Identification of Alkaloids and Repurposed Drugs as Potential Small-Molecule Inhibitors of GOLPH3 in Colorectal and Lung Cancer Using Molecular Docking, Molecular Dynamics, and MM-PBSA Analysis

Identification of Alkaloids and Repurposed Drugs as Potential Small-Molecule Inhibitors of GOLPH3 in Colorectal and Lung Cancer Using Molecular Docking, Molecular Dynamics, and MM-PBSA Analysis | 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 Alkaloids and Repurposed Drugs as Potential Small-Molecule Inhibitors of GOLPH3 in Colorectal and Lung Cancer Using Molecular Docking, Molecular Dynamics, and MM-PBSA Analysis Zechariah Oluwapelumi Oresanya, Godswill Chimezirim Eleanya, Taiwo Abdulmaruf Akande, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7524490/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract GOLPH3 is a PI(4)P-binding oncoprotein implicated in tumor progression, metastasis, and drug resistance, yet no direct small-molecule inhibitors of this target have been reported. In this study, we investigated the inhibitory potential of alkaloids and repurposable FDA-approved drugs against the GPP34 domain of GOLPH3 using molecular docking, molecular dynamics (MD) simulations, and MM-PBSA analysis. A total of 200 alkaloids and 10 representatives each from statins, anti-inflammatories, and antidepressants were screened. Docking results identified bisleuconothine A and notoamide D as the most promising alkaloids, each binding strongly to the PI(4)P-binding pocket (−10.5 kcal/mol). Among FDA-approved drugs, pitavastatin showed the highest affinity (−8.4 kcal/mol). MD simulations demonstrated that these compounds formed stable and energetically favorable complexes with GPP34, as validated by RMSD, RMSF, Rg, hydrogen-bonding, free energy landscape, and principal component analyses. MM-PBSA calculations further confirmed favorable binding free energies, with critical contributions from Phe80, Leu187, and the essential PI(4)P-binding residue Trp81. ADMET and oral bioavailability predictions indicated satisfactory pharmacokinetic profiles, particularly for the alkaloids. Collectively, this work provides the first computational evidence of alkaloid and statin scaffolds as potential GOLPH3 inhibitors, establishing a foundation for future in vitro and in vivo validation toward developing novel anti-GOLPH3 therapeutics. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1.0 INTRODUCTION GOLPH3 is a highly conserved peripheral membrane protein from yeast to humans, which dynamically localizes to the cytoplasmic face of the trans -Golgi, to the trans -Golgi network (TGN), and to plasma membranes, via the direct interaction with Phosphatidylinositol 4-phosphate (PI(4)P), mediated by its unique C-terminal GPP34 domain. At the Golgi, the GOLPH3 function is required for the maintenance of the Golgi structure and vesicle trafficking [1-3]. This GPP34 domain is essential for Golgi localization, as shown by truncation, mutational, and structural analyses. Mutations that disrupt the positively charged pocket within this domain impair both PI(4)P binding and Golgi targeting, demonstrating that PI(4)P interaction is necessary for GOLPH3 function, a mechanism conserved from yeast to humans [4]. A study proved that overexpression of wild-type GOLPH3 reduced cell-cell adhesion, increased Golgi PI(4)P levels, and enhanced invasiveness and lung metastasis in vivo, whereas the PI(4)P-binding-deficient R90L mutant failed to localize to the Golgi, did not promote migration or invasion, and lost metastatic capacity. These findings indicate that GOLPH3’s interaction with PI(4)P is essential for its role in regulating cancer cell behavior and driving metastatic progression [5]. Evidence suggests that GOLPH3, acting as a PI(4)P effector, regulates diverse intracellular vesicle pathways, including transport to the plasma membrane, intra-Golgi trafficking, and endocytic routes, while also being essential for maintaining Golgi structure and proper glycosylation [4, 6-8]. Building on these insights, emerging evidence has elucidated a critical link between PI(4)P-GOLPH3-dependent vesicular trafficking and cancer progression via the Phosphoinositide Transfer Protein Cytoplasmic 1 (PITPNC1) protein. The PITPNC1 oncogene , frequently amplified or overexpressed in various metastatic malignancies including breast, melanoma, and colorectal cancers, has been identified as a pivotal regulator in this pathway [9]. Halberg and colleagues demonstrated that PITPNC1 selectively binds to (PI(4)P) within the Golgi apparatus, facilitating the spatial localization of Rab1B , a small GTPase essential for vesicular trafficking [9]. This interaction promotes the Golgi recruitment of GOLPH3, Golgi extension, and enhanced vesicular release. The PITPNC-Rab1-GOLPH3 network drives the malignant secretion of pro-invasive and pro-angiogenic mediators, which, in turn, leads to cancer phenotypes, metastasis, and angiogenesis [2]. More evidence links deregulation of intracellular vesicle trafficking to several aspects of cancer biology [10, 11]. Rizzo et al also showed the central role of GOLPH3 in regulating the cellular sphingolipidome, thus promoting growth factor signaling and cell proliferation [12]. It has been suggested that GOLPH3 might promote cellular transformation by affecting the glycosylation of key cancer-relevant glycoproteins or glycolipids [12]. Importantly, aberrant glycosylation, such as defective processing of oligomannose glycans, incompletely processed or truncated complex N-glycan and O-glycan, altered sialylation and fucosylation of N-linked and O-linked glycans, is a universal feature of cancer cells and has been implicated in tumor progression and invasiveness [13]. Additionally, a growing body of evidence from both human studies and model organisms suggests that GOLPH3’s oncogenic potential is closely tied to its capacity to regulate the localization of glycosyltransferases across the Golgi cisternae [14] . Frappaolo et al. also stated that the PI(4)P–GOLPH3 axis plays a pivotal role in modulating glycosylation , a fundamental cellular process known to profoundly influence cancer signaling, tumor development, and metastatic progression [2]. Interestingly, several works have highlighted that GOLPH3 has been implicated in multiple oncogenic pathways across diverse cancer types, including mTOR/YB1, PI3K/AKT/mTOR, JAK2/STAT3, Wnt/β-catenin, PKD2/GOLPH3/AKT, MAPK/ERK, and PI(4)P/PI(4)KIIIβ in brain cancer; GOLPH3/MYO18A in neuroblastoma; PI4P/PI4KIIIβ and AKT/FOXO1 in breast cancer; VEGF, JAK2/STAT3, and Wnt/β-catenin in colorectal cancer; mTOR/70S6K in non-small cell lung cancer; Wnt/β-catenin in epithelial ovarian cancer; AKT/mTOR and p70S6K pathways in pancreatic and gastric cancers; and NF-κB and mTOR/70S6K in hepatocellular carcinoma [1, 15-18]. Collectively, these findings underscore that targeting GOLPH3 represents a compelling therapeutic strategy to disrupt its central role in oncogenic signaling, aberrant glycosylation, and vesicle trafficking pathways that drive tumor progression and metastasis across multiple cancer types. Despite the oncogenic role of GOLPH3 in various cancers, specifically lung and colorectal cancer, there remains a significant gap in the development of direct inhibitors targeting this protein. This dearth of research presents both a challenge and an opportunity. Targeting GOLPH3 directly could open new frontiers in cancer therapy, especially for malignancies where its overexpression correlates with poor prognosis. In response to this gap, we aim to identify hits from various classes of FDA-approved drugs that have been repurposed for cancer therapy, along with alkaloid-based compounds known for their anticancer properties, as potential inhibitors of GOLPH3. Drug repurposing involves identifying new therapeutic applications for already approved drugs. It offers several inherent advantages, including reduced development time and cost, owing to existing data on safety, dosage, and toxicity profiles. In recent years, interest in drug repurposing has grown. Successful candidates, such as chlorambucil and bufelson, were initially developed as alkylating agents inspired by the toxic chemical warfare agent mustard gas but were later found to be effective in the treatment of leukemia [19]. Similarly, thalidomide, despite its notorious history of causing severe birth defects, has been repurposed for the treatment of conditions such as leprosy and multiple myeloma [20]. Additionally, arsenic trioxide (a poison) and all-trans retinoic acid (a metabolite of vitamin A) are examples of chemical compounds approved by the FDA in 2000 for the treatment of acute promyelocytic leukemia. Thus, drug repurposing represents a promising and viable strategy for expanding cancer treatment options [21]. Alkaloids represent a highly diverse class of natural products, renowned for their wide range of pharmacological activities, including anticancer properties. These compounds employ diverse molecular mechanisms to inhibit, block, or suppress cancer cell metastasis [22]. Alkaloids have a well-established role in cancer treatment, with several alkaloid-based drugs, such as vincristine, vinblastine, docetaxel, and paclitaxel (Taxol), currently in clinical use, showcasing the practical therapeutic potential of this class of compounds [23, 24]. On the other hand, Studies have revealed the great potential of antidepressant drugs, including tricyclic antidepressants (TCAs) and selective serotonin reuptake inhibitors (SSRIs), for repurposing to cancer therapy via several mechanisms of action [25, 26]. Antidepressants have demonstrated antitumor effects primarily by inducing apoptosis through mitochondrial, death receptor, and calcium-mediated pathways, and by inhibiting pro-survival signaling such as PI3K/Akt/mTOR and ERK/NF-κB [27]. Numerous studies have further demonstrated that some antidepressants have been found to target crucial cellular pathways involved in tumorigenesis, such as the TNF-MAP4K4-JNK, PI3K/Akt/mTOR, and NF-ĸB signaling pathways in different cancers [28-30]. Additionally, Statins, widely used for their lipid-lowering effects, and Anti-inflammatory drugs have been repurposed for cancer therapy due to their ability to downregulate the tumoral cell cycle and survival that depend on PI3K/Akt signaling, induce apoptosis and ultimately inhibit the PI3K/AKT/mTOR signaling pathway, a key axis in tumorigenesis and one functionally linked to GOLPH3 [31-35]. Doumat et al also posited that the anti-cancer role of anti-inflammatory drugs in various cancers is through inhibiting various intrinsic and extrinsic pathways associated with apoptosis and the downregulation of NF-kB, caspase-9, BAX, and BCL-xL [36]. Thus, considering the established anticancer properties of these drug classes and their ability to modulate the PI3K/Akt axis and other GOLPH3-relevant signaling cascades, these compounds offer promising prospects for drug repurposing strategies targeting GOLPH3-mediated oncogenesis. This rationale underpins the present study, which seeks to identify novel GOLPH3 inhibitors through in silico screening of these pharmacological categories, with the goal of preventing GOLPH3 localization to the TGN from the cytosol [37-39]. 2.0 METHODOLOGY Identification and Preparation of Ligands for Docking The 3D structures of two hundred (200) alkaloids of various classes with reported anticancer properties [ 40 – 42 ] were downloaded in structured data format (SDF) from PubChem ( https://pubchem.ncbi.nlm.nih.gov ); a free, user-friendly database storing millions of chemical compounds [ 43 ]. Additionally, we downloaded the 3D-SDF format of FDA-approved statins, anti-inflammatories, and antidepressant drugs from PubChem. These alkaloids and drug compounds, with their respective CIDs and anticancer effects, are shown in Tables 1 and 2 of the Supplementary Data Sheet (SDS). Using PyRx Python prescription 0.8 for 200 steps, energy minimization of the ligands was performed by using Merck molecular force field (MMFF94) along with the conjugate gradient optimization algorithm [ 44 ]. Homology Modelling and Protein Preparation Homology modeling and evaluation were carried out following the procedure described by Ongtanasup, Mazumder [ 45 ]. The GPP34 domain of Golgiphosphoprotein 3 (GOLPH3) was modeled using the SWISS-MODEL server ( https://swissmodel.expasy.org/ ), and the model quality was evaluated using Qualitative Model Energy Analysis (QMEAN) ( https://swissmodel.expasy.org/qmean ). SWISS-MODEL is a completely automated tool used to estimate the three-dimensional structure of proteins. SWISS-MODEL was fed the FASTA format of GOLPH3 protein from the UniprotKB database. The projected model of GPP34 from SWISS-MODEL was an input for the QMEAN study. The QMEAN server offers access to QMEANDisco. It calculates the quality of protein structure prediction models. The Three-dimensional structure of GPP34 was prepared for molecular docking using UCSF Chimera 1.19 [ 46 ]. Hydrogens were added, and AMBER ff14SB charges assigned. Energy minimization was performed using 100 steps and a step size of 0.02 Å, with the steepest descent algorithm implemented in Chimera, to optimize the protein geometry and relieve steric clashes. ProCheck was used to verify the three-dimensional structures by creating the Ramachandran plot. Additionally, the Cavity Plus server was used to identify the binding pockets. Molecular Docking AutoDock Vina, an integral component of the PyRx software package ( https://pyrx.sourceforge.io/ ), was utilized to perform multiple ligand dockings against GPP34. PyRx, a computer-based drug discovery software, is capable of screening compound libraries against potential therapeutic targets and is one of the few docking software packages suitable for multiple docking. With optimized multithreading capabilities, the integrated AutoDock Vina in PyRx demonstrates significantly enhanced speed and efficiency. It internally determines grid charges and establishes the docking space [ 47 ]. Following the preparation of the ligands and protein, the docking process was executed using the Vina Wizard. The docking area encompassing the GPP34 was selected, with the grid box dimensions set to 50 Å × 58 Å × 60 Å. The parameter settings for AutoDock Vina included an exhaustiveness value of 8 and a maximum of 20 generated binding modes. Other optional settings were left at their default values. Subsequently, the binding energy scores and root mean square deviation (RMSD) values of the docked complexes were generated and downloaded in CSV format. Molecular Dynamics Simulation Molecular dynamics (MD) simulations of the protein-ligand complexes were conducted using GROMACS, following the method of Kushwaha, Singh [ 48 ]. After docking and ADMET studies, the lead ligands were subjected to MD simulations to evaluate their binding efficacy and assess the impact of ligand binding on the internal dynamics of the GPP34 domain, alongside the unbound protein. GROMACS (Version 2022.3) was employed for the simulations, using the CHARMM27 force field and TIP3P water model. The topologies and parameter files of the lead compounds were generated via the SwissParam server [ 49 ]. All complexes were simulated within a cubic box with a 1 Å buffer distance, and electro-neutralization was achieved by adding the required ions. Energy minimization was performed using 5,000 steps of the steepest descent method to resolve bad contacts and clashes in the protein. The energy-minimized complexes were then subjected to two stages of equilibration: 100 ps of NVT equilibration, followed by 100 ps of NPT equilibration. In order to avoid the cold solute–hot solvent difficulty, temperature coupling was applied, which was achieved by indexing the system into non-water and water components by using the gmx make_ndx module of GROMACS [ 50 ]. The system temperature was maintained at 300 K using the Berendsen thermostat [ 51 ], while pressure was controlled with the Parrinello-Rahman barostat [ 52 ]. MD simulations were carried out for 50 ns, with coordinates saved every 10 ps. Structural and conformational analyses were conducted using GROMACS analysis modules. Principal Component Analysis Principal Component Analysis (PCA) is a widely employed method for reducing the dimensionality of large datasets and is commonly used in molecular dynamics (MD) simulations to characterize slow or functionally relevant motions in biomolecules [ 53 ]. For all three complexes, the principal components were derived through diagonalization of the covariance matrices, yielding eigenvectors and eigenvalues that describe the directions and magnitudes of molecular motions, respectively. Construction and diagonalization of the covariance matrix were performed using the gmx covar module in GROMACS, while the gmx anaeig module was subsequently used to evaluate the overlap between the extracted principal components and the trajectory coordinates. Free Energy Landscapes The free energy landscape (FEL) is a commonly applied bioinformatics approach for investigating protein folding and aggregation behavior in molecular dynamics (MD) simulations, offering valuable insights into protein stability [ 54 ]. In this study, principal component (PC) values were employed as order parameters, and the FEL of the complexes was generated from the complete simulation trajectories using the gmx sham module. The Gibbs free energy was subsequently evaluated based on the standard equation: G i = − K B T In (N i /N max ) Here, K B​ denotes Boltzmann’s constant, T is the temperature, N i represents the population of bin i, and N max ​ corresponds to the most populated state. The FEL plot was visualized using a color gradient, where red indicated regions of the highest free energy and blue corresponded to the lowest energy states. Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) Binding free energy estimation and per-residue energy decomposition for all complexes were carried out using the MMPBSA.py module of AMBER in combination with the gmx_MMPBSA tool [ 55 , 56 ]. The calculations were performed for the hits docked at the active site of the target protein. Binding energies (BEs) were derived from the final 10 ns segment of the MD trajectories (40–50 ns). The free energy of binding (ΔG binding ​) was computed according to the equation: ∆G binding = G complex − (G protein + G ligand ) where G complex denotes the energy of the ligand-protein complex, while G protein and G ligand​ represent the energies of the unbound protein and ligand, respectively, in aqueous solvent. Energy decomposition and visualization of binding contributions were further analyzed using the gmx_MMPBSA analysis module. ADMET Properties and Bioactivity Prediction The SMILES of the hits were generated and uploaded to the SwissADME ( http://www.swissadme.ch ) and ADMETLab3.0 server for analysis of their drug-like characteristics and other pharmacokinetic parameters. SwissADME is a free online tool for assessing the pharmacokinetics, drug-likeness, and medicinal chemistry friendliness of small molecules [ 57 ]. 3.0 RESULTS AND DISCUSSION GOLPH3 is a PI(4)P-binding oncoprotein that regulates Golgi structure, vesicle trafficking, glycosylation, and multiple oncogenic signaling pathways, with its overexpression strongly linked to cancer progression and metastasis. Despite its central role in diverse cancers, direct inhibitors of GOLPH3 are lacking. Notably, PITPNC1 facilitates the localization of Rab1B at the Golgi, which in turn promotes the recruitment of GOLPH3, Golgi extension, and enhanced vesicular release. Since these trafficking events drive the secretion of pro-invasive and pro-angiogenic mediators, we hypothesize that modulating GOLPH3 localization to the Golgi through inhibition in GOLPH3-overexpressing cells represents a rational strategy for anticancer intervention. Considering the therapeutic promise of drug repurposing and the anticancer potential of alkaloids, antidepressants, statins, and anti-inflammatory drugs, this study aimed to identify novel inhibitors of GOLPH3 through in silico screening to modulate its localization to the Golgi and subsequent function. 3.1 Homology Modeling The 3D structure of the GPP34 domain of GOLPH3 was modeled using SWISS-MODEL, achieving a Global Model Quality Estimation (GMQE) score of 0.77. Typically, a GMQE score above 0.70 is considered reliable, underscoring the accuracy of the generated model [ 58 ]. The template used for modeling was Homo sapiens N-terminal truncated Golgi phosphoprotein 3 (PDB ID: 3KN1) with a resolution of 2.9 Å, and exhibited 100% sequence identity with the supplied amino acid sequence. The QMEANDisCo score, which reflects the average per-residue accuracy by applying distance constraints to QMEAN assessments, was 0.85 ± 0.05 for the GPP34 model, well above the general threshold of 0.6, indicating high model quality [ 59 ]. Further validation was performed using ProCheck's Ramachandran plot analysis (Fig. 1 ), which confirmed that 100% of the residues were positioned within the favored region (92.7%) and the additionally allowed region (7.3%), with no residues in the generously allowed and disallowed regions. A good quality model would be expected to have over 90% in the most favored regions [ 60 ]. Thus, these findings collectively confirm the high quality and reliability of the GOLPH3 model, making it suitable for subsequent computational ligand-receptor interaction studies. 3.2 Binding Pockets Analysis The CavityPlus web service identified 12 (three are shown) potential binding pockets on the protein for molecular docking. Among these, one pocket (the PI(4)P-binding pocket; Fig. 2 : purple) demonstrated strong druggability, while the remaining 11 showed weak druggability, as detailed in Table 1 . Table 1 presents the amino acid residues forming the first three identified binding pockets. Among these, the most druggable site, with a Pkd value of 11.76, indicates a high potential for stable ligand–target interactions. This pocket appears especially favorable for the design of molecular inhibitors, as the selection of suitable ligands for this site could facilitate the development of potent therapeutic candidates [ 61 ]. Table 1 List of amino acid residues in the top-three binding pockets of GPP34 S/N Max Pred Pkd Druggability Residues 1 11.76 strong LEU 249, ASN 172, ARG 74, LEU 89, VAL 182, LEU 69, LEU 181, GLU 175, GLU 64, LEU 68, PHE 197, GLY 76, LEU 67, THR 200, GLU 250, TRP 81, PHE 194, HIS 202, TYR 77, ASP 247, ILE 85, LEU71, PHE 80, ARG 171, MET 199, GLU 75, SER 79, CYS 84, ARG 174, VAL 66, GLN 192, PRO 203, VAL 248, ASN 251, THR 189, SER 246, ASP 73, ALA 178, ASP 198, VAL 173, ASN 82, LEU 177, LEU 204, LYS 179, LYS 72, ARG 176, LEU 187, THR 78, THR 201, GLY70 2 10.30 weak THR 143, LEU 155, LEU 164, TRP 152, GLN 144, GLU 147, PRO 145, PRO 146, GLU 142, ASN 162, LYS 165, TYR 168, PRO 163, HIS 167, ASN 151 3 9.09 weak GLY 158, TRP 161, ASN 172, THR 160, ASP 83, GLU 175, GLN 169, TRP 81, SER 86, SER 87, ARG 90, ARG 171, GLU 154, ARG 174, CYS 84, LEU 170, LEU 156, ALA 178, VAL 173, ASN 82, GLU 159, SER 157, ARG 176, ILE 153 3.3 Molecular Docking and Selection of Hits. The drug discovery pipeline benefits greatly from in silico modeling, which helps reduce attrition rates, shorten clinical trial timelines, and decrease research and development costs [ 62 ]. Considering the documented anticancer activities of various alkaloids, statins, antidepressants, and anti-inflammatory agents, their binding affinities and potential inhibitory effects on GOLPH3 were investigated. Previous studies targeting GOLPH3 in cancer have primarily relied on genetic knockdown strategies (e.g., shRNA, siRNA) rather than small-molecule inhibitors, demonstrating that silencing GOLPH3 can reverse drug resistance and suppress tumor proliferation in colon cancer and non-small cell lung cancer [ 63 , 64 ]. Mechanistic and review studies consistently highlight GOLPH3 as a promising therapeutic target, yet no direct small-molecule inhibitors have been reported to date [ 2 , 3 ]. To bridge this gap, we screened 200 alkaloids and 10 compounds each from the aforementioned drug classes, identified through literature mining, for molecular docking against the GPP34 domain. Docking results (cut-off ≤ − 10.0 kcal/mol) are summarized in Table 2 , with alkaloids exhibiting stronger binding affinities than the FDA-approved drugs. The FDA-approved drugs all showed a lesser binding affinity compared to the alkaloids; however, pitavastatin was selected for its relatively high affinity and interaction with ARG 174, a key residue involved in PI(4)P binding of GPP34. The 2D structures of the hit compounds against GPP34 are depicted in Fig. 3 . Table 2 List of the best 12 alkaloids and best statin and their respective binding affinities determined and validated by PyRx and CB-Dock scoring, respectively. S/N Ligands CID Binding Affinity (Kcal/mol) 1 6-hydroxymanzamine A 10393120 -10.8 2 notoamide D 16127841 -10.5 3 Bisleuconthine A 46881778 -10.5 4 Discorhabdin W 135466418 -10.4 5 Madangamine F 44421333 -10.2 6 Madangamine A 9980274 -10.2 7 Fumiquinazoline C 11339719 -10.1 8 Biemnadin 163156431 -10.1 9 Cytoglobosin F 46209921 -10.1 10 Dragmacidin D 15000037 -10 11 Cytoglobosin G 46209922 -10 12 Arcyriaflavin A 5327723 -10 FDA-approved Statin 1 pitavastatin 5282452 -8.4 Based on PyRx scoring, a total of 12 alkaloid compounds have binding affinities ranging from − 10 to -10.8 kcal/mol. Among these, notoamide D and bisleuconothine A were prioritized due to their strong affinities and interactions with key residues in the PI(4)P-binding pocket of GPP34. A similar rationale guided the selection of pitavastatin from the drug classes evaluated. The PI(4)P-binding residues of GOLPH3 include W81, R90, R171, and R174 [ 65 ], and increasing evidence suggests that the loss of one or more of these residues abolishes PI(4)P-binding in GOLPH3. For instance, Dippold, Ng [ 4 ] introduced charge-neutralizing mutations into human GOLPH3 while preserving protein folding. Individual mutations within the binding pocket (R90L and R171A/R174L) significantly reduced PI(4)P binding in both lipid blot and vesicle assays. Furthermore, expression of these mutants in HeLa cells demonstrated a loss of Golgi localization, underscoring the critical role of PI(4)P binding in GOLPH3 function. Thus, the selection of alkaloids and pitavastatin was based primarily on strong binding affinities, followed by the verification of interactions with PI(4)P-binding residues. In the present study, bisleuconothine A and notoamide D exhibited binding energies of -10.5 kcal/mol each, whereas pitavastatin showed a binding energy of -8.4 kcal/mol against GOLPH3. The 3D structure of the binding pose of the hit compounds and amino acids involved in the binding is depicted in Fig. 4 . Bisleuconothine A formed a hydrogen bond with Gly76 and interacted via other non-covalent bonds with Leu67, Leu71, Met199, Phe197, Phe80, and the PI(4)P-binding residue Arg174, Fig. 5 A. Notoamide D formed a hydrogen bond with HIS 202 and interacted via other non-covalent bonds with Leu67, Leu187, Leu71, Ser79, His202, Ala178, and the PI(4)P-binding residue Trp81, Fig. 5 B. On the other hand, pitavastatin formed two hydrogen bonds with Gly76 and Leu67, while forming other non-covalent bonds with Gly76, Leu67, Leu71, Leu187, Ala178, and Arg174, Fig. 5 C. As shown in Fig. 2 and Table 1 , all of these residues fall within the PI(4)P-binding pocket, having strong druggability and relatively high predicted Pkd. The binding of these hits to the PI(4)P-binding pocket, and particularly to important residues, indicates that they have the potential to inhibit GOLPH3’s binding to PI(4)P. 3.4 Molecular Dynamics Simulation On the basis of docking results, we selected bisleuconothine, notoamide D, and pitavastatin to perform molecular dynamics simulation study. The simulation was carried out on GPP34 unbound, bisleuconothine-, notoamide D-, and pitavastatin-bound systems to study the dynamic behavior of the targeted protein. Quality control of the simulated systems was assessed using temperature, density, and potential energy as validation parameters ( SDS Fig. 1 A-C). All parameters remained stable throughout the simulation. The average system temperature was approximately 300.0 K, consistent with the expected value. The average density across all systems was ~ 1013.2 kg/m³, which is close to both the experimental value for water (1000 kg/m³) and the TIP3P model prediction (1001 kg/m³). The slightly higher density observed is reasonable given the presence of protein/complex in the solvent. Finally, potential energy stabilized in the expected range of negative hundreds of thousands, confirming system equilibration. The 50 ns MD simulation results for GPP34-bisleuconothine complex (GBC), GPP34-Notoamide D complex (GNC), the unbound GPP34 protein, and GPP34-pitavastatin complex (GPC) are shown in Fig. 6 . The Radius of gyration (Rg) is a measure of a protein’s compactness. If a protein is stably folded, it will likely maintain a steady value for Rg. The Rg analysis for GNC, GBC, and GPC complexes was compared, and the results are shown in Fig. 6 A. The Rg analysis showed a consistent trend in the biomolecular systems throughout the simulation period, with all complexes exhibiting similar Rg values as the unbound protein during the 50 ns simulation period. This consistency indicates the compact and stabilized folding in the ligand-bound complex. On the other hand, the root-mean-square deviation (RMSD) calculates the average atomic displacement in a protein, helping determine the structural stability of the protein when unbound and bound to ligands [ 53 ]. The average interatomic distance in the unbound versus ligand-bound target protein provides a measure for comparing conformational changes and assessing protein stability. In the present study, the RMSD of the GNC, GBC, and GPC complexes did not show any significant deviation in comparison to the unbound protein (Fig. 6 B), indicating stable complexes were formed between GOLPH3 and the ligands. While multiple types of interactions contribute to ligand–protein complex stabilization, hydrogen bonding plays a particularly critical role. Generally, a higher frequency of hydrogen bond formation during complex assembly corresponds to increased complex stability [ 48 ]. Hydrogen bond formation between the protein and hit compounds was assessed, and the results are shown in Fig. 6 C. It can be readily observed that pitavastatin formed up to 5 hydrogen bonds, whereas bisleuconothine and notoamide D formed 2 hydrogen bonds each, which helped reinforce the structural integrity of the protein-ligand complexes during the 50 ns simulation. Root-mean-square fluctuation (RMSF) is a key parameter used to evaluate atomic fluctuations of proteins relative to a reference position over the course of a simulation. This enables comparison of residue-level flexibility in the target protein before and after ligand binding. In the present study, RMSF analysis was performed to assess the effect of hit compound binding on the flexible regions of GPP34. A marked reduction in RMSF values was observed for both GBC and GNC across most regions, indicating enhanced stability. By contrast, the pitavastatin-bound complex exhibited greater fluctuations than the unbound protein, particularly for atoms indexed between 2000 and 2500 (Fig. 6 D). 3.5 Principal Component Analysis Protein conformation is critical in defining its function, with structural rigidity being particularly essential at the binding site. Principal component analysis (PCA) was applied to examine the collective motions of the unbound GPP34 and hits-bound complex through the evaluation of eigenvectors and eigenvalues. To better characterize conformational flexibility at the atomic level, the MD trajectory was projected into phase space, yielding a spectrum of eigenvalues (EVs). Each eigenvalue corresponds to a distinct mode of motion, defining its magnitude, while the associated eigenvector specifies the direction [ 54 ]. Detailed analysis of the complexes revealed a sharp decrease in eigenvalues across the first twenty components, indicating that only the initial few eigenvectors capture the dominant motions of the systems (Fig. 7 A). The total variance (trace) of the covariance matrix was approximately 8 nm², suggesting relatively stable protein dynamics throughout the simulation. This value is lower than those reported in some other studies [ 48 , 66 ], likely due to differences in system size, simulation length, and the specific atoms considered in the PCA. The overall flexibility of unbound GPP34 and its ligand-bound complexes was evaluated along the two principal components, PC1 and PC2. The corresponding projections for all three docked complexes and the unbound protein are shown in Fig. 7 B – E. A concentrated distribution in the PCA plot indicates a narrower range of conformational changes, whereas a more dispersed distribution reflects greater flexibility and sampling of alternative conformations [ 67 ]. The 2D projections showed that all systems occupied comparable phase space, consistent with the stability trends observed in other analyses, including FEL, RMSD, and Rg. Nonetheless, closer examination revealed that the GBC complex formed distinct clusters, suggesting increased conformational diversity and flexibility. 3.6 Free Energy Landscapes Free energy landscape (FEL) analysis was carried out to examine the conformational distribution and stability of the protein-ligand complexes across the simulation trajectory. Figures 8 A-D depict the free energy landscapes (FEL) of the protein over a 50 ns molecular dynamics simulation, comparing its behavior in the presence of bisleuconothine A, notoamide D, and pitavastatin. The landscapes are presented along the first two principal components (PC1 and PC2). The FEL was visualized using a color gradient, where blue denotes low-energy states and red indicates high-energy states, with intermediate colours (green and yellow) representing moderate energy levels. The presence of a well-defined low-energy basin in the free energy landscapes suggests a region of conformational stability where the protein preferentially resides during the molecular dynamics simulation. The free energy landscape of the unbound GPP34 system (Fig. 8 A) displayed a single dominant basin, indicating structural stability in aqueous solution with conformational sampling largely restricted to local fluctuations around one state. A similar trend was observed for all complexes, consistent with the stability suggested by PCA, RMSD, and Rg analyses. However, the GBC complex (Fig. 8 B) exhibited two distinct basins, suggesting that ligand binding may have induced alternative conformational substates, consistent with PCA analysis. Generally, for all systems analyzed, the PCA revealed a compact conformational ensemble, while the free energy landscape displayed a dominant central basin corresponding to the lowest free energy state. Together, these results indicate that the protein remained structurally stable and primarily sampled local fluctuations around a single conformational state. 3.7 Binding Free Energy Estimation (MM/PBSA) and Energy Decomposition of GPP34 complexes with Hits. MM-PBSA analysis was performed on the final 10 ns of the trajectories (40–50 ns) using the gmx_MMPBSA tool. This approach was applied to compute the binding free energies and associated thermodynamic parameters. The results are summarized in Table 3 and visualized in Fig. 9 . The complexes exhibited notable stability during the last 10 ns of simulation, with binding free energies of − 37.53, − 23.17, and − 27.67 kcal/mol for GBC, GNC, and GPC, respectively (Table 3 ). Figure 9 A illustrates the contribution of individual energy components to the total binding free energy of the bisleuconothine A-GPP34 complex (GBC). Van der Waals interactions (VDWAALS) were the dominant stabilizing factor (− 57.34 kcal/mol), followed by electrostatic interactions (EEL, − 19.27 kcal/mol). In contrast, polar solvation energy (EPB) opposed binding (+ 44.66 kcal/mol), whereas non-polar solvation energy (ENPOLAR) contributed modest stabilization (− 5.58 kcal/mol). Consequently, the gas-phase energy (GGAS) contributed strongly to binding (− 76.61 kcal/mol), while the solvation energy (GSOLV) partially offset this effect (+ 39.08 kcal/mol). Comparable patterns of energetic contributions were observed for the GNC complex; however, the trend is reversed for the GPC complex, with EEL contributing more to binding (Figs. 9 B and 9 C). Table 3 Components of binding free energy of GPP34 complexes with hit ligands using MM/PBSA approach. Complexes Binding Free Energy (kcal/mol) VDWAALS EEL EPB ENPOLAR GGAS GSOLV TOTAL GBC -57.34 -19.27 44.66 -5.58 -76.61 39.08 -37.53 GNC -37.99 -5.89 24.93 -4.22 -43.88 20.71 -23.17 GPC -39.59 -77.13 93.39 -4.33 -116.72 89.06 -27.67 Furthermore, decomposition of the total binding free energy into per-residue contributions revealed critical insights into the binding mechanism (Figs. 10 A–C). In the bisleuconothine A–GPP34 complex, residues Leu67, Leu68, Leu71, Gly76, Tyr77, Ser79, Phe80, Trp81, Ala178, Leu181, Leu187, and Met199 played dominant roles in stabilizing the interaction. For notoamide D, the most significant contributors included Phe80, Trp81, Glu175, Ala178, Leu187, and Met199, while pitavastatin binding was mediated by Leu67, Leu68, Gly70, Leu71, Tyr77, Trp81, Ile85, Glu175, and Leu187. Importantly, Trp81 – an essential residue for PI(4)P binding within the PI(4)P-binding pocket – was consistently involved in ligand interactions across all three complexes. This strongly suggests that the hit compounds are not only capable of achieving stable binding but may also competitively disrupt GPP34’s natural PI(4)P interactions. By targeting this functional site, these ligands hold considerable promise as the first generation of small-molecule inhibitors of GPP34, a target that has thus far remained unexplored in cancer drug discovery. Bisleuconothine A, with its extensive network of stabilizing interactions, emerges as the most compelling lead candidate for further optimization and preclinical evaluation. 3.8 Analysis of the Bioavailability and ADMET Properties of the Hit Compounds. Table 4 Compliance of hit alkaloids with drug-likeness/bioavailability filters. S/N LIGAND CID SMILES Lipinski Veber Egan 1. Notoamide D 16127841 CC1(C = CC2 = C(O1)C = CC3 = C2N[C@@]4([C@]3(C[C@@H]5N4C(= O)[C@@H]6CCCN6C5 = O)O)C(C)(C)C = C)C Yes, 0 violations Yes, 0 violations Yes, 0 violations 2. Bisleuconthine A 46881778 CC[C@]12CCCN3[C@H]1[C@@]4(CC3)[C@@H](CC2)NC5 = C(C = CC(= C45)O)[C@@H]6C[C@]7(CCCN8[C@H]7C9 = C(CC8)C1 = CC = CC = C1N69)CC No; 2 violations: MW > 500, MLOGP > 4.15 Yes, 0 violations Yes, 0 violations Table 5 ADMET properties of the hit alkaloids and statin Ligands Bisleuconothine A Notoamide D Pitavastatin Absorption PAMPA 0–0.1; Excellent 0–0.3; Excellent 0.1–0.3; Excellent HIA 0–0.1; Excellent 0.5–0.7; Medium 0–0.1; Excellent 50% Bioavailability 0.9–1.0; Poor 0.9–1.0; Poor 0–0.1; Excellent P-gp substrate 0.9–1.0; Poor 0.5–0.7; Medium 0–0.1; Excellent P-gp inhibitor 0–0.1; Excellent 0.9–1.0; Poor 0–0.1; Excellent Distribution BBB 0.9–1.0; Poor 0–0.1; Excellent 0–0.1; Excellent PPB 92.6%; Poor 93.8%; Poor 98.8%; Poor Fu 6.2%; Excellent 5.0%; Medium 0.6%; Poor Metabolism CYP1A2 Inhibitor 0–0.1 0–0.1 0.5–0.7 CYP2C19 Inhibitor 0–0.1 0.7–0.9 0.1–0.3 CYP2C9 Inhibitor 0–0.1 0–0.1 0.9–1.0 CYP2D6 Inhibitor 0.9–1.0 0–0.1 0–0.1 CYP3A4 Inhibitor 0.9–1.0 0.9–1.0 0–0.1 Excretion CL plasma (ml/min/Kg) 7.13; Medium 6.528; Medium 9.334; Medium Toxicity Ames Toxicity 0.73; Poor 0.42; Medium 0.553; Medium Hepatotoxicity 0.811; Poor 0.719; Poor 0.938; Poor Nephrotoxicity 0.584; Medium 0.741; Poor 0.999; Poor Cardiotoxicity (hERG blocker) 0.833; Poor 0.052; Excellent 0.319; Medium In this study, the ADMET profiles of the selected alkaloid hits were evaluated using the ADMETlab 3.0 web server and compared with Pitavastatin, an FDA-approved drug. ADMETlab 3.0 is built on an extensive dataset of over 400,000 molecular records and supports automated batch analysis through its integrated API. The platform also provides uncertainty estimates to facilitate the selection of the most reliable drug candidates. Its predictive framework employs a multi-task Directed Message Passing Neural Network (DMPNN) in conjunction with molecular descriptors, enabling fast, parallel computation across multiple endpoints while preserving high accuracy and robustness [ 68 ]. The combination of these techniques allowed for a comprehensive and efficient assessment of the alkaloids' drug-like properties. Absorption analysis (Table 5 ) suggests that both Bisleuconothine A and Notoamide D possess excellent passive permeability across biological membranes. Bisleuconothine A is predicted to have excellent intestinal absorption, more closely aligning with the high absorption profile of Pitavastatin, whereas Notoamide D exhibits only moderate intestinal absorption. Despite these favorable absorption attributes, both alkaloids may have low systemic bioavailability, in contrast to the predicted higher 50% bioavailability of Pitavastatin. P-glycoprotein (P-gp) substrates are actively expelled from cells, which can lower intracellular drug levels and compromise therapeutic outcomes [ 69 , 70 ]. Bisleuconothine A is predicted to be a P-gp substrate and a non-inhibitor, suggesting that it may be susceptible to normal P-gp-mediated efflux, which may lower its bioavailability while posing minimal risk of drug–drug interactions. This also aligns with Pitavastatin’s P-gp profile. ADMET analysis also suggests that Pitavastatin may be a non-substrate and non-inhibitor of P-gp; thus, this could indicate that it is neither actively expelled from cells nor capable of blocking P-gp–mediated efflux, thereby supporting its high and consistent bioavailability. In contrast, Notoamide D is predicted to be both a P-gp inhibitor and a moderate substrate, suggesting that it may partially block its efflux and that of other P-gp substrates, thereby potentially enhancing drug absorption and retention, although its own bioavailability may still be limited at lower concentrations due to moderate efflux. Based on these results, Bisleuconothine A demonstrates a more favorable absorption profile compared to Notoamide D. Only Notoamide D and Pitavastatin indicate a potential of not crossing the blood–brain barrier (BBB), Table 5 , thus indicating minimal central nervous system (CNS) penetration and a reduced risk of CNS-related adverse effects [ 71 ]. Furthermore, plasma protein binding (PPB) is a key mechanism affecting drug uptake and distribution, strongly influencing pharmacodynamics. Drugs with high protein binding tend to have a low therapeutic index [ 72 ]. The alkaloid hits and Pitavastatin have over 90% PPB; thus, they are predicted to have a low therapeutic index due to reduced free drug concentrations. At the same time, Bisleuconothine A, with a higher fraction unbound (Fu) in plasma, is expected to potentially have a better therapeutic index than Notoamide D. Higher serum protein binding reduces the fraction of free drug available to cross cellular membranes and diffuse into tissues, thereby potentially limiting pharmacological activity [ 73 , 74 ]. Based on these results, Notoamide D is predicted to have a more favorable distribution profile compared to Bisleuconothine A. Maintaining normal cytochrome P450 (CYP) enzyme activity is essential in drug development, as inhibition can impair clearance, cause accumulation, and lead to drug–drug interactions or toxic effects [ 75 , 76 ]. The CYP2 family is the largest CYP family, with CYP2D6 and CYP2C9 being major contributors to drug metabolism. CYP2D6, the most common mutant isoform, is involved in the metabolism of approximately 25% of clinical drugs. The CYP3A subfamily, particularly CYP3A4 and CYP3A5, also plays a pivotal role in drug discovery and development, metabolizing over 30% of all clinically used drugs and representing the most abundant CYP enzymes in the human body [ 77 ]. For potential anticancer drugs, inhibition of these CYP isoforms is generally undesirable because it can lead to reduced clearance of co-administered drugs, drug–drug interactions, and increased toxicity risk [ 78 , 79 ]. As depicted in Table 5 , bisleuconothine A inhibited CYP2D6 and CYP3A4, whereas Notoamide D inhibited CYP2C19 and CYP3A4. In comparison, Pitavastatin inhibited CYP1A2 and CYP2C9, suggesting that it has a more favorable inhibition profile compared to the two alkaloid hits. These interactions indicate that Notoamide D may have a more desirable metabolic profile than Bisleuconothine A; however, careful control and monitoring of both compounds remain essential to minimize potential risks. Furthermore, Table 5 shows that bisleuconothine A and notoamide D exhibited moderate plasma clearance rates of 7.13 ml/min/kg and 6.528 ml/min/kg, respectively, while Pitavastatin also exhibited a moderate clearance rate of 9.334 ml/min/kg. This suggests that Bisleuconothine A may have a better excretion profile than Notoamide D. The predicted toxicities of the hit alkaloids are also presented in probabilities. Values very close to 1 indicate that the corresponding hit may exhibit such toxicity; those in the range of 0.3 to 0.7 show low tendencies, while those between 0 and 0.3 are not likely to exhibit the associated toxicity. Thus, Notoamide D showed a moderate tendency to be AMES mutagenic, and a propensity to cause damage to the liver, while Bisleuconothine A is predicted to be AMES mutagenic and hepatotoxic. However, while Notoamide D may cause damage to the kidneys, Bisleuconothine A is predicted not to induce such an effect. In comparison, Pitavastatin showed a tendency to be hepatotoxic and nephrotoxic. Additionally, cardiotoxicity, particularly hERG-related cardiotoxicity, is a significant concern with anti-cancer drugs due to its potential to induce arrhythmias, cardiac contractile dysfunction, coronary artery disease, and hypertension, impacting the quality of life for cancer patients [ 80 ]. According to ADMETlab 3.0, compounds with an IC₅₀ ≤ 10 µM or ≥ 50% inhibition at 10 µM are classified as hERG blockers, while those with an IC₅₀ >10 µM or < 50% inhibition at 10 µM are considered non-blockers. The output value, ranging from 0 to 1, indicates the probability of a compound being a hERG blocker. Bisleuconothine A is predicted to be an hERG blocker, while Notoamide D is predicted to be a non-hERG blocker, indicating a more favorable cardiac safety profile. This indicates that Notoamide D has a better toxicology profile than Bisleuconothine A. Overall, Notoamide D showed more favourable ADMET properties as predicted by ADMETlab 3.0 server; however, additional investigations in a preclinical trial are required to evaluate their pharmacokinetic profiles and safety profiles, to advance their candidacy for anti-GOLPH3 drug development. Overall, the ADMET analysis offered valuable insight into the pharmacokinetic and toxicity profiles of Bisleuconothine A and Notoamide D, highlighting factors that may influence their clinical potential. To complement these findings, we further evaluated their oral bioavailability using Lipinski’s Rule of Five (RoF), as well as Veber’s and Egan’s filters. While these filters are not absolute predictors, they serve as a practical first step in assessing whether a compound is likely to achieve sufficient systemic exposure when taken orally. Lipinski’s rule considers parameters such as molecular weight (≤ 500 g/mol), hydrogen bond donors (≤ 5), hydrogen bond acceptors (≤ 10), and lipophilicity (MLOGP < 4.15). Veber’s filter extends this evaluation by emphasizing molecular flexibility and polarity, requiring ≤ 10 rotatable bonds and a topological polar surface area (TPSA) ≤ 140 Ų, properties known to favor passive membrane permeability and oral absorption. Similarly, Egan’s filter combines TPSA (≤ 131.6 Ų) with WLOGP (≤ 5.88) to refine absorption prediction and reduce false negatives. [ 45 , 81 ]. Among the two alkaloids, Bisleuconothine A violated only Lipinski’s rule (MW > 500 g/mol and MLOGP > 4.15) as shown in Table 4 , reflecting its bulky and lipophilic nature, which is predicted to limit oral bioavailability. Nonetheless, many natural products and approved drugs remain orally bioavailable despite violating one or more of these rules, indicating that such violations do not necessarily preclude clinical usefulness [ 82 , 83 ]. Moreover, several drugs developed after the introduction of Lipinski’s rule occupy the beyond-RoF space, further demonstrating that noncompliance does not exclude the possibility of oral bioavailability [ 84 ]. However, It should be noted that this remains a prediction, as Lipinski’s rules were derived from a relatively small dataset (~ 2,200 drugs from the World Drug Index) and may not fully capture the diversity of modern chemical space explored in drug discovery [ 85 ]. In contrast, Notoamide D satisfied all of Lipinski’s, Veber’s, and Egan’s criteria, suggesting a higher likelihood of oral bioavailability. 4.0 Conclusion Alkaloids and certain classes of FDA-approved drugs, including statins, anti-inflammatories, and antidepressants, have well-documented roles in cancer treatment. Therefore, the computational identification of novel compounds from these categories represents a timely strategy to discover potential inhibitors of GOLPH3, an underexplored oncoprotein. In the present molecular docking study, bisleuconothine A, notoamide D, and pitavastatin emerged as promising hits, demonstrating significant binding affinity for the active site/PI(4)P-binding pocket of the GPP34 domain. Subsequent molecular dynamics (MD) simulations and MM-PBSA analyses confirmed that these compounds stabilized the GOLPH3–ligand complexes both structurally and energetically. Of particular importance, all three ligands were shown to disrupt Trp81, a residue essential for PI(4)P binding and, consequently, for GOLPH3’s oncogenic activity. Per-residue energy decomposition further highlighted Trp81 as a critical contributor to complex stabilization. In addition, ADMET and oral bioavailability predictions suggest that the alkaloid hits possess favorable drug-like properties and may serve as strong candidates for further preclinical evaluation. Collectively, these findings indicate that bisleuconothine A, notoamide D, and pitavastatin represent potential lead compounds for the development of the first generation of small-molecule anti-GOLPH3 agents in cancer therapy. Declarations Author Contributions: Conceptualization, Z.O. and R.K.; methodology, Z.O. and R.K.; software, Z.O., E.G., A.T., and J.T.; validation, E.G., and A.T.; formal analysis, Z.O., R.K., and E.G.; resources, Z.O., J.T., and A.T.; data curation, E.G., A.T., J.T., and Z.O.; writing - original draft preparation, G.C., A.T., and J.T.; writing - review and editing, Z.O., G.C., and R.K.; supervision, Z.O. and R.K. All authors have read and agreed to this version of the manuscript. Funding: This research received no external funding. Data Availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: No potential competing interests were reported by the authors References Sechi, S., et al., Oncogenic roles of GOLPH3 in the physiopathology of cancer. 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13:06:24","extension":"html","order_by":53,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195643,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/d32c212ec788dbd21f1d4ed3.html"},{"id":92950287,"identity":"99d3cf68-fdcb-4a4f-8ba3-c5dc75e190f7","added_by":"auto","created_at":"2025-10-07 13:06:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73580,"visible":true,"origin":"","legend":"\u003cp\u003eThe Ramachandran plot illustrates the phi-psi torsion angles for each GPP34 residue.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/2b728931bed6da5cac3837ac.png"},{"id":92950404,"identity":"6828fafc-5cc4-45f0-8384-20cb97a3aae4","added_by":"auto","created_at":"2025-10-07 13:06:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101967,"visible":true,"origin":"","legend":"\u003cp\u003eThe detected cavities on the GPP34 domain; purple: P(I)4P-binding pocket, strong druggability with 11.76 max pred Pkd; yellow: 10.3 max pred Pkd; and blue: 9.09 max pred Pkd\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/315c06321d6c964c9566b47f.png"},{"id":92950616,"identity":"fcfec7a8-f090-40aa-bf45-2eaf4aa4ebcc","added_by":"auto","created_at":"2025-10-07 13:06:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51192,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of the two hit alkaloids and statin: (A) bisleuconothine; (B) notoamide D; and (C) pitavastatin\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/2173f8ad9c566b6c53f090d5.png"},{"id":92951739,"identity":"266a0241-f1d5-45f6-9896-f65c5d56c099","added_by":"auto","created_at":"2025-10-07 13:14:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":602646,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction of hit compounds at the PI(4)P-binding site. (A), (C), and (E) Binding pose of bisleuconothine, notoamide D, and pitavastatin, respectively. (B), (D), and (F) Interaction of GOLPH3’s amino acids residues with bisleuconothine, notoamide D, and pitavastatin, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/7a67de1336bc66979c37b16f.png"},{"id":92950288,"identity":"c190e3fe-e576-4b91-b538-97ee09150b55","added_by":"auto","created_at":"2025-10-07 13:06:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":256266,"visible":true,"origin":"","legend":"\u003cp\u003e2D diagrams of the interactions between the amino acid residues of GOLPH3 and (A) biscleuconothine A; (B) notoamide D; and (C) pitavastatin\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/bdef409f04960b54ca2d661c.png"},{"id":92951705,"identity":"6e7b2538-eaa6-4bbc-ade6-b295dda419dc","added_by":"auto","created_at":"2025-10-07 13:14:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":163239,"visible":true,"origin":"","legend":"\u003cp\u003eMD simulation trajectory plot of GPP34 in the unbound and hit compounds bound complex. (A) The Rg of the GPP34 and bisleuconothine/notoamide D/pitavastatin-bound complex during 50 ns MD simulation. (B) RMSD of the GPP34 and bisleuconothine/notoamide D/pitavastatin-bound complex during 50 ns MD simulation. (C) Plot of hydrogen bond formation between the GPP34 and bisleuconothine/notoamide D/pitavastatin-bound complex during 50 ns MD simulation. (D) The RMSF values of the GPP34 and bisleuconothine/notoamide D/pitavastatin-bound complex during 50 ns MD simulation.\u003cstrong\u003e \u003c/strong\u003eUnbound GPP34, black color; bisleuconothine-bound complex (GBC), red; notoamide D-bound complex (GNC), green; and pitavastatin-bound complex (GPC), blue color.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/5b8dfaa9c1b7a8969322f682.png"},{"id":92950286,"identity":"1a95f224-1a95-4264-9f3c-2da289436c75","added_by":"auto","created_at":"2025-10-07 13:06:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":451848,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis of all systems over a 50 ns MD simulation. (A) Plot of eigenvalues versus eigenvectors derived from the covariance matrix of backbone atoms, constructed from the 50 ns trajectory. Only the first 20 eigenvectors are shown. (B – E) Projection plots of the first two principal components (PC1 and PC2) in phase space for unbound protein, GBC; red, GNC; green, and GPC; blue, illustrating conformational sampling over the simulation period.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/5418a6b7af3e9b8cd8b34c4d.png"},{"id":92950551,"identity":"c9b3f452-9794-422d-9a59-8e3d2489c584","added_by":"auto","created_at":"2025-10-07 13:06:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":195307,"visible":true,"origin":"","legend":"\u003cp\u003eFree energy landscape (FEL) analysis of the protein over a 50 ns molecular dynamics simulation. FEL plots comparing the (A) protein in the presence of (B) bisleuconothine A, (C) notoamide D, and (D) pitavastatin. The landscapes are presented along the first two principal components (PC1 and PC2). The FEL was visualized using a color gradient, where blue denotes low-energy states and red indicates high-energy states, with intermediate colors (green and yellow) representing moderate energy levels.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/372443702d1c6268328f948d.png"},{"id":92950411,"identity":"3657a5eb-4c06-4f97-b240-dd82692550a2","added_by":"auto","created_at":"2025-10-07 13:06:13","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":121990,"visible":true,"origin":"","legend":"\u003cp\u003eThe binding free energy terms obtained from MM/PBSA calculations concerning the interaction of (A) bisleuconothine, (B) notoamide D, and (C) pitavastatin with the GPP34 domain of GOLPH3\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/46e0717c64c1ad4407159fdb.png"},{"id":92950608,"identity":"9cb4b40d-7892-4597-a287-ce0b315ebfff","added_by":"auto","created_at":"2025-10-07 13:06:30","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":68435,"visible":true,"origin":"","legend":"\u003cp\u003ePer-residue energy decomposition analysis (MM/PBSA) showing the contributions of active-site residues to the stabilization of ligand binding. (A) Bisleuconothine A–GPP34 complex, (B) Notoamide D–GPP34 complex, and (C) Pitavastatin–GPP34 complex. Residues with significant energetic contributions highlight the key interactions driving complex stability.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/83288fd95846eb14214d293d.png"},{"id":92951746,"identity":"0b36ed15-2c60-4ed7-b69d-ae15708da847","added_by":"auto","created_at":"2025-10-07 13:14:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3457944,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/32ff4efa-71e0-46b2-977f-b84a32e2c696.pdf"},{"id":92950371,"identity":"4f4ea5ad-aadf-4c19-bf44-b9bfb4a94499","added_by":"auto","created_at":"2025-10-07 13:06:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":125916,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarydatasheet.docx","url":"https://assets-eu.researchsquare.com/files/rs-7524490/v1/f8fbadd23a8dfa19322f9ed7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Alkaloids and Repurposed Drugs as Potential Small-Molecule Inhibitors of GOLPH3 in Colorectal and Lung Cancer Using Molecular Docking, Molecular Dynamics, and MM-PBSA Analysis","fulltext":[{"header":"1.0 INTRODUCTION","content":"\u003cp\u003eGOLPH3 is a highly conserved peripheral membrane protein from yeast to humans, which\u0026nbsp;dynamically localizes to the cytoplasmic face of the \u003cem\u003etrans\u003c/em\u003e-Golgi, to the \u003cem\u003etrans\u003c/em\u003e-Golgi network (TGN), and to plasma membranes,\u0026nbsp;via the direct interaction with\u0026nbsp;Phosphatidylinositol 4-phosphate (PI(4)P), mediated by its unique C-terminal GPP34 domain.\u0026nbsp;At the Golgi, the GOLPH3 function is required for the maintenance of the Golgi structure and vesicle trafficking\u0026nbsp;[1-3]. This\u0026nbsp;GPP34 domain is essential for Golgi localization, as shown by truncation, mutational, and structural analyses. Mutations that disrupt the positively charged pocket within this domain impair both\u0026nbsp;PI(4)P\u0026nbsp;binding and Golgi targeting, demonstrating that\u0026nbsp;PI(4)P\u0026nbsp;interaction is necessary for GOLPH3 function, a mechanism conserved from yeast to humans\u0026nbsp;[4]. A\u0026nbsp;study proved that overexpression of wild-type GOLPH3 reduced cell-cell adhesion, increased Golgi PI(4)P levels, and enhanced invasiveness and lung metastasis in vivo, whereas the PI(4)P-binding-deficient R90L mutant failed to localize to the Golgi, did not promote migration or invasion, and lost metastatic capacity. These findings indicate that GOLPH3’s interaction with PI(4)P is essential for its role in regulating cancer cell behavior and driving metastatic progression\u0026nbsp;[5].\u003c/p\u003e\n\u003cp\u003eEvidence suggests that GOLPH3, acting as a PI(4)P effector, regulates diverse intracellular vesicle pathways, including transport to the plasma membrane, intra-Golgi trafficking, and endocytic routes, while also being essential for maintaining Golgi structure and proper glycosylation [4, 6-8]. Building on these insights, emerging evidence has elucidated a critical link between PI(4)P-GOLPH3-dependent vesicular trafficking and cancer progression via the Phosphoinositide Transfer Protein Cytoplasmic 1 (PITPNC1) protein. The \u003cstrong\u003e\u003cem\u003ePITPNC1\u003c/em\u003e oncogene\u003c/strong\u003e, frequently amplified or overexpressed in various metastatic malignancies including breast, melanoma, and colorectal cancers, has been identified as a pivotal regulator in this pathway [9]. Halberg and colleagues demonstrated that PITPNC1 selectively binds to (PI(4)P) within the Golgi apparatus, facilitating the spatial localization of \u003cstrong\u003eRab1B\u003c/strong\u003e, a small GTPase essential for vesicular trafficking [9]. This interaction promotes the Golgi recruitment of GOLPH3,\u0026nbsp;Golgi extension, and enhanced vesicular release. The PITPNC-Rab1-GOLPH3 network drives the malignant secretion of pro-invasive and pro-angiogenic mediators, which, in turn, leads to cancer phenotypes, metastasis, and angiogenesis\u0026nbsp;[2].\u0026nbsp;More evidence links deregulation of intracellular vesicle trafficking to several aspects of cancer biology\u0026nbsp;[10, 11]. Rizzo \u003cem\u003eet al\u003c/em\u003e also showed the central role of GOLPH3 in regulating the cellular sphingolipidome, thus promoting growth factor signaling and cell proliferation\u0026nbsp;[12].\u003c/p\u003e\n\u003cp\u003eIt has been suggested that GOLPH3 might promote cellular transformation by affecting the glycosylation of key cancer-relevant glycoproteins or glycolipids \u0026nbsp; [12]. Importantly, aberrant glycosylation, such as defective processing of oligomannose glycans, incompletely processed or truncated complex N-glycan and O-glycan, altered sialylation and fucosylation of N-linked and O-linked glycans, is a universal feature of cancer cells and has been implicated in tumor progression and invasiveness [13].\u0026nbsp;Additionally, a growing body of evidence from both human studies and model organisms suggests that \u003cstrong\u003eGOLPH3’s oncogenic potential is closely tied to its capacity to regulate the localization of glycosyltransferases across the Golgi cisternae\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[14]\u003c/strong\u003e.\u0026nbsp;Frappaolo et al. also stated that the \u003cstrong\u003ePI(4)P–GOLPH3 axis\u003c/strong\u003e plays a pivotal role in modulating \u003cstrong\u003eglycosylation\u003c/strong\u003e, a fundamental cellular process known to profoundly influence cancer signaling, tumor development, and metastatic progression\u0026nbsp;[2].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, several works have highlighted that\u0026nbsp;GOLPH3 has been implicated in multiple oncogenic pathways across diverse cancer types, including mTOR/YB1, PI3K/AKT/mTOR, JAK2/STAT3, Wnt/β-catenin, PKD2/GOLPH3/AKT, MAPK/ERK, and PI(4)P/PI(4)KIIIβ in brain cancer; GOLPH3/MYO18A in neuroblastoma; PI4P/PI4KIIIβ and AKT/FOXO1 in breast cancer; VEGF, JAK2/STAT3, and Wnt/β-catenin in colorectal cancer; mTOR/70S6K in non-small cell lung cancer; Wnt/β-catenin in epithelial ovarian cancer; AKT/mTOR and p70S6K pathways in pancreatic and gastric cancers; and NF-κB and mTOR/70S6K in hepatocellular carcinoma [1, 15-18].\u0026nbsp;Collectively, these findings underscore that targeting GOLPH3 represents a compelling therapeutic strategy to disrupt its central role in oncogenic signaling, aberrant glycosylation, and vesicle trafficking pathways that drive tumor progression and metastasis across multiple cancer types.\u003c/p\u003e\n\u003cp\u003eDespite the oncogenic role of GOLPH3 in various cancers, specifically lung and colorectal cancer, there remains a significant gap in the development of direct inhibitors targeting this protein. This dearth of research presents both a challenge and an opportunity. Targeting GOLPH3 directly could open new frontiers in cancer therapy, especially for malignancies where its overexpression correlates with poor prognosis. In response to this gap, we aim to identify hits from various classes of FDA-approved drugs that have been repurposed for cancer therapy, along with alkaloid-based compounds known for their anticancer properties, as potential inhibitors of GOLPH3.\u003c/p\u003e\n\u003cp\u003eDrug repurposing involves identifying new therapeutic applications for already approved drugs. It offers several inherent advantages, including reduced development time and cost, owing to existing data on safety, dosage, and toxicity profiles. In recent years, interest in drug repurposing has grown. Successful candidates, such as chlorambucil and bufelson, were initially developed as alkylating agents inspired by the toxic chemical warfare agent mustard gas but were later found to be effective in the treatment of leukemia [19]. Similarly, thalidomide, despite its notorious history of causing severe birth defects, has been repurposed for the treatment of conditions such as leprosy and multiple myeloma [20]. Additionally, arsenic trioxide (a poison) and all-trans retinoic acid (a metabolite of vitamin A) are examples of chemical compounds approved by the FDA in 2000 for the treatment of acute promyelocytic leukemia. Thus, drug repurposing represents a promising and viable strategy for expanding cancer treatment options [21].\u003c/p\u003e\n\u003cp\u003eAlkaloids represent a highly diverse class of natural products, renowned for their wide range of pharmacological activities, including anticancer properties. These compounds employ diverse molecular mechanisms to inhibit, block, or suppress cancer cell metastasis [22]. Alkaloids have a well-established role in cancer treatment, with several alkaloid-based drugs, such as vincristine, vinblastine, docetaxel, and paclitaxel (Taxol), currently in clinical use, showcasing the practical therapeutic potential of this class of compounds [23, 24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, Studies have revealed the great potential of antidepressant drugs, including tricyclic antidepressants (TCAs) and selective serotonin reuptake inhibitors (SSRIs), for repurposing to cancer therapy via several mechanisms of action [25, 26]. Antidepressants have demonstrated antitumor effects primarily by inducing apoptosis through mitochondrial, death receptor, and calcium-mediated pathways, and by inhibiting pro-survival signaling such as PI3K/Akt/mTOR and ERK/NF-κB [27]. Numerous studies have further demonstrated that some antidepressants have been found to target crucial cellular pathways involved in tumorigenesis, such as the TNF-MAP4K4-JNK, PI3K/Akt/mTOR, and NF-ĸB signaling pathways in different cancers [28-30]. Additionally,\u0026nbsp;Statins, widely used for their lipid-lowering effects, and Anti-inflammatory drugs\u0026nbsp;have been repurposed for cancer therapy due to their ability to\u0026nbsp;downregulate the tumoral cell cycle and survival that depend on PI3K/Akt signaling,\u0026nbsp;induce apoptosis and ultimately inhibit the PI3K/AKT/mTOR signaling pathway, a key axis in tumorigenesis and one functionally linked to GOLPH3\u0026nbsp;[31-35].\u0026nbsp;Doumat\u003cem\u003e\u0026nbsp;et al\u0026nbsp;\u003c/em\u003ealso posited that the anti-cancer role of anti-inflammatory drugs in various cancers is through inhibiting various intrinsic and extrinsic pathways associated with apoptosis and the downregulation of NF-kB, caspase-9, BAX, and BCL-xL\u0026nbsp;[36]. Thus, considering the established anticancer properties of these drug classes and their ability to modulate the PI3K/Akt axis and other GOLPH3-relevant signaling cascades, these compounds offer promising prospects for drug repurposing strategies targeting GOLPH3-mediated oncogenesis. This rationale underpins the present study, which seeks to identify novel GOLPH3 inhibitors through in \u003cem\u003esilico\u003c/em\u003e screening of these pharmacological categories,\u0026nbsp;with the goal of preventing GOLPH3 localization to the TGN from the cytosol\u0026nbsp;[37-39].\u003c/p\u003e"},{"header":"2.0 METHODOLOGY","content":"\u003cp\u003e\u003cb\u003eIdentification and Preparation of Ligands for Docking\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 3D structures of two hundred (200) alkaloids of various classes with reported anticancer properties [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] were downloaded in structured data format (SDF) from PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); a free, user-friendly database storing millions of chemical compounds [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Additionally, we downloaded the 3D-SDF format of FDA-approved statins, anti-inflammatories, and antidepressant drugs from PubChem. These alkaloids and drug compounds, with their respective CIDs and anticancer effects, are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e of the Supplementary Data Sheet (SDS). Using PyRx Python prescription 0.8 for 200 steps, energy minimization of the ligands was performed by using Merck molecular force field (MMFF94) along with the conjugate gradient optimization algorithm [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eHomology Modelling and Protein Preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHomology modeling and evaluation were carried out following the procedure described by Ongtanasup, Mazumder [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The GPP34 domain of Golgiphosphoprotein 3 (GOLPH3) was modeled using the SWISS-MODEL server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://swissmodel.expasy.org/\u003c/span\u003e\u003cspan address=\"https://swissmodel.expasy.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the model quality was evaluated using Qualitative Model Energy Analysis (QMEAN) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://swissmodel.expasy.org/qmean\u003c/span\u003e\u003cspan address=\"https://swissmodel.expasy.org/qmean\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). SWISS-MODEL is a completely automated tool used to estimate the three-dimensional structure of proteins. SWISS-MODEL was fed the FASTA format of GOLPH3 protein from the UniprotKB database. The projected model of GPP34 from SWISS-MODEL was an input for the QMEAN study. The QMEAN server offers access to QMEANDisco. It calculates the quality of protein structure prediction models. The Three-dimensional structure of GPP34 was prepared for molecular docking using UCSF Chimera 1.19 [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Hydrogens were added, and AMBER ff14SB charges assigned. Energy minimization was performed using 100 steps and a step size of 0.02 \u0026Aring;, with the steepest descent algorithm implemented in Chimera, to optimize the protein geometry and relieve steric clashes. ProCheck was used to verify the three-dimensional structures by creating the Ramachandran plot. Additionally, the Cavity Plus server was used to identify the binding pockets.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular Docking\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAutoDock Vina, an integral component of the PyRx software package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pyrx.sourceforge.io/\u003c/span\u003e\u003cspan address=\"https://pyrx.sourceforge.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was utilized to perform multiple ligand dockings against GPP34. PyRx, a computer-based drug discovery software, is capable of screening compound libraries against potential therapeutic targets and is one of the few docking software packages suitable for multiple docking. With optimized multithreading capabilities, the integrated AutoDock Vina in PyRx demonstrates significantly enhanced speed and efficiency. It internally determines grid charges and establishes the docking space [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Following the preparation of the ligands and protein, the docking process was executed using the Vina Wizard. The docking area encompassing the GPP34 was selected, with the grid box dimensions set to 50 \u0026Aring; \u0026times; 58 \u0026Aring; \u0026times; 60 \u0026Aring;. The parameter settings for AutoDock Vina included an exhaustiveness value of 8 and a maximum of 20 generated binding modes. Other optional settings were left at their default values. Subsequently, the binding energy scores and root mean square deviation (RMSD) values of the docked complexes were generated and downloaded in CSV format.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular Dynamics Simulation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMolecular dynamics (MD) simulations of the protein-ligand complexes were conducted using GROMACS, following the method of Kushwaha, Singh [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. After docking and ADMET studies, the lead ligands were subjected to MD simulations to evaluate their binding efficacy and assess the impact of ligand binding on the internal dynamics of the GPP34 domain, alongside the unbound protein. GROMACS (Version 2022.3) was employed for the simulations, using the CHARMM27 force field and TIP3P water model. The topologies and parameter files of the lead compounds were generated via the SwissParam server [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. All complexes were simulated within a cubic box with a 1 \u0026Aring; buffer distance, and electro-neutralization was achieved by adding the required ions. Energy minimization was performed using 5,000 steps of the steepest descent method to resolve bad contacts and clashes in the protein. The energy-minimized complexes were then subjected to two stages of equilibration: 100 ps of NVT equilibration, followed by 100 ps of NPT equilibration. In order to avoid the cold solute\u0026ndash;hot solvent difficulty, temperature coupling was applied, which was achieved by indexing the system into non-water and water components by using the \u003cem\u003egmx make_ndx module\u003c/em\u003e of GROMACS [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The system temperature was maintained at 300 K using the Berendsen thermostat [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], while pressure was controlled with the Parrinello-Rahman barostat [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. MD simulations were carried out for 50 ns, with coordinates saved every 10 ps. Structural and conformational analyses were conducted using GROMACS analysis modules.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrincipal Component Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrincipal Component Analysis (PCA) is a widely employed method for reducing the dimensionality of large datasets and is commonly used in molecular dynamics (MD) simulations to characterize slow or functionally relevant motions in biomolecules [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. For all three complexes, the principal components were derived through diagonalization of the covariance matrices, yielding eigenvectors and eigenvalues that describe the directions and magnitudes of molecular motions, respectively. Construction and diagonalization of the covariance matrix were performed using the \u003cem\u003egmx covar\u003c/em\u003e module in GROMACS, while the \u003cem\u003egmx anaeig\u003c/em\u003e module was subsequently used to evaluate the overlap between the extracted principal components and the trajectory coordinates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFree Energy Landscapes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe free energy landscape (FEL) is a commonly applied bioinformatics approach for investigating protein folding and aggregation behavior in molecular dynamics (MD) simulations, offering valuable insights into protein stability [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In this study, principal component (PC) values were employed as order parameters, and the FEL of the complexes was generated from the complete simulation trajectories using the \u003cem\u003egmx sham\u003c/em\u003e module. The Gibbs free energy was subsequently evaluated based on the standard equation:\u003c/p\u003e\u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003csub\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;\u0026minus;\u0026thinsp;K\u003c/b\u003e\u003csub\u003e\u003cb\u003eB\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eT In (N\u003c/b\u003e\u003csub\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e/N\u003c/b\u003e\u003csub\u003e\u003cb\u003emax\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHere, K\u003csub\u003eB​\u003c/sub\u003e denotes Boltzmann\u0026rsquo;s constant, T is the temperature, N\u003csub\u003ei\u003c/sub\u003e represents the population of bin i, and N\u003csub\u003emax\u003c/sub\u003e​ corresponds to the most populated state. The FEL plot was visualized using a color gradient, where red indicated regions of the highest free energy and blue corresponded to the lowest energy states.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular Mechanics/Poisson\u0026ndash;Boltzmann Surface Area (MM/PBSA)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBinding free energy estimation and per-residue energy decomposition for all complexes were carried out using the MMPBSA.py module of AMBER in combination with the gmx_MMPBSA \u003cb\u003etool\u003c/b\u003e [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The calculations were performed for the hits docked at the active site of the target protein. Binding energies (BEs) were derived from the final 10 ns segment of the MD trajectories (40\u0026ndash;50 ns). The free energy of binding (ΔG\u003csub\u003ebinding\u003c/sub\u003e​) was computed according to the equation:\u003c/p\u003e\u003cp\u003e∆G\u003csub\u003ebinding\u003c/sub\u003e = G\u003csub\u003ecomplex\u003c/sub\u003e \u0026minus; (G\u003csub\u003eprotein\u003c/sub\u003e + G\u003csub\u003eligand\u003c/sub\u003e)\u003c/p\u003e\u003cp\u003ewhere G\u003csub\u003ecomplex\u003c/sub\u003e denotes the energy of the ligand-protein complex, while G\u003csub\u003eprotein\u003c/sub\u003e and G\u003csub\u003eligand​\u003c/sub\u003e represent the energies of the unbound protein and ligand, respectively, in aqueous solvent. Energy decomposition and visualization of binding contributions were further analyzed using the gmx_MMPBSA analysis module.\u003c/p\u003e\u003cp\u003e\u003cb\u003eADMET Properties and Bioactivity Prediction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe SMILES of the hits were generated and uploaded to the SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ADMETLab3.0 server for analysis of their drug-like characteristics and other pharmacokinetic parameters. SwissADME is a free online tool for assessing the pharmacokinetics, drug-likeness, and medicinal chemistry friendliness of small molecules [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e"},{"header":"3.0 RESULTS AND DISCUSSION","content":"\u003cp\u003eGOLPH3 is a PI(4)P-binding oncoprotein that regulates Golgi structure, vesicle trafficking, glycosylation, and multiple oncogenic signaling pathways, with its overexpression strongly linked to cancer progression and metastasis. Despite its central role in diverse cancers, direct inhibitors of GOLPH3 are lacking. Notably, PITPNC1 facilitates the localization of Rab1B at the Golgi, which in turn promotes the recruitment of GOLPH3, Golgi extension, and enhanced vesicular release. Since these trafficking events drive the secretion of pro-invasive and pro-angiogenic mediators, we hypothesize that modulating GOLPH3 localization to the Golgi through inhibition in GOLPH3-overexpressing cells represents a rational strategy for anticancer intervention. Considering the therapeutic promise of drug repurposing and the anticancer potential of alkaloids, antidepressants, statins, and anti-inflammatory drugs, this study aimed to identify novel inhibitors of GOLPH3 through in silico screening to modulate its localization to the Golgi and subsequent function.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Homology Modeling\u003c/h2\u003e\u003cp\u003eThe 3D structure of the GPP34 domain of GOLPH3 was modeled using SWISS-MODEL, achieving a Global Model Quality Estimation (GMQE) score of 0.77. Typically, a GMQE score above 0.70 is considered reliable, underscoring the accuracy of the generated model [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The template used for modeling was \u003cem\u003eHomo sapiens\u003c/em\u003e N-terminal truncated Golgi phosphoprotein 3 (PDB ID: 3KN1) with a resolution of 2.9 \u0026Aring;, and exhibited 100% sequence identity with the supplied amino acid sequence. The QMEANDisCo score, which reflects the average per-residue accuracy by applying distance constraints to QMEAN assessments, was 0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 for the GPP34 model, well above the general threshold of 0.6, indicating high model quality [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Further validation was performed using ProCheck's Ramachandran plot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which confirmed that 100% of the residues were positioned within the favored region (92.7%) and the additionally allowed region (7.3%), with no residues in the generously allowed and disallowed regions. A good quality model would be expected to have over 90% in the most favored regions [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Thus, these findings collectively confirm the high quality and reliability of the GOLPH3 model, making it suitable for subsequent computational ligand-receptor interaction studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Binding Pockets Analysis\u003c/h2\u003e\u003cp\u003eThe CavityPlus web service identified 12 (three are shown) potential binding pockets on the protein for molecular docking. Among these, one pocket (the PI(4)P-binding pocket; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: purple) demonstrated strong druggability, while the remaining 11 showed weak druggability, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the amino acid residues forming the first three identified binding pockets. Among these, the most druggable site, with a Pkd value of 11.76, indicates a high potential for stable ligand\u0026ndash;target interactions. This pocket appears especially favorable for the design of molecular inhibitors, as the selection of suitable ligands for this site could facilitate the development of potent therapeutic candidates [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of amino acid residues in the top-three binding pockets of GPP34\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMax Pred Pkd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDruggability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResidues\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003estrong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLEU 249, ASN 172, ARG 74, LEU 89, VAL 182, LEU 69, LEU 181, GLU 175, GLU 64, LEU 68, PHE 197, GLY 76, LEU 67, THR 200, GLU 250, TRP 81, PHE 194, HIS 202, TYR 77, ASP 247, ILE 85, LEU71, PHE 80, ARG 171, MET 199, GLU 75, SER 79, CYS 84, ARG 174, VAL 66, GLN 192, PRO 203, VAL 248, ASN 251, THR 189, SER 246, ASP 73, ALA 178, ASP 198, VAL 173, ASN 82, LEU 177, LEU 204, LYS 179, LYS 72, ARG 176, LEU 187, THR 78, THR 201, GLY70\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eweak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTHR 143, LEU 155, LEU 164, TRP 152, GLN 144, GLU 147, PRO 145, PRO 146, GLU 142, ASN 162, LYS 165, TYR 168, PRO 163, HIS 167, ASN 151\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eweak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGLY 158, TRP 161, ASN 172, THR 160, ASP 83, GLU 175, GLN 169, TRP 81, SER 86, SER 87, ARG 90, ARG 171, GLU 154, ARG 174, CYS 84, LEU 170, LEU 156, ALA 178, VAL 173, ASN 82, GLU 159, SER 157, ARG 176, ILE 153\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=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Molecular Docking and Selection of Hits.\u003c/h2\u003e\u003cp\u003eThe drug discovery pipeline benefits greatly from in silico modeling, which helps reduce attrition rates, shorten clinical trial timelines, and decrease research and development costs [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Considering the documented anticancer activities of various alkaloids, statins, antidepressants, and anti-inflammatory agents, their binding affinities and potential inhibitory effects on GOLPH3 were investigated. Previous studies targeting GOLPH3 in cancer have primarily relied on genetic knockdown strategies (e.g., shRNA, siRNA) rather than small-molecule inhibitors, demonstrating that silencing GOLPH3 can reverse drug resistance and suppress tumor proliferation in colon cancer and non-small cell lung cancer [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Mechanistic and review studies consistently highlight GOLPH3 as a promising therapeutic target, yet no direct small-molecule inhibitors have been reported to date [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To bridge this gap, we screened 200 alkaloids and 10 compounds each from the aforementioned drug classes, identified through literature mining, for molecular docking against the GPP34 domain. Docking results (cut-off \u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;10.0 kcal/mol) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with alkaloids exhibiting stronger binding affinities than the FDA-approved drugs. The FDA-approved drugs all showed a lesser binding affinity compared to the alkaloids; however, pitavastatin was selected for its relatively high affinity and interaction with ARG 174, a key residue involved in PI(4)P binding of GPP34. The 2D structures of the hit compounds against GPP34 are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\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\u003eList of the best 12 alkaloids and best statin and their respective binding affinities determined and validated by PyRx and CB-Dock scoring, respectively.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLigands\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBinding Affinity (Kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6-hydroxymanzamine A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10393120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003enotoamide D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16127841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBisleuconthine A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46881778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiscorhabdin W\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135466418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMadangamine F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44421333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMadangamine A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9980274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFumiquinazoline C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11339719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBiemnadin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163156431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCytoglobosin F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46209921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDragmacidin D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15000037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCytoglobosin G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46209922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArcyriaflavin A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5327723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFDA-approved Statin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epitavastatin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5282452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-8.4\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\u003eBased on PyRx scoring, a total of 12 alkaloid compounds have binding affinities ranging from \u0026minus;\u0026thinsp;10 to -10.8 kcal/mol. Among these, notoamide D and bisleuconothine A were prioritized due to their strong affinities and interactions with key residues in the PI(4)P-binding pocket of GPP34. A similar rationale guided the selection of pitavastatin from the drug classes evaluated. The PI(4)P-binding residues of GOLPH3 include W81, R90, R171, and R174 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and increasing evidence suggests that the loss of one or more of these residues abolishes PI(4)P-binding in GOLPH3. For instance, Dippold, Ng [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] introduced charge-neutralizing mutations into human GOLPH3 while preserving protein folding. Individual mutations within the binding pocket (R90L and R171A/R174L) significantly reduced PI(4)P binding in both lipid blot and vesicle assays. Furthermore, expression of these mutants in HeLa cells demonstrated a loss of Golgi localization, underscoring the critical role of PI(4)P binding in GOLPH3 function. Thus, the selection of alkaloids and pitavastatin was based primarily on strong binding affinities, followed by the verification of interactions with PI(4)P-binding residues.\u003c/p\u003e\u003cp\u003eIn the present study, bisleuconothine A and notoamide D exhibited binding energies of -10.5 kcal/mol each, whereas pitavastatin showed a binding energy of -8.4 kcal/mol against GOLPH3. The 3D structure of the binding pose of the hit compounds and amino acids involved in the binding is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Bisleuconothine A formed a hydrogen bond with Gly76 and interacted via other non-covalent bonds with Leu67, Leu71, Met199, Phe197, Phe80, and the PI(4)P-binding residue Arg174, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA. Notoamide D formed a hydrogen bond with HIS 202 and interacted via other non-covalent bonds with Leu67, Leu187, Leu71, Ser79, His202, Ala178, and the PI(4)P-binding residue Trp81, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. On the other hand, pitavastatin formed two hydrogen bonds with Gly76 and Leu67, while forming other non-covalent bonds with Gly76, Leu67, Leu71, Leu187, Ala178, and Arg174, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, all of these residues fall within the PI(4)P-binding pocket, having strong druggability and relatively high predicted Pkd. The binding of these hits to the PI(4)P-binding pocket, and particularly to important residues, indicates that they have the potential to inhibit GOLPH3\u0026rsquo;s binding to PI(4)P.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Molecular Dynamics Simulation\u003c/h2\u003e\u003cp\u003eOn the basis of docking results, we selected bisleuconothine, notoamide D, and pitavastatin to perform molecular dynamics simulation study. The simulation was carried out on GPP34 unbound, bisleuconothine-, notoamide D-, and pitavastatin-bound systems to study the dynamic behavior of the targeted protein. Quality control of the simulated systems was assessed using temperature, density, and potential energy as validation parameters (\u003cb\u003eSDS\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C). All parameters remained stable throughout the simulation. The average system temperature was approximately 300.0 K, consistent with the expected value. The average density across all systems was ~\u0026thinsp;1013.2 kg/m\u0026sup3;, which is close to both the experimental value for water (1000 kg/m\u0026sup3;) and the TIP3P model prediction (1001 kg/m\u0026sup3;). The slightly higher density observed is reasonable given the presence of protein/complex in the solvent. Finally, potential energy stabilized in the expected range of negative hundreds of thousands, confirming system equilibration. The 50 ns MD simulation results for GPP34-bisleuconothine complex (GBC), GPP34-Notoamide D complex (GNC), the unbound GPP34 protein, and GPP34-pitavastatin complex (GPC) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Radius of gyration (Rg) is a measure of a protein\u0026rsquo;s compactness. If a protein is stably folded, it will likely maintain a steady value for Rg. The Rg analysis for GNC, GBC, and GPC complexes was compared, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA. The Rg analysis showed a consistent trend in the biomolecular systems throughout the simulation period, with all complexes exhibiting similar Rg values as the unbound protein during the 50 ns simulation period. This consistency indicates the compact and stabilized folding in the ligand-bound complex. On the other hand, the root-mean-square deviation (RMSD) calculates the average atomic displacement in a protein, helping determine the structural stability of the protein when unbound and bound to ligands [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The average interatomic distance in the unbound versus ligand-bound target protein provides a measure for comparing conformational changes and assessing protein stability. In the present study, the RMSD of the GNC, GBC, and GPC complexes did not show any significant deviation in comparison to the unbound protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), indicating stable complexes were formed between GOLPH3 and the ligands.\u003c/p\u003e\u003cp\u003eWhile multiple types of interactions contribute to ligand\u0026ndash;protein complex stabilization, hydrogen bonding plays a particularly critical role. Generally, a higher frequency of hydrogen bond formation during complex assembly corresponds to increased complex stability [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Hydrogen bond formation between the protein and hit compounds was assessed, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC. It can be readily observed that pitavastatin formed up to 5 hydrogen bonds, whereas bisleuconothine and notoamide D formed 2 hydrogen bonds each, which helped reinforce the structural integrity of the protein-ligand complexes during the 50 ns simulation. Root-mean-square fluctuation (RMSF) is a key parameter used to evaluate atomic fluctuations of proteins relative to a reference position over the course of a simulation. This enables comparison of residue-level flexibility in the target protein before and after ligand binding. In the present study, RMSF analysis was performed to assess the effect of hit compound binding on the flexible regions of GPP34. A marked reduction in RMSF values was observed for both GBC and GNC across most regions, indicating enhanced stability. By contrast, the pitavastatin-bound complex exhibited greater fluctuations than the unbound protein, particularly for atoms indexed between 2000 and 2500 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Principal Component Analysis\u003c/h2\u003e\u003cp\u003eProtein conformation is critical in defining its function, with structural rigidity being particularly essential at the binding site. Principal component analysis (PCA) was applied to examine the collective motions of the unbound GPP34 and hits-bound complex through the evaluation of eigenvectors and eigenvalues. To better characterize conformational flexibility at the atomic level, the MD trajectory was projected into phase space, yielding a spectrum of eigenvalues (EVs). Each eigenvalue corresponds to a distinct mode of motion, defining its magnitude, while the associated eigenvector specifies the direction [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Detailed analysis of the complexes revealed a sharp decrease in eigenvalues across the first twenty components, indicating that only the initial few eigenvectors capture the dominant motions of the systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The total variance (trace) of the covariance matrix was approximately 8 nm\u0026sup2;, suggesting relatively stable protein dynamics throughout the simulation. This value is lower than those reported in some other studies [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], likely due to differences in system size, simulation length, and the specific atoms considered in the PCA.\u003c/p\u003e\u003cp\u003eThe overall flexibility of unbound GPP34 and its ligand-bound complexes was evaluated along the two principal components, PC1 and PC2. The corresponding projections for all three docked complexes and the unbound protein are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB \u0026ndash; E. A concentrated distribution in the PCA plot indicates a narrower range of conformational changes, whereas a more dispersed distribution reflects greater flexibility and sampling of alternative conformations [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The 2D projections showed that all systems occupied comparable phase space, consistent with the stability trends observed in other analyses, including FEL, RMSD, and Rg. Nonetheless, closer examination revealed that the GBC complex formed distinct clusters, suggesting increased conformational diversity and flexibility.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Free Energy Landscapes\u003c/h2\u003e\u003cp\u003eFree energy landscape (FEL) analysis was carried out to examine the conformational distribution and stability of the protein-ligand complexes across the simulation trajectory. Figures\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-D depict the free energy landscapes (FEL) of the protein over a 50 ns molecular dynamics simulation, comparing its behavior in the presence of bisleuconothine A, notoamide D, and pitavastatin. The landscapes are presented along the first two principal components (PC1 and PC2). The FEL was visualized using a color gradient, where blue denotes low-energy states and red indicates high-energy states, with intermediate colours (green and yellow) representing moderate energy levels. The presence of a well-defined low-energy basin in the free energy landscapes suggests a region of conformational stability where the protein preferentially resides during the molecular dynamics simulation. The free energy landscape of the unbound GPP34 system (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) displayed a single dominant basin, indicating structural stability in aqueous solution with conformational sampling largely restricted to local fluctuations around one state. A similar trend was observed for all complexes, consistent with the stability suggested by PCA, RMSD, and Rg analyses. However, the GBC complex (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) exhibited two distinct basins, suggesting that ligand binding may have induced alternative conformational substates, consistent with PCA analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGenerally, for all systems analyzed, the PCA revealed a compact conformational ensemble, while the free energy landscape displayed a dominant central basin corresponding to the lowest free energy state. Together, these results indicate that the protein remained structurally stable and primarily sampled local fluctuations around a single conformational state.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Binding Free Energy Estimation (MM/PBSA) and Energy Decomposition of GPP34 complexes with Hits.\u003c/h2\u003e\u003cp\u003eMM-PBSA analysis was performed on the final 10 ns of the trajectories (40\u0026ndash;50 ns) using the gmx_MMPBSA tool. This approach was applied to compute the binding free energies and associated thermodynamic parameters. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The complexes exhibited notable stability during the last 10 ns of simulation, with binding free energies of \u0026minus;\u0026thinsp;37.53, \u0026minus;\u0026thinsp;23.17, and \u0026minus;\u0026thinsp;27.67 kcal/mol for GBC, GNC, and GPC, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA illustrates the contribution of individual energy components to the total binding free energy of the bisleuconothine A-GPP34 complex (GBC). Van der Waals interactions (VDWAALS) were the dominant stabilizing factor (\u0026minus;\u0026thinsp;57.34 kcal/mol), followed by electrostatic interactions (EEL, \u0026minus;\u0026thinsp;19.27 kcal/mol). In contrast, polar solvation energy (EPB) opposed binding (+\u0026thinsp;44.66 kcal/mol), whereas non-polar solvation energy (ENPOLAR) contributed modest stabilization (\u0026minus;\u0026thinsp;5.58 kcal/mol). Consequently, the gas-phase energy (GGAS) contributed strongly to binding (\u0026minus;\u0026thinsp;76.61 kcal/mol), while the solvation energy (GSOLV) partially offset this effect (+\u0026thinsp;39.08 kcal/mol). Comparable patterns of energetic contributions were observed for the GNC complex; however, the trend is reversed for the GPC complex, with EEL contributing more to binding (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC).\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\u003eComponents of binding free energy of GPP34 complexes with hit ligands using MM/PBSA approach.\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=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eComplexes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e\u003cp\u003eBinding Free Energy (kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVDWAALS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEEL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEPB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eENPOLAR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGGAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGSOLV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTOTAL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-57.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-19.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-76.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-37.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGNC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-37.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-43.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-23.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-39.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-77.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-116.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-27.67\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\u003eFurthermore, decomposition of the total binding free energy into per-residue contributions revealed critical insights into the binding mechanism (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA\u0026ndash;C). In the bisleuconothine A\u0026ndash;GPP34 complex, residues Leu67, Leu68, Leu71, Gly76, Tyr77, Ser79, Phe80, Trp81, Ala178, Leu181, Leu187, and Met199 played dominant roles in stabilizing the interaction. For notoamide D, the most significant contributors included Phe80, Trp81, Glu175, Ala178, Leu187, and Met199, while pitavastatin binding was mediated by Leu67, Leu68, Gly70, Leu71, Tyr77, Trp81, Ile85, Glu175, and Leu187. Importantly, Trp81 \u0026ndash; an essential residue for PI(4)P binding within the PI(4)P-binding pocket \u0026ndash; was consistently involved in ligand interactions across all three complexes. This strongly suggests that the hit compounds are not only capable of achieving stable binding but may also competitively disrupt GPP34\u0026rsquo;s natural PI(4)P interactions. By targeting this functional site, these ligands hold considerable promise as the first generation of small-molecule inhibitors of GPP34, a target that has thus far remained unexplored in cancer drug discovery. Bisleuconothine A, with its extensive network of stabilizing interactions, emerges as the most compelling lead candidate for further optimization and preclinical evaluation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.8 Analysis of the Bioavailability and ADMET Properties of the Hit Compounds.\u003c/b\u003e\u003c/h2\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\u003eCompliance of hit alkaloids with drug-likeness/bioavailability filters.\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=\"left\" 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=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLIGAND\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSMILES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLipinski\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVeber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEgan\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\u003eNotoamide D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16127841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCC1(C\u0026thinsp;=\u0026thinsp;CC2\u0026thinsp;=\u0026thinsp;C(O1)C\u0026thinsp;=\u0026thinsp;CC3\u0026thinsp;=\u0026thinsp;C2N[C@@]4([C@]3(C[C@@H]5N4C(=\u0026thinsp;O)[C@@H]6CCCN6C5\u0026thinsp;=\u0026thinsp;O)O)C(C)(C)C\u0026thinsp;=\u0026thinsp;C)C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes, 0 violations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes, 0 violations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes, 0 violations\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\u003eBisleuconthine A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46881778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCC[C@]12CCCN3[C@H]1[C@@]4(CC3)[C@@H](CC2)NC5\u0026thinsp;=\u0026thinsp;C(C\u0026thinsp;=\u0026thinsp;CC(=\u0026thinsp;C45)O)[C@@H]6C[C@]7(CCCN8[C@H]7C9\u0026thinsp;=\u0026thinsp;C(CC8)C1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C1N69)CC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo; 2 violations: MW\u0026thinsp;\u0026gt;\u0026thinsp;500, MLOGP\u0026thinsp;\u0026gt;\u0026thinsp;4.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes, 0 violations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes, 0 violations\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\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eADMET properties of the hit alkaloids and statin\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLigands\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBisleuconothine A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNotoamide D\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePitavastatin\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsorption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAMPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;0.1; Excellent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;0.3; Excellent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u0026ndash;0.3; Excellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;0.1; Excellent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u0026ndash;0.7; Medium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;0.1; Excellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50% Bioavailability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9\u0026ndash;1.0; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9\u0026ndash;1.0; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;0.1; Excellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-gp substrate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9\u0026ndash;1.0; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u0026ndash;0.7; Medium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;0.1; Excellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-gp inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;0.1; Excellent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9\u0026ndash;1.0; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;0.1; Excellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDistribution\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9\u0026ndash;1.0; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;0.1; Excellent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;0.1; Excellent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.6%; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93.8%; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98.8%; Poor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.2%; Excellent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.0%; Medium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6%; Poor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMetabolism\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP1A2 Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u0026ndash;0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP2C19 Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7\u0026ndash;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u0026ndash;0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP2C9 Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u0026ndash;1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP2D6 Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9\u0026ndash;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCYP3A4 Inhibitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9\u0026ndash;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9\u0026ndash;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExcretion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCL plasma (ml/min/Kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.13; Medium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.528; Medium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.334; Medium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eToxicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmes Toxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.73; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42; Medium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.553; Medium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatotoxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.811; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.719; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.938; Poor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNephrotoxicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.584; Medium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.741; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.999; Poor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiotoxicity (hERG blocker)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.833; Poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.052; Excellent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.319; Medium\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\u003eIn this study, the ADMET profiles of the selected alkaloid hits were evaluated using the ADMETlab 3.0 web server and compared with Pitavastatin, an FDA-approved drug. ADMETlab 3.0 is built on an extensive dataset of over 400,000 molecular records and supports automated batch analysis through its integrated API. The platform also provides uncertainty estimates to facilitate the selection of the most reliable drug candidates. Its predictive framework employs a multi-task Directed Message Passing Neural Network (DMPNN) in conjunction with molecular descriptors, enabling fast, parallel computation across multiple endpoints while preserving high accuracy and robustness [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The combination of these techniques allowed for a comprehensive and efficient assessment of the alkaloids' drug-like properties. Absorption analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) suggests that both Bisleuconothine A and Notoamide D possess excellent passive permeability across biological membranes. Bisleuconothine A is predicted to have excellent intestinal absorption, more closely aligning with the high absorption profile of Pitavastatin, whereas Notoamide D exhibits only moderate intestinal absorption. Despite these favorable absorption attributes, both alkaloids may have low systemic bioavailability, in contrast to the predicted higher 50% bioavailability of Pitavastatin. P-glycoprotein (P-gp) substrates are actively expelled from cells, which can lower intracellular drug levels and compromise therapeutic outcomes [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Bisleuconothine A is predicted to be a P-gp substrate and a non-inhibitor, suggesting that it may be susceptible to normal P-gp-mediated efflux, which may lower its bioavailability while posing minimal risk of drug\u0026ndash;drug interactions. This also aligns with Pitavastatin\u0026rsquo;s P-gp profile. ADMET analysis also suggests that Pitavastatin may be a non-substrate and non-inhibitor of P-gp; thus, this could indicate that it is neither actively expelled from cells nor capable of blocking P-gp\u0026ndash;mediated efflux, thereby supporting its high and consistent bioavailability. In contrast, Notoamide D is predicted to be both a P-gp inhibitor and a moderate substrate, suggesting that it may partially block its efflux and that of other P-gp substrates, thereby potentially enhancing drug absorption and retention, although its own bioavailability may still be limited at lower concentrations due to moderate efflux. Based on these results, Bisleuconothine A demonstrates a more favorable absorption profile compared to Notoamide D.\u003c/p\u003e\u003cp\u003eOnly Notoamide D and Pitavastatin indicate a potential of not crossing the blood\u0026ndash;brain barrier (BBB), Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, thus indicating minimal central nervous system (CNS) penetration and a reduced risk of CNS-related adverse effects [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Furthermore, plasma protein binding (PPB) is a key mechanism affecting drug uptake and distribution, strongly influencing pharmacodynamics. Drugs with high protein binding tend to have a low therapeutic index [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. The alkaloid hits and Pitavastatin have over 90% PPB; thus, they are predicted to have a low therapeutic index due to reduced free drug concentrations. At the same time, Bisleuconothine A, with a higher fraction unbound (Fu) in plasma, is expected to potentially have a better therapeutic index than Notoamide D. Higher serum protein binding reduces the fraction of free drug available to cross cellular membranes and diffuse into tissues, thereby potentially limiting pharmacological activity [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Based on these results, Notoamide D is predicted to have a more favorable distribution profile compared to Bisleuconothine A.\u003c/p\u003e\u003cp\u003eMaintaining normal cytochrome P450 (CYP) enzyme activity is essential in drug development, as inhibition can impair clearance, cause accumulation, and lead to drug\u0026ndash;drug interactions or toxic effects [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. The CYP2 family is the largest CYP family, with CYP2D6 and CYP2C9 being major contributors to drug metabolism. CYP2D6, the most common mutant isoform, is involved in the metabolism of approximately 25% of clinical drugs. The CYP3A subfamily, particularly CYP3A4 and CYP3A5, also plays a pivotal role in drug discovery and development, metabolizing over 30% of all clinically used drugs and representing the most abundant CYP enzymes in the human body [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. For potential anticancer drugs, inhibition of these CYP isoforms is generally undesirable because it can lead to reduced clearance of co-administered drugs, drug\u0026ndash;drug interactions, and increased toxicity risk [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. As depicted in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, bisleuconothine A inhibited CYP2D6 and CYP3A4, whereas Notoamide D inhibited CYP2C19 and CYP3A4. In comparison, Pitavastatin inhibited CYP1A2 and CYP2C9, suggesting that it has a more favorable inhibition profile compared to the two alkaloid hits. These interactions indicate that Notoamide D may have a more desirable metabolic profile than Bisleuconothine A; however, careful control and monitoring of both compounds remain essential to minimize potential risks.\u003c/p\u003e\u003cp\u003eFurthermore, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that bisleuconothine A and notoamide D exhibited moderate plasma clearance rates of 7.13 ml/min/kg and 6.528 ml/min/kg, respectively, while Pitavastatin also exhibited a moderate clearance rate of 9.334 ml/min/kg. This suggests that Bisleuconothine A may have a better excretion profile than Notoamide D. The predicted toxicities of the hit alkaloids are also presented in probabilities. Values very close to 1 indicate that the corresponding hit may exhibit such toxicity; those in the range of 0.3 to 0.7 show low tendencies, while those between 0 and 0.3 are not likely to exhibit the associated toxicity. Thus, Notoamide D showed a moderate tendency to be AMES mutagenic, and a propensity to cause damage to the liver, while Bisleuconothine A is predicted to be AMES mutagenic and hepatotoxic. However, while Notoamide D may cause damage to the kidneys, Bisleuconothine A is predicted not to induce such an effect. In comparison, Pitavastatin showed a tendency to be hepatotoxic and nephrotoxic. Additionally, cardiotoxicity, particularly hERG-related cardiotoxicity, is a significant concern with anti-cancer drugs due to its potential to induce arrhythmias, cardiac contractile dysfunction, coronary artery disease, and hypertension, impacting the quality of life for cancer patients [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. According to ADMETlab 3.0, compounds with an IC₅₀ \u0026le; 10 \u0026micro;M or \u0026ge;\u0026thinsp;50% inhibition at 10 \u0026micro;M are classified as hERG blockers, while those with an IC₅₀ \u0026gt;10 \u0026micro;M or \u0026lt;\u0026thinsp;50% inhibition at 10 \u0026micro;M are considered non-blockers. The output value, ranging from 0 to 1, indicates the probability of a compound being a hERG blocker. Bisleuconothine A is predicted to be an hERG blocker, while Notoamide D is predicted to be a non-hERG blocker, indicating a more favorable cardiac safety profile. This indicates that Notoamide D has a better toxicology profile than Bisleuconothine A. Overall, Notoamide D showed more favourable ADMET properties as predicted by ADMETlab 3.0 server; however, additional investigations in a preclinical trial are required to evaluate their pharmacokinetic profiles and safety profiles, to advance their candidacy for anti-GOLPH3 drug development.\u003c/p\u003e\u003cp\u003eOverall, the ADMET analysis offered valuable insight into the pharmacokinetic and toxicity profiles of Bisleuconothine A and Notoamide D, highlighting factors that may influence their clinical potential. To complement these findings, we further evaluated their oral bioavailability using Lipinski\u0026rsquo;s Rule of Five (RoF), as well as Veber\u0026rsquo;s and Egan\u0026rsquo;s filters. While these filters are not absolute predictors, they serve as a practical first step in assessing whether a compound is likely to achieve sufficient systemic exposure when taken orally. Lipinski\u0026rsquo;s rule considers parameters such as molecular weight (\u0026le;\u0026thinsp;500 g/mol), hydrogen bond donors (\u0026le;\u0026thinsp;5), hydrogen bond acceptors (\u0026le;\u0026thinsp;10), and lipophilicity (MLOGP\u0026thinsp;\u0026lt;\u0026thinsp;4.15). Veber\u0026rsquo;s filter extends this evaluation by emphasizing molecular flexibility and polarity, requiring\u0026thinsp;\u0026le;\u0026thinsp;10 rotatable bonds and a topological polar surface area (TPSA)\u0026thinsp;\u0026le;\u0026thinsp;140 \u0026Aring;\u0026sup2;, properties known to favor passive membrane permeability and oral absorption. Similarly, Egan\u0026rsquo;s filter combines TPSA (\u0026le;\u0026thinsp;131.6 \u0026Aring;\u0026sup2;) with WLOGP (\u0026le;\u0026thinsp;5.88) to refine absorption prediction and reduce false negatives. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Among the two alkaloids, Bisleuconothine A violated only Lipinski\u0026rsquo;s rule (MW\u0026thinsp;\u0026gt;\u0026thinsp;500 g/mol and MLOGP\u0026thinsp;\u0026gt;\u0026thinsp;4.15) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, reflecting its bulky and lipophilic nature, which is predicted to limit oral bioavailability. Nonetheless, many natural products and approved drugs remain orally bioavailable despite violating one or more of these rules, indicating that such violations do not necessarily preclude clinical usefulness [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Moreover, several drugs developed after the introduction of Lipinski\u0026rsquo;s rule occupy the beyond-RoF space, further demonstrating that noncompliance does not exclude the possibility of oral bioavailability [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. However, It should be noted that this remains a prediction, as Lipinski\u0026rsquo;s rules were derived from a relatively small dataset (~\u0026thinsp;2,200 drugs from the World Drug Index) and may not fully capture the diversity of modern chemical space explored in drug discovery [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. In contrast, Notoamide D satisfied all of Lipinski\u0026rsquo;s, Veber\u0026rsquo;s, and Egan\u0026rsquo;s criteria, suggesting a higher likelihood of oral bioavailability.\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0 Conclusion","content":"\u003cp\u003eAlkaloids and certain classes of FDA-approved drugs, including statins, anti-inflammatories, and antidepressants, have well-documented roles in cancer treatment. Therefore, the computational identification of novel compounds from these categories represents a timely strategy to discover potential inhibitors of GOLPH3, an underexplored oncoprotein. In the present molecular docking study, bisleuconothine A, notoamide D, and pitavastatin emerged as promising hits, demonstrating significant binding affinity for the active site/PI(4)P-binding pocket of the GPP34 domain. Subsequent molecular dynamics (MD) simulations and MM-PBSA analyses confirmed that these compounds stabilized the GOLPH3\u0026ndash;ligand complexes both structurally and energetically. Of particular importance, all three ligands were shown to disrupt Trp81, a residue essential for PI(4)P binding and, consequently, for GOLPH3\u0026rsquo;s oncogenic activity. Per-residue energy decomposition further highlighted Trp81 as a critical contributor to complex stabilization. In addition, ADMET and oral bioavailability predictions suggest that the alkaloid hits possess favorable drug-like properties and may serve as strong candidates for further preclinical evaluation. Collectively, these findings indicate that bisleuconothine A, notoamide D, and pitavastatin represent potential lead compounds for the development of the first generation of small-molecule anti-GOLPH3 agents in cancer therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, Z.O. and R.K.; methodology, Z.O. and R.K.; software, Z.O., E.G., A.T., and J.T.; validation, E.G., and A.T.; formal analysis, Z.O., R.K., and E.G.; resources, Z.O., J.T., and A.T.; data curation, E.G., A.T., J.T., and Z.O.; writing - original draft preparation, G.C., A.T., and J.T.; writing - review and editing, Z.O., G.C., and R.K.; supervision, Z.O. and R.K. All authors have read and agreed to this version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e No potential competing interests were reported by the authors\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSechi, S., et al., \u003cem\u003eOncogenic roles of GOLPH3 in the physiopathology of cancer.\u003c/em\u003e International journal of molecular sciences, 2020. \u003cstrong\u003e21\u003c/strong\u003e(3): p. 933.\u003c/li\u003e\n\u003cli\u003eFrappaolo, A., G. 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Doak, and J. Kihlberg, \u003cem\u003eOpportunities and guidelines for discovery of orally absorbed drugs in beyond rule of 5 space.\u003c/em\u003e Curr Opin Chem Biol, 2018. \u003cstrong\u003e44\u003c/strong\u003e: p. 23-29.\u003c/li\u003e\n\u003cli\u003eWalters, W.P., \u003cem\u003eGoing further than Lipinski\u0026apos;s rule in drug design.\u003c/em\u003e Expert Opinion on Drug Discovery, 2012. \u003cstrong\u003e7\u003c/strong\u003e(2): p. 99-107.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7524490/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7524490/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGOLPH3 is a PI(4)P-binding oncoprotein implicated in tumor progression, metastasis, and drug resistance, yet no direct small-molecule inhibitors of this target have been reported. In this study, we investigated the inhibitory potential of alkaloids and repurposable FDA-approved drugs against the GPP34 domain of GOLPH3 using molecular docking, molecular dynamics (MD) simulations, and MM-PBSA analysis. A total of 200 alkaloids and 10 representatives each from statins, anti-inflammatories, and antidepressants were screened. Docking results identified bisleuconothine A and notoamide D as the most promising alkaloids, each binding strongly to the PI(4)P-binding pocket (−10.5 kcal/mol). Among FDA-approved drugs, pitavastatin showed the highest affinity (−8.4 kcal/mol). MD simulations demonstrated that these compounds formed stable and energetically favorable complexes with GPP34, as validated by RMSD, RMSF, Rg, hydrogen-bonding, free energy landscape, and principal component analyses. MM-PBSA calculations further confirmed favorable binding free energies, with critical contributions from Phe80, Leu187, and the essential PI(4)P-binding residue Trp81. ADMET and oral bioavailability predictions indicated satisfactory pharmacokinetic profiles, particularly for the alkaloids. Collectively, this work provides the first computational evidence of alkaloid and statin scaffolds as potential GOLPH3 inhibitors, establishing a foundation for future in vitro and in vivo validation toward developing novel anti-GOLPH3 therapeutics.\u003c/p\u003e","manuscriptTitle":"Identification of Alkaloids and Repurposed Drugs as Potential Small-Molecule Inhibitors of GOLPH3 in Colorectal and Lung Cancer Using Molecular Docking, Molecular Dynamics, and MM-PBSA Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 12:55:11","doi":"10.21203/rs.3.rs-7524490/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-29T12:14:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T03:50:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46905593938641946664222207792517300840","date":"2025-10-14T10:02:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T06:09:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99751312929258405252354969515100489613","date":"2025-10-14T05:24:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288477740628001236793014545227210468493","date":"2025-10-13T09:16:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254193205790669780965233372918916659810","date":"2025-10-12T13:16:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-12T09:51:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-08T07:46:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T08:21:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-20T09:15:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Chemistry","date":"2025-09-20T09:11:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e1660a6-fead-44ff-9bae-b1ad45f4778f","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T06:24:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-07 12:55:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7524490","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7524490","identity":"rs-7524490","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}