Computational Screening of Alternaria Metabolites as Potential DPP-4 Inhibitors: ADMET, Molecular Docking, Molecular Dynamics, and Network Pharmacology Analysis

Computational Screening of Alternaria Metabolites as Potential DPP-4 Inhibitors: ADMET, Molecular Docking, Molecular Dynamics, and Network Pharmacology 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 Computational Screening of Alternaria Metabolites as Potential DPP-4 Inhibitors: ADMET, Molecular Docking, Molecular Dynamics, and Network Pharmacology Analysis Piyush Kumar, Sai Anand Kannakazhi Kantari, Malleswara Dharanikota This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8483182/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 4 You are reading this latest preprint version Abstract Type 2 diabetes represents a major global health burden, necessitating the development of safer and more effective therapeutic strategies. While fungi exhibit exceptional metabolic diversity, the genus Alternaria remains largely unexplored for antidiabetic bioactive compounds. This study investigates Alternaria as a potential source of natural inhibitors against dipeptidyl peptidase-4 (DPP4), a validated therapeutic target regulating blood glucose levels through incretin hormone modulation. A library of 278 Alternaria metabolites was systematically screened using an integrated computational workflow. Candidates were prioritized through ADMET profiling to ensure drug-likeness and molecular docking to identify high-affinity binders. Subsequently, molecular dynamics simulations and MM-PBSA calculations substantiated the stability and binding free energy of the top hits. Additionally, network pharmacology approaches were employed to predict mechanistic pathways, elucidating the lead compound's potential as a multi-target therapeutic. Among the prioritized candidates, Anthrininone B (–23.35 ± 3.40 kcal/mol) and Alternatain D (–20.06 ± 4.07 kcal/mol) exhibited significantly stronger binding affinities than the reference drug linagliptin (–10.55 ± 0.34 kcal/mol). Structural analysis revealed that Anthrininone B achieved superior inhibition through stable interactions with Ser630, Tyr547, and the S2′ pocket residue Trp629, emerging as the most promising natural DPP4 inhibitor. Further analysis characterized Anthrininone B as a pleiotropic candidate modulating critical diabetic pathways, including cAMP-mediated insulin secretion, inflammatory suppression via NF-κB1, and β-cell preservation through HIF1α. These findings highlight Alternaria metabolites as promising candidates for natural-based antidiabetic therapeutics and support the further experimental validation of Anthrininone B. Alternaria DPP4 inhibition Type 2 diabetes (T2D) molecular dynamics natural products MM-PBSA Network Pharmacology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Type 2 diabetes mellitus (T2DM) is a major global health crisis, characterized by the body's ineffective use of insulin and inadequate insulin production [ 1 ]. The frequency of this long-term metabolic disorder has been increasing globally [ 2 ]. 382 million people were affected by T2DM in 2013, with a projected population of 783 million being at risk of developing T2DM by 2035 [ 3 , 4 ] . Approximately 90% of global diabetes cases are classified as type 2 diabetes mellitus (T2DM) [ 1 ]. T2DM is a multifactorial metabolic disorder resulting from genetic and environmental factors, characterized by progressive pancreatic β-cell dysfunction and insulin resistance in peripheral tissues. Disease mechanisms encompass β-cell dedifferentiation, trans differentiation, loss of cellular identity mediated by glucotoxicity and lipotoxicity, and impaired insulin signalling in skeletal muscle, adipose tissue, and the liver. Emerging evidence implicates mitochondrial dysfunction, endoplasmic reticulum stress, dysbiotic gut microbiota, and chronic low-grade systemic inflammation in disease progression [ 5 ]. Untreated T2DM can cause nephropathy leading to renal failure, increased risk of foot ulcers, retinopathy with blindness, elevated cardiovascular risk, and links to neurodegenerative diseases. Metformin is the primary therapy, but not all patients respond, and recent trends emphasize personalized treatment based on individual risks for renal and cardiovascular complications. Key drug targets include DPP-4 inhibitors (DPP4i), GLP-1 receptor agonists, SGLT2 inhibitors, and thiazolidinediones [ 5 ]. Despite available interventions, new therapies are needed to control glycemia and limit side effects such as hypoglycemia or weight gain. DPP4 inhibitors, by enhancing incretin (GLP-1) production, promote glucose-induced insulin secretion, beta-cell preservation, weight loss, reduced hypoglycemia risk, and immunomodulation, supporting their future potential in T2DM management.[ 6 ] . Continuous administration of gliptins which are currently in the market like sitagliptin, saxagliptin, vidagliptin, linagliptin and alogliptin has been associated with multiple side effects among various studies including pancreatitis nasopharyngitis, skin rash, and mild gastrointestinal disturbances [ 5 , 7 , 8 ]. In this scenario, there is an unmet need for discovery of novel DPP-4 inhibitors, possibly from natural sources, like fungi. These inhibitors with unique scaffolds must effectively bind to and inhibit the function of DPP-4 and must be suitable for long time sustained dosage with minimal side effects. [ 5 ]. Recent studies have highlighted that molecules derived from Alternaria species exhibit potent bioactivities, including antimicrobial, anticancer, antioxidant, and immunomodulatory effects, making them promising candidates for the development of novel pharmaceuticals targeting a wide spectrum of diseases [ 9 , 10 ]. Of particular interest is the anti-diabetic potential of Alternaria -derived metabolites. These natural compounds have demonstrated the ability to inhibit key enzymes involved in glucose metabolism, such as α-glucosidase and α-amylase, thereby contributing to the regulation of postprandial blood glucose levels and improvement of insulin sensitivity [ 11 , 12 ]. However, their potential to inhibit DPP-4 and contribute to diabetes treatment is still underexplored. These secondary metabolites demonstrate extensive structural diversity (Kumar et al., 2025), potent enzyme inhibition, notably against α-glucosidase and pancreatic lipase, multitarget mechanisms of action [ 11 ], and desirable pharmacokinetics, suggesting their promise for diabetes therapy [ 13 ]. Inspired by this abundance of bioactivity of secondary metabolites from this promising endophytic-fungi, we have investigated 278 secondary metabolites from fungi, Alternaria , and assessed their interaction dynamics with DPP-4. Robust in-silico assessment, comprising of ADMET analysis, molecular docking, molecular dynamic simulation, and MM-PBSA calculations, has been carried out to facilitate the identification of novel compounds with strong binding affinities to DPP-4 and desirable drug-like properties. Moreover, these moieties also exhibited strong antioxidant properties, as per previous studies. We envision that these molecules may play a crucial role in therapeutic intervention of T2D, mediated by inhibition of DPP-4, with minimal or no side effects. Materials and Methods Software tools: ChemDraw 19.1 version, MolSoft 3.9 version, Pymol 4.6.0, ProTox 3.0, Chimera 1.18 version, AutoDock vina, Pubchem, Ligplot+, Swiss PdbViewer 4.1.0 version, Avogadro 1.2.0 version, Open babel 2.4.1 version, GROMAC and gmx_MMPBSA v1.5.7 Drug-Likeness and Physicochemical Property Prediction An initial in silico screening of all 278 metabolites was performed using Molsoft software to predict their molecular properties and assess their drug-likeness. This platform evaluates key physicochemical parameters, including hydrogen bond donors (HBD), lipophilicity, aqueous solubility, polar surface area (PSA), and acid/base strength (pKa). The software generates a Drug-Likeness Model Score (DLS) to provide a rapid assessment of a compound's potential as a drug candidate. A DLS threshold of ≥ 0.18was set to distinguish between drug-like and non-drug-like molecules. Compounds that scored above this value, indicating properties consistent with known drugs, were selected for further analysis. This initial filtration step reduced the library to 49 candidate molecules (Kannakazhi Kantari et al., 2025). In Silico Toxicity Assessment The 49 compounds that passed the drug-likeness filter were subsequently evaluated for toxicity using the ProTox 3.0 web server. ProTox 3.0 predicts various toxicity endpoints, including acute toxicity (LD50), organ toxicity, and carcinogenicity, based on molecular similarity and machine learning models (Arulanandam et al., 2022; Banerjee et al., 2018). The platform classifies compounds into six toxicity classes according to their predicted LD50 values: Classes I, II, and III (LD50 ≤ 300 mg/kg) are considered highly toxic; Class IV (300 < LD50 ≤ 2000 mg/kg) indicates slight toxicity; Class V (2000 5000 mg/kg) represents non-toxic compounds. In this study, compounds categorized as highly toxic (Classes I–III) were excluded, while only those predicted to fall into Class IV or higher were retained. This toxicity assessment further refined the selection, reducing the number of compounds from 49 to 33, which were subsequently advanced for molecular docking analysis. (Banerjee et al., 2018, 2024). Molecular docking All 33 test molecules along with the positive control, linagliptin, were prepared prior to docking. The 3D structures of all ligands were downloaded in .sdf format and processed through a systematic preparation workflow to ensure chemically accurate and energetically favorable conformations suitable for docking studies. Ligand structures were first imported into Avogadro, where geometry optimization was performed using the Generalized Amber Force Field (GAFF) with 1,000 minimization steps to eliminate steric clashes and obtain low-energy conformations (Hanwell et al., 2012; J. Wang et al., 2004). The minimized ligand structures were exported in .pdb format and opened in UCSF Chimera for further refinement. Hydrogen atoms were added to ensure correct protonation states at physiological pH, which is critical for accurate docking interactions (Pettersen et al., 2004). Partial charges were assigned using the Gasteiger method, widely employed for docking compatibility (Gasteiger & Marsili, n.d.). Finally, the prepared structures were saved in PDB format for docking simulations. For the target protein (DPP-4, PDB ID: 2ONC), the structure was obtained from the Protein Data Bank. As some residues were missing, homology modelling using AlphaFold2 was employed to reconstruct the full-length protein model (Jumper et al., 2021). Protein preparation was carried out in UCSF Chimera by removing water molecules, ions, extraneous chains, and bound ligands, followed by hydrogen addition and charge assignment using the Amber ff14SB force field (Pettersen et al., 2004). Energy minimization was then performed in Swiss-PDBViewer using the GROMOS96 force field to relieve steric clashes (Ciucx & Peitsrh Urctrophuresis, 1997). The docking was performed using the Lamarckian Genetic Algorithm (LGA) method in AutoDock Vina, with the search grid defined by center coordinates at (x = 0.257, y = -1.335, z = 8.206) and dimensions of 80 × 80 × 80 Å, encompassing the active binding site comprehensively while balancing computational efficiency (Ghanta et al., 2022; Trott & Olson, 2010). Docking results were analysed to identify ligands with lower binding energy compared to the control (linagliptin), which were further subjected to molecular dynamics (MD) simulations for stability assessment (Eberhardt et al., 2021). Binding interactions of protein-ligand complexes were visualized with LigPlot+, providing detailed 2D interaction diagrams for hydrogen bonding and hydrophobic contacts (Laskowski & Swindells, 2011). Molecular Dynamics Simulation Six protein-ligand complexes exhibiting superior binding affinity compared to the positive control linagliptin were selected for comprehensive molecular dynamics (MD) simulations alongside the 2ONC-linagliptin reference complex. Both unbound protein (apo form) and bound protein-ligand complexes were subjected to 300 ns MD simulations using GROMACS Version 5.0 to evaluate structural stability and dynamic behaviour under physiological conditions (Lemkul, 2024). Protein topology files were generated using the Amber99SB-ILDN force field, which provides enhanced accuracy for amino acid side-chain conformations through optimized torsion potentials of amino acid residues (Lindorff-Larsen et al., 2010). The force field was implemented in its GPU-optimized version to accelerate computational performance. For ligand parametrization, ACPYPE (AnteChamber PYthon Parser interfacE) was employed to generate GROMACS-compatible topology files using the General Amber Force Field (GAFF) parameters (Kagami et al., 2023). ACPYPE serves as a Python interface to Antechamber, facilitating automated partial charge assignment and topology generation for small organic molecules (Sousa Da Silva & Vranken, 2012). Each complex was positioned within a cubic water box maintaining a 1.0 nm minimum distance from the solute to the box edges. The systems were solvated with explicit water molecules and neutralized through addition of sodium (Na⁺) and chloride (Cl⁻) ions to maintain physiological ionic concentration. This setup ensures realistic electrostatic screening and maintains charge neutrality essential for accurate MD simulations. Steepest descent energy minimization eliminated unfavorable atomic contacts and structural strain. Two-phase equilibration was performed: 0.5 ns NVT equilibration (canonical ensemble) brought systems to target temperature of 300 K with position restraints on heavy atoms, followed by 0.5 ns NPT equilibration (isothermal-isobaric ensemble) to stabilize density at 1 bar pressure. This sequential approach prevents instabilities from simultaneous velocity generation and barostat application (Berendsen et al., 1987; Parrinello & Rahman, 1981). Production MD simulations were executed for 300 ns using 2 fs time step with temperature maintained at 300 K using Berendsen thermostat and pressure controlled at 1 bar using Parrinello-Rahman barostat. Periodic boundary conditions and Particle-Mesh Ewald electrostatics ensured accurate long-range interactions. Trajectory analysis included RMSD, RMSF, hydrogen bonding, and protein-ligand contacts using GROMACS analysis tools. LigPlot+ visualization provided detailed 2D interaction diagrams for binding mode analysis (Lemkul, 2024). Binding Free Energy Calculations Using MM-PBSA To assess binding affinity between protein-ligand complexes, the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method was implemented using the gmx_MMPBSA tool. This computational approach provides superior predictive performance compared to conventional docking scoring functions while maintaining computational efficiency for estimating binding free energies in molecular recognition processes. The MM-PBSA method calculates binding free energy (ΔG bind ) by decomposing the total free energy change into distinct energetic components: ΔG bind = ΔE MM + ΔGpol + ΔGnp − TΔS, where ΔE MM encompasses molecular mechanics energies including bonded interactions (bonds, angles, dihedrals) and non-bonded contributions (electrostatic and van der Waals forces), ΔGpol represents polar solvation energy calculated via the Poisson-Boltzmann equation, ΔGnp corresponds to non-polar solvation energy, and TΔS accounts for entropic contributions. (Genheden & Ryde, 2015; Valdés-Tresanco et al., 2021). Calculations were performed on trajectory snapshots extracted from the final 10 ns of molecular dynamics simulations using the single-trajectory approach: ΔG binding = G complex − (G protein + G ligand ), where each free energy term incorporates both molecular mechanics energy in vacuum and corresponding solvation contributions. Polar solvation energies employed dielectric constants of ε solvent = 80 for water and ε solute = 2-4 for protein interior. Entropy contributions were evaluated using interaction entropy methods when computationally feasible, with ensemble averaging performed over multiple snapshots to obtain statistically meaningful binding free energies and associated uncertainties (Valdés-Tresanco et al., 2021). Network Pharmacology Network pharmacology analysis was performed to identify putative diabetes-related targets of the final lead compound following MMPBSA calculations. The SMILES of the compound was submitted to AI/ML -powered platform SuperPred (https://prediction.charite.de/), 3DStarPred (https://targetprediction.org/), and AmiActive (https://www.amiactive.ai/) for target prediction, where candidate targets were filtered using the following criteria: SuperPred probability ≥ 70%, 3DStarPred maximum similarity ≥ 0.6, and AmiActive pro-active score ≥ 0.7 combined with Matthews Correlation Coefficient (MCC) ≥ 0.6. In parallel, diabetes-associated genes were retrieved from the GeneCards database ( Type 2 Diabetes Related Genes - GeneCards Search Results , n.d.). “Type 2 diabetes” was used to filter the genes using a significance threshold of p-value < 0.05, yielding a comprehensive disease-related gene set. The overlap between the predicted targets of the compound and the diabetes gene set was determined using Venny 2.1 ( Venny 2.1.0 , n.d.) to obtain common targets for subsequent network construction. These common genes were then imported into the STRING database to construct a high-confidence protein–protein interaction (PPI) network, which was visualized and further analyzed to explore the interaction landscape of the targets. Finally, Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using Enrichr ( Enrichr , n.d.) to elucidate the principal biological functions and signaling pathways associated with the common targets, with enriched terms selected at p-value < 0.05 (Anand et al., 2025; S. Kumar et al., 2025). Results Virtual screening of the Alternaria Molecules The library of 278 molecules was initially screened for the Drug-likeness property (ADMET property) using Molsoft and for toxicity screening using ProTox 3.0 software. After this initial screening, a total of 33 molecules were filtered out and taken for the further analysis based on druglikeness score greater than 0.18 and toxicity class above 4 as given in the Supplementary data (S1 & S2). Molecular docking Molecular docking was performed on 33 pre-screened molecules against the DPP4 target protein (PDB ID: 2QMJ) using AutoDock Vina software. This initial phase of the study aimed to identify molecules with strong potential for inhibiting DPP4, a crucial enzyme in glucose metabolism. Linagliptin, a known DPP4 inhibitor, was included as a positive control look for better molecules in comparison. The screening process was rigorous, focusing on molecules that exhibited a binding affinity greater than -7.2 Kcal/mol as above this binding energy it exhibited strong and high binding affinity in drug discovery study (Dankwa et al., 2022; Singh et al., 2024). In addition, the number of hydrogen bond Based on these results, six molecules were selected and further examined using LigPlot+ software, followed by molecular dynamics simulation for detailed analysis. The docking result of other molecules is given in supplementary data (S4). Table 1 Molecular docking result of the best six protein-ligand complexes (selected for MD) along with control S. No Ligands name Binding energy (kcal/mol) No of hydrogen bonds Hydrogen bond interaction Hydrophobic bond interaction 1 Anthrininones B -8.692 5 Glu206 (2), Phe357, Arg358 (2) Ser209, Arg356, Pro359, Ser360, Tyr666, Arg669, 2 15-hydroxytricycloalternarenes -8.283 6 Glu205, Phe357, Ser630,3(Asp709 &Asp708) Arg125, Glu206, Ser209, Tyr547, Tyr666, Arg669, Asn710 3 Bicycloalternarene F -7.967 4 Glu205, Arg356, Phe357, Arg358 Glu206, Val207, Ser209, Tyr547, Tyr662, Tyr666, Arg669, Asn710 4 Alternatain D -7.95 5 Glu206, Ser209, Tyr547, Glu553, Arg669 Phe357, Arg358, Gln553, Tyr666 5 Bostrycin -7.76 5 Glu205, Glu206 (2), Phe357, Tyr547, Val207, Ser209, Tyr666, Arg669 6 Tricyclicalternan R -7.553 5 Glu206, Phe357, Arg358 (3), Val207, Ser209, Arg356, Pro359, Ser360, Tyr666 7 Linagliptin (control) -8.503 6 Glu205, Ser209, His740 3(Asp709 &Asp708) Arg125, Glu206, Phe357, Tyr547, Lys554 Molecular Dynamics simulation In order to evaluate the dynamic behaviour of the molecules selected during the docking process, further investigation was conducted through molecular dynamics simulations. A total of eight simulations is carried out: one for the protein alone, and one for each of the seven chosen ligands, control Linagliptin, Anthrininones B, 15-hydroxytricycloalternarenes, Bicycloalternarene F, Alternatain D, Bostrycin, and Tricyclicalternan R. RMSD analysis Root-mean-square deviation (RMSD) analysis revealed diverse dynamic behaviors across the unbound protein and its various complexes. The unbound protein displayed an initial conformational shift early in the simulation, transitioning from a low RMSD state (0.12- 0.15 nm) to a stable, higher plateau (0.22-0.25 nm). This suggests the protein adopted a new, stable conformation after an initial period of adjustment. Among the ligands, Tricyclicalternan R and Bicycloalternarene F exhibited the highest stability. Their RMSD values consistently stayed within a narrow, low range (0.15-0.18 nm and 0.15-0.20 nm, respectively) with minimal fluctuations, indicating strong, persistent interactions of the compounds with the protein's binding site. In contrast, Bostrycin showed the most dynamic behavior, characterized by a spike in RMSD to 0.32-0.33 nm, suggesting a significant conformational rearrangement or a potential partial unbinding event before re-stabilizing. The Linagliptin control compound also showed a dynamic profile, with a period of increased RMSD (0.25-0.30 nm) followed by a return to lower values, suggesting it explored alternative conformational states before settling into a more stable arrangement. The remaining complexes, Anthrininones B, Alternatain D, and 15-hydroxytricycloalternarenes, all showed progressive increases in RMSD over time. Anthrininones B had two distinct transitions, reaching a final plateau of 0.20-0.23 nm. Alternatain D and 15-hydroxytricycloalternarenes exhibited a more gradual, continuous increase, suggesting a slow refinement of their binding interactions. This contrasts with the sharp, discrete shifts seen in other complexes. Overall, these findings highlight the dynamic interaction profiles of each compound with the protein. However, during the last 100ns of MD (200ns to 300ns), all the complexes stabilized with an average RMSD of 0.2 nm, indicating stability of the complexes. RMSF analysis RMSF analysis of the 2ONC protein and seven ligand complexes identified key flexible regions, with several residues exceeding the 2 Å fluctuation cutoff. Common flexible residues across most ligands include 96–97, 243–247, and 276–280, while residues 331–333 showed flexibility in most complexes except linagliptin, bostrycin, and bicycloalternarene F. Anthrininones B showed the highest peaks at ligand-specific residues 125 and 690. Alternatain D induced widespread high flexibility at unique residues 190, 390, and 519, while Bostrycin affected distinct residues 447, 590, and 675–676. Bicycloalternarene F caused flexibility at residues 189 and 447. Linagliptin and 15-hydroxytricycloalternarenes both showed peaks at residue 488, with linagliptin additionally displaying flexibility at 675–676. Hydrogen Bond Interaction Analysis The hydrogen bond analysis reveals a diverse range of protein-ligand interactions across the complexes over the 300ns simulation. Alternatain D and Tricyclicalternan R demonstrated the most robust and consistent hydrogen bonding profiles, maintaining a continuous band of 1-4 and 1-3 bonds, respectively, with very few instances of the count dropping to zero. This suggests a stable and persistent anchoring of these ligands within the binding pocket. In contrast, 15-Hydroxytricycloalternarenes exhibited the weakest and most unstable profile, characterized by frequent periods with minimal or no hydrogen bonds, indicating an intermittent and less stable interaction. Bicycloalternarene F and Bostrycin displayed more variable patterns, with Bicycloalternarene F showing the highest count of non-control ligands, reaching 6 hydrogen bonds, while also having a wide range of fluctuations. Anthrininones B showed a variable pattern with a notable interruption around 150 ns before stabilizing with 2-4 bonds. The control compound, Linagliptin, began with the strongest interaction, reaching 7 bonds, before settling into a consistent pattern of 2-3 bonds for the remainder of the simulation. This highlights that while some ligands form consistently stable interactions, others display more dynamic and variable binding modes, with some even showing signs of intermittent unbinding. Ligplot Analysis The 300 ns molecular dynamics simulation revealed that Anthrininones B and Alternatain D emerged as the most stable inhibitors, achieving potent binding through distinct subsite strategies consistent with known DPP-4 inhibitor classes. Anthrininones B demonstrated robust engagement with the S2' subsite (Trp629, His740) and Tyr547, with hydrophobic contacts increasing from 3 to 8 over the simulation period, paralleling the potency-enhancing interactions observed in clinical inhibitors like Linagliptin. Alternatain D established critical anchoring through Glu206 (S2 subsite) and extensive hydrophobic interactions with Phe357 and Glu205 (S2/S2 extensive), while maintaining contacts with Tyr666 in the S1 region. Both compounds maintained their core interactions from 10 ns to 300 ns, indicating conformational stability and specific recognition of the catalytic pocket architecture. Tricyclicalternan R exhibited consistent and stable interaction throughout the simulation, particularly with Tyr547and Ser630 (S2' subsite/ S1' subsite), Trp629 (S2' subsite), Asn710 (S1 subsite). In contrast, compounds such as, 15-hydroxytricycloalternarenes, and Bicycloalternarene F showed reduced occupancy of essential S2 anchors (Glu205/Glu206) and interacted with peripheral residues by 300 ns, suggesting weaker active site affinity. Table 2 Result of LigPlot+ analysis at the end of 300ns for the best six protein-ligand complexes along with control Compound Names No H-bond No of Hydrophobic bond H-bond Interaction Hydrophobic interaction Linagliptin (control) 2 6 Glu206, Tyr547 Tyr662, Asn710, Arg125, Asp663, Gln553, Lys554 Anthrininones B 3 8 Ser630, Lys554, Asp545 Trp629, Tyr752, Tyr48, Trp563, Trp627, Asp710, Tyr547, His740 Alternatain D 3 5 Glu206, Cys551, Gln553 Glu205, Phe357, Ser552, Tyr547, Tyr666 Tricyclicalternan R 2 3 Tyr547, Lys554 Ser630, Asn710, Trp629 15-hydroxytricycloalternarenes 1 4 Ser473 Pro478, Tyr480, Leu57, Val558 Bicycloalternarene_F 1 2 Glu693 Ser690, Arg691 Bostrycin 2 3 Gln553, Lys554 Tyr547, Cys551, Ser552 MM-PBSA Energy Analysis of 2ONC Protein-Ligand Complexes The MM/PBSA calculations conducted over a 300 ns molecular dynamics simulation revealed significant binding free energy differences between natural compounds and the control drug Linagliptin. The total binding free energy (Δ TOTAL ) hierarchy showed Anthrininone B with the most favourable binding (-23.35 ± 3.40 kcal/mol), followed by Alternanatin D (-20.06 ± 4.07 kcal/mol), Tricyclicalternan R (-16.35 ± 4.05 kcal/mol), Bicycloalternarene F (-11.80 ± 6.24 kcal/mol), Bostryin (-9.64 ± 4.29 kcal/mol), and 15-hydroxytricycloalternarenes (-0.27 ± 1.27 kcal/mol), with Linagliptin showed moderate binding (-10.55 ± 0.34 kcal/mol). Energy decomposition analysis revealed distinct interaction patterns across components: electrostatic interactions (ΔA EEL ) were strongest for Alternanatin D (-46.09 ± 8.97 kcal/mol), followed by Anthrininone B (-37.40 ± 4.87 kcal/mol) and Tricyclicalternan R (-34.33 ± 8.17 kcal/mol), while van der Waals interactions (Δ VDWAALS ) favored Anthrininone B (-34.65 ± 2.77 kcal/mol) over Alternanatin D (-28.43 ± 2.80 kcal/mol) and Tricyclicalternan R (-28.02 ± 2.46 kcal/mol). Gas-phase interaction energies (Δ GAS ) reinforced the top three compounds' performance with Alternanatin D (-74.52 ± 9.24 kcal/mol), Anthrininone B (-72.05 ± 0.22 kcal/mol), and Tricyclicalternan R (-62.35 ± 8.13 kcal/mol), while solvation energies (ΔG SOLV ) demonstrated manageable desolvation penalties effectively compensated by favourable gas-phase interactions. The binding affinity improvements over Linagliptin were substantial for Anthrininone B, Alternanatin D, and Tricyclicalternan R, demonstrating significant therapeutic potential for these natural compounds. Per-Residue Energy Decomposition Analysis Per-residue decomposition of binding free energies using both the Generalized Born (GB) and Poisson–Boltzmann (PB) models revealed compound-specific secondary interaction patterns. For Anthrininone B, Trp629 emerged as the primary hotspot with the strongest binding affinity (~–4.5 kcal/mol in both GB and PB), followed by Ser630 (–3 kcal/mol, GB and PB), Tyr547 (–2 to –3 kcal/mol, GB and PB), Asp545 (–2 kcal/mol, GB only), and additional contacts at Trp627. In Alternanatin D, Glu206 served as the key electrostatic hotspot residue (–3.5 kcal/mol, GB), supported by aromatic contributions from Phe357 (–3 kcal/mol, GB and PB), Cys551 (–2.8 kcal/mol, GB and PB), and Tyr547 (–2 kcal/mol, GB). Tricyclicalternan R displayed a more balanced network, with Tyr547 (–2.6 kcal/mol, GB and PB) and Trp629 (–2.5 kcal/mol, GB and PB) as major contributors, alongside Ser630 (–2 kcal/mol, GB only) and Trp631 (–1.8 kcal/mol, GB and PB). These per-residue energy profiles underpin the distinct molecular recognition mechanisms exhibited by each compound. Table 3 MMPBSA calculation of the protein-ligand complexs that was carried out for the 300ns Energy Component (kcal/mol) Linagliptin (control) Alternatain D Anthriniones B Bicycloalternarene F Bostrycin 15-hydroxytricycloalternarenes Tricyclicalternan R Δ VDWAALS -33.01 ± 0.25 -28.43 ± 2.80 -34.65 ± 2.77 -15.82 ± 6.38 -26.80 ± 2.59 -0.00 ± 0.00 -28.02 ± 2.46 Δ EEL -90.60 ± 0.66 -46.09 ± 8.97 -37.40 ± 4.87 -8.97 ± 8.37 -14.87 ± 9.63 -0.01 ± 0.17 -34.33 ± 8.17 Δ1-4 VDW 0.00 ± 0.00 -0.00 ± 0.09 0.00 ± 0.00 0.00 ± 0.09 0.00 ± 0.08 0.00 ± 0.00 -0.00 ± 0.00 ΔE PB -64.07 ± 0.61 57.51 ± 8.00 52.21 ± 4.59 14.87 ± 7.83 34.78 ± 8.83 -0.26 ± 1.28 49.34 ± 6.42 ΔEN POLAR -4.0 ± 0.02 -3.04 ± 0.12 -3.52 ± 0.09 -1.88 ± 0.69 -2.75 ± 0.14 -0.00 ± 0.00 -3.33 ± 0.18 ΔG GAS 57.59 ± 0.72 -74.52 ± 9.24 -72.05 ± 0.22 -24.79 ± 12.43 -41.67 ± 9.13 -0.01 ± 0.17 -62.35 ± 8.13 ΔG SOLV -68.14 ± 0.60 54.47 ± 7.96 48.70 ± 0.09 12.99 ± 7.38 32.03 ± 8.80 -0.26 ± 1.28 46.00 ± 6.32 ΔTOTAL -10.55 ± 0.34 -20.06 ± 4.07 -23.35 ± 3.40 -11.80 ± 6.24 -9.64 ± 4.29 -0.27 ± 1.27 -16.35 ± 4.05 Targets Gene Predication and Diabetes Association Target genes for Anthrininone B were systematically identified through three independent artificial intelligence-based prediction tools. This multi-tool approach yielded 118 candidate genes. Subsequent cross-referencing with Type 2 Diabetes (T2D) disease genes from curated biomedical databases (GeneCards) revealed substantial overlap, with 84 genes (71.2%) demonstrating established associations with T2D pathophysiology shown in Fig. 5A. Protein-Protein Interaction (PPI) Network Analysis To explore the functional interactome of the 84 identified T2D-associated targets, a Protein-Protein Interaction (PPI) network was constructed using the STRING database. This analysis reveals how these targets functionally connect and synergize to exert their therapeutic effects. The resulting PPI network demonstrates a highly interconnected architecture, visualized with nodes representing the target proteins and edges representing the protein-protein interactions (Fig. 5B). The complex web of interactions suggests that the targets do not act in isolation but rather form a dense regulatory network. The distinct coloured clusters in the diagram indicate functional modules, where proteins with similar biological roles or signalling pathways are grouped together like the GPCR signalling cluster on the left and the metabolic regulation core in the centre. As shown in the centre of the interaction map, key high-degree targets include STAT3, HIF1A, NFKB1, HSP90AA1, and CTNNB. Peripheral clusters, such as the adrenergic and dopaminergic receptor group (e.g., ADRA1A, ADRA2A, DRD1) seen on the left side of the network, interact with this central core, indicating a multi-pathway mechanism where the drug candidates may simultaneously modulate insulin signalling, inflammation, and metabolic stress. Canonical Signaling Pathway Enrichment Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis identified significantly enriched pathways associated with the T2D-Anthrinone B gene network (adjusted p value ≤ 0.05). The pathways with the strongest statistical enrichment were Neuroactive Ligand-Receptor Interaction pathway, followed by Calcium Signaling Pathway, cAMP Signaling Pathway, Lipid and Atherosclerosis etc as shown in the Fig. 5C. Hub Gene Identification and Centrality Analysis Hub gene analysis was conducted using CytoHubba to identify proteins with the highest regulatory influence within the network. 10 core hub proteins were identified including CTNNB1 HDAC1 HSP90AB1 HIF1A KDM1A CSNK2A1 NFE2L2 NFKB1 STAT3 and HSP90AA1, as shown in the Fig. 6. Discussion Long term metabolic diseases like Type II diabetes (T2DM), are a major reason for manifestation of other co-morbidities. Often, T2DM complicates the treatment for other diseases, making it a worrisome clinical phenomenon. While multiple therapeutic options exist for T2DM, these treatment methods are bridled with side effects that affect the overall health of the patient. DPP-4 inhibitors are currently emerging as future therapeutics due to overall benefit for the management of T2D. With many DPP-4 inhibitors under clinical trials, there is still a need for natural or nature-identical therapeutics with less side effects for clinical management of T2DM. There has been an ever-increasing interest in identification of therapeutic compounds from fungi like Alternaria . Several studies have identified compounds from this Alternaria that possess antidiabetic activity [ 11 , 42 ]. In this study, we have observed the implications of these molecules in the clinical management of T2DM, by profiling their roles as potential DPP-4 inhibitors. We have observed that three out of six tested molecules had better binding dynamics to the protein confirmed by MM-PBSA calculations. Anthrininone B achieves the highest binding affinity through an exceptionally rare binding mode. It accesses the difficult-to-target S2' pocket (Trp629) via π-stacking interactions while maintaining the tryptophan in its native, energetically relaxed conformation. This contrasts sharply with conventional S2'-directed inhibitors that force Trp629 into unnatural positions, incurring an energetic penalty that limits potency. Concurrent engagement of the catalytic residue Ser630 and oxyanion hole Tyr547, both retained in native conformations (RMSF < 0.2 Å), demonstrates that Anthrininone B achieves exceptional potency through optimal molecular fit rather than conformational distortion of the enzyme. This thermodynamic efficiency binding without forcing protein rearrangement represents a hallmark of evolutionarily optimized natural products and directly explains its superior potency among the compounds analyzed. The ability to exploit three critical regions simultaneously (S2' pocket, catalytic triad, oxyanion hole) without conformational cost confers exceptional nanomolar-range binding affinity and prolonged inhibitor stability within the enzyme active site. Alternatain D employs a mechanistically distinct strategy that prioritizes selectivity through engagement of residues unique to DPP-IV. Its binding is anchored by sustained hydrogen bonding with Glu206, mimicking the salt-bridge interaction conserved across all approved DPP-IV inhibitors and validated by evolutionary selection in clinical drugs. Crucially, Alternatain D targets Phe357 and Cys551 in the S2 subsite through π–π stacking and hydrogen bonding interactions. Since these residues are replaced by structurally different amino acids in DPP-8 and DPP-9, Phe357 becomes His or Cys, while Cys551 becomes Gln, direct interactions with these specific positions effectively prevent off-target binding. This selectivity mechanism represents a naturally optimized scaffold where inhibitory potency is coupled to isoform specificity, reducing the risk of off-target side effects that commonly plague broad-spectrum protease inhibitors. With a binding affinity of − 20.06 kcal/mol, Alternatain D validates that evolutionary optimization extends beyond raw potency to encompass selectivity as an integrated pharmacophoric property. Tricyclialternan R engages the active site through broader hydrophobic interactions with Tyr547 and Trp629, combined with hydrophobic contacts to the catalytic residue Ser630 rather than the direct hydrogen bonding observed with Anthrininone B. While this binding mode maintains thermodynamic stability (− 16.35 kcal/mol binding affinity), the reliance on general hydrophobic interactions rather than specialized anchoring limits both absolute potency and the mechanistic precision necessary for exceptional selectivity. Tricyclialternan R serves as a structurally complementary scaffold that validates the broader chemical class of fungal metabolites as DPP-IV inhibitors, yet its more generalized binding strategy positions it as a secondary candidate for lead optimization compared to the mechanistic sophistication of Anthrininone B and the selectivity advantages of Alternatain D. Anthrininone B's rare ability to exploit the S2' pocket without conformational distortion identifies it as a naturally optimized inhibitor scaffold with exceptional potential for selective, potent anti-diabetic therapeutics [ 43 , 44 ]. Network pharmacology analysis of Anthrininone B identified multiple targets modulating key pathways implicated in Type 2 Diabetes (T2D) pathogenesis. Predicted adrenergic receptor targets align with both AmIActive and 3STarPred algorithms (> 90% accuracy), highlighting Anthrininone B’s potential role in modulating the neuroactive ligand-receptor interaction pathway, which regulates systemic glucose homeostasis and insulin secretion [ 45 ]. A central mechanism involves PDEA1 (predicted with > 90% confidence by AmIActive), a phosphodiesterase enzyme localized within calcium signaling pathways. Phosphodiesterase inhibition elevates intracellular cAMP, a critical second messenger for glucose-stimulated insulin secretion from pancreatic β cells. By enhancing cAMP-dependent signaling, Anthrininone may improve insulin secretion and glycemic control. [ 46 ]. Anthrininone B’s predicted inhibition of NFKB1 (98% accuracy by SuperPred) is therapeutically significant, as NFKB1 mediates inflammatory signalling in the cAMP pathway. NFKB1 suppression reduces chronic inflammation, a hallmark of T2D complications including β-cell dysfunction and vascular dysfunction [ 47 ]. Additionally, predicted inhibition of STAT3 may favor β-cell preservation, as STAT3 suppression promotes pancreatic cellular reprogramming into insulin-producing cells (prediction confidence 70%) [ 48 ]. A notable high-confidence prediction (> 90% accuracy by SuperPred) is HIF1α inhibition. HIF1α elevation in T2D islets promotes β-cell dedifferentiation and impairs glucose-stimulated insulin secretion; its suppression may preserve mature β-cell phenotype and functional capacity. This mechanism complements STAT3 inhibition in supporting β-cell preservation and function [ 49 , 50 ]. HDAC inhibition by Anthrininone B (prediction confidence 62%) adds therapeutic value, as HDAC inhibitors are documented to improve insulin sensitivity and protect pancreatic β-cell function [ 51 ]. Furthermore, Anthrininone's targeting of MAOA (prediction confidence 65%) reduces ROS production and mitigates oxidative stress and mitochondrial dysfunction—pathological hallmarks of T2D [ 52 ]. Collectively, these network pharmacology predictions reveal Anthrininone B as a multi-targeted modulator of dysregulated pathways in Type 2 Diabetes, simultaneously enhancing insulin secretion through PDE1A-cAMP signaling, suppressing chronic inflammation via NF-κB1 inhibition, preserving β-cell function through STAT3 and HIF1α inhibition, mitigating oxidative stress via MAOA targeting, and restoring epigenetic control through HDAC modulation. The coordinated engagement of these mechanisms—spanning secretory capacity, inflammatory protection, transcriptional stability, and metabolic detoxification—positions Anthrininone B as a pleiotropic therapeutic candidate for T2D management. Progression toward clinical translation requires enzymatic inhibition assays to quantify IC₅₀ values and kinetic constants, determining whether computational binding affinities translate to functional inhibition. Selectivity screening against DPP-8 and DPP-9 will experimentally validate the predicted isoform specificity, particularly for Alternatain D's sequence-dependent selectivity mechanism. Cell-based glucose uptake assays in insulin-responsive models will confirm functional DPP-IV inhibition in physiologically relevant systems. In vivo efficacy in Type 2 diabetes models (db/db mice, high-fat diet-induced obesity) will establish therapeutic translation and pharmacokinetic/pharmacodynamic relationships. X-ray crystallography or cryo-EM structure determination will experimentally confirm the computationally predicted binding poses and validate the conformational stability that distinguishes these natural products from conventional synthetic inhibitors. Fermentation optimization and synthetic route development will establish production scalability necessary for clinical development of these naturally derived leads. Conclusion While humans continue to develop multiple metabolic diseases due to lifestyle changes or other causes, nature has always been a valuable resource for discovering suitable cures. Naturally occurring fungi have been the source of numerous therapeutic molecules instrumental in clinical interventions for various diseases. Fungi such as Alternaria have contributed a wide range of antioxidant and anti-inflammatory metabolites. Among these, Anthrininone B (Ant B) demonstrates a remarkably superior binding profile towards DPP4, with an exceptional binding strength − 23.35 kcal/mol. This potency arises from its ability to simultaneously engage the catalytic Ser630, the oxyanion hole residue Tyr547, and the elusive S2′ pocket residue Trp629 in their natural conformations, ensuring highly stable and efficient inhibition. Furthermore, Alternatain D exhibited the second most favourable binding affinity of − 20.06 kcal/mol, forming key interactions with residues Glu206, Cys551, and Phe357 within the active site of the protein, further supporting its potential inhibitory action. In this study, we identified novel antidiabetic properties of Alternaria metabolites that exhibited better in-silico binding characteristics toward DPP-4 compared to the current therapeutic drug, linagliptin. Additionally, network pharmacology analysis revealed Anthrininone B as a promising multi-target therapeutic candidate for Type 2 Diabetes, demonstrating synergistic effects through enhanced insulin secretion, inflammation control, β-cell preservation, oxidative stress reduction, and epigenetic regulation. Overall, our findings highlight that natural products from Alternaria represent promising candidates for next-generation antidiabetic therapy, warranting further experimental validation and development aimed at maintaining glucose homeostasis with minimal adverse effects. These results further emphasize the significance of fungi as reservoirs of potent metabolites with wide therapeutic potential in disease treatment and prevention. Declarations Author Contribution Piyush Kumar: Designing of methodology, data analysis, presentation of data and preparation of original draft. KK Sai Anand: data analysis, presentation and reviewing the work. Malleswara D: Writing - Review & Editing, Supervision. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors. Conflict of Interest The authors declare no conflicts of interest. Ethical approval: Not applicable Acknowledgments The authors are ever grateful to the Founder Chancellor Bhagawan Sri Sathya Sai Baba of Sri Sathya Sai Institute of Higher Learning (SSSIHL), for his constant inspiration. The authors are thankful to SSSIHL and UGC-SAP for providing financial support to carry out this work. The authors are grateful to Science, Analytics & Innovation with High Performance Computing (SAI-HPC) Facility at the Central Research Instruments Facility (CRIF), SSSIHL, for providing all necessary facilities. The authors wish to thank Arun Sai Kumar Peketi, Sudam Bhoi and Anuj Garg for help in designing the script for simulation and help in carrying out this work. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process. During the preparation of this work, the author used Perplexity to check grammar and rephrasing of sentences. 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Elsevier Masson s.r.l. https://doi.org/10.1016/j.biopha.2024.117010 Wang, Q., Liu, Y., Fu, Q., Xu, B., Zhang, Y., Kim, S., Tan, R., Barbagallo, F., West, T., Anderson, E., Wei, W., Abel, E. D., & Xiang, Y. K. (2017). Inhibiting insulin-mediated β 2-adrenergic receptor activation prevents diabetes-associated cardiac dysfunction. Circulation , 135 (1), 73–88. https://doi.org/10.1161/CIRCULATIONAHA.116.022281 Zhang, P., Zhou, X., Duan, L., Yang, X., Copyright, fmicb, Zhao, S., Li, J., Liu, J., Xiao, S., Yang, S., Mei, J., Ren, M., Wu, S., & Zhang, H. (n.d.). Secondary metabolites of Alternaria: A comprehensive review of chemical diversity and pharmacological properties . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8483182","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569702071,"identity":"b2b6e266-ee5e-4f3c-b7cb-816c7655c1a5","order_by":0,"name":"Piyush Kumar","email":"","orcid":"","institution":"Sri Sathya Sai Institute of Higher Learning","correspondingAuthor":false,"prefix":"","firstName":"Piyush","middleName":"","lastName":"Kumar","suffix":""},{"id":569702074,"identity":"fe8ac7ec-fa5e-46d6-aa7d-26e5b899c049","order_by":1,"name":"Sai Anand Kannakazhi Kantari","email":"","orcid":"","institution":"Sri Sathya Sai Institute of Higher Learning","correspondingAuthor":false,"prefix":"","firstName":"Sai","middleName":"Anand Kannakazhi","lastName":"Kantari","suffix":""},{"id":569702076,"identity":"36a53b0c-c695-4428-86d4-5f5ee7e24729","order_by":2,"name":"Malleswara Dharanikota","email":"data:image/png;base64,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","orcid":"","institution":"Sri Sathya Sai Institute of Higher Learning","correspondingAuthor":true,"prefix":"","firstName":"Malleswara","middleName":"","lastName":"Dharanikota","suffix":""}],"badges":[],"createdAt":"2025-12-30 16:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8483182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8483182/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99878682,"identity":"24e4c387-12a7-48e0-942a-71ba07b8a3f1","added_by":"auto","created_at":"2026-01-09 10:47:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":311488,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD analysis of Protein (2QMJ) and protein-ligand complexes carried out for the 300ns\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/5f045ddcf94264d8a7814f9e.png"},{"id":99878707,"identity":"21ed388e-fe8e-449d-a8bc-0ba3f5d36f48","added_by":"auto","created_at":"2026-01-09 10:47:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137322,"visible":true,"origin":"","legend":"\u003cp\u003eRMSF analysis of the Protein and Protein-ligand complexes\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/c2e57083b75a0714de261078.png"},{"id":99878698,"identity":"e4f907b9-069b-4f22-a1f8-74d5238da785","added_by":"auto","created_at":"2026-01-09 10:47:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126237,"visible":true,"origin":"","legend":"\u003cp\u003eHydrogen bonding interactions of protein-ligand complexes for 300ns\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/fbc472b454c41b6fc53343da.png"},{"id":100358526,"identity":"7e7fab57-6afc-4f25-9bcf-0cbd376edd44","added_by":"auto","created_at":"2026-01-16 07:21:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":325249,"visible":true,"origin":"","legend":"\u003cp\u003ePer-Residue MM-PBSA Energy Decomposition Profiles for Key DPP4-ligand complexes: (A) Anthrininones B, (B) Alternatain D, and (C) Tricyclialternan R\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/07a3643e48b4b6c68cac3b9a.png"},{"id":100358592,"identity":"db01b40e-7f4c-4d47-87d1-68b6bea99c79","added_by":"auto","created_at":"2026-01-16 07:21:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":475087,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork pharmacology analysis of the compound against Type 2 Diabetes (T2D). (A) Venn diagram illustrating the intersection of potential compound-related targets and known T2D-related targets. (B) Protein-Protein Interaction (PPI) network constructed for the 84 overlapping genes identified. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis performed on the 84 core targets.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/2570e0eb9d07a6a87ddf1fe2.png"},{"id":100358310,"identity":"99e08b35-69ab-47b9-83d9-b7eedecb76b0","added_by":"auto","created_at":"2026-01-16 07:20:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":130452,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork visualization highlighting the top ten ranked hub genes, analyzed via the CytoHubba plugin in Cytoscape.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/9201f83c1edac3efd4dae171.png"},{"id":100379946,"identity":"0c6f2db1-b38f-40b9-bad8-18b4be7b990d","added_by":"auto","created_at":"2026-01-16 09:56:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2269513,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/29ddb4e5-2f99-4429-99cf-d5f354f365f1.pdf"},{"id":99878685,"identity":"d648c032-b36b-495d-9c57-f3d051f8dcd8","added_by":"auto","created_at":"2026-01-09 10:47:39","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53762,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementrydata1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/80767a61eae2923ce72f1441.xlsx"},{"id":100358516,"identity":"944a4b68-1379-4e19-bca4-a6c920b0fbef","added_by":"auto","created_at":"2026-01-16 07:21:07","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1249881,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementrydata2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/091d166e77665a7c2023962b.xlsx"},{"id":99878710,"identity":"f8e44edb-77e3-4fbc-a4e8-38859fbb8d19","added_by":"auto","created_at":"2026-01-09 10:47:39","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2860954,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementrydata3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/db388b626dcf926deca31ed7.docx"},{"id":100358725,"identity":"68dfd60d-051c-4350-a65e-3d1b72920d74","added_by":"auto","created_at":"2026-01-16 07:21:17","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":835804,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8483182/v1/406f1ef6fd2a5db36066a990.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational Screening of Alternaria Metabolites as Potential DPP-4 Inhibitors: ADMET, Molecular Docking, Molecular Dynamics, and Network Pharmacology Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is a major global health crisis, characterized by the body's ineffective use of insulin and inadequate insulin production [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The frequency of this long-term metabolic disorder has been increasing globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. 382\u0026nbsp;million people were affected by T2DM in 2013, with a projected population of 783\u0026nbsp;million being at risk of developing T2DM by 2035 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eApproximately 90% of global diabetes cases are classified as type 2 diabetes mellitus (T2DM) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. T2DM is a multifactorial metabolic disorder resulting from genetic and environmental factors, characterized by progressive pancreatic β-cell dysfunction and insulin resistance in peripheral tissues. Disease mechanisms encompass β-cell dedifferentiation, trans differentiation, loss of cellular identity mediated by glucotoxicity and lipotoxicity, and impaired insulin signalling in skeletal muscle, adipose tissue, and the liver. Emerging evidence implicates mitochondrial dysfunction, endoplasmic reticulum stress, dysbiotic gut microbiota, and chronic low-grade systemic inflammation in disease progression [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Untreated T2DM can cause nephropathy leading to renal failure, increased risk of foot ulcers, retinopathy with blindness, elevated cardiovascular risk, and links to neurodegenerative diseases. Metformin is the primary therapy, but not all patients respond, and recent trends emphasize personalized treatment based on individual risks for renal and cardiovascular complications. Key drug targets include DPP-4 inhibitors (DPP4i), GLP-1 receptor agonists, SGLT2 inhibitors, and thiazolidinediones [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite available interventions, new therapies are needed to control glycemia and limit side effects such as hypoglycemia or weight gain. DPP4 inhibitors, by enhancing incretin (GLP-1) production, promote glucose-induced insulin secretion, beta-cell preservation, weight loss, reduced hypoglycemia risk, and immunomodulation, supporting their future potential in T2DM management.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eContinuous administration of gliptins which are currently in the market like sitagliptin, saxagliptin, vidagliptin, linagliptin and alogliptin has been associated with multiple side effects among various studies including pancreatitis nasopharyngitis, skin rash, and mild gastrointestinal disturbances [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this scenario, there is an unmet need for discovery of novel DPP-4 inhibitors, possibly from natural sources, like fungi. These inhibitors with unique scaffolds must effectively bind to and inhibit the function of DPP-4 and must be suitable for long time sustained dosage with minimal side effects. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have highlighted that molecules derived from \u003cem\u003eAlternaria\u003c/em\u003e species exhibit potent bioactivities, including antimicrobial, anticancer, antioxidant, and immunomodulatory effects, making them promising candidates for the development of novel pharmaceuticals targeting a wide spectrum of diseases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Of particular interest is the anti-diabetic potential of \u003cem\u003eAlternaria\u003c/em\u003e-derived metabolites. These natural compounds have demonstrated the ability to inhibit key enzymes involved in glucose metabolism, such as α-glucosidase and α-amylase, thereby contributing to the regulation of postprandial blood glucose levels and improvement of insulin sensitivity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, their potential to inhibit DPP-4 and contribute to diabetes treatment is still underexplored. These secondary metabolites demonstrate extensive structural diversity (Kumar et al., 2025), potent enzyme inhibition, notably against α-glucosidase and pancreatic lipase, multitarget mechanisms of action [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and desirable pharmacokinetics, suggesting their promise for diabetes therapy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInspired by this abundance of bioactivity of secondary metabolites from this promising endophytic-fungi, we have investigated 278 secondary metabolites from fungi, \u003cem\u003eAlternaria\u003c/em\u003e, and assessed their interaction dynamics with DPP-4. Robust \u003cem\u003ein-silico\u003c/em\u003e assessment, comprising of ADMET analysis, molecular docking, molecular dynamic simulation, and MM-PBSA calculations, has been carried out to facilitate the identification of novel compounds with strong binding affinities to DPP-4 and desirable drug-like properties. Moreover, these moieties also exhibited strong antioxidant properties, as per previous studies. We envision that these molecules may play a crucial role in therapeutic intervention of T2D, mediated by inhibition of DPP-4, with minimal or no side effects.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eSoftware tools: ChemDraw 19.1 version, MolSoft 3.9 version, Pymol 4.6.0, ProTox 3.0, Chimera 1.18 version, AutoDock vina, Pubchem, Ligplot+, Swiss PdbViewer 4.1.0 version, Avogadro 1.2.0 version, Open babel 2.4.1 version, GROMAC and gmx_MMPBSA v1.5.7\u003c/p\u003e\n\u003cp\u003eDrug-Likeness and Physicochemical Property Prediction\u003c/p\u003e\n\u003cp\u003eAn initial \u003cem\u003ein silico\u003c/em\u003e screening of all 278 metabolites was performed using Molsoft software to predict their molecular properties and assess their drug-likeness. This platform evaluates key physicochemical parameters, including hydrogen bond donors (HBD), lipophilicity, aqueous solubility, polar surface area (PSA), and acid/base strength (pKa). The software generates a Drug-Likeness Model Score (DLS) to provide a rapid assessment of a compound\u0026apos;s potential as a drug candidate. A DLS threshold of \u0026ge; 0.18was set to distinguish between drug-like and non-drug-like molecules. Compounds that scored above this value, indicating properties consistent with known drugs, were selected for further analysis. This initial filtration step reduced the library to 49 candidate molecules (Kannakazhi Kantari et al., 2025).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn Silico\u003c/em\u003e Toxicity Assessment\u003c/p\u003e\n\u003cp\u003eThe 49 compounds that passed the drug-likeness filter were subsequently evaluated for toxicity using the ProTox 3.0 web server. ProTox 3.0 predicts various toxicity endpoints, including acute toxicity (LD50), organ toxicity, and carcinogenicity, based on molecular similarity and machine learning models (Arulanandam et al., 2022; Banerjee et al., 2018).\u003c/p\u003e\n\u003cp\u003eThe platform classifies compounds into six toxicity classes according to their predicted LD50 values: Classes I, II, and III (LD50 \u0026le; 300 mg/kg) are considered highly toxic; Class IV (300 \u0026lt; LD50 \u0026le; 2000 mg/kg) indicates slight toxicity; Class V (2000 \u0026lt; LD50 \u0026le; 5000 mg/kg) denotes potentially harmful compounds; and Class VI (LD50 \u0026gt; 5000 mg/kg) represents non-toxic compounds. In this study, compounds categorized as highly toxic (Classes I\u0026ndash;III) were excluded, while only those predicted to fall into Class IV or higher were retained. This toxicity assessment further refined the selection, reducing the number of compounds from 49 to 33, which were subsequently advanced for molecular docking analysis. (Banerjee et al., 2018, 2024).\u003c/p\u003e\n\u003cp\u003eMolecular docking\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll 33 test molecules along with the positive control, linagliptin, were prepared prior to docking. The 3D structures of all ligands were downloaded in .sdf format and processed through a systematic preparation workflow to ensure chemically accurate and energetically favorable conformations suitable for docking studies.\u003c/p\u003e\n\u003cp\u003eLigand structures were first imported into Avogadro, where geometry optimization was performed using the Generalized Amber Force Field (GAFF) with 1,000 minimization steps to eliminate steric clashes and obtain low-energy conformations (Hanwell et al., 2012; J. Wang et al., 2004). The minimized ligand structures were exported in .pdb format and opened in UCSF Chimera for further refinement. Hydrogen atoms were added to ensure correct protonation states at physiological pH, which is critical for accurate docking interactions (Pettersen et al., 2004). Partial charges were assigned using the Gasteiger method, widely employed for docking compatibility (Gasteiger \u0026amp; Marsili, n.d.). Finally, the prepared structures were saved in PDB format for docking simulations. For the target protein (DPP-4, PDB ID: 2ONC), the structure was obtained from the Protein Data Bank. As some residues were missing, homology modelling using AlphaFold2 was employed to reconstruct the full-length protein model (Jumper et al., 2021). Protein preparation was carried out in UCSF Chimera by removing water molecules, ions, extraneous chains, and bound ligands, followed by hydrogen addition and charge assignment using the Amber ff14SB force field (Pettersen et al., 2004). Energy minimization was then performed in Swiss-PDBViewer using the GROMOS96 force field to relieve steric clashes (Ciucx \u0026amp; Peitsrh Urctrophuresis, 1997).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe docking was performed using the Lamarckian Genetic Algorithm (LGA) method in AutoDock Vina, with the search grid defined by center coordinates at (x = 0.257, y = -1.335, z = 8.206) and dimensions of 80 \u0026times; 80 \u0026times; 80 \u0026Aring;, encompassing the active binding site comprehensively while balancing computational efficiency (Ghanta et al., 2022; Trott \u0026amp; Olson, 2010). Docking results were analysed to identify ligands with lower binding energy compared to the control (linagliptin), which were further subjected to molecular dynamics (MD) simulations for stability assessment (Eberhardt et al., 2021). Binding interactions of protein-ligand complexes were visualized with LigPlot+, providing detailed 2D interaction diagrams for hydrogen bonding and hydrophobic contacts (Laskowski \u0026amp; Swindells, 2011).\u003c/p\u003e\n\u003cp\u003eMolecular Dynamics Simulation\u003c/p\u003e\n\u003cp\u003eSix protein-ligand complexes exhibiting superior binding affinity compared to the positive control linagliptin were selected for comprehensive molecular dynamics (MD) simulations alongside the 2ONC-linagliptin reference complex. Both unbound protein (apo form) and bound protein-ligand complexes were subjected to 300 ns MD simulations using GROMACS Version 5.0 to evaluate structural stability and dynamic behaviour under physiological conditions (Lemkul, 2024). Protein topology files were generated using the Amber99SB-ILDN force field, which provides enhanced accuracy for amino acid side-chain conformations through optimized torsion potentials of amino acid residues (Lindorff-Larsen et al., 2010). The force field was implemented in its GPU-optimized version to accelerate computational performance. For ligand parametrization, ACPYPE (AnteChamber PYthon Parser interfacE) was employed to generate GROMACS-compatible topology files using the General Amber Force Field (GAFF) parameters (Kagami et al., 2023). ACPYPE serves as a Python interface to Antechamber, facilitating automated partial charge assignment and topology generation for small organic molecules (Sousa Da Silva \u0026amp; Vranken, 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach complex was positioned within a cubic water box maintaining a 1.0 nm minimum distance from the solute to the box edges. The systems were solvated with explicit water molecules and neutralized through addition of sodium (Na⁺) and chloride (Cl⁻) ions to maintain physiological ionic concentration. This setup ensures realistic electrostatic screening and maintains charge neutrality essential for accurate MD simulations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSteepest descent energy minimization eliminated unfavorable atomic contacts and structural strain. Two-phase equilibration was performed: 0.5 ns NVT equilibration (canonical ensemble) brought systems to target temperature of 300 K with position restraints on heavy atoms, followed by 0.5 ns NPT equilibration (isothermal-isobaric ensemble) to stabilize density at 1 bar pressure. This sequential approach prevents instabilities from simultaneous velocity generation and barostat application (Berendsen et al., 1987; Parrinello \u0026amp; Rahman, 1981).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProduction MD simulations were executed for 300 ns using 2 fs time step with temperature maintained at 300 K using Berendsen thermostat and pressure controlled at 1 bar using Parrinello-Rahman barostat. Periodic boundary conditions and Particle-Mesh Ewald electrostatics ensured accurate long-range interactions. Trajectory analysis included RMSD, RMSF, hydrogen bonding, and protein-ligand contacts using GROMACS analysis tools. LigPlot+ visualization provided detailed 2D interaction diagrams for binding mode analysis (Lemkul, 2024).\u003c/p\u003e\n\u003cp\u003eBinding Free Energy Calculations Using MM-PBSA\u003c/p\u003e\n\u003cp\u003eTo assess binding affinity between protein-ligand complexes, the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method was implemented using the gmx_MMPBSA tool. This computational approach provides superior predictive performance compared to conventional docking scoring functions while maintaining computational efficiency for estimating binding free energies in molecular recognition processes. The MM-PBSA method calculates binding free energy (\u0026Delta;G\u003csub\u003ebind\u003c/sub\u003e) by decomposing the total free energy change into distinct energetic components: \u0026Delta;G\u003csub\u003ebind\u003c/sub\u003e = \u0026Delta;E\u003csub\u003eMM\u0026nbsp;\u003c/sub\u003e+ \u0026Delta;Gpol + \u0026Delta;Gnp \u0026minus; T\u0026Delta;S, where \u0026Delta;E\u003csub\u003eMM\u003c/sub\u003e encompasses molecular mechanics energies including bonded interactions (bonds, angles, dihedrals) and non-bonded contributions (electrostatic and van der Waals forces), \u0026Delta;Gpol represents polar solvation energy calculated via the Poisson-Boltzmann equation, \u0026Delta;Gnp corresponds to non-polar solvation energy, and T\u0026Delta;S accounts for entropic contributions. (Genheden \u0026amp; Ryde, 2015; Vald\u0026eacute;s-Tresanco et al., 2021).\u003c/p\u003e\n\u003cp\u003eCalculations were performed on trajectory snapshots extracted from the final 10 ns of molecular dynamics simulations using the single-trajectory approach: \u0026Delta;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), where each free energy term incorporates both molecular mechanics energy in vacuum and corresponding solvation contributions. Polar solvation energies employed dielectric constants of \u0026epsilon;\u003csub\u003esolvent\u003c/sub\u003e = 80 for water and \u0026epsilon;\u003csub\u003esolute\u003c/sub\u003e = 2-4 for protein interior. Entropy contributions were evaluated using interaction entropy methods when computationally feasible, with ensemble averaging performed over multiple snapshots to obtain statistically meaningful binding free energies and associated uncertainties (Vald\u0026eacute;s-Tresanco et al., 2021).\u003c/p\u003e\n\u003cp\u003eNetwork Pharmacology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNetwork pharmacology analysis was performed to identify putative diabetes-related targets of the final lead compound following MMPBSA calculations. The SMILES of the compound was submitted to \u0026nbsp;AI/ML -powered platform SuperPred (https://prediction.charite.de/), 3DStarPred (https://targetprediction.org/), and AmiActive (https://www.amiactive.ai/) for target prediction, where candidate targets were filtered using the following criteria: SuperPred probability \u0026ge; 70%, 3DStarPred maximum similarity \u0026ge; 0.6, and AmiActive pro-active score \u0026ge; 0.7 combined with Matthews Correlation Coefficient (MCC) \u0026ge; 0.6. In parallel, diabetes-associated genes were retrieved from the GeneCards database (\u003cem\u003eType 2 Diabetes Related Genes - GeneCards Search Results\u003c/em\u003e, n.d.). \u0026ldquo;Type 2 diabetes\u0026rdquo; was used to filter the genes using a significance threshold of p-value \u0026lt; 0.05, yielding a comprehensive disease-related gene set. The overlap between the predicted targets of the compound and the diabetes gene set was determined using Venny 2.1 (\u003cem\u003eVenny 2.1.0\u003c/em\u003e, n.d.) to obtain common targets for subsequent network construction. These common genes were then imported into the STRING database to construct a high-confidence protein\u0026ndash;protein interaction (PPI) network, which was visualized and further analyzed to explore the interaction landscape of the targets. Finally, Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using Enrichr (\u003cem\u003eEnrichr\u003c/em\u003e, n.d.) to elucidate the principal biological functions and signaling pathways associated with the common targets, with enriched terms selected at p-value \u0026lt; 0.05 (Anand et al., 2025; S. Kumar et al., 2025).\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eVirtual screening of the \u003cem\u003eAlternaria\u003c/em\u003e Molecules\u003c/h2\u003e\n\u003cp\u003eThe library of 278 molecules was initially screened for the Drug-likeness property (ADMET property) using Molsoft and for toxicity screening using ProTox 3.0 software. After this initial screening, a total of 33 molecules were filtered out and taken for the further analysis based on druglikeness score greater than 0.18 and toxicity class above 4 as given in the Supplementary data (S1 \u0026amp; S2).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMolecular docking\u003c/h2\u003e\n\u003cp\u003eMolecular docking was performed on 33 pre-screened molecules against the DPP4 target protein (PDB ID: 2QMJ) using AutoDock Vina software. This initial phase of the study aimed to identify molecules with strong potential for inhibiting DPP4, a crucial enzyme in glucose metabolism. Linagliptin, a known DPP4 inhibitor, was included as a positive control \u0026nbsp; look for better molecules in comparison. The screening process was rigorous, focusing on molecules that exhibited a binding affinity greater than -7.2 Kcal/mol as above this binding energy it exhibited strong and high binding affinity in drug discovery study (Dankwa et al., 2022; Singh et al., 2024). In addition, the number of hydrogen bond Based on these results, six molecules were selected and further examined using LigPlot+ software, followed by molecular dynamics simulation for detailed analysis. The docking result of other molecules is given in supplementary data (S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Molecular docking result of the best six protein-ligand complexes (selected for MD) along with control\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLigands name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBinding energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo of hydrogen bonds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bond interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrophobic bond interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eAnthrininones B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-8.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGlu206 (2), Phe357, Arg358 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eSer209, Arg356, Pro359, Ser360, Tyr666, Arg669,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e15-hydroxytricycloalternarenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-8.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGlu205, Phe357, Ser630,3(Asp709 \u0026amp;Asp708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eArg125, Glu206, Ser209, Tyr547, Tyr666, Arg669, Asn710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBicycloalternarene F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-7.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGlu205, Arg356, Phe357, Arg358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eGlu206, Val207, Ser209, Tyr547, Tyr662, Tyr666, Arg669, Asn710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eAlternatain D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGlu206, Ser209, Tyr547, Glu553, Arg669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003ePhe357, Arg358, Gln553, Tyr666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBostrycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-7.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGlu205, Glu206 (2), Phe357, Tyr547,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eVal207, Ser209, Tyr666, Arg669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eTricyclicalternan R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-7.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGlu206, Phe357, Arg358 (3),\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eVal207, Ser209, Arg356, Pro359, Ser360, Tyr666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eLinagliptin (control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-8.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGlu205, Ser209, His740 3(Asp709 \u0026amp;Asp708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eArg125, Glu206, Phe357, Tyr547, Lys554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cbr\u003e\u003c/h2\u003e\n\u003ch2\u003eMolecular Dynamics simulation\u003c/h2\u003e\n\u003cp\u003eIn order to evaluate the dynamic behaviour of the molecules selected during the docking process, further investigation was conducted through molecular dynamics simulations. A total of eight simulations is carried out: one for the protein alone, and one for each of the seven chosen ligands, control Linagliptin, Anthrininones B, 15-hydroxytricycloalternarenes, Bicycloalternarene F, Alternatain D, Bostrycin, and Tricyclicalternan R.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRMSD analysis\u003c/p\u003e\n\u003cp\u003eRoot-mean-square deviation (RMSD) analysis revealed diverse dynamic behaviors across the unbound protein and its various complexes. The unbound protein displayed an initial conformational shift early in the simulation, transitioning from a low RMSD state (0.12-\u003c/p\u003e\n\u003cp\u003e0.15 nm) to a stable, higher plateau (0.22-0.25 nm). This suggests the protein adopted a new, stable conformation after an initial period of adjustment. Among the ligands, Tricyclicalternan R and Bicycloalternarene F exhibited the highest stability. Their RMSD values consistently stayed within a narrow, low range (0.15-0.18 nm and 0.15-0.20 nm, respectively) with minimal fluctuations, indicating strong, persistent interactions of the compounds with the protein\u0026apos;s binding site. In contrast, Bostrycin showed the most dynamic behavior, characterized by a spike in RMSD to 0.32-0.33 nm, suggesting a significant conformational rearrangement or a potential partial unbinding event before re-stabilizing. The Linagliptin control compound also showed a dynamic profile, with a period of increased RMSD (0.25-0.30 nm) followed by a return to lower values, suggesting it explored alternative conformational states before settling into a more stable arrangement. The remaining complexes, Anthrininones B, Alternatain D, and 15-hydroxytricycloalternarenes, all showed progressive increases in RMSD over time. Anthrininones B had two distinct transitions, reaching a final plateau of 0.20-0.23 nm. Alternatain D and 15-hydroxytricycloalternarenes exhibited a more gradual, continuous increase, suggesting a slow refinement of their binding interactions. This contrasts with the sharp, discrete shifts seen in other complexes. Overall, these findings highlight the dynamic interaction profiles of each compound with the protein. However, during the last 100ns of MD (200ns to 300ns), all the complexes stabilized with an average RMSD of 0.2 nm, indicating stability of the complexes.\u003c/p\u003e\n\u003ch3\u003eRMSF analysis\u003c/h3\u003e\n\u003cp\u003eRMSF analysis of the 2ONC protein and seven ligand complexes identified key flexible regions, with several residues exceeding the 2 \u0026Aring; fluctuation cutoff. Common flexible residues across most ligands include 96\u0026ndash;97, 243\u0026ndash;247, and 276\u0026ndash;280, while residues 331\u0026ndash;333 showed flexibility in most complexes except linagliptin, bostrycin, and bicycloalternarene F. Anthrininones B showed the highest peaks at ligand-specific residues 125 and 690. Alternatain D induced widespread high flexibility at unique residues 190, 390, and 519, while Bostrycin affected distinct residues 447, 590, and 675\u0026ndash;676. Bicycloalternarene F caused flexibility at residues 189 and 447. Linagliptin and 15-hydroxytricycloalternarenes both showed peaks at residue 488, with linagliptin additionally displaying flexibility at 675\u0026ndash;676.\u003c/p\u003e\n\u003ch3\u003eHydrogen Bond Interaction Analysis\u003c/h3\u003e\n\u003cp\u003eThe hydrogen bond analysis reveals a diverse range of protein-ligand interactions across the complexes over the 300ns simulation. Alternatain D and Tricyclicalternan R demonstrated the most robust and consistent hydrogen bonding profiles, maintaining a continuous band of 1-4 and 1-3 bonds, respectively, with very few instances of the count dropping to zero. This suggests a stable and persistent anchoring of these ligands within the binding pocket. In contrast, 15-Hydroxytricycloalternarenes exhibited the weakest and most unstable profile, characterized by frequent periods with minimal or no hydrogen bonds, indicating an intermittent and less stable interaction. Bicycloalternarene F and Bostrycin displayed more variable patterns, with Bicycloalternarene F showing the highest count of non-control ligands, reaching 6 hydrogen bonds, while also having a wide range of fluctuations. Anthrininones B showed a variable pattern with a notable interruption around 150 ns before stabilizing with 2-4 bonds. The control compound, Linagliptin, began with the strongest interaction, reaching 7 bonds, before settling into a consistent pattern of 2-3 bonds for the remainder of the simulation. This highlights that while some ligands form consistently stable interactions, others display more dynamic and variable binding modes, with some even showing signs of intermittent unbinding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLigplot Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 300 ns molecular dynamics simulation revealed that Anthrininones B and Alternatain D emerged as the most stable inhibitors, achieving potent binding through distinct subsite strategies consistent with known DPP-4 inhibitor classes. Anthrininones B demonstrated robust engagement with the S2\u0026apos; subsite (Trp629, His740) and Tyr547, with hydrophobic contacts increasing from 3 to 8 over the simulation period, paralleling the potency-enhancing interactions observed in clinical inhibitors like Linagliptin. Alternatain D established critical anchoring through Glu206 (S2 subsite) and extensive hydrophobic interactions with Phe357 and Glu205 (S2/S2 extensive), while maintaining contacts with Tyr666 in the S1 region. Both compounds maintained their core interactions from 10 ns to 300 ns, indicating conformational stability and specific recognition of the catalytic pocket architecture. Tricyclicalternan R exhibited consistent and stable interaction throughout the simulation, particularly with Tyr547and Ser630 (S2\u0026apos; subsite/ S1\u0026apos; subsite), Trp629 (S2\u0026apos; subsite), Asn710 (S1 subsite). In contrast, compounds such as, 15-hydroxytricycloalternarenes, and Bicycloalternarene F showed reduced occupancy of essential S2 anchors (Glu205/Glu206) and interacted with peripheral residues by 300 ns, suggesting weaker active site affinity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Result of LigPlot+ analysis at the end of 300ns for the best six protein-ligand complexes along with control\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"540\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompound Names\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo H-bond\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo of Hydrophobic bond\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH-bond Interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrophobic interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;Linagliptin (control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGlu206, Tyr547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTyr662, Asn710, Arg125, Asp663, Gln553, Lys554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eAnthrininones B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSer630, Lys554, Asp545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTrp629, Tyr752, Tyr48, Trp563, Trp627, Asp710, Tyr547, His740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;Alternatain D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGlu206, Cys551, Gln553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eGlu205, Phe357, Ser552, Tyr547, Tyr666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;Tricyclicalternan R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eTyr547, Lys554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSer630, Asn710, Trp629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;15-hydroxytricycloalternarenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSer473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePro478, Tyr480, Leu57, Val558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;Bicycloalternarene_F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGlu693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSer690, Arg691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;Bostrycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eGln553, Lys554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTyr547, Cys551, Ser552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMM-PBSA Energy Analysis of 2ONC Protein-Ligand Complexes\u003c/h2\u003e\n\u003cp\u003eThe MM/PBSA calculations conducted over a 300 ns molecular dynamics simulation revealed significant binding free energy differences between natural compounds and the control drug Linagliptin. The total binding free energy (\u0026Delta;\u003csub\u003eTOTAL\u003c/sub\u003e) hierarchy\u0026nbsp;showed Anthrininone B with the most favourable binding (-23.35 \u0026plusmn; 3.40 kcal/mol), followed by Alternanatin D (-20.06 \u0026plusmn; 4.07 kcal/mol), Tricyclicalternan R (-16.35 \u0026plusmn; 4.05 kcal/mol), Bicycloalternarene F (-11.80 \u0026plusmn; 6.24 kcal/mol), Bostryin (-9.64 \u0026plusmn; 4.29 kcal/mol), and 15-hydroxytricycloalternarenes (-0.27 \u0026plusmn; 1.27 kcal/mol), with Linagliptin showed moderate binding (-10.55 \u0026plusmn; 0.34 kcal/mol). Energy decomposition analysis revealed distinct interaction patterns across components: electrostatic interactions (\u0026Delta;A\u003csub\u003eEEL\u003c/sub\u003e) were strongest for Alternanatin D (-46.09 \u0026plusmn; 8.97 kcal/mol), followed by Anthrininone B (-37.40 \u0026plusmn; 4.87 kcal/mol) and Tricyclicalternan R (-34.33 \u0026plusmn; 8.17 kcal/mol), while van der Waals interactions (\u0026Delta;\u003csub\u003eVDWAALS\u003c/sub\u003e) favored Anthrininone B (-34.65 \u0026plusmn; 2.77 kcal/mol) over Alternanatin D (-28.43 \u0026plusmn; 2.80 kcal/mol) and Tricyclicalternan R (-28.02 \u0026plusmn; 2.46 kcal/mol). Gas-phase interaction energies (\u0026Delta;\u003csub\u003eGAS\u003c/sub\u003e) reinforced the top three compounds\u0026apos; performance with Alternanatin D (-74.52 \u0026plusmn; 9.24 kcal/mol), Anthrininone B (-72.05 \u0026plusmn; 0.22 kcal/mol), and Tricyclicalternan R (-62.35 \u0026plusmn; 8.13 kcal/mol), while solvation energies (\u0026Delta;G\u003csub\u003eSOLV\u003c/sub\u003e) demonstrated manageable desolvation penalties effectively compensated by favourable gas-phase interactions. The binding affinity improvements over Linagliptin were substantial for Anthrininone B, Alternanatin D, and Tricyclicalternan R, demonstrating significant therapeutic potential for these natural compounds.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003ePer-Residue Energy Decomposition Analysis\u003c/h3\u003e\n\u003cp\u003ePer-residue decomposition of binding free energies using both the Generalized Born (GB) and Poisson\u0026ndash;Boltzmann (PB) models revealed compound-specific secondary interaction patterns. For Anthrininone B, Trp629 emerged as the primary hotspot with the strongest binding affinity (~\u0026ndash;4.5 kcal/mol in both GB and PB), followed by Ser630 (\u0026ndash;3 kcal/mol, GB and PB), Tyr547 (\u0026ndash;2 to \u0026ndash;3 kcal/mol, GB and PB), Asp545 (\u0026ndash;2 kcal/mol, GB only), and additional contacts at Trp627. In Alternanatin D, Glu206 served as the key electrostatic hotspot residue (\u0026ndash;3.5 kcal/mol, GB), supported by aromatic contributions from Phe357 (\u0026ndash;3 kcal/mol, GB and PB), Cys551 (\u0026ndash;2.8 kcal/mol, GB and PB), and Tyr547 (\u0026ndash;2 kcal/mol, GB). Tricyclicalternan R displayed a more balanced network, with Tyr547 (\u0026ndash;2.6 kcal/mol, GB and PB) and Trp629 (\u0026ndash;2.5 kcal/mol, GB and PB) as major contributors, alongside Ser630 (\u0026ndash;2 kcal/mol, GB only) and Trp631 (\u0026ndash;1.8 kcal/mol, GB and PB). These per-residue energy profiles underpin the distinct molecular recognition mechanisms exhibited by each compound.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e MMPBSA calculation of the protein-ligand complexs that was carried out for the 300ns\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnergy Component\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLinagliptin (control)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlternatain D\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnthriniones B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBicycloalternarene F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBostrycin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15-hydroxytricycloalternarenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTricyclicalternan R\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026Delta;\u003csub\u003eVDWAALS\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-33.01 \u0026plusmn; 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-28.43 \u0026plusmn; 2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-34.65 \u0026plusmn; 2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-15.82 \u0026plusmn; 6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-26.80 \u0026plusmn; 2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.00 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-28.02 \u0026plusmn; 2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026Delta;\u003csub\u003eEEL\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-90.60 \u0026plusmn; 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-46.09 \u0026plusmn; 8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-37.40 \u0026plusmn; 4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-8.97 \u0026plusmn; 8.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-14.87 \u0026plusmn; 9.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.01 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-34.33 \u0026plusmn; 8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026Delta;1-4 \u003csub\u003eVDW\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.00 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.00 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026Delta;E\u003csub\u003ePB\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-64.07\u0026nbsp;\u0026plusmn; 0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e57.51 \u0026plusmn; 8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e52.21 \u0026plusmn; 4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e14.87 \u0026plusmn; 7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e34.78 \u0026plusmn; 8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.26 \u0026plusmn; 1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e49.34 \u0026plusmn; 6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026Delta;EN\u003csub\u003ePOLAR\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-4.0 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-3.04 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-3.52 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.88 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-2.75 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.00 \u0026plusmn; 0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-3.33 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026Delta;G\u003csub\u003eGAS\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e57.59\u0026nbsp;\u0026plusmn; 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-74.52 \u0026plusmn; 9.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-72.05 \u0026plusmn; 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-24.79 \u0026plusmn; 12.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-41.67 \u0026plusmn; 9.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.01 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-62.35 \u0026plusmn; 8.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026Delta;G\u003csub\u003eSOLV\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-68.14\u0026nbsp;\u0026plusmn; 0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e54.47 \u0026plusmn; 7.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e48.70 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e12.99 \u0026plusmn; 7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e32.03 \u0026plusmn; 8.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.26 \u0026plusmn; 1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e46.00 \u0026plusmn; 6.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;TOTAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-10.55 \u0026plusmn; 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-20.06 \u0026plusmn; 4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-23.35 \u0026plusmn; 3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-11.80 \u0026plusmn; 6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-9.64 \u0026plusmn; 4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.27 \u0026plusmn; 1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-16.35 \u0026plusmn; 4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eTargets Gene Predication and Diabetes Association\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eTarget genes for Anthrininone B were systematically identified through three independent artificial intelligence-based prediction tools. This multi-tool approach yielded 118 candidate genes. Subsequent cross-referencing with Type 2 Diabetes (T2D) disease genes from curated biomedical databases (GeneCards) revealed substantial overlap, with 84 genes (71.2%) demonstrating established associations with T2D pathophysiology shown in Fig. 5A.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eProtein-Protein Interaction (PPI) Network Analysis\u003c/h3\u003e\n\u003cp\u003eTo explore the functional interactome of the 84 identified T2D-associated targets, a Protein-Protein Interaction (PPI) network was constructed using the STRING database. This analysis reveals how these targets functionally connect and synergize to exert their therapeutic effects. The resulting PPI network demonstrates a highly interconnected architecture, visualized with nodes representing the target proteins and edges representing the protein-protein interactions (Fig. 5B). The complex web of interactions suggests that the targets do not act in isolation but rather form a dense regulatory network. The distinct coloured clusters in the diagram indicate functional modules, where proteins with similar biological roles or signalling pathways are grouped together like the GPCR signalling cluster on the left and the metabolic regulation core in the centre. As shown in the centre of the interaction map, key high-degree targets include STAT3, HIF1A, NFKB1, HSP90AA1, and CTNNB. Peripheral clusters, such as the adrenergic and dopaminergic receptor group (e.g., ADRA1A, ADRA2A, DRD1) seen on the left side of the network, interact with this central core, indicating a multi-pathway mechanism where the drug candidates may simultaneously modulate insulin signalling, inflammation, and metabolic stress.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCanonical Signaling Pathway Enrichment\u003c/h3\u003e\n\u003cp\u003eKyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis identified significantly enriched pathways associated with the T2D-Anthrinone B gene network (adjusted p value \u0026le; 0.05). The pathways with the strongest statistical enrichment were Neuroactive Ligand-Receptor Interaction pathway, followed by Calcium Signaling Pathway, cAMP Signaling Pathway, Lipid and Atherosclerosis etc as shown in the Fig. 5C.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eHub Gene Identification and Centrality Analysis\u003c/h3\u003e\n\u003cp\u003eHub gene analysis was conducted using CytoHubba to identify proteins with the highest regulatory influence within the network. 10 core hub proteins were identified including CTNNB1 HDAC1 HSP90AB1 HIF1A KDM1A CSNK2A1 NFE2L2 NFKB1 STAT3 and HSP90AA1, as shown in the Fig. 6.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLong term metabolic diseases like Type II diabetes (T2DM), are a major reason for manifestation of other co-morbidities. Often, T2DM complicates the treatment for other diseases, making it a worrisome clinical phenomenon. While multiple therapeutic options exist for T2DM, these treatment methods are bridled with side effects that affect the overall health of the patient. DPP-4 inhibitors are currently emerging as future therapeutics due to overall benefit for the management of T2D. With many DPP-4 inhibitors under clinical trials, there is still a need for natural or nature-identical therapeutics with less side effects for clinical management of T2DM. There has been an ever-increasing interest in identification of therapeutic compounds from fungi like \u003cem\u003eAlternaria\u003c/em\u003e. Several studies have identified compounds from this \u003cem\u003eAlternaria\u003c/em\u003e that possess antidiabetic activity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we have observed the implications of these molecules in the clinical management of T2DM, by profiling their roles as potential DPP-4 inhibitors. We have observed that three out of six tested molecules had better binding dynamics to the protein confirmed by MM-PBSA calculations. Anthrininone B achieves the highest binding affinity through an exceptionally rare binding mode. It accesses the difficult-to-target S2' pocket (Trp629) via π-stacking interactions while maintaining the tryptophan in its native, energetically relaxed conformation. This contrasts sharply with conventional S2'-directed inhibitors that force Trp629 into unnatural positions, incurring an energetic penalty that limits potency. Concurrent engagement of the catalytic residue Ser630 and oxyanion hole Tyr547, both retained in native conformations (RMSF\u0026thinsp;\u0026lt;\u0026thinsp;0.2 \u0026Aring;), demonstrates that Anthrininone B achieves exceptional potency through optimal molecular fit rather than conformational distortion of the enzyme. This thermodynamic efficiency binding without forcing protein rearrangement represents a hallmark of evolutionarily optimized natural products and directly explains its superior potency among the compounds analyzed. The ability to exploit three critical regions simultaneously (S2' pocket, catalytic triad, oxyanion hole) without conformational cost confers exceptional nanomolar-range binding affinity and prolonged inhibitor stability within the enzyme active site.\u003c/p\u003e \u003cp\u003eAlternatain D employs a mechanistically distinct strategy that prioritizes selectivity through engagement of residues unique to DPP-IV. Its binding is anchored by sustained hydrogen bonding with Glu206, mimicking the salt-bridge interaction conserved across all approved DPP-IV inhibitors and validated by evolutionary selection in clinical drugs. Crucially, Alternatain D targets Phe357 and Cys551 in the S2 subsite through π\u0026ndash;π stacking and hydrogen bonding interactions. Since these residues are replaced by structurally different amino acids in DPP-8 and DPP-9, Phe357 becomes His or Cys, while Cys551 becomes Gln, direct interactions with these specific positions effectively prevent off-target binding. This selectivity mechanism represents a naturally optimized scaffold where inhibitory potency is coupled to isoform specificity, reducing the risk of off-target side effects that commonly plague broad-spectrum protease inhibitors. With a binding affinity of \u0026minus;\u0026thinsp;20.06 kcal/mol, Alternatain D validates that evolutionary optimization extends beyond raw potency to encompass selectivity as an integrated pharmacophoric property.\u003c/p\u003e \u003cp\u003eTricyclialternan R engages the active site through broader hydrophobic interactions with Tyr547 and Trp629, combined with hydrophobic contacts to the catalytic residue Ser630 rather than the direct hydrogen bonding observed with Anthrininone B. While this binding mode maintains thermodynamic stability (\u0026minus;\u0026thinsp;16.35 kcal/mol binding affinity), the reliance on general hydrophobic interactions rather than specialized anchoring limits both absolute potency and the mechanistic precision necessary for exceptional selectivity. Tricyclialternan R serves as a structurally complementary scaffold that validates the broader chemical class of fungal metabolites as DPP-IV inhibitors, yet its more generalized binding strategy positions it as a secondary candidate for lead optimization compared to the mechanistic sophistication of Anthrininone B and the selectivity advantages of Alternatain D. Anthrininone B's rare ability to exploit the S2' pocket without conformational distortion identifies it as a naturally optimized inhibitor scaffold with exceptional potential for selective, potent anti-diabetic therapeutics [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNetwork pharmacology analysis of Anthrininone B identified multiple targets modulating key pathways implicated in Type 2 Diabetes (T2D) pathogenesis. Predicted adrenergic receptor targets align with both AmIActive and 3STarPred algorithms (\u0026gt;\u0026thinsp;90% accuracy), highlighting Anthrininone B\u0026rsquo;s potential role in modulating the neuroactive ligand-receptor interaction pathway, which regulates systemic glucose homeostasis and insulin secretion [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. A central mechanism involves PDEA1 (predicted with \u0026gt;\u0026thinsp;90% confidence by AmIActive), a phosphodiesterase enzyme localized within calcium signaling pathways. Phosphodiesterase inhibition elevates intracellular cAMP, a critical second messenger for glucose-stimulated insulin secretion from pancreatic β cells. By enhancing cAMP-dependent signaling, Anthrininone may improve insulin secretion and glycemic control. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Anthrininone B\u0026rsquo;s predicted inhibition of NFKB1 (98% accuracy by SuperPred) is therapeutically significant, as NFKB1 mediates inflammatory signalling in the cAMP pathway. NFKB1 suppression reduces chronic inflammation, a hallmark of T2D complications including β-cell dysfunction and vascular dysfunction [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Additionally, predicted inhibition of STAT3 may favor β-cell preservation, as STAT3 suppression promotes pancreatic cellular reprogramming into insulin-producing cells (prediction confidence 70%) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA notable high-confidence prediction (\u0026gt;\u0026thinsp;90% accuracy by SuperPred) is HIF1α inhibition. HIF1α elevation in T2D islets promotes β-cell dedifferentiation and impairs glucose-stimulated insulin secretion; its suppression may preserve mature β-cell phenotype and functional capacity. This mechanism complements STAT3 inhibition in supporting β-cell preservation and function [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. HDAC inhibition by Anthrininone B (prediction confidence 62%) adds therapeutic value, as HDAC inhibitors are documented to improve insulin sensitivity and protect pancreatic β-cell function [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Furthermore, Anthrininone's targeting of MAOA (prediction confidence 65%) reduces ROS production and mitigates oxidative stress and mitochondrial dysfunction\u0026mdash;pathological hallmarks of T2D [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCollectively, these network pharmacology predictions reveal Anthrininone B as a multi-targeted modulator of dysregulated pathways in Type 2 Diabetes, simultaneously enhancing insulin secretion through PDE1A-cAMP signaling, suppressing chronic inflammation via NF-κB1 inhibition, preserving β-cell function through STAT3 and HIF1α inhibition, mitigating oxidative stress via MAOA targeting, and restoring epigenetic control through HDAC modulation. The coordinated engagement of these mechanisms\u0026mdash;spanning secretory capacity, inflammatory protection, transcriptional stability, and metabolic detoxification\u0026mdash;positions Anthrininone B as a pleiotropic therapeutic candidate for T2D management.\u003c/p\u003e \u003cp\u003eProgression toward clinical translation requires enzymatic inhibition assays to quantify IC₅₀ values and kinetic constants, determining whether computational binding affinities translate to functional inhibition. Selectivity screening against DPP-8 and DPP-9 will experimentally validate the predicted isoform specificity, particularly for Alternatain D's sequence-dependent selectivity mechanism. Cell-based glucose uptake assays in insulin-responsive models will confirm functional DPP-IV inhibition in physiologically relevant systems. \u003cem\u003eIn vivo\u003c/em\u003e efficacy in Type 2 diabetes models (db/db mice, high-fat diet-induced obesity) will establish therapeutic translation and pharmacokinetic/pharmacodynamic relationships. X-ray crystallography or cryo-EM structure determination will experimentally confirm the computationally predicted binding poses and validate the conformational stability that distinguishes these natural products from conventional synthetic inhibitors. Fermentation optimization and synthetic route development will establish production scalability necessary for clinical development of these naturally derived leads.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhile humans continue to develop multiple metabolic diseases due to lifestyle changes or other causes, nature has always been a valuable resource for discovering suitable cures. Naturally occurring fungi have been the source of numerous therapeutic molecules instrumental in clinical interventions for various diseases. Fungi such as \u003cem\u003eAlternaria\u003c/em\u003e have contributed a wide range of antioxidant and anti-inflammatory metabolites. Among these, Anthrininone B (Ant B) demonstrates a remarkably superior binding profile towards DPP4, with an exceptional binding strength \u0026minus;\u0026thinsp;23.35 kcal/mol. This potency arises from its ability to simultaneously engage the catalytic Ser630, the oxyanion hole residue Tyr547, and the elusive S2\u0026prime; pocket residue Trp629 in their natural conformations, ensuring highly stable and efficient inhibition. Furthermore, Alternatain D exhibited the second most favourable binding affinity of \u0026minus;\u0026thinsp;20.06 kcal/mol, forming key interactions with residues Glu206, Cys551, and Phe357 within the active site of the protein, further supporting its potential inhibitory action. In this study, we identified novel antidiabetic properties of \u003cem\u003eAlternaria\u003c/em\u003e metabolites that exhibited better \u003cem\u003ein-silico\u003c/em\u003e binding characteristics toward DPP-4 compared to the current therapeutic drug, linagliptin.\u003c/p\u003e \u003cp\u003eAdditionally, network pharmacology analysis revealed Anthrininone B as a promising multi-target therapeutic candidate for Type 2 Diabetes, demonstrating synergistic effects through enhanced insulin secretion, inflammation control, β-cell preservation, oxidative stress reduction, and epigenetic regulation. Overall, our findings highlight that natural products from \u003cem\u003eAlternaria\u003c/em\u003e represent promising candidates for next-generation antidiabetic therapy, warranting further experimental validation and development aimed at maintaining glucose homeostasis with minimal adverse effects. These results further emphasize the significance of fungi as reservoirs of potent metabolites with wide therapeutic potential in disease treatment and prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eAuthor Contribution\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePiyush Kumar: Designing of methodology, data analysis, presentation of data and preparation of original draft. KK Sai Anand: data analysis, presentation and reviewing the work. Malleswara D: Writing - Review \u0026amp; Editing, Supervision.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEthical approval:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors are ever grateful to the Founder Chancellor Bhagawan Sri Sathya Sai Baba of Sri Sathya Sai Institute of Higher Learning (SSSIHL), for his constant inspiration. The authors are thankful to SSSIHL and UGC-SAP for providing financial support to carry out this work. The authors are grateful to Science, Analytics \u0026amp; Innovation with High Performance Computing (SAI-HPC) Facility at the Central Research Instruments Facility (CRIF), SSSIHL, for providing all necessary facilities. The authors wish to thank Arun Sai Kumar Peketi, Sudam Bhoi and Anuj Garg for help in designing the script for simulation and help in carrying out this work.\u003c/p\u003e\n\u003cp\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process.\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the author used Perplexity to check grammar and rephrasing of sentences. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the final version of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAletti, R., \u0026amp; Cheng-Lai, A. (2012). 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Elsevier Masson s.r.l. https://doi.org/10.1016/j.biopha.2024.117010\u003c/li\u003e\n \u003cli\u003eWang, Q., Liu, Y., Fu, Q., Xu, B., Zhang, Y., Kim, S., Tan, R., Barbagallo, F., West, T., Anderson, E., Wei, W., Abel, E. D., \u0026amp; Xiang, Y. K. (2017). Inhibiting insulin-mediated \u0026beta; 2-adrenergic receptor activation prevents diabetes-associated cardiac dysfunction. \u003cem\u003eCirculation\u003c/em\u003e, \u003cem\u003e135\u003c/em\u003e(1), 73\u0026ndash;88. https://doi.org/10.1161/CIRCULATIONAHA.116.022281\u003c/li\u003e\n \u003cli\u003eZhang, P., Zhou, X., Duan, L., Yang, X., Copyright, fmicb, Zhao, S., Li, J., Liu, J., Xiao, S., Yang, S., Mei, J., Ren, M., Wu, S., \u0026amp; Zhang, H. (n.d.). \u003cem\u003eSecondary metabolites of Alternaria: A comprehensive review of chemical diversity and pharmacological properties\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Alternaria, DPP4 inhibition, Type 2 diabetes (T2D), molecular dynamics, natural products, MM-PBSA, Network Pharmacology","lastPublishedDoi":"10.21203/rs.3.rs-8483182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8483182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eType 2 diabetes represents a major global health burden, necessitating the development of safer and more effective therapeutic strategies. While fungi exhibit exceptional metabolic diversity, the genus \u003cem\u003eAlternaria\u003c/em\u003e remains largely unexplored for antidiabetic bioactive compounds. This study investigates \u003cem\u003eAlternaria\u003c/em\u003e as a potential source of natural inhibitors against dipeptidyl peptidase-4 (DPP4), a validated therapeutic target regulating blood glucose levels through incretin hormone modulation. A library of 278 \u003cem\u003eAlternaria\u003c/em\u003e metabolites was systematically screened using an integrated computational workflow. Candidates were prioritized through ADMET profiling to ensure drug-likeness and molecular docking to identify high-affinity binders. Subsequently, molecular dynamics simulations and MM-PBSA calculations substantiated the stability and binding free energy of the top hits. Additionally, network pharmacology approaches were employed to predict mechanistic pathways, elucidating the lead compound's potential as a multi-target therapeutic. Among the prioritized candidates, Anthrininone B (\u0026ndash;23.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40 kcal/mol) and Alternatain D (\u0026ndash;20.06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07 kcal/mol) exhibited significantly stronger binding affinities than the reference drug linagliptin (\u0026ndash;10.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 kcal/mol). Structural analysis revealed that Anthrininone B achieved superior inhibition through stable interactions with Ser630, Tyr547, and the S2\u0026prime; pocket residue Trp629, emerging as the most promising natural DPP4 inhibitor. Further analysis characterized Anthrininone B as a pleiotropic candidate modulating critical diabetic pathways, including cAMP-mediated insulin secretion, inflammatory suppression via NF-κB1, and β-cell preservation through HIF1α. These findings highlight \u003cem\u003eAlternaria\u003c/em\u003e metabolites as promising candidates for natural-based antidiabetic therapeutics and support the further experimental validation of Anthrininone B.\u003c/p\u003e","manuscriptTitle":"Computational Screening of Alternaria Metabolites as Potential DPP-4 Inhibitors: ADMET, Molecular Docking, Molecular Dynamics, and Network Pharmacology Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 10:47:28","doi":"10.21203/rs.3.rs-8483182/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T16:16:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-31T14:17:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-31T07:15:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Diversity","date":"2025-12-30T16:35:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"44d04dc5-43ec-45f4-9513-67a1bfd44711","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T10:39:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 10:47:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8483182","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8483182","identity":"rs-8483182","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}