Multitargeted Molecular Mechanisms of Triphaladi Ghana Vati in Cataract Management: An Integrative In-Silico Study Using Docking and Dynamics

Multitargeted Molecular Mechanisms of Triphaladi Ghana Vati in Cataract Management: An Integrative In-Silico Study Using Docking and Dynamics | 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 Multitargeted Molecular Mechanisms of Triphaladi Ghana Vati in Cataract Management: An Integrative In-Silico Study Using Docking and Dynamics Prem Kumar Goud, Rashmi Sahu, Akanksha Thakur, Prashant Kumar Gupta, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6104893/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 Cataracts are a leading cause of vision impairment globally. This study investigates the potential of Triphaladi Ghana Vati (TGV) , an Ayurvedic formulation, in cataract management through integrative computational approaches. High-resolution mass spectrometry identified 100 bioactives, of which 22 met ADME criteria. Network pharmacology and molecular docking revealed six overlapping targets between TGV and cataracts, including HDAC8, ALDH1A1, GSTP1, and CASP3. Promazine sulfoxide demonstrated significant interactions with ALDH1A1, achieving a docking score of -8.339. Molecular dynamics simulations validated its stable binding, with RMSD values below 6.4 Å and MMGBSA binding free energy of -59.28 kcal/mol. Gene ontology and KEGG enrichment analyses highlighted pathways like oxidative stress and nitric oxide homeostasis, implicating TGV in cataract modulation. These findings propose TGV as a promising multi-target therapeutic candidate for cataract prevention and treatment, warranting further experimental validation. Triphala Cataract Network Pharmacology Molecular Docking Molecular Dynamic Simulations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Cataract, characterized by lens opacification, is the leading cause of visual impairment globally, representing a significant public health challenge. 1 The primary role of the lens is to focus light onto the retina; however, with advancing age, oxidative modifications of lens proteins lead to opacity and progressive vision loss. 2 ˒ 3 Cataracts are categorized into nuclear, cortical, and subcapsular types, with age-related (senile) cataract being the most prevalent form. 4 Research indicates a higher prevalence of cataracts in females, often developing bilaterally, though one eye typically shows earlier symptoms. 5 Of the 2.2 billion individuals worldwide experiencing vision impairment, cataracts account for 94 million cases. 6 While cataract surgery remains the primary treatment option, preventive measures are crucial for patients. Ayurvedic formulations, such as Triphaladi Ghana Vati , are being investigated for their potential to slow the progression of immature senile cataracts, g to their rejuvenation and ocular health benefits. 7 Ayurinformatics is a promising approach to accelerating drug discovery and development within Ayurveda. 8 This article seeks to explore the bioactive compounds, molecular targets, pathways, and gene ontology associated with Triphaladi Ghana Vati and cataract, thereby paving the way for further experimental studies and validation in laboratory settings. Materials and methods Screening the active ingredients of Triphaladi Ghana Vati ( TGV ) The bioactive compounds of TGV were identified through an HR-LCMS study conducted at IIT, Bombay and subsequently analyzed in PUBCHEM to retrieve their canonical SMILES. After obtaining the canonical SMILES of the phytochemicals from PUBCHEM, these compounds were further subjected to drug-likeness and ADMET profiling using SwissADME. The criteria used for screening included water solubility, gastrointestinal (GI) absorption, drug-likeness (fulfillment of at least three out of five established rules), and bioavailability score. 9 Screening the active ingredient targets of TGV To determine the targets of these compounds, they were analyzed using the "Find My Compound's Target" tool available in Binding DB.⁹ A similarity score threshold of 0.7 was applied for the analysis. The corresponding gene names associated with these target proteins were then identified through UniProt, ensuring the selection of entries specific to the Homo sapiens species. The names of the proteins and their respective genes were extracted from UniProt for further evaluation. 11 Collection of target genes related to cataract All identified targets were analyzed using DisGeNet 12 to explore potential associations with various diseases, with a particular emphasis on cataract. By employing the keyword "Cataract," 878 cataract-related targets were retrieved. To illustrate the overlapping targets between TGV and cataract, a Venn diagram was constructed, providing a clear visualization of the intersections. Construction of the bioactive-target-disease network A visually comprehensive network was developed using Cytoscape 3.10.2 13 , a Java-based software platform, to map the interactions among the bioactives of the herb ( TGV ), the intersecting targets, and the disease (cataract). Construction of the protein-protein interaction network The protein-protein interaction (PPI) network was constructed using the STRING 14 database. The overlapping targets of TGV and cataract were input into the STRING platform to generate the PPI network. For data reliability, a minimum interaction score of 0.4 was applied, with Homo sapiens selected as the species of focus. Proteins not connected to the network were excluded from the final visualization. Gene ontology (GO) and the KYOTO encyclopedia of genes and genomes (KEGG) pathway enrichment To further investigate the pathways associated with the disease, the shared targets of TGV and cataract were analyzed using the DAVID database ( https://david.ncifcrf.gov/) . 15 This online biological knowledgebase and analytical tool facilitates the extraction of various biological insights, including gene functional classification, functional annotation, and pathway enrichment. GO enrichment analysis spans three categories: biological process (BP), cellular component (CC), and molecular function (MF). Additionally, KEGG serves as a repository for understanding molecular interactions and elucidating established metabolic pathways. The analysis was conducted with a focus on Homo sapiens to perform GO 16 and KEGG 17 pathway enrichment analyses. Molecular docking and molecular dynamics simulation Protein Preparation The crystallographic structures of the target proteins were obtained from the Protein Data Bank (PDB). The receptor-binding domains (RBDs) were extracted from these PDB structures and prepared using the "Protein Preparation Wizard" tool of the Schrödinger suite 18 (Schrödinger Release 2021-4: Protein Preparation Wizard; Epik, Schrödinger, LLC, New York, NY, 2021). The preparation process included removing water molecules and cofactors, correcting mislabeled elements, adding hydrogen atoms, assigning bond orders, optimizing hydrogen bonding, and performing restrained energy minimization using the OPLS4 force field. These prepared protein structures were subsequently used for grid generation through the "Receptor Grid Generation" panel in the Glide module of the Schrödinger suite. Ligand preparation The ligands used in this study were prepared using the Schrödinger suite, with the PubChem database serving as the source for the phytochemical 3D sdf. structures. A comprehensive search for relevant phytochemicals was initially performed in the PubChem database, and the 3D sdf. structures of the selected compounds were downloaded. These structures were then imported into the Schrödinger suite and processed using the LigPrep module 19 (Schrödinger Release € 2021-4: LigPrep, Schrödinger, LLC, New York, NY €, 2021). The preparation process involved optimizing the phytochemicals at a pH of 7 ± 2 using the Epik tool. Subsequently, the optimized structures underwent energy minimization using the OPLS4 force field to ensure precise molecular conformations for the ensuing docking studies. The resulting ligand library was then employed for molecular docking and dynamics simulations to evaluate its potential as modulators of the target proteins. Molecular Docking Molecular docking studies were conducted using the Glide module from the Schrödinger Suite 20 (Schrödinger Release € 2021-4: Glide, Schrödinger, LLC, New York, NY €, 2021). The standard precision (SP) mode was employed for ligand docking to evaluate the binding interactions between the ligand and the target protein. Flexible ligand docking was performed considering the conformational flexibility of the ligand during the docking process, which allowed for the exploration of various potential binding modes. The SP mode’s default settings were utilized, striking a balance between computational efficiency and accuracy. The scoring function of Glide was applied to rank ligand poses based on their predicted binding affinity to the target protein. The docking results were analyzed to identify the most favourable binding conformations and interactions. Molecular Dynamics Simulations Molecular dynamics (MD) simulations were performed using the Desmond module within the Schrödinger Suite 21 (Schrödinger Release € 2021-4: Desmond, Schrödinger, LLC, New York, NY €, 2021). The protein-ligand complex, prepared from the docking studies, served as the initial structure for the simulations. The system was solvated in an orthorhombic simulation box containing explicit water molecules, modeled using the TIP3P water model. Appropriate counterions (Na + or Cl-) were added to neutralize the system, and additional ions were introduced to achieve an ionic strength of 0.15 M. The system was then minimized and equilibrated in several stages to resolve any steric clashes or unfavorable interactions. The MD simulations were carried out under periodic boundary conditions using the OPLS4 force field. The simulation parameters included a temperature of 300 K, controlled by a Nosé-Hoover thermostat, and a pressure of 1 atm, maintained by a Martyna-Tobias-Klein barostat. The total simulation time was 100 ns, with a timestep of 2 fs, and the trajectory data were recorded at 10 ps intervals. The trajectory data were analyzed to evaluate the stability of the protein-ligand complex, focusing on parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), and hydrogen bonding interactions throughout the simulation. The binding affinity in the molecular simulations was calculated using the thermal_mmgbsa scripts. Results TGV-Bioactives A total of 100 bioactives were obtained from HR-LCMS(High Resolution- Liquid Chromatography-Mass Spectroscopy). Among these compounds were 4-Chloro-3,5-dimethoxybenzyl alcohol, 4-Amino-2-methylenebutanoic acid, D-Pipecolic acid, L-trans-5-Hydroxy-2-piperidinecarboxylic acid, (Z)-5-[(5-Methyl-2-thienyl)methylene]-2(5H)-furanone, (+)-Chebulic acid, Medicanine, L-2-Aminobutyric acid, Lentiginosine, Melosatin B(12), Neuraminic acid, WIN56291, DL-Dopa, Metyrosine, Lentiginosine, Bakers yeast extract, 1-(1-Pyrrolidinyl)-2-propanone, Lysyl-Asparagine, N-Isovalerylglycine methyl ester, Hypoxylone, D-Tryptophan, Cis-Caffeoyl tartaric acid, 3beta,6beta-Dihydroxynortropane, Sarmentosin, Ketotifen(4), N-(1-Deoxy-1-fructosyl)phenylalanine, Irenolone, 3-Hydroxy-carbofuran, N-Isovalerylglycine methyl ester, Laccarin, 2-Methoxy-3-(1-methylpropyl)pyrazine, Histidinyl-Arginine, Ethyl N-ethylanthranilate(6), 4-Hydroxybutanoic acid, Promacyl(1), HC Blue No. 2, Ruspolinone(5), N-n-Hexanoylglycine methyl ester, 4,4'-Methylene-bis-(2-chloroaniline), (S)-Edulinine, (-)-Hygroline, Licoagroside B, Convolamine(7), γ-Glutamyl-β-cyanoalanine, Capsaicin(8), Coronatine, Tyrosyl-Aspartate, Variotin, Coixinden B, Codeine N-oxide, N-Ethylmaleimide-S-glutathione, Portulacaxanthin III, Valclavam, Muricinine, (S)-Edulinine, HMR1556, 3-(Acetyloxy)-9-mercaptoandrosta-3,5-diene-11,17-dione, Americine, (1beta,2alpha,3alpha)-1,2,3,24-Tetrahydroxy-12-oleanen-28-oic acid, Schleicherastatin 6, Glycyrrhizin, Portulacaxanthin III, C16 Sphinganine, Dinoseb acetate, Nitramine, Promazine sulfoxide(31), Monomenthyl succinate(5), Methyl 2-furoate, 3,4-Heptanedione(2), 1-(5-Methyl-3-pyridinyl)-1-decanone, Nitramine. The bioactive structures were sourced from PUBCHEM, with 72 of 100 bioactive structures successfully retrieved. These 72 structures were then analyzed using the Swiss ADME, which assessed various criteria including physicochemical properties, water solubility, gastrointestinal absorption, and drug likeness based on Lipinski, Ghose, Veber, Egan, and Muegge rules. The bioavailability score for these compounds was 0.55. Ultimately, only 22 compounds met the ADME criteria, as shown in Table 1 and Fig. 1 . Bioactives and the Target In our analysis, we examined 22 phytochemicals with a similarity index of 0.7, identifying 11 compounds that interacted with 81 targets. The compound with the highest number of interactions was Promazine sulfoxide, which engaged with 31 targets, followed by Melosatin B with 11 targets, Capsaicin with 8 targets, Convolamine with 7 targets, Ethyl N-ethylanthranilate with 5 targets, Ruspolinone with 5 targets, Monomenthyl succinate with 5 targets, Ketotifen with 4 targets, 3,4-Heptanedione with 2 targets, 1-(5-Methyl-3-pyridinyl)-1-decanone with 2 targets, and Promacyl with 1 target. Detailed information about the bioactive compounds and their associated targets is provided in Table 2 and Fig. 2 . Table 2 Bioactive and their target genes S.No. Bioactive Target/Gene 1. Melosatin B HTR1A, HTR1B, HTR1D, ADRA2B, MAOA, MAOB, CASP3, CASP7, DRD3, DRD4, DRD2 2. Ketotifen DRD2, DRD3, GSR, HRH1 3. Ethyl N-ethylanthranilate ADRA1A, ADRA1B, ADRA1D, CDC25A, CDC25C 4. Promacyl ACHE 5. Ruspolinone CA1, CA2, CTNNB1, KDM2B, TAAR1 6. Convolamine HTR3A, ABCB1, SLC29A1, CHRM1, CHRM2, CHRM4, ST14 7. Capsaicin ACHE, BACE1, EPHX2, CNR1, CNR2, HDAC8, KDM1A, TRPV1 8. Promazine sulfoxide HTR1A, HTR2A, HTR2B, HTR2C, HTR3A, HTR6, HTR7, ACHE, ALDH1A1, AOX1, ADRA2A, ADRA2C, ABCB1, BCHE, CYP2D6, DRD1, DRD5 DRD2, GSTP1, HRH3, HRH4, MALT1, CHRM1, CHRM2, CHRM3, CHRM4, CHRM5, KCNH2, PTGS1, SIGMAR1, SLC6A4 9. 3,4-Heptanedione CES2, CES1 10. 1-(5-Methyl-3-pyridinyl)-1-decanone HDAC1, HDAC8 11. Monomenthyl succinate HSD11B1, CYP19A1, GPBAR1, OPRM1, PPM1B TGV- EyeDisease other diseases network The 71 targets/genes were queried in DisGeNet and a total of 15,151 results were obtained.Wide range of diseases, including Cataract, Cornelia de Lange Syndrome, Cardiomegaly, Hypertension, Intellectual Disability, Autism, Anxiety, Glaucoma, Conductive and Sensorineural Hearing Loss, Atrial and Ventricular Septal Defects, Neuroblastoma, Seizures, Sleep Disorders, Strabismus, Cleft Palate, Hypogonadism, Polycystic Kidney Disease, Microcephaly, Micrognathism, Myopia, Hirsutism, Mood Swings, Premature Birth, Short Stature, Delayed Puberty, Obsessive-Compulsive Behaviour, Attention Deficit Hyperactivity Disorder (ADHD), Cerebral Ventriculomegaly, Renal Insufficiency, X-linked Inheritance, Malignant Neoplasms (including breast, liver, and bladder carcinomas), Neurodegenerative Disorders, and several other congenital, developmental, and cancer-related disorders. TGV - Cataract Network A Venn diagram analysis (Fig. 3 ) revealed six common targets shared between TGV and Cataract. These targets are HDAC8, ALDH1A1, GSTP1, ABCB1, CYP19A1, and CASP3. Five bioactive compounds were identified as interacting with these targets. Specifically, 1-(5-Methyl-3-pyridinyl)-1-decanone interacts with HDAC8, Promazine sulfoxide interacts with ALDH1A1 and GSTP1, Convolamine interacts with ABCB1, Monomenthyl succinate interacts with CYP19A1, and melassin B interacts with CASP3. Notably, all these targets are associated with cataract, with particular emphasis on CASP3, which plays a key role in cataract development. (Fig. 3 ) Protein-protein interaction (PPI) Network The current study analyzed the protein network of all intersecting genes related to cataract using the STRING database. Functional associations between protein nodes were represented by lines of varying colors, each corresponding to different types of evidence. The confidence in these associations was indicated by the distance between the nodes, which was calculated using the Bayesian scoring system. The network consists of 11 nodes connected by 32 edges, with an average node degree of 5.82 and an average local clustering coefficient of 0.604. The expected number of edges was 7, and the PPI enrichment p-value was 2.06e-12. (Fig. 4 ) GO and KEGG pathway enrichment To explore the mechanism of TGV in cataract, overlapping target genes were analyzed using the DAVID database for GO and KEGG pathway analysis. The results revealed enrichment in 20 biological processes (BP), 0 cellular components (CC), and 20 molecular functions (MF). The key biological processes included Benzaldehyde dehydrogenase activity, Nitric oxide binding, Androgen binding, Protein decrotonylase activity, and Glyceraldehyde-3-phosphate dehydrogenase activity. Molecular functions involved Nitric oxide homeostasis, Estradiol secretion, Detoxification, and Response to toxic substances. No significant cellular components were identified. KEGG enrichment analysis revealed three significant pathways: Platinum drug resistance, MicroRNAs in cancer, and Viral carcinogenesis. These pathways are shown in Fig. 5 , with the Y-axis representing the pathway names and the X-axis indicating the fold enrichment ratio between target genes and background genes. The colour gradient from blue (low enrichment) to red (high enrichment) highlights the significance of each pathway. ( Fig. 6 ) Molecular docking In this investigation, molecular docking was conducted to assess the binding interactions of several ligands with diverse protein targets associated with cataract. Among the analyzed complexes, gene ALDH1A1 (aldehyde dehydrogenase 1A1) with 4WPN, 7UM9, 5AC2, 5L2N, 6DUM, and 4WB9. Promazine sulfoxide demonstrated with- 4WPN one pi-cation PHE171 with nitrogen, several non-bonding interactions involving TYR297, GLY294, HIP293, TYR457, GLY458, VAL460, GLU269, PHE468, ASP122, ASN121, TRP178, MET175, VAL174, CYS302 and CYS303, ILE304, ALA305 and docking score was obtained − 8.339. 7UM9 One H-bond with TRY297 and one pi-cation with TRY297 others several non-bonding interactions ASN121, ASP122, GLY125, PHE171, VAL174, MET175, HIE293, GLY294, CYS302, CYS303, ILE304, TYR457, GLY458 and VAL460 and docking score was − 8.086. 5AC2 H- bond GLY294 and, others several non-bonding interactions HIP293, TYR297, CYS302, ILE304, CYS303, CYS302, ASN121, ASP122, PHE171, VAL174, MET175, TRP178, TYR457, GLY458, VAL460, PHE466 and docking score was − 7.560. 5L2N all non-bonding interactions ASN121, CYS302, ILE304, PHE171, VAL174, PHE290, HIS293, GLY294, TYR297, TYR457, GLY458 and VAL460 and docking score was − 6.642. 6DUM One H-bond with SER247, five salt bridge with GLU349, CL605, CL610 and CL611, One pi-pi stacking TRP169 others non-bonding interactions IlE166, GLY226, GLN350, LYS193, ALA195, GLU196, GLN197, PHE244, THR245, GLY246 AND VAL250 and docking score was obtained − 6.468. 4WB9 two pi-pi stacking with TRP169, one H- bond withSER247, two salt bridge with GLU349, and others was non-bonding interactions PRO168, ALA195, GLU196, GLN197, PHE244, GLY246, GLU249, VAL250, GLN350, LYS353, PHE402 and docking score was obtained 6.090. Gene HDAC8(Histone Deacetylase 8) with 1T69 docking score was − 6.254 and bioactive 1-(5-Methyl-3-pyridinyl)-1-decanone one pi-pi stacking with PHE152, H-bond with TYR306, one metal coordination with ZN378 and others non-bonding interactions ASP101, PHE208, MET274, ASP178, HIS180, HIS143, HIE142, TRP141, GLY151, CYS153, TYR154, LEU31, ALA32, LYS33, 1LE34, ARG37, GLY303, GLY304, ASP267. Gene ABCB1 with 6Y6H docking score was − 5.583 and bioactive Convolamine one salt bridge with GLU117 and others non-bonding interactions ARG41, GLY40, LEU39, LYS37, VAL47, ALA60, ILE94, LEU110, GLU111, TYR112, ALA113, ALA114, GLY115, GLY116, GLU117, LYS160, GLN162, ASN163, LEU165 AND VAL178. Gene CYP19A1 with 3S79 and 5JKV bioactive with monomethyl succinate docking score was obtained respectively 5.645 and 5.299. 3S79 all non- bonding interactions ARG115, PHE134, ILE133, PHE221, TRP224, ILE305, ALA306, ASN309, THR310, VAL369, VAL370, LEU372, VAL373, MET374, LEU477, SER478 and HEM600. 5JKV one salt bridge with HEM601 and others non-bonding interactions ARG115, ILE133, PHE134, PHE221, TRP224, ALA306, ASH309, THR310, VAL369, VAL370, LEU372, VAL373, MET374, LEU477 and SER478. Gene CASP3 with 1NMQ and 1NMS bioactive with Melosatin B and docking score was obtained respectively − 5.530 and − 5.383. 1NMQ three H-bond with ARG64, SER120 and GLN161, two pi-pi stacking with HIE and PHE256, others non-bonding interactions THR62, HIE121, GLY122, ALA162, CYS163, LEU168, TYR204, SER205, TRP206 and ARG207. 1NMS four H-bond with ARG64, SER120, GLN161 and ARG207, three pi-pi stacking HIE121, TYR204 and TRP206 and others non-bonding interactions GLY122, ALA 162, CYS163, SER205, ARG207, ASN208, SER213, TRP214, SER249, PHE250, SER251 and PHE256. Table 3 and Fig. 7 A- 7 L Table 3 Gene- Bioactive and their Docking score Sr. No. Gene-Name Uniprot-ID Bioactive PDB_ID PCID Docking-Score Key interactions 1. ALDH1A1 P00352 Promazine sulfoxide 4WPN 547559 -8.339 One pi-cation PHE171 with nitrogen, several non-bonding interaction involving TYR297, GLY294, HIP293, TYR457, GLY458, VAL460, GLU269, PHE468, ASP122, ASN121, TRP178, MET175, VAL174, CYS302 and CYS303, ILE304, ALA305 2. ALDH1A1 P00352 Promazine sulfoxide 7UM9 547559 -8.086 One H-bond with TRY297 and one pi-cation with TRY297 others several non bonding interactions ASN121, ASP122, GLY125, PHE171, VAL174, MET175, HIE293, GLY294, CYS302, CYS303, ILE304, TYR457, GLY458 and VAL460 3. ALDH1A1 P00352 Promazine sulfoxide 5AC2 547559 -7.560 One H- bond GLY294 and, others several non-bonding interactions HIP293, TYR297, CYS302, ILE304, CYS303, CYS302, ASN121, ASP122, PHE171, VAL174, MET175, TRP178, TYR457, GLY458, VAL460, PHE466 4. ALDH1A1 P00352 Promazine sulfoxide 5L2N 547559 -6.642 All non-bonding interactions ASN121, CYS302, ILE304, PHE171, VAL174, PHE290, HIS293, GLY294, TYR297, TYR457, GLY458 and VAL460 5. ALDH1A1 P00352 Promazine sulfoxide 6DUM 547559 -6.468 One H-bond with SER247, five salt bridge with GLU349, CL605, CL610 and CL611, One pi-pi stacking TRP169 others non-bonding interactions IlE166, GLY226, GLN350, LYS193, ALA195, GLU196, GLN197, PHE244, THR245, GLY246 AND VAL250 6. ALDH1A1 P00352 Promazine sulfoxide 4WB9 547559 -6.090 Two pi-pi stacking with TRP169, one H- bond with SER247, two salt bridge with GLU349, and others was non-bonding interactions PRO168, ALA195, GLU196, GLN197, PHE244, GLY246, GLU249, VAL250, GLN350, LYS353, PHE402 7. HDAC8 Q9BY41 1-(5-Methyl-3-pyridinyl)-1-decanone 1T69 71347088 -6.254 One pi-pi stacking with PHE152, H-bond with TYR306, one metal coordination with ZN378 and others non-bonding interactions ASP101, PHE208, MET274, ASP178, HIS180, HIS143, HIE142, TRP141, GLY151, CYS153, TYR154, LEU31, ALA32, LYS33, 1LE34, ARG37, GLY303, GLY304, ASP267 8. ABCB1 P08183 Convolamine 6Y6H 420422 -5.583 One salt bridge with GLU117 and others non-bonding interactions ARG41, GLY40, LEU39, LYS37, VAL47, ALA60, ILE94, LEU110, GLU111, TYR112, ALA113, ALA114, GLY115, GLY116, GLU117, LYS160, GLN162, ASN163, LEU165 AND VAL178 9. CYP19A1 P11511 Monomenthyl succinate 3S79 10199004 -5.645 All non- bonding interactions ARG115, PHE134, ILE133, PHE221, TRP224, ILE305, ALA306, ASN309, THR310, VAL369, VAL370, LEU372, VAL373, MET374, LEU477, SER478 and HEM600 10. CYP19A1 P11511 Monomenthyl succinate 5JKV 10199004 -5.299 One salt bridge with HEM601 and others non-bonding interactions ARG115, ILE133, PHE134, PHE221, TRP224, ALA306, ASH309, THR310, VAL369, VAL370, LEU372, VAL373, MET374, LEU477 and SER478 11. CASP3 P42574 Melosatin B 1NMQ 188038 -5.530 Three H-bond with ARG64, SER120 and GLN161, two pi-pi stacking with HIE and PHE256, others non-bonding interactions THR62, HIE121, GLY122, ALA162, CYS163, LEU168, TYR204, SER205, TRP206 and ARG207 12. CASP3 P42574 Melosatin B 1NMS 188038 -5.383 Four H-bond with ARG64, SER120, GLN161 and ARG207, three pi-pi stacking HIE121, TYR204 and TRP206 and others non-bonding interactions GLY122, ALA 162, CYS163, SER205, ARG207, ASN208, SER213, TRP214, SER249, PHE250, SER251 and PHE256 MOLECULAR DYNAMIC SIMULATIONS Selected phytochemicals from TGV, based on their highest docking scores against respective targets, were subjected to molecular dynamics simulations for 100 ns. The protein RMSD was used to assess the stability of the protein structures during thermodynamic movement. In this study, the RMSD values of cataract-related proteins were analysed for their interaction with TGV phytochemicals. One such phytochemical, Promazine sulfoxide, was studied in complex with three aldehyde dehydrogenase proteins (PDB: 4PWN) through molecular dynamics simulation. The results showed similar RMSD trends for the two protein-ligand complexes. For 4PWN and 6DUM with 547559, the RMSD remained stable below 6.4 Å, while 5AC2 with 547559 showed stability below 5.4 Å. The 4PWN-Promazine sulfoxide(547559) complex demonstrated significant hydrophobic pi-pi stacking interactions with A: TYR297 (41%). The highest interaction fraction was 0.8 for the hydrophobic, ionic, and water bridge interactions. The protein’s secondary structure consisted of 34.84% helices, 16.77% strands, and 51.61% total secondary structure elements (SSE) (Fig. 8 A-9A). The MMGBSA binding free energy was − 59.28, and the Prime energy was − 14827.07 (Fig. 10 A, Table 4 ). Table 4 MMGBSA for Protein-Ligand complex Protein-Ligand complex Prime Energy MMGBSA dG Bind MMGBSA dG Bind (NS) MMGBSA dG Bind (NS) Coulomb MMGBSA dG Bind (NS) Covalent MMGBSA dG Bind (NS) vdW 4WPN_547559 -14827.07 -59.28 -60.84 -7.62 -0.00 -44.98 6DUM_547559 -15088.01 -47.64 -49.06 -20.87 1.13 -40.73 5AC2_547559 -15504.48 -62.71 -63.55 -14.68 -0.00 -38.57 The 6DUM-Promazine sulfoxide (547559) complex showed notable interactions, including two water-bridged hydrogen bonds with A: CYS303 (78%, 78%) and A: ILE304 (76%, 76%). The strongest interaction fraction of 0.8 was observed for the hydrophobic and water bridges. The secondary structure of the protein was 34.38% helices, 16.08% strands, and 50.46% total SSE (Fig. 8 B-9B). The MMGBSA binding free energy was − 47.64, and the Prime energy was − 15088.01 (Fig. 10 B, Table 4 ). The 5AC2-Promazine sulfoxide (547559) complex exhibited significant interactions, including a hydrogen bond with A: TYR297 (91%) and two pi-pi stacking interactions with A: TRP178 (55% and 35%). This complex had the highest interaction fraction of 1.2 for hydrogen bonds, hydrophobic interactions, and water bridges. The protein’s secondary structure comprised 34.22% helices, 16.66% strands, and 50.88% total SSE (Fig. 8 C-9C). The MMGBSA binding free energy was − 62.71, while the Prime energy was − 15504.48 (Fig. 10 C, Table 4 ). Discussion Triphaladi Ghana Vati is known for its versatile therapeutic uses in various clinical conditions, specifically eye disorders. Documented ethnopharmacological use includes ophthalmia, conjunctivitis, swelling of eye, diabetes, digestive and respiratory illness, 22 fever, edema, arthritis, 23,24 skin diseases, 25 disorders of male 26 and female reproductive system. 27-30 The reported pharmacological activities of TGV are anti-cataractogenic effect, immunomodulatory, 31-34 anti-cancer, 35-42 anti-diabetic and hypoglycemic, 43-45 anti-fibrinolytic, 46-52 anti-inflammatory, 53-55 anti-bacterial, 56-58 anti-fungal, 59 anti-oxidant, 60-62 anti-cataract and free radical scavenger, 63-66 This study investigates the binding interactions of Promazine sulfoxide with key protein targets, including ALDH1A1, using molecular docking and molecular dynamics (MD) simulations. The results highlight the significant potential of Promazine sulfoxide in modulating the protein targets associated with cataract and related conditions. In the molecular docking analysis, Promazine sulfoxide demonstrated notable binding interactions with three ALDH1A1 protein structures (PDB: 4WPN, 6DUM, and 5AC2). The highest docking score was observed for the 4WPN complex, with a score of -8.339. The interactions of 4WPN suggest that Promazine sulfoxide forms a stable complex with ALDH1A1, contributing to its potential therapeutic effects. Additionally, Promazine sulfoxide with the 6DUM protein exhibited a docking score of -6.468. These docking results indicate that Promazine sulfoxide interacts with ALDH1A1 through a variety of interaction types, including hydrogen bonds, pi-pi stacking, and hydrophobic interactions, contributing to its stability and affinity. The RMSD values for the 4WPN and 6DUM complexes with Promazine sulfoxide remained below 6.4 Å, indicating relatively stable interactions. The 5AC2 complex with Promazine sulfoxide exhibited even greater stability, with RMSD values below 5.4 Å, suggesting a more stable protein-ligand complex. The MMGBSA binding energy for 4WPN was -59.28 kcal/mol, and the Prime energy was -14827.07, reflecting a strong binding affinity. The MMGBSA binding free energy for 6DUM was -47.64 kcal/mol, with a Prime energy of -15088.01, indicating stable binding. The MMGBSA binding energy was -62.71 kcal/mol, and the Prime energy was -15504.48, indicating a very stable and energetically favorable binding. Overall, the findings from both the docking and molecular dynamics simulations suggest that a phytochemical of TGV , i.e. Promazine sulfoxide forms stable, energetically favorable complexes with ALDH1A1 and other related protein targets. The strong binding affinity and stability of these complexes, especially with 5AC2, indicate the potential of Promazine sulfoxide as a promising inhibitor of aldehyde dehydrogenase and other relevant targets in cataract treatment and other diseases. Conclusion This study employed HRLMS-based phytochemical identification followed by Network Pharmacology to investigate the active compounds in Triphaladi Ghana Vati and their potential multitarget effects in the treatment of cataract. The findings suggest that TGV's therapeutic mechanisms may be mediated through the aldehyde dehydrogenase signaling pathways. Molecular docking and dynamic simulations revealed interactions with several bioactive compounds. Notably, Promazine sulfoxide exhibited robust, stable interactions with cataract-related protein targets, as evidenced by RMSD values and MMGBSA binding free energies. This compound emerged as a promising therapeutic candidate with potential applications in managing oxidative stress, which is implicated in cataract formation. However, further in vitro and in vivo validation is essential to substantiate its efficacy and therapeutic potential in cataract prevention and treatment. Declarations Contribution – MR and PKG planned, supervised, and edited the study and manuscript, PG, RS and AT performed the computational work, draft and constructed the manuscript. Acknowledgements – We acknowledge all freely accessed databases, All India Institute of Ayurveda Library for academic support, and Ayurinformatics laboratory for access to all licensed software. We acknowledge Dr Vinod Devaraji, Schrodinger for technical support. Conflict of Interest – The authors declare no conflict of interest regarding the publication of this article. All authors have reviewed and approved the manuscript and affirm that there are no other conflicts of interest to disclose. 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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-6104893","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432384236,"identity":"8e141e7f-3065-4ee1-9950-701c3118170c","order_by":0,"name":"Prem Kumar Goud","email":"data:image/png;base64,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","orcid":"","institution":"All India Institute of Ayurveda, New Delhi","correspondingAuthor":true,"prefix":"","firstName":"Prem","middleName":"Kumar","lastName":"Goud","suffix":""},{"id":432384237,"identity":"81c7770c-2832-4c15-9970-c0559fc02328","order_by":1,"name":"Rashmi Sahu","email":"","orcid":"","institution":"All India Institute of Ayurveda, New Delhi","correspondingAuthor":false,"prefix":"","firstName":"Rashmi","middleName":"","lastName":"Sahu","suffix":""},{"id":432384238,"identity":"cf395828-0055-4135-92f3-caf974aace24","order_by":2,"name":"Akanksha Thakur","email":"","orcid":"","institution":"All India Institute of Ayurveda, New Delhi","correspondingAuthor":false,"prefix":"","firstName":"Akanksha","middleName":"","lastName":"Thakur","suffix":""},{"id":432384239,"identity":"833bb4ab-7daf-4434-94c4-1439aab1abdc","order_by":3,"name":"Prashant Kumar Gupta","email":"","orcid":"","institution":"All India Institute of Ayurveda, New Delhi","correspondingAuthor":false,"prefix":"","firstName":"Prashant","middleName":"Kumar","lastName":"Gupta","suffix":""},{"id":432384240,"identity":"47155978-2b96-47e2-be24-a7094857ff10","order_by":4,"name":"Manjusha Rajagopala","email":"","orcid":"","institution":"All India Institute of Ayurveda, New Delhi","correspondingAuthor":false,"prefix":"","firstName":"Manjusha","middleName":"","lastName":"Rajagopala","suffix":""}],"badges":[],"createdAt":"2025-02-25 11:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6104893/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6104893/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79186094,"identity":"00bfde3e-0896-4d5c-81f0-20aef0e3931f","added_by":"auto","created_at":"2025-03-25 11:36:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37768,"visible":true,"origin":"","legend":"\u003cp\u003eADME qualified bioactive of \u003cem\u003eTriphaladi Ghana Vati\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/2c7e16ca7c0eb2b08c74be6c.jpg"},{"id":79186951,"identity":"8cc3d529-affd-4721-956e-e0b093f6de4c","added_by":"auto","created_at":"2025-03-25 11:44:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTriphaladi Ghana Vati(TVG)\u003c/em\u003eBioactive – Cataract Disease Network\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/a707c564a09fc2a38ce5fd97.jpg"},{"id":79186946,"identity":"105f37f2-0a97-4375-b6a2-7fd39af85583","added_by":"auto","created_at":"2025-03-25 11:44:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15174,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn diagram of intersection targets of TGV and Cataract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/b63dcf43471283f00b3bc573.jpg"},{"id":79186110,"identity":"a09fe911-e396-49be-823c-9cde81e6a572","added_by":"auto","created_at":"2025-03-25 11:36:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53735,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-Protein Interaction Network of intersecting targets of TGV \u0026amp; Cataract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/866ed1467300790ef2f49790.jpg"},{"id":79186953,"identity":"09c9bc2e-9ed2-434c-9dc7-194dec44a3b0","added_by":"auto","created_at":"2025-03-25 11:44:39","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":120776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO Biological Functional Analysis of Intersection target genes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/f8196c939f2e884c13942e10.jpg"},{"id":79186099,"identity":"9d63c4fc-59c3-472f-b512-95a4f80d4da8","added_by":"auto","created_at":"2025-03-25 11:36:39","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":21407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG pathway analysis of intersection target genes through DAVID database\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/12ba7b11fb630e49041dd85a.jpg"},{"id":79186097,"identity":"b76420de-4b04-4bef-b921-6b3344986229","added_by":"auto","created_at":"2025-03-25 11:36:39","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":80630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eand 9(A-C) Molecular Dynamics\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/d9df78d94c1055ec9dd6d8e3.jpg"},{"id":79186967,"identity":"aa8a1735-14b3-42c2-98a7-5d6987cc1fce","added_by":"auto","created_at":"2025-03-25 11:44:40","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":89039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A-C) Molecular Dynamics\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/9f18ca66e33360ab5fa272fc.jpg"},{"id":79186124,"identity":"0d780d84-89f9-4b5b-8ced-5f9b5185f231","added_by":"auto","created_at":"2025-03-25 11:36:40","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":105340,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A-C) Molecular Dynamics\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/60473173bfed6664575b9dac.jpg"},{"id":79188384,"identity":"787907cf-273e-4915-9191-d8c29fd2105d","added_by":"auto","created_at":"2025-03-25 12:00:39","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":37728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A-C) MMGBSA\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/eaafcaddfc9e6001b3969042.jpg"},{"id":79208322,"identity":"92dc5d41-6af4-4891-8b66-8655ead66360","added_by":"auto","created_at":"2025-03-25 16:16:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1876443,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/5dc9aa4d-5499-4267-b291-e344134da1ac.pdf"},{"id":79186092,"identity":"edcfaf18-7cf2-4f61-b4e8-8e80397a5700","added_by":"auto","created_at":"2025-03-25 11:36:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":107005,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6104893/v1/0920d01b33663c9a48b4993a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multitargeted Molecular Mechanisms of Triphaladi Ghana Vati in Cataract Management: An Integrative In-Silico Study Using Docking and Dynamics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCataract, characterized by lens opacification, is the leading cause of visual impairment globally, representing a significant public health challenge.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The primary role of the lens is to focus light onto the retina; however, with advancing age, oxidative modifications of lens proteins lead to opacity and progressive vision loss.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e˒\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Cataracts are categorized into nuclear, cortical, and subcapsular types, with age-related (senile) cataract being the most prevalent form.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Research indicates a higher prevalence of cataracts in females, often developing bilaterally, though one eye typically shows earlier symptoms.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Of the 2.2\u0026nbsp;billion individuals worldwide experiencing vision impairment, cataracts account for 94\u0026nbsp;million cases.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e While cataract surgery remains the primary treatment option, preventive measures are crucial for patients. Ayurvedic formulations, such as \u003cem\u003eTriphaladi Ghana Vati\u003c/em\u003e, are being investigated for their potential to slow the progression of immature senile cataracts, g to their rejuvenation and ocular health benefits.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAyurinformatics is a promising approach to accelerating drug discovery and development within Ayurveda.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e This article seeks to explore the bioactive compounds, molecular targets, pathways, and gene ontology associated with \u003cem\u003eTriphaladi Ghana Vati\u003c/em\u003e and cataract, thereby paving the way for further experimental studies and validation in laboratory settings.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003eScreening the active ingredients of\u003c/b\u003e \u003cb\u003eTriphaladi Ghana Vati\u003c/b\u003e\u003cb\u003e(\u003c/b\u003e\u003cb\u003eTGV\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe bioactive compounds of \u003cem\u003eTGV\u003c/em\u003e were identified through an HR-LCMS study conducted at IIT, Bombay and subsequently analyzed in PUBCHEM to retrieve their canonical SMILES. After obtaining the canonical SMILES of the phytochemicals from PUBCHEM, these compounds were further subjected to drug-likeness and ADMET profiling using SwissADME. The criteria used for screening included water solubility, gastrointestinal (GI) absorption, drug-likeness (fulfillment of at least three out of five established rules), and bioavailability score.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eScreening the active ingredient targets of\u003c/b\u003e \u003cb\u003eTGV\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo determine the targets of these compounds, they were analyzed using the \"Find My Compound's Target\" tool available in Binding DB.⁹ A similarity score threshold of 0.7 was applied for the analysis. The corresponding gene names associated with these target proteins were then identified through UniProt, ensuring the selection of entries specific to the Homo sapiens species. The names of the proteins and their respective genes were extracted from UniProt for further evaluation.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection of target genes related to cataract\u003c/h2\u003e \u003cp\u003eAll identified targets were analyzed using DisGeNet\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e to explore potential associations with various diseases, with a particular emphasis on cataract. By employing the keyword \"Cataract,\" 878 cataract-related targets were retrieved. To illustrate the overlapping targets between \u003cem\u003eTGV\u003c/em\u003e and cataract, a Venn diagram was constructed, providing a clear visualization of the intersections.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction of the bioactive-target-disease network\u003c/h3\u003e\n\u003cp\u003eA visually comprehensive network was developed using Cytoscape 3.10.2\u003csup\u003e13\u003c/sup\u003e, a Java-based software platform, to map the interactions among the bioactives of the herb (\u003cem\u003eTGV\u003c/em\u003e), the intersecting targets, and the disease (cataract).\u003c/p\u003e\n\u003ch3\u003eConstruction of the protein-protein interaction network\u003c/h3\u003e\n\u003cp\u003eThe protein-protein interaction (PPI) network was constructed using the STRING\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e database. The overlapping targets of \u003cem\u003eTGV\u003c/em\u003e and cataract were input into the STRING platform to generate the PPI network. For data reliability, a minimum interaction score of 0.4 was applied, with Homo sapiens selected as the species of focus. Proteins not connected to the network were excluded from the final visualization.\u003c/p\u003e\n\u003ch3\u003eGene ontology (GO) and the KYOTO encyclopedia of genes and genomes (KEGG) pathway enrichment\u003c/h3\u003e\n\u003cp\u003eTo further investigate the pathways associated with the disease, the shared targets of \u003cem\u003eTGV\u003c/em\u003e and cataract were analyzed using the DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/)\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003csup\u003e15\u003c/sup\u003e This online biological knowledgebase and analytical tool facilitates the extraction of various biological insights, including gene functional classification, functional annotation, and pathway enrichment. GO enrichment analysis spans three categories: biological process (BP), cellular component (CC), and molecular function (MF). Additionally, KEGG serves as a repository for understanding molecular interactions and elucidating established metabolic pathways. The analysis was conducted with a focus on \u003cem\u003eHomo sapiens\u003c/em\u003e to perform GO\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and KEGG\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e pathway enrichment analyses.\u003c/p\u003e\n\u003ch3\u003eMolecular docking and molecular dynamics simulation\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProtein Preparation\u003c/h2\u003e \u003cp\u003eThe crystallographic structures of the target proteins were obtained from the Protein Data Bank (PDB). The receptor-binding domains (RBDs) were extracted from these PDB structures and prepared using the \"Protein Preparation Wizard\" tool of the Schr\u0026ouml;dinger suite\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e (Schr\u0026ouml;dinger Release 2021-4: Protein Preparation Wizard; Epik, Schr\u0026ouml;dinger, LLC, New York, NY, 2021). The preparation process included removing water molecules and cofactors, correcting mislabeled elements, adding hydrogen atoms, assigning bond orders, optimizing hydrogen bonding, and performing restrained energy minimization using the OPLS4 force field. These prepared protein structures were subsequently used for grid generation through the \"Receptor Grid Generation\" panel in the Glide module of the Schr\u0026ouml;dinger suite.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLigand preparation\u003c/h3\u003e\n\u003cp\u003eThe ligands used in this study were prepared using the Schr\u0026ouml;dinger suite, with the PubChem database serving as the source for the phytochemical 3D sdf. structures. A comprehensive search for relevant phytochemicals was initially performed in the PubChem database, and the 3D sdf. structures of the selected compounds were downloaded. These structures were then imported into the Schr\u0026ouml;dinger suite and processed using the LigPrep module\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e (Schr\u0026ouml;dinger Release \u0026euro; 2021-4: LigPrep, Schr\u0026ouml;dinger, LLC, New York, NY \u0026euro;, 2021). The preparation process involved optimizing the phytochemicals at a pH of 7\u0026thinsp;\u0026plusmn;\u0026thinsp;2 using the Epik tool. Subsequently, the optimized structures underwent energy minimization using the OPLS4 force field to ensure precise molecular conformations for the ensuing docking studies. The resulting ligand library was then employed for molecular docking and dynamics simulations to evaluate its potential as modulators of the target proteins.\u003c/p\u003e\n\u003ch3\u003eMolecular Docking\u003c/h3\u003e\n\u003cp\u003eMolecular docking studies were conducted using the Glide module from the Schr\u0026ouml;dinger Suite\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e (Schr\u0026ouml;dinger Release \u0026euro; 2021-4: Glide, Schr\u0026ouml;dinger, LLC, New York, NY \u0026euro;, 2021). The standard precision (SP) mode was employed for ligand docking to evaluate the binding interactions between the ligand and the target protein. Flexible ligand docking was performed considering the conformational flexibility of the ligand during the docking process, which allowed for the exploration of various potential binding modes. The SP mode\u0026rsquo;s default settings were utilized, striking a balance between computational efficiency and accuracy. The scoring function of Glide was applied to rank ligand poses based on their predicted binding affinity to the target protein. The docking results were analyzed to identify the most favourable binding conformations and interactions.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamics Simulations\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulations were performed using the Desmond module within the Schr\u0026ouml;dinger Suite\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (Schr\u0026ouml;dinger Release \u0026euro; 2021-4: Desmond, Schr\u0026ouml;dinger, LLC, New York, NY \u0026euro;, 2021). The protein-ligand complex, prepared from the docking studies, served as the initial structure for the simulations. The system was solvated in an orthorhombic simulation box containing explicit water molecules, modeled using the TIP3P water model. Appropriate counterions (Na\u0026thinsp;+\u0026thinsp;or Cl-) were added to neutralize the system, and additional ions were introduced to achieve an ionic strength of 0.15 M. The system was then minimized and equilibrated in several stages to resolve any steric clashes or unfavorable interactions.\u003c/p\u003e \u003cp\u003eThe MD simulations were carried out under periodic boundary conditions using the OPLS4 force field. The simulation parameters included a temperature of 300 K, controlled by a Nos\u0026eacute;-Hoover thermostat, and a pressure of 1 atm, maintained by a Martyna-Tobias-Klein barostat. The total simulation time was 100 ns, with a timestep of 2 fs, and the trajectory data were recorded at 10 ps intervals. The trajectory data were analyzed to evaluate the stability of the protein-ligand complex, focusing on parameters such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), and hydrogen bonding interactions throughout the simulation. The binding affinity in the molecular simulations was calculated using the thermal_mmgbsa scripts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTGV-Bioactives\u003c/h2\u003e \u003cp\u003eA total of 100 bioactives were obtained from HR-LCMS(High Resolution- Liquid Chromatography-Mass Spectroscopy). Among these compounds were 4-Chloro-3,5-dimethoxybenzyl alcohol, 4-Amino-2-methylenebutanoic acid, D-Pipecolic acid, L-trans-5-Hydroxy-2-piperidinecarboxylic acid, (Z)-5-[(5-Methyl-2-thienyl)methylene]-2(5H)-furanone, (+)-Chebulic acid, Medicanine, L-2-Aminobutyric acid, Lentiginosine, Melosatin B(12), Neuraminic acid, WIN56291, DL-Dopa, Metyrosine, Lentiginosine, Bakers yeast extract, 1-(1-Pyrrolidinyl)-2-propanone, Lysyl-Asparagine, N-Isovalerylglycine methyl ester, Hypoxylone, D-Tryptophan, Cis-Caffeoyl tartaric acid, 3beta,6beta-Dihydroxynortropane, Sarmentosin, Ketotifen(4), N-(1-Deoxy-1-fructosyl)phenylalanine, Irenolone, 3-Hydroxy-carbofuran, N-Isovalerylglycine methyl ester, Laccarin, 2-Methoxy-3-(1-methylpropyl)pyrazine, Histidinyl-Arginine, Ethyl N-ethylanthranilate(6), 4-Hydroxybutanoic acid, Promacyl(1), HC Blue No. 2, Ruspolinone(5), N-n-Hexanoylglycine methyl ester, 4,4'-Methylene-bis-(2-chloroaniline), (S)-Edulinine, (-)-Hygroline, Licoagroside B, Convolamine(7), γ-Glutamyl-β-cyanoalanine, Capsaicin(8), Coronatine, Tyrosyl-Aspartate, Variotin, Coixinden B, Codeine N-oxide, N-Ethylmaleimide-S-glutathione, Portulacaxanthin III, Valclavam, Muricinine, (S)-Edulinine, HMR1556, 3-(Acetyloxy)-9-mercaptoandrosta-3,5-diene-11,17-dione, Americine, (1beta,2alpha,3alpha)-1,2,3,24-Tetrahydroxy-12-oleanen-28-oic acid, Schleicherastatin 6, Glycyrrhizin, Portulacaxanthin III, C16 Sphinganine, Dinoseb acetate, Nitramine, Promazine sulfoxide(31), Monomenthyl succinate(5), Methyl 2-furoate, 3,4-Heptanedione(2), 1-(5-Methyl-3-pyridinyl)-1-decanone, Nitramine.\u003c/p\u003e \u003cp\u003eThe bioactive structures were sourced from PUBCHEM, with 72 of 100 bioactive structures successfully retrieved. These 72 structures were then analyzed using the Swiss ADME, which assessed various criteria including physicochemical properties, water solubility, gastrointestinal absorption, and drug likeness based on Lipinski, Ghose, Veber, Egan, and Muegge rules. The bioavailability score for these compounds was 0.55. Ultimately, only 22 compounds met the ADME criteria, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBioactives and the Target\u003c/h2\u003e \u003cp\u003eIn our analysis, we examined 22 phytochemicals with a similarity index of 0.7, identifying 11 compounds that interacted with 81 targets. The compound with the highest number of interactions was Promazine sulfoxide, which engaged with 31 targets, followed by Melosatin B with 11 targets, Capsaicin with 8 targets, Convolamine with 7 targets, Ethyl N-ethylanthranilate with 5 targets, Ruspolinone with 5 targets, Monomenthyl succinate with 5 targets, Ketotifen with 4 targets, 3,4-Heptanedione with 2 targets, 1-(5-Methyl-3-pyridinyl)-1-decanone with 2 targets, and Promacyl with 1 target. Detailed information about the bioactive compounds and their associated targets is provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBioactive and their target genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBioactive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget/Gene\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelosatin B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHTR1A, HTR1B, HTR1D, ADRA2B, MAOA, MAOB, CASP3, CASP7, DRD3, DRD4, DRD2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKetotifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDRD2, DRD3, GSR, HRH1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthyl N-ethylanthranilate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADRA1A, ADRA1B, ADRA1D, CDC25A, CDC25C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePromacyl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACHE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRuspolinone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCA1, CA2, CTNNB1, KDM2B, TAAR1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConvolamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHTR3A, ABCB1, SLC29A1, CHRM1, CHRM2, CHRM4, ST14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapsaicin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACHE, BACE1, EPHX2, CNR1, CNR2, HDAC8, KDM1A, TRPV1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePromazine sulfoxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHTR1A, HTR2A, HTR2B, HTR2C, HTR3A, HTR6,\u003c/p\u003e \u003cp\u003eHTR7, ACHE, ALDH1A1, AOX1, ADRA2A, ADRA2C, ABCB1, BCHE, CYP2D6, DRD1, DRD5\u003c/p\u003e \u003cp\u003eDRD2, GSTP1, HRH3, HRH4, MALT1, CHRM1, CHRM2, CHRM3, CHRM4, CHRM5, KCNH2, PTGS1, SIGMAR1, SLC6A4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,4-Heptanedione\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCES2, CES1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-(5-Methyl-3-pyridinyl)-1-decanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHDAC1, HDAC8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonomenthyl succinate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSD11B1, CYP19A1, GPBAR1, OPRM1, PPM1B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTGV-\u003c/b\u003e \u003cb\u003eEyeDisease other diseases network\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe 71 targets/genes were queried in DisGeNet and a total of 15,151 results were obtained.Wide range of diseases, including Cataract, Cornelia de Lange Syndrome, Cardiomegaly, Hypertension, Intellectual Disability, Autism, Anxiety, Glaucoma, Conductive and Sensorineural Hearing Loss, Atrial and Ventricular Septal Defects, Neuroblastoma, Seizures, Sleep Disorders, Strabismus, Cleft Palate, Hypogonadism, Polycystic Kidney Disease, Microcephaly, Micrognathism, Myopia, Hirsutism, Mood Swings, Premature Birth, Short Stature, Delayed Puberty, Obsessive-Compulsive Behaviour, Attention Deficit Hyperactivity Disorder (ADHD), Cerebral Ventriculomegaly, Renal Insufficiency, X-linked Inheritance, Malignant Neoplasms (including breast, liver, and bladder carcinomas), Neurodegenerative Disorders, and several other congenital, developmental, and cancer-related disorders.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTGV\u003c/b\u003e \u003cb\u003e- Cataract Network\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA Venn diagram analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed six common targets shared between \u003cem\u003eTGV\u003c/em\u003e and Cataract. These targets are HDAC8, ALDH1A1, GSTP1, ABCB1, CYP19A1, and CASP3. Five bioactive compounds were identified as interacting with these targets. Specifically, 1-(5-Methyl-3-pyridinyl)-1-decanone interacts with HDAC8, Promazine sulfoxide interacts with ALDH1A1 and GSTP1, Convolamine interacts with ABCB1, Monomenthyl succinate interacts with CYP19A1, and melassin B interacts with CASP3. Notably, all these targets are associated with cataract, with particular emphasis on CASP3, which plays a key role in cataract development. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein interaction (PPI) Network\u003c/h2\u003e \u003cp\u003eThe current study analyzed the protein network of all intersecting genes related to cataract using the STRING database. Functional associations between protein nodes were represented by lines of varying colors, each corresponding to different types of evidence. The confidence in these associations was indicated by the distance between the nodes, which was calculated using the Bayesian scoring system. The network consists of 11 nodes connected by 32 edges, with an average node degree of 5.82 and an average local clustering coefficient of 0.604. The expected number of edges was 7, and the PPI enrichment p-value was 2.06e-12. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG pathway enrichment\u003c/h2\u003e \u003cp\u003eTo explore the mechanism of \u003cem\u003eTGV\u003c/em\u003e in cataract, overlapping target genes were analyzed using the DAVID database for GO and KEGG pathway analysis. The results revealed enrichment in 20 biological processes (BP), 0 cellular components (CC), and 20 molecular functions (MF). The key biological processes included Benzaldehyde dehydrogenase activity, Nitric oxide binding, Androgen binding, Protein decrotonylase activity, and Glyceraldehyde-3-phosphate dehydrogenase activity. Molecular functions involved Nitric oxide homeostasis, Estradiol secretion, Detoxification, and Response to toxic substances. No significant cellular components were identified.\u003c/p\u003e \u003cp\u003eKEGG enrichment analysis revealed three significant pathways: Platinum drug resistance, MicroRNAs in cancer, and Viral carcinogenesis. These pathways are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, with the Y-axis representing the pathway names and the X-axis indicating the fold enrichment ratio between target genes and background genes. The colour gradient from blue (low enrichment) to red (high enrichment) highlights the significance of each pathway.\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking\u003c/h2\u003e \u003cp\u003eIn this investigation, molecular docking was conducted to assess the binding interactions of several ligands with diverse protein targets associated with cataract. Among the analyzed complexes, gene ALDH1A1 (aldehyde dehydrogenase 1A1) with 4WPN, 7UM9, 5AC2, 5L2N, 6DUM, and 4WB9. Promazine sulfoxide demonstrated with- 4WPN one pi-cation PHE171 with nitrogen, several non-bonding interactions involving TYR297, GLY294, HIP293, TYR457, GLY458, VAL460, GLU269, PHE468, ASP122, ASN121, TRP178, MET175, VAL174, CYS302 and CYS303, ILE304, ALA305 and docking score was obtained \u0026minus;\u0026thinsp;8.339. 7UM9 One H-bond with TRY297 and one pi-cation with TRY297 others several non-bonding interactions ASN121, ASP122, GLY125, PHE171, VAL174, MET175, HIE293, GLY294, CYS302, CYS303, ILE304, TYR457, GLY458 and VAL460 and docking score was \u0026minus;\u0026thinsp;8.086. 5AC2 H- bond GLY294 and, others several non-bonding interactions HIP293, TYR297, CYS302, ILE304, CYS303, CYS302, ASN121, ASP122, PHE171, VAL174, MET175, TRP178, TYR457, GLY458, VAL460, PHE466 and docking score was \u0026minus;\u0026thinsp;7.560. 5L2N all non-bonding interactions ASN121, CYS302, ILE304, PHE171, VAL174, PHE290, HIS293, GLY294, TYR297, TYR457, GLY458 and VAL460 and docking score was \u0026minus;\u0026thinsp;6.642. 6DUM One H-bond with SER247, five salt bridge with GLU349, CL605, CL610 and CL611, One pi-pi stacking TRP169 others non-bonding interactions IlE166, GLY226, GLN350, LYS193, ALA195, GLU196, GLN197, PHE244, THR245, GLY246 AND VAL250 and docking score was obtained \u0026minus;\u0026thinsp;6.468. 4WB9 two pi-pi stacking with TRP169, one H- bond withSER247, two salt bridge with GLU349, and others was non-bonding interactions PRO168, ALA195, GLU196, GLN197, PHE244, GLY246, GLU249, VAL250, GLN350, LYS353, PHE402 and docking score was obtained 6.090. Gene HDAC8(Histone Deacetylase 8) with 1T69 docking score was \u0026minus;\u0026thinsp;6.254 and bioactive 1-(5-Methyl-3-pyridinyl)-1-decanone one pi-pi stacking with PHE152, H-bond with TYR306, one metal coordination with ZN378 and others non-bonding interactions ASP101, PHE208, MET274, ASP178, HIS180, HIS143, HIE142, TRP141, GLY151, CYS153, TYR154, LEU31, ALA32, LYS33, 1LE34, ARG37, GLY303, GLY304, ASP267.\u003c/p\u003e \u003cp\u003eGene ABCB1 with 6Y6H docking score was \u0026minus;\u0026thinsp;5.583 and bioactive Convolamine one salt bridge with GLU117 and others non-bonding interactions ARG41, GLY40, LEU39, LYS37, VAL47, ALA60, ILE94, LEU110, GLU111, TYR112, ALA113, ALA114, GLY115, GLY116, GLU117, LYS160, GLN162, ASN163, LEU165 AND VAL178. Gene CYP19A1 with 3S79 and 5JKV bioactive with monomethyl succinate docking score was obtained respectively 5.645 and 5.299. 3S79 all non- bonding interactions ARG115, PHE134, ILE133, PHE221, TRP224, ILE305, ALA306, ASN309, THR310, VAL369, VAL370, LEU372, VAL373, MET374, LEU477, SER478 and HEM600. 5JKV one salt bridge with HEM601 and others non-bonding interactions ARG115, ILE133, PHE134, PHE221, TRP224, ALA306, ASH309, THR310, VAL369, VAL370, LEU372, VAL373, MET374, LEU477 and SER478.\u003c/p\u003e \u003cp\u003eGene CASP3 with 1NMQ and 1NMS bioactive with Melosatin B and docking score was obtained respectively \u0026minus;\u0026thinsp;5.530 and \u0026minus;\u0026thinsp;5.383. 1NMQ three H-bond with ARG64, SER120 and GLN161, two pi-pi stacking with HIE and PHE256, others non-bonding interactions THR62, HIE121, GLY122, ALA162, CYS163, LEU168, TYR204, SER205, TRP206 and ARG207. 1NMS four H-bond with ARG64, SER120, GLN161 and ARG207, three pi-pi stacking HIE121, TYR204 and TRP206 and others non-bonding interactions GLY122, ALA 162, CYS163, SER205, ARG207, ASN208, SER213, TRP214, SER249, PHE250, SER251 and PHE256. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eL\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene- Bioactive and their Docking score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene-Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniprot-ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBioactive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePDB_ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePCID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDocking-Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKey interactions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALDH1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP00352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromazine sulfoxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4WPN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e547559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-8.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOne pi-cation PHE171 with nitrogen, several non-bonding interaction involving TYR297, GLY294, HIP293, TYR457, GLY458, VAL460, GLU269, PHE468, ASP122, ASN121, TRP178, MET175, VAL174, CYS302 and CYS303, ILE304, ALA305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALDH1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP00352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromazine sulfoxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7UM9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e547559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-8.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOne H-bond with TRY297 and one pi-cation with TRY297 others several non bonding interactions ASN121, ASP122, GLY125, PHE171, VAL174, MET175, HIE293, GLY294, CYS302, CYS303, ILE304, TYR457, GLY458 and VAL460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALDH1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP00352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromazine sulfoxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5AC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e547559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-7.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOne H- bond GLY294 and, others several non-bonding interactions HIP293, TYR297, CYS302, ILE304, CYS303, CYS302, ASN121, ASP122, PHE171, VAL174, MET175, TRP178, TYR457, GLY458, VAL460, PHE466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALDH1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP00352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromazine sulfoxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5L2N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e547559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAll non-bonding interactions ASN121, CYS302, ILE304, PHE171, VAL174, PHE290, HIS293, GLY294, TYR297, TYR457, GLY458 and VAL460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALDH1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP00352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromazine sulfoxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6DUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e547559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOne H-bond with SER247, five salt bridge with GLU349, CL605, CL610 and CL611, One pi-pi stacking TRP169 others non-bonding interactions IlE166, GLY226, GLN350, LYS193, ALA195, GLU196, GLN197, PHE244, THR245, GLY246 AND VAL250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALDH1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP00352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromazine sulfoxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4WB9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e547559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTwo pi-pi stacking with TRP169, one H- bond with SER247, two salt bridge with GLU349, and others was non-bonding interactions PRO168, ALA195, GLU196, GLN197, PHE244, GLY246, GLU249, VAL250, GLN350, LYS353, PHE402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHDAC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9BY41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1-(5-Methyl-3-pyridinyl)-1-decanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1T69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71347088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOne pi-pi stacking with PHE152, H-bond with TYR306, one metal coordination with ZN378 and others non-bonding interactions ASP101, PHE208, MET274, ASP178, HIS180, HIS143, HIE142, TRP141, GLY151, CYS153, TYR154, LEU31, ALA32, LYS33, 1LE34, ARG37, GLY303, GLY304, ASP267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABCB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP08183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvolamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6Y6H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e420422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOne salt bridge with GLU117 and others non-bonding interactions ARG41, GLY40, LEU39, LYS37, VAL47, ALA60, ILE94, LEU110, GLU111, TYR112, ALA113, ALA114, GLY115, GLY116, GLU117, LYS160, GLN162, ASN163, LEU165 AND VAL178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP19A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP11511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonomenthyl succinate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3S79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10199004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAll non- bonding interactions ARG115, PHE134, ILE133, PHE221, TRP224, ILE305, ALA306, ASN309, THR310, VAL369, VAL370, LEU372, VAL373, MET374, LEU477, SER478 and HEM600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP19A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP11511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonomenthyl succinate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5JKV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10199004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOne salt bridge with HEM601 and others non-bonding interactions ARG115, ILE133, PHE134, PHE221, TRP224, ALA306, ASH309, THR310, VAL369, VAL370, LEU372, VAL373, MET374, LEU477 and SER478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP42574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMelosatin B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1NMQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e188038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eThree H-bond with ARG64, SER120 and GLN161, two pi-pi stacking with HIE and PHE256, others non-bonding interactions THR62, HIE121, GLY122, ALA162, CYS163, LEU168, TYR204, SER205, TRP206 and ARG207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP42574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMelosatin B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1NMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e188038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFour H-bond with ARG64, SER120, GLN161 and ARG207, three pi-pi stacking HIE121, TYR204 and TRP206 and others non-bonding interactions GLY122, ALA 162, CYS163, SER205, ARG207, ASN208, SER213, TRP214, SER249, PHE250, SER251 and PHE256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMOLECULAR DYNAMIC SIMULATIONS\u003c/h2\u003e \u003cp\u003eSelected phytochemicals from TGV, based on their highest docking scores against respective targets, were subjected to molecular dynamics simulations for 100 ns. The protein RMSD was used to assess the stability of the protein structures during thermodynamic movement. In this study, the RMSD values of cataract-related proteins were analysed for their interaction with TGV phytochemicals. One such phytochemical, Promazine sulfoxide, was studied in complex with three aldehyde dehydrogenase proteins (PDB: 4PWN) through molecular dynamics simulation. The results showed similar RMSD trends for the two protein-ligand complexes.\u003c/p\u003e \u003cp\u003eFor 4PWN and 6DUM with 547559, the RMSD remained stable below 6.4 \u0026Aring;, while 5AC2 with 547559 showed stability below 5.4 \u0026Aring;. The 4PWN-Promazine sulfoxide(547559) complex demonstrated significant hydrophobic pi-pi stacking interactions with A: TYR297 (41%). The highest interaction fraction was 0.8 for the hydrophobic, ionic, and water bridge interactions. The protein\u0026rsquo;s secondary structure consisted of 34.84% helices, 16.77% strands, and 51.61% total secondary structure elements (SSE) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-9A). The MMGBSA binding free energy was \u0026minus;\u0026thinsp;59.28, and the Prime energy was \u0026minus;\u0026thinsp;14827.07 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMMGBSA for Protein-Ligand complex\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein-Ligand complex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrime Energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMMGBSA dG Bind\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMMGBSA dG Bind (NS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMMGBSA dG Bind (NS) Coulomb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMMGBSA dG Bind (NS) Covalent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMMGBSA dG Bind (NS) vdW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4WPN_547559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-14827.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-59.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-60.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-44.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6DUM_547559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15088.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-47.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-49.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-20.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-40.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5AC2_547559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-15504.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-62.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-63.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-14.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-38.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe 6DUM-Promazine sulfoxide (547559) complex showed notable interactions, including two water-bridged hydrogen bonds with A: CYS303 (78%, 78%) and A: ILE304 (76%, 76%). The strongest interaction fraction of 0.8 was observed for the hydrophobic and water bridges. The secondary structure of the protein was 34.38% helices, 16.08% strands, and 50.46% total SSE (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB-9B). The MMGBSA binding free energy was \u0026minus;\u0026thinsp;47.64, and the Prime energy was \u0026minus;\u0026thinsp;15088.01 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 5AC2-Promazine sulfoxide (547559) complex exhibited significant interactions, including a hydrogen bond with A: TYR297 (91%) and two pi-pi stacking interactions with A: TRP178 (55% and 35%). This complex had the highest interaction fraction of 1.2 for hydrogen bonds, hydrophobic interactions, and water bridges. The protein\u0026rsquo;s secondary structure comprised 34.22% helices, 16.66% strands, and 50.88% total SSE (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-9C). The MMGBSA binding free energy was \u0026minus;\u0026thinsp;62.71, while the Prime energy was \u0026minus;\u0026thinsp;15504.48 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eTriphaladi Ghana Vati\u003c/em\u003e is known for its versatile therapeutic uses in various clinical conditions, specifically eye disorders. Documented ethnopharmacological use includes ophthalmia, conjunctivitis, swelling of eye, diabetes, digestive and respiratory illness,\u003csup\u003e22\u0026nbsp;\u003c/sup\u003efever, edema, arthritis,\u003csup\u003e23,24\u003c/sup\u003eskin diseases,\u003csup\u003e25\u003c/sup\u003edisorders of male\u003csup\u003e26\u003c/sup\u003e and female reproductive system.\u003csup\u003e27-30\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe reported pharmacological activities of TGV are anti-cataractogenic effect, immunomodulatory,\u003csup\u003e31-34\u003c/sup\u003e anti-cancer,\u003csup\u003e35-42\u003c/sup\u003e anti-diabetic and hypoglycemic,\u003csup\u003e43-45\u0026nbsp;\u003c/sup\u003eanti-fibrinolytic,\u003csup\u003e46-52\u003c/sup\u003e anti-inflammatory,\u003csup\u003e53-55\u0026nbsp;\u003c/sup\u003eanti-bacterial,\u003csup\u003e56-58\u003c/sup\u003e anti-fungal,\u003csup\u003e59\u003c/sup\u003e anti-oxidant,\u003csup\u003e60-62\u0026nbsp;\u003c/sup\u003eanti-cataract and free radical scavenger,\u003csup\u003e63-66\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThis study investigates the binding interactions of Promazine sulfoxide with key protein targets, including ALDH1A1, using molecular docking and molecular dynamics (MD) simulations. The results highlight the significant potential of Promazine sulfoxide in modulating the protein targets associated with cataract and related conditions.\u003c/p\u003e\n\u003cp\u003eIn the molecular docking analysis, Promazine sulfoxide demonstrated notable binding interactions with three ALDH1A1 protein structures (PDB: 4WPN, 6DUM, and 5AC2). The highest docking score was observed for the 4WPN complex, with a score of -8.339. The interactions of 4WPN suggest that Promazine sulfoxide forms a stable complex with ALDH1A1, contributing to its potential therapeutic effects. Additionally, Promazine sulfoxide with the 6DUM protein exhibited a docking score of -6.468. These docking results indicate that Promazine sulfoxide interacts with ALDH1A1 through a variety of interaction types, including hydrogen bonds, pi-pi stacking, and hydrophobic interactions, contributing to its stability and affinity.\u003c/p\u003e\n\u003cp\u003eThe RMSD values for the 4WPN and 6DUM complexes with Promazine sulfoxide remained below 6.4 \u0026Aring;, indicating relatively stable interactions. The 5AC2 complex with Promazine sulfoxide exhibited even greater stability, with RMSD values below 5.4 \u0026Aring;, suggesting a more stable protein-ligand complex. The MMGBSA binding energy for 4WPN was -59.28 kcal/mol, and the Prime energy was -14827.07, reflecting a strong binding affinity. The MMGBSA binding free energy for 6DUM was -47.64 kcal/mol, with a Prime energy of -15088.01, indicating stable binding. The MMGBSA binding energy was -62.71 kcal/mol, and the Prime energy was -15504.48, indicating a very stable and energetically favorable binding.\u003c/p\u003e\n\u003cp\u003eOverall, the findings from both the docking and molecular dynamics simulations suggest that a phytochemical of \u003cem\u003eTGV\u003c/em\u003e, i.e. Promazine sulfoxide forms stable, energetically favorable complexes with ALDH1A1 and other related protein targets. The strong binding affinity and stability of these complexes, especially with 5AC2, indicate the potential of Promazine sulfoxide as a promising inhibitor of aldehyde dehydrogenase and other relevant targets in cataract treatment and other diseases.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study employed HRLMS-based phytochemical identification followed by Network Pharmacology to investigate the active compounds \u003cem\u003ein Triphaladi Ghana Vati\u003c/em\u003e and their potential multitarget effects in the treatment of cataract. The findings suggest that \u003cem\u003eTGV\u0026apos;s\u003c/em\u003e therapeutic mechanisms may be mediated through the aldehyde dehydrogenase signaling pathways. Molecular docking and dynamic simulations revealed interactions with several bioactive compounds. Notably, Promazine sulfoxide exhibited robust, stable interactions with cataract-related protein targets, as evidenced by RMSD values and MMGBSA binding free energies. This compound emerged as a promising therapeutic candidate with potential applications in managing oxidative stress, which is implicated in cataract formation. However, further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e validation is essential to substantiate its efficacy and therapeutic potential in cataract prevention and treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContribution\u0026nbsp;\u003c/strong\u003e– MR and PKG planned, supervised, and edited the study and manuscript, PG, RS and AT performed the computational work, draft and constructed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e – We acknowledge all freely accessed databases, All India Institute of Ayurveda Library for academic support, and Ayurinformatics laboratory for access to all licensed software. We acknowledge Dr Vinod Devaraji, Schrodinger for technical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e – The authors declare no conflict of interest regarding the publication of this article. All authors have reviewed and approved the manuscript and affirm that there are no other conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial Intelligence utility declaration\u003c/strong\u003e – No AI tool is used for Manuscript writing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJinC YK. Cochrane Library Cochrane Database of Systematic Reviews Dierent-sized incisions for phacoemulsification in age-related cataract (Review). Published online 2017. doi:10.1002/14651858.CD010510.pub2\u003c/li\u003e\n\u003cli\u003eSharma KK, Santhoshkumar P. Lens aging: effects of crystallins. \u003cem\u003eBiochim Biophys Acta\u003c/em\u003e. 2009;1790(10):1095-1108. doi:10.1016/J.BBAGEN.2009.05.008\u003c/li\u003e\n\u003cli\u003eCataracts - Symptoms and causes - Mayo Clinic. Accessed September 9, 2022. https://www.mayoclinic.org/diseases-conditions/cataracts/symptoms-causes/syc-20353790\u003c/li\u003e\n\u003cli\u003eTypes of Cataracts \u0026amp; Their Treatments: Posterior Subcapsular, Nuclear Sclerosis, Cortical. Accessed September 9, 2022. https://www.heartoftexaseye.com/blog/cataract-types-and-treatments/\u003c/li\u003e\n\u003cli\u003eZetterberg M, Celojevic D. Gender and cataract--the role of estrogen. \u003cem\u003eCurr Eye Res\u003c/em\u003e. 2015;40(2):176-190. doi:10.3109/02713683.2014.898774\u003c/li\u003e\n\u003cli\u003eBourne RRA, Steinmetz JD, Saylan M, et al. 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S19, 2005.\u003c/li\u003e\n\u003cli\u003eMehrotra S, Singh VK, Agarwal SS, Maurya R, and Srimal RC, \u0026ldquo;Antilymphoproliferative activity of ethanolic extract of Boerhaavia diffusa roots,\u0026rdquo; Experimental and Molecular Pathology, vol. 72, no. 3, pp. 236\u0026ndash;242, 2002.\u003c/li\u003e\n\u003cli\u003eChopra M, Srivastava R, Saluja D, and Dwarakanath BS, \u0026ldquo;Inhibition of human cervical cancer cell growth by ethanolic extract of Boerhaavia diffusa Linn. (punarnava) root,\u0026rdquo; Evidencebased Complementary and Alternative Medicine, vol. 2011, Article ID 427031, 13 pages, 2011.\u003c/li\u003e\n\u003cli\u003eSreeja S and Sreeja S, \u0026ldquo;An in vitro study on antiproliferative and antiestrogenic effects of Boerhaavia diffusa L. extracts,\u0026rdquo; Journal of Ethnopharmacology, vol. 126, no. 2, pp. 221\u0026ndash;225, 2009.\u003c/li\u003e\n\u003cli\u003eLeyon PV, Lini CC, and Kuttan G, \u0026ldquo;Inhibitory effect of Boerhaavia diffusa on experimental metastasis by B16F10 melanoma in C57BL/6 mice,\u0026rdquo; Life Sciences, vol. 76, no. 12, pp. 1339\u0026ndash;1349, 2005.\u003c/li\u003e\n\u003cli\u003eManu KA and Kuttan G, \u0026ldquo;Effect of punarnavine, an alkaloid from Boerhaavia diffusa, on cell-mediated immune responses and TIMP-1 in B16F-10 metastatic melanoma-bearing mice,\u0026rdquo; Immunopharmacology and Immunotoxicology, vol. 29, no. 3-4, pp. 569\u0026ndash;586, 2007.\u003c/li\u003e\n\u003cli\u003eManu KA and Kuttan G, \u0026ldquo;Anti-metastatic potential of Punarnavine, an alkaloid from Boerhaavia diffusa Linn,\u0026rdquo; Immunobiology, vol. 214, no. 4, pp. 245\u0026ndash;255, 2009.\u003c/li\u003e\n\u003cli\u003eManu KA, Leyon PV, and Kuttan G, \u0026ldquo;Studies on the protective effects of Boerhaavia diffusa L. against gamma radiationinduced damage in mice,\u0026rdquo; Integrative Cancer Therapies, vol. 6, no. 4, pp. 381\u0026ndash;388, 2007.\u003c/li\u003e\n\u003cli\u003eChude MA, Orisakwe OE, Afonne OJ, Gamaniel KS, Vongtau OH, and Obi E, \u0026ldquo;Hypoglycaemic effect of the aqueous extract of Boerhavia diffusa leaves,\u0026rdquo; Indian Journal of Pharmacology, vol. 33, no. 3, pp. 215\u0026ndash;216, 2001.\u003c/li\u003e\n\u003cli\u003ePari L and Satheesh MA, \u0026ldquo;Antidiabetic effect of Boerhavia diffusa: effect on serum and tissue lipids in experimental diabetes,\u0026rdquo; Journal of Medicinal Food, vol. 7, no. 4, pp. 472\u0026ndash;476, 2004.\u003c/li\u003e\n\u003cli\u003eSatheesh MA and Pari L, \u0026ldquo;Antioxidant effect of Boerhavia diffusa L. in tissues of alloxan induced diabetic rats,\u0026rdquo; Indian Journal of Experimental Biology, vol. 42, no. 10, pp. 989\u0026ndash;992, 2004.\u003c/li\u003e\n\u003cli\u003eRao KN, Boini KM, and Srinivas R, \u0026ldquo;Effect of chronic administration of BD L. leaf extract on experimental diabetes in rats,\u0026rdquo; Tropical Journal of Pharmaceutical Research, vol. 3, pp. 305\u0026ndash;309, 2004.\u003c/li\u003e\n\u003cli\u003eGulati R, Agarwal S, and Agarwal SS, \u0026ldquo;Hepatoprotective activity of Boerhaavia diffusa linn. against country made liquor induced hepatotoxicity in albino rats fed on controlled calorie diet,\u0026rdquo; Indian Journal of Pharmacology, vol. 23, pp. 264\u0026ndash;267, 1991.\u003c/li\u003e\n\u003cli\u003eSrivastava K, Srivastava GN, Rizvi NS, and Dasgupta PK, \u0026ldquo;Effect of Boerhaavia diffusa on IUD-induced bleeding in rhesus monkeys,\u0026rdquo; Contraceptive Delivery Systems, vol. 2, pp. 157\u0026ndash; 161, 1981.\u003c/li\u003e\n\u003cli\u003eSrivastava K and Dasgupta PK, \u0026ldquo;NAD-dependent-15- hydroxy-prostagtandin dehydrogenase activity in the endometrium of IUD- and Boerhaavia diffusa Linn.treated female rhesus monkeys,\u0026rdquo; Malaysian Journal of Reproductive Health, vol. 4, pp. 1\u0026ndash;5, 1986.\u003c/li\u003e\n\u003cli\u003eBarthwal M, Dasgupta PK, and Srivastava K, \u0026ldquo;Vascular and tissue plasminogen activator activity in IUD-fitted female rhesus monkeys (Macaca mulatta),\u0026rdquo; Singapore Journal of Obstetrics \u0026amp; Gynecology, vol. 19, pp. 94\u0026ndash;97, 1988.\u003c/li\u003e\n\u003cli\u003eBarthwal M and Srivastava K, \u0026ldquo;Histologic studies on endometrium of menstruating monkeys wearing IUDs: comparative evaluation of drugs,\u0026rdquo; Advances in Contraception, vol. 6, no. 2, pp. 113\u0026ndash;124, 1990.\u003c/li\u003e\n\u003cli\u003eBarthwal M and Srivastava K, \u0026ldquo;Mangement of IUD-associated menorrhagia in female rhesus monkeys (Macaca mulatta),\u0026rdquo; Advances in Contraception, vol. 7, no. 1, pp. 67\u0026ndash;76, 1991.\u003c/li\u003e\n\u003cli\u003eMudgal V, \u0026ldquo;Studies on medicinal properties of Convolvulus pluricaulis and Boerhaavia diffusa,\u0026rdquo; Planta Medica, vol. 28, no. 1, pp. 62\u0026ndash;68, 1975.\u003c/li\u003e\n\u003cli\u003eHiruma-Lima CA, Gracioso JS, Bighetti EJB, Germonsen Robineou L, and Souza Brito ARM, \u0026ldquo;The juice of \u0026acute; fresh leaves of Boerhaavia diffusa L. 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Tulsi \u0026ndash; \u003cem\u003eOcimum sanctum\u003c/em\u003e: A herb for all reason. J Ayurveda Integr Med 2014;5:251-9.\u003c/li\u003e\n\u003cli\u003eSaha S, Ghosh S \u003cem\u003eTinospora cordifolia\u003c/em\u003e: One plant, many roles. Can Sci Life 2012;31:151-9.\u003c/li\u003e\n\u003cli\u003eAraujo CC, Leon LL. Biological activities of \u003cem\u003eCurcuma longa\u003c/em\u003e L. Mem Inst Oswaldo Cruz 2001;96:723-8.\u003c/li\u003e\n\u003cli\u003eGacche RN, Dhole NA. Profile of Aldose reductase inhibition, anti-cataract and free radical scavenging activity of selected medical plants: An attempt to standardize the the botanicals for amelioration of diabetes complications. Food Chem Toxicol 2011;49:1806-13.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Triphala, Cataract, Network Pharmacology, Molecular Docking, Molecular Dynamic Simulations","lastPublishedDoi":"10.21203/rs.3.rs-6104893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6104893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCataracts are a leading cause of vision impairment globally. This study investigates the potential of \u003cem\u003eTriphaladi Ghana Vati (TGV)\u003c/em\u003e, an Ayurvedic formulation, in cataract management through integrative computational approaches. High-resolution mass spectrometry identified 100 bioactives, of which 22 met ADME criteria. Network pharmacology and molecular docking revealed six overlapping targets between TGV and cataracts, including HDAC8, ALDH1A1, GSTP1, and CASP3. Promazine sulfoxide demonstrated significant interactions with ALDH1A1, achieving a docking score of -8.339. Molecular dynamics simulations validated its stable binding, with RMSD values below 6.4 \u0026Aring; and MMGBSA binding free energy of -59.28 kcal/mol. Gene ontology and KEGG enrichment analyses highlighted pathways like oxidative stress and nitric oxide homeostasis, implicating \u003cem\u003eTGV\u003c/em\u003e in cataract modulation. These findings propose \u003cem\u003eTGV\u003c/em\u003e as a promising multi-target therapeutic candidate for cataract prevention and treatment, warranting further experimental validation.\u003c/p\u003e","manuscriptTitle":"Multitargeted Molecular Mechanisms of Triphaladi Ghana Vati in Cataract Management: An Integrative In-Silico Study Using Docking and Dynamics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 11:36:34","doi":"10.21203/rs.3.rs-6104893/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":"16ca74a3-f42c-44a8-baec-a4d315b14f05","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-25T16:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-25 11:36:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6104893","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6104893","identity":"rs-6104893","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}