigand Preparation\u003c/h3\u003e\n\u003cp\u003eBy selecting the optimised ligands, torsion tree feature is assigned to the ligand so as to recognize the flexibility of that ligand and determine the smallest of molecules interacting with the protein [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. By assigning choose and detect root options, the software analyses the ligand in the form of a tree, where the rigid core is the root and the flexible parts are the branches. This also helps in incorporating the free energy which gets released because of the rotational bonds of the ligand [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGrid Box Generation\u003c/h3\u003e\n\u003cp\u003eTo enable comprehensive docking analyses, a three-dimensional grid box was strategically positioned to encompass the entirety of the receptor proteins. This grid box was carefully positioned to fit the receptor proteins as well as the ligands (compounds), guaranteeing a thorough investigation of possible binding interactions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The dimensions of the grid box were tailored to match the spatial requirements of the ligands and receptors, with adjustments made to the Z, Y, and X dimensions to optimize the fit. The dimensions of grid box are receptor\u0026thinsp;=\u0026thinsp;3am8.pdbqt with exhaustiveness\u0026thinsp;=\u0026thinsp;8, center_x\u0026thinsp;=\u0026thinsp;18.3765651107, center_y\u0026thinsp;=\u0026thinsp;23.2233903109, center_z\u0026thinsp;=\u0026thinsp;11.7902580453, size_x\u0026thinsp;=\u0026thinsp;40.9508026378, size_y\u0026thinsp;=\u0026thinsp;40.9508026378, and size_z\u0026thinsp;=\u0026thinsp;40.9508026378. The adjustments in grid box size were guided by the desired spacing between grid points, set at 0.5 \u0026Aring;, which approximately corresponds to one-quarter of the length of a carbon single bonding atom. This spacing was chosen to sufficiently capture the spatial intricacies of the ligand-receptor interactions while maintaining computational efficiency [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Subsequently, the ligand was positioned within the grid box, ensuring its ideal alignment within the receptor proteins' active location. Upon finalizing the grid box configuration, the dimensions were saved for subsequent processing and analysis, facilitating further exploration of ligand-receptor interactions and the determination of docking scores.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePharmacokinetic Profiling and Drug Candidate Optimization\u003c/h2\u003e \u003cp\u003eThe efficacy of a potential drug candidate relies not only on its ability to reach its target within the body but also on its capacity to maintain a bioactive state at sufficient concentrations for the requisite duration to elicit desired biological responses. Drug development involves a thorough assessment of the features of absorption, distribution, metabolism, and excretion (ADME). Among them, pharmacokinetic characteristics are essential for directing the choice and refinement of new therapeutic compounds [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Assessing pharmacokinetic properties is integral to the identification of drug candidates with optimal biological activity and pharmacokinetic profiles. To this end, we employed two widely utilized tools, SwissADME [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and ADMElab 2.0 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], to analyze the pharmacokinetic properties of candidate compounds. A crucial aspect of pharmacokinetic evaluation involves the assessment of P-glycoprotein (Pgp), a transmembrane efflux pump predominantly expressed in hepatic tissues. Pgp s essential to the development and discovery of new drugs by regulating the efflux of various xenobiotics and therapeutic agents from cells [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Inhibition of Pgp by a drug molecule can enhance its bioavailability and efficacy by preventing its expulsion from target cells. Conversely, if a drug molecule exhibits potent inhibition of Pgp, it may be considered a promising candidate due to its potential to overcome multidrug resistance mechanisms and improve therapeutic outcomes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLipinski's Rule of Five for Predicting Drug-like Characteristics\u003c/h3\u003e\n\u003cp\u003eLipinski in 2007, established the \"Rule of Five.\", a fundamental criterion widely employed to assess drug-likeness, as documented by [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This criterion serves as a pivotal filter for evaluating the potential oral absorption of drug candidates, delineating four key rules to ascertain a molecule's suitability for oral administration. A compound could be deemed orally active if it adheres to these rules; however, violation of two or more rules may indicate diminished oral bioavailability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We utilized the zinc online server and SwissADME to forecast adherence to Lipinski's rule and drug-like charcteristics including hydrogen bond donors (HBD), acceptors of hydrogen bonds (HBA), partition coefficient (Log P), and molecular weight (MW) and the quantity of rotatable bonds. These computational tools offer robust predictive capabilities, enabling the assessment of molecular characteristics crucial for oral absorption and pharmacokinetic viability [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Through the utilization of these resources, we sought to identify drug candidates exhibiting favorable physicochemical properties aligned with Lipinski's criteria, thereby facilitating in making choices throughout the medication development and discovery process.\u003c/p\u003e\n\u003ch3\u003eMolecular Dynamics Simulation\u003c/h3\u003e\n\u003cp\u003eThe current study utilized a program for simulating molecular dynamics called Schrodinger's Desmond that is widely recognized for its specialized functionalities and capacity to compute binding free energies and analyze protein-ligand interactions [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It was performed on Ubuntu 22.04 on an Acer workstation using Desmond version 2021-4 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The force field in OPLS-2005 was used to create the protein-ligand complex's topology. In order to develop the complex, the system builder platform was utilized in conjunction with an orthorhombic simulation box and simple point-charge clear water models. Following that, an adequate amount of Na\u003csup\u003e+\u003c/sup\u003e/Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e counterions were introduced to neutralize the solvated complex system. To replicate physiological conditions, a salt concentration of 0.15 M was introduced [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A transparent fluid model with SPC water atoms was used to confine the system in an orthorhombic simulation box. The calculations were carried out at a temperature of 300 K (NPT or NVT) and a pressure of 1.0325 bar. Prior to the commencement of the molecular dynamics simulations, the system underwent an initial minimization phase lasting 100 picoseconds (ps) to alleviate potential steric clashes and unfavorable interactions. Subsequently, the system was allowed to relax using default relaxation techniques before initiating the 100 nanoseconds (ns) trajectory. This all-encompassing strategy made sure that the conformational dynamics and interactions within the receptor-ligand complex system were thoroughly investigated under physiologically relevant circumstances.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThrough Molecular Docking analysis, Diosgenin shows a higher binding score of -8.93 Kcal/mol with MHC-I, therefore, it can be deduced that Diosgenin while interacting with MHC-I could help in enhancing the immune response against cancer, since it possesses pharmacokinetic properties. In Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(a), the Ligplot visualisation shows hydrophobic interactions along with hydrogen bond interaction of the ligand Diosgenin with TYR 67(D) of MHC-I, showing H-bond distance of 3.11 \u0026Aring;. In Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(b), through 2D visualisation by Discovery Studio, van der Waals interaction can be observed in TYR 26(D) and 67(D), SER 52(D), THR 233(B), PHE 241(B) and ASP 30(B); conventional hydrogen bonding with ARG 48(B); and alkyl to pi-alkyl interactions with ALA 211(B), TYR 27(B) and 63(D), PRO 235(B) and LEU 65(D). Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e(c) shows the 3D representation of these interactions, generated using Discovery Studio. These \u003cem\u003ein silico\u003c/em\u003e analyses may act as a substantial foundation for subsequent \u0026ldquo;\u003cem\u003ein-vitro\u0026rdquo;\u003c/em\u003e and \u0026ldquo;\u003cem\u003ein-vivo\u0026rdquo;\u003c/em\u003e tests to investigate the biological activity of the bioactive compound in an effort to identify its potential as a treatment for tumor disease. This compound is presently undergoing \u003cem\u003ein-vitro\u003c/em\u003e analysis in our laboratory (Lovely Professional University and Thyme Phyto BioMed Pvt. Ltd.). Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e lists the docking outcomes for the topmost five compounds.\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eComprehensive ADME Profiling and Enzyme Interaction Analysis of Diosgenin for Drug Development\u003c/h2\u003e\n \u003cp\u003eADME prediction serves as a critical component in the process of finding new drugs and development mechanism, aiming to anticipate \u003cem\u003ein vivo\u003c/em\u003e pharmacokinetic behavior of prospective therapeutic molecules. ADME evaluations are essential for defining the safety and effectiveness profiles of pharmaceutical drugs when used in tandem with molecular docking studies [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. Tables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e present a comprehensive overview of the pharmacological characteristics, wherein the evaluated compounds exhibit values within acceptable ranges. Notably, Diosgenin, a natural molecule under scrutiny, demonstrates adherence to Lipinski\u0026apos;s Rule of Five (RO5), suggesting favorable oral absorption and desirable pharmacokinetic attributes. Diosgenin exhibits excellent permeability, indicating its potential for increased bioavailability and efficacy as an healing therapeutic agent in contrast to substances that have been screened, sourced from the Zinc database and PubChem. Moreover, Diosgenin\u0026apos;s interaction with key enzymes involved in drug metabolism, notably cytochrome P450 (CYP) subtypes, was assessed. Results indicate that Diosgenin is not a substrate for CYP2D6 and CYP3A4, suggesting minimal metabolism via these pathways. While Diosgenin is not predicted to inhibit CYP2C19, it displays inhibitory potential against CYP2C9 and CYP2D6, hinting at potential metabolic processing in the liver. The description for the same regarding the best 5 ligands has been shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFurthermore, predictions regarding Diosgenin\u0026apos;s interaction with P-glycoprotein (P-gp), a key efflux pump implicated in drug transport, were evaluated using SwissADME. P-gp, predominantly expressed in various tissues, plays a crucial role in cellular drug efflux mechanisms [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. Notably, Diosgenin\u0026apos;s interaction with P-gp suggests potential involvement in drug transport processes, with implications for its pharmacokinetic profile and tissue distribution.\u003c/p\u003e\n \u003cp\u003eThese findings underscore Diosgenin\u0026apos;s promising pharmacological attributes, suggesting its potential as a viable therapeutic candidate with favorable pharmacokinetic properties and metabolic characteristics. Further elucidation of Diosgenin\u0026apos;s pharmacological profile by \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies is warranted to confirm its therapeutic potential and inform subsequent drug development efforts.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eBoiled Egg Plot\u003c/h2\u003e\n \u003cp\u003eApart from ADME forecasting, the effectiveness and security of small molecules are often compromised by unfavorable pharmacokinetic attributes. Notably, adverse pharmacokinetic characteristics can contribute to the failure of minor molecule candidates in drug development efforts.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the pharmacokinetic properties of Diosgenin using a Boiled Egg plot, [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. This plot highlights two favorable attributes of Diosgenin: gastrointestinal absorption and Blood-Brain Barrier (BBB) permeability. Diosgenin exhibits a high rate of BBB, evident by its depiction within the yellow zone of the egg in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Consequently, Diosgenin is indicated to be gastrointestinal impermeable, suggesting limited absorption across the gastrointestinal.\u003c/p\u003e\n \u003cp\u003eFurthermore, Diosgenin emerged as the paramount efficacious drug candidate in our investigation. However, it is imperative to note that further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e research on this natural ligand is warranted to validate and corroborate the result of this study. Comprehensive characterization of Diosgenin\u0026apos;s pharmacological profile through rigorous experimental studies is essential to ascertain its efficacy, safety, and therapeutic potential in the context of cancer treatment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eMolecular Dynamics Evaluation of Diosgenin-3AM8 Complex Stability and Interaction Dynamics\u003c/h2\u003e\n \u003cp\u003eRoot Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and hydrogen bonds (H-bonds) were among the molecular dynamics parameters that were analysed in order to assess the stability of the Diosgenin-3AM8 protein complex and identify the type of interactions that take place during their bindingThe RMSD profile of the Diosgenin-3AM8 complex during a 100-ns solvent-environment molecular dynamics simulation is displayed in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The ligand and protein\u0026apos;s respective RMSD values exhibited a consistent range during the simulation. Specifically, the protein values fluctuated between 2.4 and 2.8 \u0026Aring;, while the ligand values fluctuated between 2 and 2.7 \u0026Aring;. Initial fluctuations in the RMSD, particularly observed between 0 and 15 ns, are attributed to movements in the 3AM8 protein\u0026apos;s activation loop. However, minimal deviations were observed thereafter, indicating that the 3AM8-Diosgenin interaction remained stable throughout the simulation duration, with no dissociation of Diosgenin from the protein. These findings suggest a consistent RMSD profile with no significant deviations, indicative of a stable protein-ligand complex. The protein and Ligand complex\u0026apos;s structural stability, as determined by RMSD study, is shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The stability of the Diosgenin-3AM8 complex is further supported by the state stability of observed at 100 ns, which shows that both the initial and final simulated structures retained stable conformations throughout the simulation period. The stability of the Diosgenin-3AM8 complex is further supported by the state stability of observed at 100 ns, which shows that both the initial and final simulated structures retained stable conformations throughout the simulation period. The state stability observed at 100 ns indicates that both the initial and final simulated structures maintained stable conformations throughout the simulation period, further corroborating the stability of the Diosgenin-3AM8 complex.\u003c/p\u003e\n \u003cp\u003eFurthermore, RMSF analysis was conducted to assess the adaptiblity of the 3AM8 protein\u0026apos;s activation loop, as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. This analysis revealed variations in specific amino acid residues, primarily localized to regions of loop flexibility and disorder. Notably, residues exhibiting RMSF peaks exceeding 0.1 nm were identified, although these fluctuations did not impact the stability of the protein-Diosgenin interaction, as observed in docking studies. Consequently, the trajectory analysis suggests the prospective and active nature of the Diosgenin-3AM8 complex, underscoring its potential as a promising therapeutic candidate requiring further research through \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies.\u003c/p\u003e\n \u003cp\u003eIn the current study, dynamic simulation analyses were performed to assess the protein\u0026rsquo;s structural stability over a 100-ns duration. Key parameters, including the radius of gyration (Rg) and Root Mean Square Deviation, were utilized to evaluate protein compactness and structural deviation, respectively.\u003c/p\u003e\n \u003cp\u003eProtein compactness was measured using the radius of gyration (Rg), which was found to be 4.50 \u0026Aring;, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. This metric provides insights into the overall spatial distribution of atoms within the protein structure, with lower Rg values indicative of greater compactness.\u003c/p\u003e\n \u003cp\u003eFurthermore, trajectory analysis of RMSD values revealed minimal structural deviation throughout the Simulation time of 100 ns, using RMSD values spaning from 0.6 to 0.9 \u0026Aring;, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. RMSD serves as a metric for quantifying the extent of structural fluctuations or deviations from the initial reference structure. The observed low RMSD values suggest that the protein maintained its conformational integrity with negligible distortion or deviation from the starting structure during the simulation duration.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eProtein Secondary Structure\u003c/h2\u003e\n \u003cp\u003eThe protein\u0026apos;s secondary structural elements (SSE), including beta- and alpha-strands, were monitored throughout the molecular dynamics simulation to elucidate their spread across the protein structure. The plot depicting SSE distribution by residue index provides insight into the temporal evolution of SSE assignments througout the simulation. Specifically, the lower plot tracks the SSE assignment for each residue over time, while the plot below offers an overview of SSE components for every frame of the trajectory throughout the simulation, as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. It is important to note that while computational studies offer invalable insights, they are subject to certain limitations. To address these limitations and provide comprehensive validation of computational findings, \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e investigations are essential.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe computational analyses conducted in this study identify Diosgenin as a promising candidate for cancer therapy, demonstrating superior binding affinity to the 3AM8 protein, as evidenced by its favorable binding affinity score of -8.93 kcal/mol. Comprehensive pharmacokinetic profiling and ADME analyses confirm Diosgenin's compliance with Lipinski's Rule of Five and other drug-like criteria, including high BBB permeability and favorable pharmacokinetic attributes. Molecular dynamics simulations further validate the stability of the Diosgenin-3AM8 complex over a 100-ns timeframe, supporting its possible efficacy as a therapeutic agent. The stability of the Diosgenin-3AM8 complex further corroborates its potential as an effective therapeutic agent. These simulations reveal robust binding stability, suggesting that Diosgenin could maintain its bioactive conformation within the target site, thereby enhancing its therapeutic efficacy. These findings provide a strong rationale for advancing Diosgenin to \u0026ldquo;\u003cem\u003ein vitro\u0026rdquo;\u003c/em\u003e and \u0026ldquo;\u003cem\u003ein vivo\u0026rdquo;\u003c/em\u003e studies, paving way for its potential development as a targeted cancer therapy. The results not only highlight Diosgenin's potential as a lead compound for cancer therapy but also contribute to the larger area of study on cancer by identifying novel therapeutic targets and approaches. Future empirical research is essential to validate these \u003cem\u003ein silico\u003c/em\u003e findings and to fully elucidate the therapeutic potential of Diosgenin in clinical settings, eventually playing a part in the development of targeted cancer therapies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to Lovely Professional University's School of Bioengineering and Biosciences in Phagwara, Punjab, for all of their help.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSJ, TS, SS, SA and AKV performed research and analyzed data, writing - entire original draft preparation; SJ, TS, SS, CC and AKV: Writing - review and editing; AKV, RS and AK: Supervision; AK and AKV designed the research, provided the facility and edited the paper for the final version. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM. Wieczorek \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Major Histocompatibility Complex (MHC) Class I and MHC Class II Proteins: Conformational Plasticity in Antigen Presentation,\u0026rdquo; \u003cem\u003eFront. 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Dyn.\u003c/em\u003e, vol. 42, no. 17, pp. 9105\u0026ndash;9117, Nov. 2024, doi: 10.1080/07391102.2023.2250451.\u003c/li\u003e\n\u003cli\u003eM. J. Ruiz G\u0026oacute;mez, A. Souviron Rodr\u0026iacute;guez, and M. Mart\u0026iacute;nez Morillo, \u0026ldquo;La glicoprote\u0026iacute;na-P una bomba de membrana que representa una barrera a la quimioterapia de los pacientes con c\u0026aacute;ncer,\u0026rdquo; in \u003cem\u003eAnales de Medicina Interna\u003c/em\u003e, SciELO Espana, 2002, pp. 49\u0026ndash;57.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 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":"Lovely Professional University","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":"MHC-I, Diosgenin, Phytomedicine, In silico, Computational analysis, Molecular Docking, MD Simulation","lastPublishedDoi":"10.21203/rs.3.rs-6024968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6024968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImmunotherapeutic strategies targeting MHC-I have gained significant attention for combating cancer, a leading global health. However, the emergence of therapeutic resistance and the manifestation of adverse concerneffects associated with conventional treatment modalities underscore the imperative for innovative therapeutic strategies. In the current study, we conducted an “\u003cem\u003ein silico”\u003c/em\u003einvestigation to identify potential phytomedicines for cancer treatment targeting MHC-I (3AM8). Using AutodockTools software, 408 natural secondary polyphenols were screened against MHC-I, with Diosgenin exhibiting the highest affinity for binding (−8.93 Kcal/mol). Pharmacokinetic profiling of the highest-ranking ligands elucidated their suitability for subsequent development and optimization. Molecular dynamics simulations, conducted using the Desmond software suite, demonstrated pronounced stability of the Diosgenin-MHC-I complex throughout a 100-ns trajectory. Our findings suggest that Diosgenin holds significant promise as a candidate for cancer therapeutics; however, further verifications by use of extensive “\u003cem\u003ein vitro”\u003c/em\u003e and “\u003cem\u003ein vivo”\u003c/em\u003eresearch is warranted to substantiate its validity and therapeutic potential. This study highlights the potential of Diosgenin in upcoming trial regimens and advances the choices for treating cancer.\u003c/p\u003e","manuscriptTitle":"In-Silico Identification and Evaluation of Diosgenin as a Promising Phytomedicine Targeting MHC-I for Cancer Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-26 12:53:59","doi":"10.21203/rs.3.rs-6024968/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":"bf75466b-ae24-498d-ac0f-445b7f6b7ac8","owner":[],"postedDate":"February 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44828879,"name":"Immunology"},{"id":44828880,"name":"Bioinformatics"},{"id":44828881,"name":"Computational Biology"},{"id":44828882,"name":"Toxicology"},{"id":44828883,"name":"Drug Discovery, Design, \u0026 Development"},{"id":44828884,"name":"Pharmacokinetics"}],"tags":[],"updatedAt":"2025-02-26T12:53:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-26 12:53:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6024968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6024968","identity":"rs-6024968","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}