Identification of potential inhibitors of 3‑Mercaptopyruvate Sulfurtransferase with a deep-learning based screening of natural products

Identification of potential inhibitors of 3‑Mercaptopyruvate Sulfurtransferase with a deep-learning based screening of natural products | 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 Identification of potential inhibitors of 3‑Mercaptopyruvate Sulfurtransferase with a deep-learning based screening of natural products Changkang Wang, Yin Yu, Huimin Ding, Zhensuo Sha, Yifan Zhu, Dongliang Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8278911/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Apr, 2026 Read the published version in Journal of Computer-Aided Molecular Design → Version 1 posted 11 You are reading this latest preprint version Abstract 3-Mercaptopyruvate sulfurtransferase (3-MST), a key enzyme in sulfur metabolism, has emerged as a promising anticancer target. In this study, a library of 3,744 natural products was virtually screened against human 3-MST using machine learning-based screening and molecular docking. Top-ranking candidates were further analyzed via molecular dynamics (MD) simulations and MM-PBSA binding free energy calculations. Methylophiopogonanone A ( 4 ), Daphnoretin ( 5 ), and L-asarinin ( 9 ) exhibited stable binding with favorable energetics, displaying binding free energies comparable to the reference ligand 7NC301. Binding mode analyses revealed that Methylophiopogonanone A primarily engaged in hydrophobic interactions, whereas Daphnoretin and L-asarinin formed extensive polar contacts, accompanied by higher desolvation penalties. In vitro cytotoxicity assays showed that, Methylophiopogonanone A and L-asarinin reduced HCT116 cell lines viability by 40.3% and 26.3% at 25 µM, which is consist with their inhibitory to 3-MST with IC 50 = 5.83 ± 0.69 µM and IC 50 = 19.11 ± 3.37 µM, respectively. These results suggested that the natural products identified in this study should be promising start point for the development of novel anticancer agents targeting 3-MST. 3-MST colon cancer natural products deep learning MM-PBSA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Hydrogen sulfide (H₂S) is increasingly recognized as a critical endogenous gasotransmitter, alongside nitric oxide (NO) and carbon monoxide (CO), regulating diverse physiological processes including vasodilation, neuromodulation, inflammation, and cellular metabolism 1-3 . In cancer biology, H₂S has emerged as a pro-oncogenic mediator, supporting tumor cell proliferation, migration, bioenergetics, and angiogenesis 4,5 . Elevated H₂S levels have been documented in various tumor types, driven by overexpression of the enzymes responsible for its biosynthesis: cystathionine β-synthase (CBS), cystathionine γ-lyase (CSE), and 3-mercaptopyruvate sulfurtransferase (3-MST) 6-8 . 3-MST, which localized in both the cytosol and mitochondria, has gained attention as a distinct and underexplored target for anticancer therapy, particularly in colorectal malignancies 6,9 . 3-MST catalyzes the conversion of 3-mercaptopyruvate (3-MP), derived from cysteine via aminotransferase activity, into pyruvate and a protein-bound persulfide intermediate 10-12 . This sulfane sulfur-containing species can subsequently release H₂S through physiological reductants such as thioredoxin or dihydrolipoic acid 9,13,14 . While CBS and CSE have been extensively studied in cancer, the role of 3-MST in tumor biology is less defined, despite evidence of its upregulation in colon, renal, and brain cancers 13,15 . In colon cancer specifically, 3-MST expression correlates with increased H₂S production and tumor cell viability, positioning this enzyme as a promising target for small-molecule inhibition 16-18 . Recent advances have led to the identification of novel 3-MST inhibitors through structure-based drug design campaigns 19-21 . Structural and molecular dynamics studies have highlighted the significance of key interactions between inhibitor scaffolds and the active site cysteine (Cys248) of 3-MST, underscoring opportunities for structure-based drug design 22 . However, relatively few efforts have explored natural products as potential 3-MST inhibitors, despite their structural diversity and favorable pharmacological profiles. We plan to utilize a combination of classic CADD tools, such as pharmacophore modeling combined with docking, for the screening of hit compounds. However, an accurate pharmacophore model that can be used for screening requires a larger variety of compounds with greater differences in their activity values. There has been limited reporting on inhibitors of the 3-MST, which clearly does not meet these requirements. Using docking software alone for the screening of a large compound library would also result in relatively low efficiency. With the widespread adoption of AI and related technologies, the classical screening protocol can be partially or entirely replaced by AI tools. In this study, we employed an integrative AI-based in silico and in vitro approaches to identify and characterize natural products as inhibitors of 3-MST ( Figure 1 ). A library of natural compounds was screened against the 3-MST active site via an AI-based screening tool (DiffDock) 23 running on deep-learning and then a docking-based (Autodock Vina) screening, followed by molecular dynamics simulations, and free energy calculations. Hit compounds were prioritized based on their predicted binding stability and interaction energetics, which were further validated by in vitro anticancer evaluation using HCT116 colon cancer cells. Our findings provide new insight into natural-product-based 3-MST inhibition and lay a foundation for further development of selective modulators targeting H₂S signaling in colorectal cancer. To support the virtual screening and biological evaluation described below, a standardized computational–experimental workflow was applied. The crystal structure of human 3-MST (PDB 5WQK) was prepared in UCSF Chimera by adding hydrogens, completing missing loops, and assigning Gasteiger charges. A natural-product library (TargetMol) containing 3,744 compounds was processed using the Prepare Ligands module in Discovery Studio to generate optimized 3D conformations. Virtual screening was performed through DiffDock (an deep-learning-based tool) coupled with AutoDock Vina. DiffDock is a generative molecular docking method, which is based on a diffusion model. It reinterprets the traditional "search-scoring" process as a generative modeling problem within the protein-ligand conformational space. The model employs SE(3)-isotropic graph neural networks to perform diffusion and denoising within the space of translational, rotational, and twisting degrees of freedom of the ligand. Starting from a random initial pose, it gradually generates reasonable binding conformations. Additionally, it uses a separate confidence model to rank the generated poses, thereby significantly enhancing the precision and stability of the docking process. In studies involving novel targets for which no ligands have been reported (such as proteins predicted by AlphaFold) or targets for which only a limited number of ligands have been reported (such as 3-MST), traditional screening methods based on pharmacophore-based approaches are largely ineffective. In contrast, DiffDock can accurately identify top-ranking hits, especially when performed on GPU-based computing environments, where it can efficiently generate results. The binding pocket was defined by redocking the co-crystallized ligand of 3-MST, and a cubic grid centered on its coordinates was used for docking all library molecules. Top-ranked complexes were further refined by 10-ns molecular dynamics simulations in AMBER (ff19SB protein force field and GAFF2 ligand parameters), followed by MM-PBSA calculations using snapshots from the equilibrated trajectories. For preliminary biological assessment, the two most promising natural products were evaluated for cytotoxicity against HCT116 colorectal cancer cells using the CCK-8 assay. Detailed computational and experimental parameters are provided in the Supporting Information. 3-Mercaptopyruvate sulfurtransferase (3-MST) is a key enzyme in sulfur metabolism. Its crystal structure, resolved at 2.15 Å, reveals a two-domain architecture characteristic of the rhodanese-like fold, with N-terminal (residues 1-138) and C-terminal (residues 165-285) domains connected by a linker (residues 139-164) that wraps around and stabilizes both domains. The active site is located in the interdomain cleft, where 3-mercaptopyruvate (3-MP) binds 20 ( Figure 2A ). In complex with compound 7NC301, Cys248 is observed in a persulfurated state, engaging in an unusual interaction with the 4-pyrimidone-like aromatic ring of the ligand, characterized by a perpendicular orientation and a sulfur-π distance exceeding 3.45 Å. Additional stabilization arises from direct hydrogen bonding with R188 and S250, water-mediated interactions involving E195 and R197, and van der Waals contacts with multiple active-site residues( Figure 2B ). After being provided with a natural product library containing 3744 molecules and the 3-MST protein, DiffDock successfully generated a filtered result set with ranked scoring values on a GPU-computing platform (see supporting information). Top 200 molecules with top DiffDock_scores were selected and sent to Autodock Vina. The crystal structure of 3-MST complexed with the known ligand 7NC301 (PDB: 5WQK) served as the receptor model. To validate the docking protocol, the co-crystallized ligand was redocked into the binding site. The resulting pose closely aligned with the experimental conformation, yielding a root-mean-square deviation (RMSD) of 0.973 Å and a binding affinity of -9.3 kcal/mol. An RMSD below 2.0 Å confirms the reliability of the docking parameters, and the redocked ligand reproduced key interactions observed in the crystal structure. The top 10 hits, ranked by binding energy, are summarized in Table 1 , with their chemical structures shown in Figure 3 . These compounds exhibited docking scores ranging from -9.9 to -8.7 kcal/mol, indicating favorable binding to the active site. Among the top hits, (-)-Zuonin A ( 1 ) demonstrated the highest predicted affinity (-9.9 kcal/mol), exceeding that of the reference ligand 7NC301. Other notable compounds include Licarin B (-9.2 kcal/mol), Chrysophanol 8-O-glucoside and Methylophiopogonanone A (both -8.9 kcal/mol), as well as Daphnoretin and Plantagoside (-8.8 kcal/mol). The comparable or superior docking scores of these candidates relative to the positive control suggest their potential as scaffolds for further development of 3-MST inhibitors. Table 1 . The scores predicted by DiffDock and binding energy obtained from molecular docking and MM-PBSA calculation of top 10 hits. No. compound ID DiffDock_score Binding energy (kcal/mol) Autodock vina MM-PBSA 1 (-)-Zuonin A ZINC000257520722 9.996295 -9.9 -18.76 ± 3.81 2 Licarin B ZINC000001587485 9.990738 -9.2 -19.56 ± 3.26 3 Chrysophanol 8-O-glucoside ZINC00004098657 9.985181 -8.9 -8.47 ± 3.69 4 Methylophiopogonanone A ZINC000013481899 9.975920 -8.9 -21.08 ± 3.10 5 Daphnoretin ZINC000000689683 9.966658 -8.8 -20.24 ± 3.87 6 Plantagoside ZINC000257543460 9.957396 -8.8 -0.27 ± 5.05 7 Mebendazole ZINC00000121541 9.948134 -8.7 -15.32 ± 3.91 8 Anisindione ZINC0010015486 9.938873 -8.7 -13.63 ± 3.3 9 L-asarinin ZINC00001668768 9.925906 -8.7 -20.41 ± 2.73 10 6'-O-b-D-Glucosylgentiopicroside ZINC00085799038 9.912940 -8.6 -1.03 ± 0.37 11 7NC301 \ -9.3 -24.95 ± 2.75 To further assess the dynamic stability and binding behavior of the top 10 hit compounds within the 3-MST active site, 10 ns molecular dynamics (MD) simulations were performed for each protein-ligand complex. While molecular docking provides useful insights into potential binding conformations, its limitations - particularly the rigid treatment of the receptor - can result in suboptimal binding poses. In contrast, MD simulations allow both ligand and protein flexibility under near-physiological conditions, offering a more accurate depiction of complex behavior over time. Analysis of the MD trajectories focused on root-mean-square deviation (RMSD) values to evaluate structural stability and convergence. The backbone RMSD of the protein in all complexes stabilized within 1-2.5 Å, indicating minimal global conformational drift during the simulation. Ligand RMSDs remained within the 0.5-2.0 Å range for most compounds, except for 6'-O-β-D-Glucosylgentiopicroside ( 10 ), which exhibited slightly higher fluctuations (average RMSD ~3.0 Å), suggesting less stable binding or greater conformational flexibility. Figure 4 illustrated the RMSD profiles of selected complexes. Notably, (-)-Zuonin A ( 1 ), Licarin B ( 2 ), Methylophiopogonanone A ( 4 ), Anisindione ( 8 ), and L-Asarinin ( 9 ) displayed minimal ligand RMSD values and rapid equilibration, supporting the reliability of their initial docking poses and conformational stability throughout the simulation. All systems achieved equilibrium within the first 3 ns and remained stable for the duration of the simulations, with backbone fluctuations consistently below 2.0 Å. These findings suggested that the selected natural compounds are not only capable of occupying the substrate-binding site of 3-MST but also form stable complexes under dynamic conditions, reinforcing their potential as viable leads for further development. The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) technique was used to calculate the binding free energy of protein-ligand complexes using the trajectory from the last 4 ns of MD simulation. Subsequently, the energy contribution of individual residues was evaluated. The binding energies were calculated and compared with native ligand 7NC301 as positive control. 7NC301 showed the binding energies of -24.95 ± 2.75 kcal/mol. The binding free energy of the top 10 compounds ranged from -0.27 ± 5.05 to -21.08 ± 3.10 kcal/mol, which is lower than the native ligand and indicated the potential protease inhibitor. Methylophiopogonanone A ( 4 ) exhibited the lowest binding energy of -21.08 ± 3.10kcal/mol, followed by Daphnoretin ( 5 ) and L-asarinin ( 9 ), with binding energy below -20 kcal/mol. The interactions of these molecules with a binding pocket were primarily due to hydrophobic interactions. Moreover, (-)-Zuonin A ( 1 ) and Licarin B ( 2 ) also provided excellent binding affinity with binding energies of -18.76 ± 3.81 and -19.56 ± 3.26 kcal/mol, respectively. Our studies revealed that molecular dynamics and MM-PBSA calculation results supported the feasibility of these compounds to find candidate compounds for in vitro experiments. We identified three most promising hit compounds for further development as 3-MST inhibitors using in silico methods, including Methylophiopogonanone A ( 4 ), Daphnoretin ( 5 ) and L-asarinin ( 9 ). The root-mean-square deviation (RMSD) values of the protein backbone and ligands were calculated using a least-squares fit to their respective initial structures. As shown in Figure 4 , the reference complex with 7NC301 exhibited backbone RMSD values between 1.0 and 1.5 Å during the equilibrium phase. In comparison, protein backbones complexed with the hit compounds displayed RMSD values ranging from 1.0 to 2.5 Å. Although slightly elevated relative to the native ligand complex, these values remained within acceptable limits, reflecting minor conformational adaptations upon ligand binding. All systems reached structural equilibrium within approximately 3 ns and maintained stability for the remainder of the 10 ns simulation. Among the candidate compounds, the ligands Methylophiopogonanone A ( 4 ), Daphnoretin ( 5 ), and L-asarinin ( 9 ) achieved stable conformations rapidly. Their RMSD profiles stabilized within 0.5 to 1.5 Å, with only minor fluctuations observed for compounds 4 and 9 , indicating well-defined initial binding poses. Daphnoretin ( 5 ), however, exhibited greater variation (1.0-2.0 Å), suggesting a structural adjustment during the simulation that may have led to a more favorable binding conformation. To further examine residue-level flexibility, root-mean-square fluctuation (RMSF) analysis was performed on the protein backbone atoms over the 10 ns trajectory ( Figure 5 ). All protein-ligand complexes exhibited fluctuation patterns generally consistent with the native 7NC301-bound form, suggesting preservation of the overall secondary structure. Notably, the complex with Daphnoretin ( 5 ) showed enhanced RMSF values across several regions, implying increased local flexibility and a less stable interaction profile relative to other compounds. Overall, the MD simulations support that compounds 4 and 9 maintain stable and favorable binding conformations within the active site of 3-MST, while Daphnoretin ( 5 ) appears to induce greater conformational dynamics in the protein, possibly reflecting lower binding affinity or stability. Binding free energies of the candidate ligands were estimated using the MM-PBSA method, with results summarized in Table 2 . Methylophiopogonanone A ( 4 ), Daphnoretin ( 5 ), and L-asarinin ( 9 ) exhibited binding free energies of -21.08 ± 3.10, -20.24 ± 3.87, and -20.41 ± 2.73 kcal/mol, respectively. Among the tested compounds, Methylophiopogonanone A ( 4 ) demonstrated the most favorable binding affinity, primarily driven by strong van der Waals interactions and a relatively low polar solvation penalty. Although Daphnoretin and L-asarinin displayed comparable overall free energies (~-20 kcal/mol), their binding profiles were characterized by higher polar desolvation costs, which partially offset favorable electrostatic contributions. This distinction suggests that while electrostatics play a role, hydrophobic interactions are the predominant driving force for ligand binding in this system, particularly for Methylophiopogonanone A ( 4 ). In comparison to the reference ligand 7NC301, the natural compounds exhibit slightly weaker binding affinities; however, their energetically favorable profiles and structural diversity position them as promising lead scaffolds for further optimization in the development of 3-MST inhibitors. Table 2 . Energy Contribution of the Various Components to the Total Binding Free Energies of the Simulated Systems. compound free energy (kcal/mol) Methylophiopogonanone A ( 4 ) -35.22 ± 2.34 -3.58 ± 2.98 22.19 ± 2.64 -4.47 ± 0.13 -21.08 ± 3.10 Daphnoretin ( 5 ) -28.72 ± 3.24 -23.07 ± 9.89 36.31 ± 8.93 -4.75 ± 1.93 -20.24 ± 3.87 L-asarinin ( 9 ) -35.23 ± 1.89 -15.98 ± 2.81 35.68 ± 3.11 -4.83 ± 0.18 -20.41 ± 2.74 7NSC301 -34.63 ± 2.74 -42.16 ± 6.83 56.67 ± 6.90 -4.82 ± 0.15 -24.95 ± 2.75 The protein-ligand interactions of the three hit compounds were depicted in Figure 6 . Methylophiopogonanone A ( 4 ) primarily engaged in hydrophobic and π-π stacking interactions, notably with Leu38, Val251, Trp36, and His74. Additional contacts with Tyr108 and Arg112 likely contributed through dispersion and electrostatic effects. The absence of classical polar interactions was consistent with its favorable van der Waals energy and moderate polar solvation penalty, indicating a hydrophobic-driven binding mode. In contrast, Daphnoretin ( 5 ) formed an extensive polar interaction network, including hydrogen bonds with Arg184 and Lys40, π-π stacking with Pro196, and ionic interactions with Arg188 and Glu199, correlating with substantial electrostatic contributions. However, a high polar solvation penalty offset its binding affinity. L-asarinin ( 9 ) exhibited both aromatic (Trp36, Tyr108) and π-cation (Arg197) interactions, yielding balanced van der Waals and electrostatic contributions, though attenuated by solvation effects. Collectively, Methylophiopogonanone A’s hydrophobic binding conferred thermodynamic favorability, whereas Daphnoretin and L-asarinin incurred greater desolvation penalties despite stable polar contacts. The cytotoxic potential of three hit compounds was assessed against the human colon cancer cell line HCT116 at a concentration of 25 mM using the CCK-8 assay ( Figure 7A ). The anticancer activity (61.67%) of Daphnoretin ( 5 ) against colon cancer lines is consistent with the data previously reported in the literature. Methylophiopogonanone A ( 4 ) demonstrated a more pronounced inhibitory effect, reducing cell viability by 40.34%, while L-asarinin ( 9 ) induced a 26.29% decrease. Subsequently, we conducted tests to assess the inhibitory activity of these three compounds using the 3-MST assay ( Figure 7B ). Consistent with cellular activity, 4 (IC 50 = 5.83 ± 0.69 mM) demonstrated superior inhibitory activity compared to 9 (IC 50 = 19.11 ± 3.37 mM). Interesting, Daphnoretin ( 5 ) (IC 50 = 9.59 ± 1.22 mM) demonstrates slightly weaker 3-MST inhibitory activity compared to 4 , which is consistent with the results from the MM-PBSA calculations; however, its inhibitory activity towards cancer cell proliferation is better, as Daphnoretin not only exhibits inhibitory activity towards 3-MST but also has agonistic activity towards protein kinase C (PKC). 24 It is possible that these factors contribute to its enhanced ability to inhibit cancer cell proliferation. These results indicated that three natural products exhibited measurable cytotoxicity, with Methylophiopogonanone A showing greater potential as a lead compound for further development of 3-MST inhibitors in colorectal cancer therapy. As a crucial source for drug discovery and development, natural products require clear delineation of their botanical origins, traditional medicinal contexts, and the specific plant parts enriched with active constituents. This clarity is pivotal for guiding subsequent research endeavors, including compound isolation and purification, structural optimization, and in-depth investigation into their mechanisms of action. The three potential 3-MST inhibitors, Methylophiopogonanone A ( 4 ), Daphnoretin ( 5 ), and L-asarinin ( 9 ), identified in this study are all derived from traditional medicinal plants and exhibit well-defined patterns of active ingredient accumulation in specific medicinal parts. Methylophiopogonanone A ( 4 ) is a steroid saponin derivative, whose primary botanical source is the dried tuberous roots of Ophiopogon japonicus (L. f.) Ker-Gawl , a plant of the genus Ophiopogon in the family Liliaceae. Recent pharmacological studies have confirmed its potential activities in immune regulation, antioxidation, and antitumor research 25-27 . Daphnoretin ( 5 ) is a benzophenone-type natural product, primarily derived from the dried flower buds and root barks of Daphne genkwa Sieb. et Zucc , a plant belonging to the genus Daphne in the family Thymelaeaceae. The isolation of toxic components (e.g., genkwanin) from Daphne genkwa ’s active constituents has long been a key focus of research. As a non-toxic active component, Daphnoretin has been identified in recent years to possess significant antitumor and anti-inflammatory activities 28-30 . L-asarinin ( 9 ) is a bisbenzylisoquinoline alkaloid, primarily derived from the dried whole herbs of Asarum heterotropoides Fr. Schmidt var. mandshuricum (Maxim.) Kitag., Asarum sieboldii Miq. var. seoulense Nakai , or Asarum sieboldii Miq , all of which belong to the genus Asarum in the family Aristolochiaceae. Recent studies have demonstrated that its components (including L-asarinin) exhibit definite activities in inhibiting tumor cell proliferation and anti-platelet aggregation 31-33 . Based on the analysis of binding modes, preliminary conclusions can be drawn regarding the structure-activity relationship (SAR) rules for 3-MST inhibitors: 1). The molecular structure should contain moderate hydrophobic groups (e.g., the steroidal nucleus in Methylophiopogonanone A) to enhance interactions with hydrophobic residues in the active site; 2). The number and distribution of polar groups should be controlled to avoid high solvation penalty caused by excessive polarity. For example, methylation modification of the hydroxyl groups in Daphnoretin can reduce the number of polar groups, decrease solvation cost, and retain hydrogen bonding interactions with key residues (e.g., Arg184),and this is expected to further improve its binding affinity and in vitro activity. This study screened anti-colon cancer natural products using 3-MST as the target, with the core basis being the specific high expression of 3-MST in colon cancer and its key regulatory role in H₂S synthesis. Existing studies have confirmed that in colon cancer cell lines (e.g., HCT116 and SW480) and colon cancer tissue samples, the mRNA and protein expression levels of 3-MST are 2-3 fold higher than those in normal colonic epithelial cells-and its expression level is positively correlated with tumor stage (expression in patients with stage Ⅲ-Ⅳ is significantly higher than that in stage Ⅰ-Ⅱ) 35 . This high expression directly leads to increased H₂S concentration in the tumor microenvironment (up to 1.5-2.0 fold that of normal tissues). As an oncogenic gas signaling molecule, H₂S can promote tumor cell proliferation and inhibit apoptosis by activating the PI3K/Akt/mTOR pathway; simultaneously, it can induce angiogenesis by upregulating VEGF expression, thereby providing nutritional support for tumor growth. In vitro experiments in this study validate the effectiveness of 3-MST as a therapeutic target for colon cancer and confirm the feasibility of natural products exerting anti-colon cancer effects by inhibiting 3-MST. In conclusion, this study identified Methylophiopogonanone A ( 4 ), Daphnoretin ( 5 ), and L-asarinin ( 9 ) as promising 3-MST inhibitors through in silico approaches including deep-learning-based screening combined with docking screening, molecular dynamics simulations, and free energy calculations. All the retained compounds exhibited stable binding at the 3-MST active site, with Methylophiopogonanone A displaying the most favorable binding free energy, primarily driven by hydrophobic interactions and minimal polar solvation penalties. Notably, Methylophiopogonanone A and L-asarinin inhibited HCT116 colon cancer cell proliferation, supporting the AI-assisted computational predictions and underscoring their therapeutic potential. These findings provide a basis for the rational development of selective, natural product-derived 3-MST inhibitors and merit further preclinical evaluation. Declarations Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution C.W. wrote the first draft of the manuscript. Y.Y., H.D. and Z.S. performed material preparation and data analysis. 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Supplementary Files 3MSTDiffDock3744.xlsx Graphicalabstract.tiff Cite Share Download PDF Status: Published Journal Publication published 04 Apr, 2026 Read the published version in Journal of Computer-Aided Molecular Design → Version 1 posted Editorial decision: Revision requested 20 Dec, 2025 Reviews received at journal 20 Dec, 2025 Reviews received at journal 18 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 11 Dec, 2025 Editor assigned by journal 11 Dec, 2025 Submission checks completed at journal 04 Dec, 2025 First submitted to journal 04 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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17:05:27","extension":"xml","order_by":42,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96941,"visible":true,"origin":"","legend":"","description":"","filename":"41e21fa5b324405db320dc5cb84c55c01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/d9e3a680ad8bcf1a62d859de.xml"},{"id":98403249,"identity":"c7b619b4-df14-4293-b1a4-cba12f55dd47","added_by":"auto","created_at":"2025-12-17 12:04:17","extension":"html","order_by":43,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110186,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/731148c1a7fddf616120c8b3.html"},{"id":98403202,"identity":"fd69e1f5-152a-4847-8503-25c330f1afa3","added_by":"auto","created_at":"2025-12-17 12:04:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2210824,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the workflow of this study.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/7379fe867be1104d9fcdfa84.jpg"},{"id":98441184,"identity":"5df8be7e-0c35-4f13-a1d6-c022fecac502","added_by":"auto","created_at":"2025-12-17 17:05:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5517908,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of human MST. (\u003cstrong\u003eA\u003c/strong\u003e) Crystal structure of MST displayed in a ribbon drawing (N-terminal domain shown in cyan; C-terminal domain shown in green; linker region between the two domains shown in magenta) (PDB: 4JGT). (\u003cstrong\u003eB\u003c/strong\u003e) Crystal structure of 3-MST in complex with inhibitor 7NC301 (green stick), and the key interactions were showed with hydrogen bonds in green, p-cation in orange and p-alkyl in pale magenta (PDB: 5WQK).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/b35f3fe39cd6478e6e8e11ac.jpg"},{"id":98441507,"identity":"185548aa-88f3-4b3f-b028-f0ef75e6f1ba","added_by":"auto","created_at":"2025-12-17 17:05:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2754348,"visible":true,"origin":"","legend":"\u003cp\u003eChemical Structures of the top 10 hit Compounds\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/4fd9d8f63ba82cfbaae6427c.jpg"},{"id":98440863,"identity":"b51881fd-206a-45ba-bbf0-802661253a20","added_by":"auto","created_at":"2025-12-17 17:04:31","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5910463,"visible":true,"origin":"","legend":"\u003cp\u003eRoot-mean-square deviation (RMSD) of protein backbone (\u003cstrong\u003eA\u003c/strong\u003e) and ligand (\u003cstrong\u003eB\u003c/strong\u003e) with respect to the initial structure from 10 ns MD simulation.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/27359361cf5bbf206939f82d.jpg"},{"id":98440717,"identity":"e50b9089-4997-444f-bb3f-173880b8c921","added_by":"auto","created_at":"2025-12-17 17:04:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3312227,"visible":true,"origin":"","legend":"\u003cp\u003eRoot-mean-square fluctuation (RMSF) of the protein backbone of 3-MST-ligand complexes.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/b5a0dcc4c63a94027d3ab481.jpg"},{"id":98403213,"identity":"d9642d68-f7e8-4d24-af1b-eafd8bb15b17","added_by":"auto","created_at":"2025-12-17 12:04:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3865875,"visible":true,"origin":"","legend":"\u003cp\u003eCalculated binding modes of Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e), Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e) and L-asarinin (\u003cstrong\u003e9\u003c/strong\u003e) in complex with 3-MST.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/3727ec8310fecfae53f2e92d.jpg"},{"id":98441615,"identity":"5172713a-aa89-4d38-a250-264a0c57da3d","added_by":"auto","created_at":"2025-12-17 17:05:38","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1776064,"visible":true,"origin":"","legend":"\u003cp\u003eInhibition rates of the retained natural products (25 mM) against the growth of HCT116 cells. Inhibition rate was the result of three independent experiments. Untreated cells were used as negative control, and 5-FU (10 μM) as positive control.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/fea0a8f137a3113fb29a06af.jpg"},{"id":106344467,"identity":"41e6addf-1252-42c7-96f1-a27821da15ee","added_by":"auto","created_at":"2026-04-07 16:14:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25982597,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/f9d20bf5-8c20-4ea8-8f6f-6ca147305527.pdf"},{"id":98441578,"identity":"87e91648-a8fc-4876-baac-a73a1e3b171c","added_by":"auto","created_at":"2025-12-17 17:05:36","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":218660,"visible":true,"origin":"","legend":"","description":"","filename":"3MSTDiffDock3744.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/b616aff5f2a663f68daf5ec2.xlsx"},{"id":98403204,"identity":"e9a0b0a1-9e7d-4c3c-94b3-81580c4e1938","added_by":"auto","created_at":"2025-12-17 12:04:16","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1106973,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.tiff","url":"https://assets-eu.researchsquare.com/files/rs-8278911/v1/505ccb256873891ad8ec8bd4.tiff"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIdentification of potential inhibitors of 3‑Mercaptopyruvate Sulfurtransferase with a deep-learning based screening of natural products\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"\u003cp\u003eHydrogen sulfide (H₂S) is increasingly recognized as a critical endogenous gasotransmitter, alongside nitric oxide (NO) and carbon monoxide (CO), regulating diverse physiological processes including vasodilation, neuromodulation, inflammation, and cellular metabolism\u003csup\u003e1-3\u003c/sup\u003e. In cancer biology, H₂S has emerged as a pro-oncogenic mediator, supporting tumor cell proliferation, migration, bioenergetics, and angiogenesis\u003csup\u003e4,5\u003c/sup\u003e. Elevated H₂S levels have been documented in various tumor types, driven by overexpression of the enzymes responsible for its biosynthesis: cystathionine \u0026beta;-synthase (CBS), cystathionine \u0026gamma;-lyase (CSE), and 3-mercaptopyruvate sulfurtransferase (3-MST)\u003csup\u003e6-8\u003c/sup\u003e. 3-MST, which localized in both the cytosol and mitochondria, has gained attention as a distinct and underexplored target for anticancer therapy, particularly in colorectal malignancies\u003csup\u003e6,9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e3-MST catalyzes the conversion of 3-mercaptopyruvate (3-MP), derived from cysteine \u003cem\u003evia\u003c/em\u003e aminotransferase activity, into pyruvate and a protein-bound persulfide intermediate\u003csup\u003e10-12\u003c/sup\u003e. This sulfane sulfur-containing species can subsequently release H₂S through physiological reductants such as thioredoxin or dihydrolipoic acid\u003csup\u003e9,13,14\u003c/sup\u003e. While CBS and CSE have been extensively studied in cancer, the role of 3-MST in tumor biology is less defined, despite evidence of its upregulation in colon, renal, and brain cancers\u003csup\u003e13,15\u003c/sup\u003e. In colon cancer specifically, 3-MST expression correlates with increased H₂S production and tumor cell viability, positioning this enzyme as a promising target for small-molecule inhibition\u003csup\u003e16-18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRecent advances have led to the identification of novel 3-MST inhibitors through structure-based drug design campaigns\u003csup\u003e19-21\u003c/sup\u003e. Structural and molecular dynamics studies have highlighted the significance of key interactions between inhibitor scaffolds and the active site cysteine (Cys248) of 3-MST, underscoring opportunities for structure-based drug design\u003csup\u003e22\u003c/sup\u003e. However, relatively few efforts have explored natural products as potential 3-MST inhibitors, despite their structural diversity and favorable pharmacological profiles.\u003c/p\u003e\n\u003cp\u003eWe plan to utilize a combination of classic CADD tools, such as pharmacophore modeling combined with docking, for the screening of hit compounds. However, an accurate pharmacophore model that can be used for screening requires a larger variety of compounds with greater differences in their activity values. There has been limited reporting on inhibitors of the 3-MST, which clearly does not meet these requirements. Using docking software alone for the screening of a large compound library would also result in relatively low efficiency. With the widespread adoption of AI and related technologies, the classical screening protocol can be partially or entirely replaced by AI tools. In this study, we employed an integrative AI-based \u003cem\u003ein silico\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e approaches to identify and characterize natural products as inhibitors of 3-MST (\u003cstrong\u003eFigure 1\u003c/strong\u003e). A library of natural compounds was screened against the 3-MST active site via an AI-based screening tool (DiffDock)\u003csup\u003e23\u003c/sup\u003e running on deep-learning and then a docking-based (Autodock Vina) screening, followed by molecular dynamics simulations, and free energy calculations. Hit compounds were prioritized based on their predicted binding stability and interaction energetics, which were further validated by in vitro anticancer evaluation using HCT116 colon cancer cells. Our findings provide new insight into natural-product-based 3-MST inhibition and lay a foundation for further development of selective modulators targeting H₂S signaling in colorectal cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo support the virtual screening and biological evaluation described below, a standardized computational\u0026ndash;experimental workflow was applied. The crystal structure of human 3-MST (PDB 5WQK) was prepared in UCSF Chimera by adding hydrogens, completing missing loops, and assigning Gasteiger charges. A natural-product library (TargetMol) containing 3,744 compounds was processed using the Prepare Ligands module in Discovery Studio to generate optimized 3D conformations. Virtual screening was performed through DiffDock (an deep-learning-based tool) coupled with AutoDock Vina. DiffDock is a generative molecular docking method, which is based on a diffusion model. It reinterprets the traditional \u0026quot;search-scoring\u0026quot; process as a generative modeling problem within the protein-ligand conformational space. The model employs SE(3)-isotropic graph neural networks to perform diffusion and denoising within the space of translational, rotational, and twisting degrees of freedom of the ligand. Starting from a random initial pose, it gradually generates reasonable binding conformations. Additionally, it uses a separate confidence model to rank the generated poses, thereby significantly enhancing the precision and stability of the docking process. In studies involving novel targets for which no ligands have been reported (such as proteins predicted by AlphaFold) or targets for which only a limited number of ligands have been reported (such as 3-MST), traditional screening methods based on pharmacophore-based approaches are largely ineffective. In contrast, DiffDock can accurately identify top-ranking hits, especially when performed on GPU-based computing environments, where it can efficiently generate results. The binding pocket was defined by redocking the co-crystallized ligand of 3-MST, and a cubic grid centered on its coordinates was used for docking all library molecules. Top-ranked complexes were further refined by 10-ns molecular dynamics simulations in AMBER (ff19SB protein force field and GAFF2 ligand parameters), followed by MM-PBSA calculations using snapshots from the equilibrated trajectories.\u003c/p\u003e\n\u003cp\u003eFor preliminary biological assessment, the two most promising natural products were evaluated for cytotoxicity against HCT116 colorectal cancer cells using the CCK-8 assay. Detailed computational and experimental parameters are provided in the Supporting Information.\u003c/p\u003e\n\u003cp\u003e3-Mercaptopyruvate sulfurtransferase (3-MST) is a key enzyme in sulfur metabolism. Its crystal structure, resolved at 2.15 \u0026Aring;, reveals a two-domain architecture characteristic of the rhodanese-like fold, with N-terminal (residues 1-138) and C-terminal (residues 165-285) domains connected by a linker (residues 139-164) that wraps around and stabilizes both domains. The active site is located in the interdomain cleft, where 3-mercaptopyruvate (3-MP) binds\u003csup\u003e20\u003c/sup\u003e (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). In complex with compound 7NC301, Cys248 is observed in a persulfurated state, engaging in an unusual interaction with the 4-pyrimidone-like aromatic ring of the ligand, characterized by a perpendicular orientation and a sulfur-\u0026pi; distance exceeding 3.45 \u0026Aring;. Additional stabilization arises from direct hydrogen bonding with R188 and S250, water-mediated interactions involving E195 and R197, and van der Waals contacts with multiple active-site residues(\u003cstrong\u003eFigure 2B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAfter being provided with a natural product library containing 3744 molecules and the 3-MST protein, DiffDock successfully generated a filtered result set with ranked scoring values on a GPU-computing platform (see supporting information). Top 200 molecules with top DiffDock_scores were selected and sent to Autodock Vina. The crystal structure of 3-MST complexed with the known ligand 7NC301 (PDB: 5WQK) served as the receptor model. To validate the docking protocol, the co-crystallized ligand was redocked into the binding site. The resulting pose closely aligned with the experimental conformation, yielding a root-mean-square deviation (RMSD) of 0.973 \u0026Aring; and a binding affinity of -9.3 kcal/mol. An RMSD below 2.0 \u0026Aring; confirms the reliability of the docking parameters, and the redocked ligand reproduced key interactions observed in the crystal structure.\u003c/p\u003e\n\u003cp\u003eThe top 10 hits, ranked by binding energy, are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e, with their chemical structures shown in\u003cstrong\u003e\u0026nbsp;Figure 3\u003c/strong\u003e. These compounds exhibited docking scores ranging from -9.9 to -8.7 kcal/mol, indicating favorable binding to the active site. Among the top hits, (-)-Zuonin A (\u003cstrong\u003e1\u003c/strong\u003e) demonstrated the highest predicted affinity (-9.9 kcal/mol), exceeding that of the reference ligand 7NC301. Other notable compounds include Licarin B (-9.2 kcal/mol), Chrysophanol 8-O-glucoside and Methylophiopogonanone A (both -8.9 kcal/mol), as well as Daphnoretin and Plantagoside (-8.8 kcal/mol). The comparable or superior docking scores of these candidates relative to the positive control suggest their potential as scaffolds for further development of 3-MST inhibitors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. The scores predicted by DiffDock and binding energy obtained from molecular docking and MM-PBSA calculation of top 10 hits.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecompound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiffDock_score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBinding energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAutodock vina\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMM-PBSA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e(-)-Zuonin A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eZINC000257520722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.996295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-18.76\u0026nbsp;\u0026plusmn;\u0026nbsp;3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eLicarin B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eZINC000001587485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.990738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-19.56\u0026nbsp;\u0026plusmn;\u0026nbsp;3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eChrysophanol 8-O-glucoside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eZINC00004098657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.985181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-8.47\u0026nbsp;\u0026plusmn;\u0026nbsp;3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMethylophiopogonanone A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eZINC000013481899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.975920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-21.08\u0026nbsp;\u0026plusmn;\u0026nbsp;3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDaphnoretin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eZINC000000689683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.966658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-20.24\u0026nbsp;\u0026plusmn;\u0026nbsp;3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ePlantagoside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eZINC000257543460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.957396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.27\u0026nbsp;\u0026plusmn;\u0026nbsp;5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMebendazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eZINC00000121541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.948134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-15.32\u0026nbsp;\u0026plusmn;\u0026nbsp;3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAnisindione\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eZINC0010015486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.938873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-13.63\u0026nbsp;\u0026plusmn;\u0026nbsp;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eL-asarinin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eZINC00001668768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.925906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-20.41\u0026nbsp;\u0026plusmn;\u0026nbsp;2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e6\u0026apos;-O-b-D-Glucosylgentiopicroside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eZINC00085799038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.912940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-1.03\u0026nbsp;\u0026plusmn;\u0026nbsp;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7NC301\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\\\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-9.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-24.95\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTo further assess the dynamic stability and binding behavior of the top 10 hit compounds within the 3-MST active site, 10 ns molecular dynamics (MD) simulations were performed for each protein-ligand complex. While molecular docking provides useful insights into potential binding conformations, its limitations - particularly the rigid treatment of the receptor - can result in suboptimal binding poses. In contrast, MD simulations allow both ligand and protein flexibility under near-physiological conditions, offering a more accurate depiction of complex behavior over time.\u003c/p\u003e\n\u003cp\u003eAnalysis of the MD trajectories focused on root-mean-square deviation (RMSD) values to evaluate structural stability and convergence. The backbone RMSD of the protein in all complexes stabilized within 1-2.5 \u0026Aring;, indicating minimal global conformational drift during the simulation. Ligand RMSDs remained within the 0.5-2.0 \u0026Aring; range for most compounds, except for 6\u0026apos;-O-\u0026beta;-D-Glucosylgentiopicroside (\u003cstrong\u003e10\u003c/strong\u003e), which exhibited slightly higher fluctuations (average RMSD ~3.0 \u0026Aring;), suggesting less stable binding or greater conformational flexibility. \u003cstrong\u003eFigure 4\u003c/strong\u003e illustrated the RMSD profiles of selected complexes. Notably, (-)-Zuonin A (\u003cstrong\u003e1\u003c/strong\u003e), Licarin B (\u003cstrong\u003e2\u003c/strong\u003e), Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e), Anisindione (\u003cstrong\u003e8\u003c/strong\u003e), and L-Asarinin (\u003cstrong\u003e9\u003c/strong\u003e) displayed minimal ligand RMSD values and rapid equilibration, supporting the reliability of their initial docking poses and conformational stability throughout the simulation. All systems achieved equilibrium within the first 3 ns and remained stable for the duration of the simulations, with backbone fluctuations consistently below 2.0 \u0026Aring;.\u003c/p\u003e\n\u003cp\u003eThese findings suggested that the selected natural compounds are not only capable of occupying the substrate-binding site of 3-MST but also form stable complexes under dynamic conditions, reinforcing their potential as viable leads for further development.\u003c/p\u003e\n\u003cp\u003eThe molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) technique was used to calculate the binding free energy of protein-ligand complexes using the trajectory from the last 4 ns of MD simulation. Subsequently, the energy contribution of individual residues was evaluated. The binding energies were calculated and compared with native ligand 7NC301 as positive control. 7NC301 showed the binding energies of -24.95\u0026nbsp;\u0026plusmn;\u0026nbsp;2.75 kcal/mol. The binding free energy of the top 10 compounds ranged from -0.27\u0026nbsp;\u0026plusmn;\u0026nbsp;5.05 to -21.08\u0026nbsp;\u0026plusmn;\u0026nbsp;3.10 kcal/mol, which is lower than the native ligand and indicated the potential protease inhibitor. Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e) exhibited the lowest binding energy of -21.08\u0026nbsp;\u0026plusmn;\u0026nbsp;3.10kcal/mol, followed by Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e) and L-asarinin (\u003cstrong\u003e9\u003c/strong\u003e), with binding energy below -20 kcal/mol. The interactions of these molecules with a binding pocket were primarily due to hydrophobic interactions. Moreover, (-)-Zuonin A (\u003cstrong\u003e1\u003c/strong\u003e) and Licarin B (\u003cstrong\u003e2\u003c/strong\u003e) also provided excellent binding affinity with binding energies of -18.76 \u0026plusmn; 3.81 and -19.56 \u0026plusmn; 3.26 kcal/mol, respectively. Our studies revealed that molecular dynamics and MM-PBSA calculation results supported the feasibility of these compounds to find candidate compounds for \u003cem\u003ein vitro\u003c/em\u003e experiments.\u003c/p\u003e\n\u003cp\u003eWe identified three most promising hit compounds for further development as 3-MST inhibitors using \u003cem\u003ein silico\u003c/em\u003e methods, including Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e), Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e) and L-asarinin (\u003cstrong\u003e9\u003c/strong\u003e). The root-mean-square deviation (RMSD) values of the protein backbone and ligands were calculated using a least-squares fit to their respective initial structures. As shown in \u003cstrong\u003eFigure 4\u003c/strong\u003e, the reference complex with 7NC301 exhibited backbone RMSD values between 1.0 and 1.5 \u0026Aring; during the equilibrium phase. In comparison, protein backbones complexed with the hit compounds displayed RMSD values ranging from 1.0 to 2.5 \u0026Aring;. Although slightly elevated relative to the native ligand complex, these values remained within acceptable limits, reflecting minor conformational adaptations upon ligand binding. All systems reached structural equilibrium within approximately 3 ns and maintained stability for the remainder of the 10 ns simulation. Among the candidate compounds, the ligands Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e), Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e), and L-asarinin (\u003cstrong\u003e9\u003c/strong\u003e) achieved stable conformations rapidly. Their RMSD profiles stabilized within 0.5 to 1.5 \u0026Aring;, with only minor fluctuations observed for compounds \u003cstrong\u003e4\u003c/strong\u003e and \u003cstrong\u003e9\u003c/strong\u003e, indicating well-defined initial binding poses. Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e), however, exhibited greater variation (1.0-2.0 \u0026Aring;), suggesting a structural adjustment during the simulation that may have led to a more favorable binding conformation.\u003c/p\u003e\n\u003cp\u003eTo further examine residue-level flexibility, root-mean-square fluctuation (RMSF) analysis was performed on the protein backbone atoms over the 10 ns trajectory (\u003cstrong\u003eFigure 5\u003c/strong\u003e). All protein-ligand complexes exhibited fluctuation patterns generally consistent with the native 7NC301-bound form, suggesting preservation of the overall secondary structure. Notably, the complex with Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e) showed enhanced RMSF values across several regions, implying increased local flexibility and a less stable interaction profile relative to other compounds.\u003c/p\u003e\n\u003cp\u003eOverall, the MD simulations support that compounds \u003cstrong\u003e4\u003c/strong\u003e and \u003cstrong\u003e9\u003c/strong\u003e maintain stable and favorable binding conformations within the active site of 3-MST, while Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e) appears to induce greater conformational dynamics in the protein, possibly reflecting lower binding affinity or stability.\u003c/p\u003e\n\u003cp\u003eBinding free energies of the candidate ligands were estimated using the MM-PBSA method, with results summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e. Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e), Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e), and L-asarinin (\u003cstrong\u003e9\u003c/strong\u003e) exhibited binding free energies of -21.08 \u0026plusmn; 3.10, -20.24 \u0026plusmn; 3.87, and -20.41 \u0026plusmn; 2.73 kcal/mol, respectively. Among the tested compounds, Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e) demonstrated the most favorable binding affinity, primarily driven by strong van der Waals interactions and a relatively low polar solvation penalty. Although Daphnoretin and L-asarinin displayed comparable overall free energies (~-20 kcal/mol), their binding profiles were characterized by higher polar desolvation costs, which partially offset favorable electrostatic contributions. This distinction suggests that while electrostatics play a role, hydrophobic interactions are the predominant driving force for ligand binding in this system, particularly for Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e). In comparison to the reference ligand 7NC301, the natural compounds exhibit slightly weaker binding affinities; however, their energetically favorable profiles and structural diversity position them as promising lead scaffolds for further optimization in the development of 3-MST inhibitors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Energy Contribution of the Various Components to the Total Binding Free Energies of the Simulated Systems.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecompound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 389px;\"\u003e\n \u003cp\u003e\u003cstrong\u003efree energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cimg width=\"33\" height=\"14\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cimg width=\"29\" height=\"14\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cimg width=\"53\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cimg width=\"59\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cimg width=\"50\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eMethylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e-35.22\u0026nbsp;\u0026plusmn;\u0026nbsp;2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-3.58\u0026nbsp;\u0026plusmn;\u0026nbsp;2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e22.19\u0026nbsp;\u0026plusmn;\u0026nbsp;2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-4.47\u0026nbsp;\u0026plusmn;\u0026nbsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-21.08\u0026nbsp;\u0026plusmn;\u0026nbsp;3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eDaphnoretin (\u003cstrong\u003e5\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e-28.72\u0026nbsp;\u0026plusmn;\u0026nbsp;3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-23.07\u0026nbsp;\u0026plusmn;\u0026nbsp;9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e36.31\u0026nbsp;\u0026plusmn;\u0026nbsp;8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-4.75\u0026nbsp;\u0026plusmn;\u0026nbsp;1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.24\u0026nbsp;\u0026plusmn;\u0026nbsp;3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eL-asarinin (\u003cstrong\u003e9\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e-35.23\u0026nbsp;\u0026plusmn;\u0026nbsp;1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-15.98\u0026nbsp;\u0026plusmn;\u0026nbsp;2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e35.68\u0026nbsp;\u0026plusmn;\u0026nbsp;3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-4.83\u0026nbsp;\u0026plusmn;\u0026nbsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.41\u0026nbsp;\u0026plusmn;\u0026nbsp;2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7NSC301\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e-34.63\u0026nbsp;\u0026plusmn;\u0026nbsp;2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-42.16\u0026nbsp;\u0026plusmn;\u0026nbsp;6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e56.67\u0026nbsp;\u0026plusmn;\u0026nbsp;6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-4.82\u0026nbsp;\u0026plusmn;\u0026nbsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-24.95\u0026nbsp;\u0026plusmn;\u0026nbsp;2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe protein-ligand interactions of the three hit compounds were depicted in \u003cstrong\u003eFigure 6\u003c/strong\u003e.\u0026nbsp;Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e) primarily engaged in hydrophobic and \u0026pi;-\u0026pi; stacking interactions, notably with Leu38, Val251, Trp36, and His74. Additional contacts with Tyr108 and Arg112 likely contributed through dispersion and electrostatic effects. The absence of classical polar interactions was consistent with its favorable van der Waals energy and moderate polar solvation penalty, indicating a hydrophobic-driven binding mode. In contrast, Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e) formed an extensive polar interaction network, including hydrogen bonds with Arg184 and Lys40, \u0026pi;-\u0026pi; stacking with Pro196, and ionic interactions with Arg188 and Glu199, correlating with substantial electrostatic contributions. However, a high polar solvation penalty offset its binding affinity. L-asarinin (\u003cstrong\u003e9\u003c/strong\u003e) exhibited both aromatic (Trp36, Tyr108) and \u0026pi;-cation (Arg197) interactions, yielding balanced van der Waals and electrostatic contributions, though attenuated by solvation effects. Collectively, Methylophiopogonanone A\u0026rsquo;s hydrophobic binding conferred thermodynamic favorability, whereas Daphnoretin and L-asarinin incurred greater desolvation penalties despite stable polar contacts.\u003c/p\u003e\n\u003cp\u003eThe cytotoxic potential of three hit compounds was assessed against the human colon cancer cell line HCT116 at a concentration of 25 mM using the CCK-8 assay (\u003cstrong\u003eFigure 7A\u003c/strong\u003e). The anticancer activity (61.67%) of Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e) against colon cancer lines is consistent with the data previously reported in the literature. Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e) demonstrated a more pronounced inhibitory effect, reducing cell viability by 40.34%, while L-asarinin (\u003cstrong\u003e9\u003c/strong\u003e) induced a 26.29% decrease. Subsequently, we conducted tests to assess the inhibitory activity of these three compounds using the 3-MST assay (\u003cstrong\u003eFigure 7B\u003c/strong\u003e). Consistent with cellular activity, \u003cstrong\u003e4\u003c/strong\u003e (IC\u003csub\u003e50\u003c/sub\u003e= 5.83 \u0026plusmn; 0.69 mM) demonstrated superior inhibitory activity compared to \u003cstrong\u003e9\u003c/strong\u003e (IC\u003csub\u003e50\u003c/sub\u003e= 19.11 \u0026plusmn; 3.37\u0026nbsp;mM). Interesting, Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e) (IC\u003csub\u003e50\u003c/sub\u003e= 9.59 \u0026plusmn; 1.22 mM) demonstrates slightly weaker 3-MST inhibitory activity compared to \u003cstrong\u003e4\u003c/strong\u003e, which is consistent with the results from the MM-PBSA calculations; however, its inhibitory activity towards cancer cell proliferation is better, as Daphnoretin not only exhibits inhibitory activity towards 3-MST but also has agonistic activity towards protein kinase C (PKC).\u003csup\u003e24\u003c/sup\u003e It is possible that these factors contribute to its enhanced ability to inhibit cancer cell proliferation. These results indicated that three natural products exhibited measurable cytotoxicity, with Methylophiopogonanone A showing greater potential as a lead compound for further development of 3-MST inhibitors in colorectal cancer therapy.\u003c/p\u003e\n\u003cp\u003eAs a crucial source for drug discovery and development, natural products require clear delineation of their botanical origins, traditional medicinal contexts, and the specific plant parts enriched with active constituents. This clarity is pivotal for guiding subsequent research endeavors, including compound isolation and purification, structural optimization, and in-depth investigation into their mechanisms of action. The three potential 3-MST inhibitors, Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e), Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e), and L-asarinin (\u003cstrong\u003e9\u003c/strong\u003e), identified in this study are all derived from traditional medicinal plants and exhibit well-defined patterns of active ingredient accumulation in specific medicinal parts. Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e) is a steroid saponin derivative, whose primary botanical source is the dried tuberous roots of \u003cem\u003eOphiopogon japonicus (L. f.) Ker-Gawl\u003c/em\u003e, a plant of the genus \u003cem\u003eOphiopogon\u003c/em\u003e in the family Liliaceae. Recent pharmacological studies have confirmed its potential activities in immune regulation, antioxidation, and antitumor research\u003csup\u003e25-27\u003c/sup\u003e. Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e) is a benzophenone-type natural product, primarily derived from the dried flower buds and root barks of Daphne\u003cem\u003e\u0026nbsp;genkwa Sieb. et Zucc\u003c/em\u003e , a plant belonging to the genus Daphne in the family Thymelaeaceae. The isolation of toxic components (e.g., genkwanin) from \u003cem\u003eDaphne genkwa\u003c/em\u003e\u0026rsquo;s active constituents has long been a key focus of research. As a non-toxic active component, Daphnoretin has been identified in recent years to possess significant antitumor and anti-inflammatory activities\u003csup\u003e28-30\u003c/sup\u003e. L-asarinin (\u003cstrong\u003e9\u003c/strong\u003e) is a bisbenzylisoquinoline alkaloid, primarily derived from the dried whole herbs of \u003cem\u003eAsarum heterotropoides Fr. Schmidt var. mandshuricum (Maxim.) Kitag., Asarum sieboldii Miq. var. seoulense Nakai\u003c/em\u003e, or \u003cem\u003eAsarum sieboldii Miq\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eall of which belong to the genus \u003cem\u003eAsarum\u003c/em\u003e in the family Aristolochiaceae. Recent studies have demonstrated that its components (including L-asarinin) exhibit definite activities in inhibiting tumor cell proliferation and anti-platelet aggregation\u003csup\u003e31-33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBased on the analysis of binding modes, preliminary conclusions can be drawn regarding the structure-activity relationship (SAR) rules for 3-MST inhibitors: 1). The molecular structure should contain moderate hydrophobic groups (e.g., the steroidal nucleus in Methylophiopogonanone A) to enhance interactions with hydrophobic residues in the active site; 2). The number and distribution of polar groups should be controlled to avoid high solvation penalty caused by excessive polarity. For example, methylation modification of the hydroxyl groups in Daphnoretin can reduce the number of polar groups, decrease solvation cost, and retain hydrogen bonding interactions with key residues (e.g., Arg184),and this is expected to further improve its binding affinity and in vitro activity.\u003c/p\u003e\n\u003cp\u003eThis study screened anti-colon cancer natural products using 3-MST as the target, with the core basis being the specific high expression of 3-MST in colon cancer and its key regulatory role in H₂S synthesis. Existing studies have confirmed that in colon cancer cell lines (e.g., HCT116 and SW480) and colon cancer tissue samples, the mRNA and protein expression levels of 3-MST are 2-3 fold higher than those in normal colonic epithelial cells-and its expression level is positively correlated with tumor stage (expression in patients with stage Ⅲ-Ⅳ is significantly higher than that in stage Ⅰ-Ⅱ)\u003csup\u003e35\u003c/sup\u003e. This high expression directly leads to increased H₂S concentration in the tumor microenvironment (up to 1.5-2.0 fold that of normal tissues). As an oncogenic gas signaling molecule, H₂S can promote tumor cell proliferation and inhibit apoptosis by activating the PI3K/Akt/mTOR pathway; simultaneously, it can induce angiogenesis by upregulating VEGF expression, thereby providing nutritional support for tumor growth. In vitro experiments in this study validate the effectiveness of 3-MST as a therapeutic target for colon cancer and confirm the feasibility of natural products exerting anti-colon cancer effects by inhibiting 3-MST.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study identified Methylophiopogonanone A (\u003cstrong\u003e4\u003c/strong\u003e), Daphnoretin (\u003cstrong\u003e5\u003c/strong\u003e), and L-asarinin (\u003cstrong\u003e9\u003c/strong\u003e) as promising 3-MST inhibitors through \u003cem\u003ein silico\u003c/em\u003e approaches including deep-learning-based screening combined with docking screening, molecular dynamics simulations, and free energy calculations. All the retained compounds exhibited stable binding at the 3-MST active site, with Methylophiopogonanone A displaying the most favorable binding free energy, primarily driven by hydrophobic interactions and minimal polar solvation penalties. Notably, Methylophiopogonanone A and L-asarinin inhibited HCT116 colon cancer cell proliferation, supporting the AI-assisted computational predictions and underscoring their therapeutic potential. These findings provide a basis for the rational development of selective, natural product-derived 3-MST inhibitors and merit further preclinical evaluation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.W. wrote the first draft of the manuscript. Y.Y., H.D. and Z.S. performed material preparation and data analysis. Y.Z. performed the calculations and managed the publication process. The study was conceived and supervised by X.X. and D.Z. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThis work is supported by Jiangsu Province Traditional Chinese Medicine Technology Development Plan Project (Project No. YB201976); Zhenjiang Innovation Capacity Building Plan - Zhenjiang TCM Spleen and Stomach Disease Clinical Medical Research Center (Project No. SS2021005).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuang Y-Q, Jin H-F, Zhang H et al (2021) Interaction among hydrogen sulfide and other gasotransmitters in mammalian physiology and pathophysiology. \u003cem\u003eAdv Exp Med Biol.\u003c/em\u003e 2021;205\u0026ndash;236\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCirino G, Vellecco V, Bucci M (2017) Nitric oxide and hydrogen sulfide: the gasotransmitter paradigm of the vascular system. 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Biol Chem 278(48):48219\u0026ndash;48227\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-computer-aided-molecular-design","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcam","sideBox":"Learn more about [Journal of Computer-Aided Molecular Design](http://link.springer.com/journal/10822)","snPcode":"10822","submissionUrl":"https://submission.nature.com/new-submission/10822/3","title":"Journal of Computer-Aided Molecular Design","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"3-MST, colon cancer, natural products, deep learning, MM-PBSA","lastPublishedDoi":"10.21203/rs.3.rs-8278911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8278911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e3-Mercaptopyruvate sulfurtransferase (3-MST), a key enzyme in sulfur metabolism, has emerged as a promising anticancer target. In this study, a library of 3,744 natural products was virtually screened against human 3-MST using machine learning-based screening and molecular docking. Top-ranking candidates were further analyzed via molecular dynamics (MD) simulations and MM-PBSA binding free energy calculations. Methylophiopogonanone A (\u003cb\u003e4\u003c/b\u003e), Daphnoretin (\u003cb\u003e5\u003c/b\u003e), and L-asarinin (\u003cb\u003e9\u003c/b\u003e) exhibited stable binding with favorable energetics, displaying binding free energies comparable to the reference ligand 7NC301. Binding mode analyses revealed that Methylophiopogonanone A primarily engaged in hydrophobic interactions, whereas Daphnoretin and L-asarinin formed extensive polar contacts, accompanied by higher desolvation penalties. \u003cem\u003eIn vitro\u003c/em\u003e cytotoxicity assays showed that, Methylophiopogonanone A and L-asarinin reduced HCT116 cell lines viability by 40.3% and 26.3% at 25 \u0026micro;M, which is consist with their inhibitory to 3-MST with IC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69 \u0026micro;M and IC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;19.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37 \u0026micro;M, respectively. These results suggested that the natural products identified in this study should be promising start point for the development of novel anticancer agents targeting 3-MST.\u003c/p\u003e","manuscriptTitle":"Identification of potential inhibitors of 3‑Mercaptopyruvate Sulfurtransferase with a deep-learning based screening of natural products","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 12:04:11","doi":"10.21203/rs.3.rs-8278911/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-21T02:19:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T21:37:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-18T09:12:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316786914976867265704895855930438645789","date":"2025-12-12T07:43:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12008648302010659045895862338503521688","date":"2025-12-12T05:32:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329581494102287422735500104908632421004","date":"2025-12-12T03:05:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16528723977044410134844817542115619047","date":"2025-12-12T00:34:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T00:32:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-12T00:29:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T02:53:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Computer-Aided Molecular Design","date":"2025-12-04T11:36:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-computer-aided-molecular-design","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcam","sideBox":"Learn more about [Journal of Computer-Aided Molecular Design](http://link.springer.com/journal/10822)","snPcode":"10822","submissionUrl":"https://submission.nature.com/new-submission/10822/3","title":"Journal of Computer-Aided Molecular Design","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d51614a0-7aab-4ee6-973c-cb6959a6e543","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:09:23+00:00","versionOfRecord":{"articleIdentity":"rs-8278911","link":"https://doi.org/10.1007/s10822-026-00795-5","journal":{"identity":"journal-of-computer-aided-molecular-design","isVorOnly":false,"title":"Journal of Computer-Aided Molecular Design"},"publishedOn":"2026-04-04 15:59:54","publishedOnDateReadable":"April 4th, 2026"},"versionCreatedAt":"2025-12-17 12:04:11","video":"","vorDoi":"10.1007/s10822-026-00795-5","vorDoiUrl":"https://doi.org/10.1007/s10822-026-00795-5","workflowStages":[]},"version":"v1","identity":"rs-8278911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8278911","identity":"rs-8278911","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}