Prodigiosin as a Potential Multi-Target Antidiabetic Agent: Molecular Docking and Dynamics Simulations with Type 2 Diabetes-Related Proteins
Cite as: Rahman, T., Hossain Bappi, M. S., & Hossain, T. J. (2025). Prodigiosin as a Potential Multi-Target Antidiabetic Agent: Molecular Docking and Dynamics Simulations with Type 2 Diabetes-Related Proteins [Preprint].
Article Type: Full Length Original Research Article
Published version: N/A
RESEARCH PAPER
Tanjilur Rahman1,2, Mohammed Sajjad Hossain Bappi1,2, Tanim Jabid Hossain1,2*
1 Department of Biochemistry and Molecular Biology, University of Chittagong, Chattogram, Bangladesh
2 Biotechnology, Informatics and Genomics (BIG) Unit, Laboratory for Health, Omics and Pathway Exploration (HOPE Lab), Chattogram, Bangladesh
Corresponding Author: Tanim Jabid Hossain
Email: tanim.bmb@gmail.com
Type 2 diabetes (T2D) is a global health crisis that urgently requires new antidiabetic leads. A network of proteins regulating carbohydrate metabolism, nutrient transport, and insulin signaling contributes to the onset and progression of T2D, and targeting these proteins offers a promising therapeutic approach. Here, we performed in-silico evaluation of prodigiosin, a tripyrrole bacterial pigment, against 19 T2D-relevant protein targets using molecular docking. Eight proteins with docking scores ≤ −8.0 kcal/mol, including Sodium-Glucose Cotransporter-2 (SGLT-2), Glycogen Synthase Kinase-3 Beta (GSK-3β), Aldose Reductase (AR), KATP Subunit (Kir6.2), Alpha-Glucosidase (AG), Sirtuin1 (SIRT1), Fructose-1,6-Bisphosphatase (FBPase), and Peroxisome Proliferator-Activated Receptor Gamma (PPARγ), were further analyzed by MM-GBSA rescoring and 100-ns molecular dynamics (MD) simulations. Our MD and MM-GBSA analyses indicate that prodigiosin forms stable complexes and shows moderate-to-strong predicted inhibitory potential against SGLT-2, GSK-3β, AR, Kir6.2, and AG, as evidenced by sustained RMSD/RMSF profiles and persistent interactions with functional motifs. Interaction with SIRT1 appears likely to be an undesirable off-target, while binding to PPARγ is modulatory with unclear therapeutic direction; interaction with FBPase is weak and likely neutral. These findings highlight prodigiosin as a potential antidiabetic lead acting on multiple diabetes-relevant proteins, although experimental validation is required to confirm its potency, selectivity, and safety.
Cite: Rahman, T., Hossain Bappi, M. S., & Hossain, T. J. (2025). Prodigiosin as a Potential Multi-Target Antidiabetic Agent: Molecular Docking and Dynamics Simulations with Type 2 Diabetes-Related Proteins [Preprint].
Preprint at Researchgate: https://www.researchgate.net/publication/396890723
Full Text:
Type 2 diabetes mellitus (T2D) is one of the most serious global health concerns of this century. It is a chronic metabolic disorder marked by insulin resistance and the gradual failure of pancreatic β-cells [1]. As of 2021, it affects around 537 million adults aged 20 to 79 worldwide. Without effective control measures, the number is expected to grow to 643 million by 2030 and 783 million by 2045 [2]. Recent predictions even suggest that by 2050, it could pass 1.31 billion [3]. The condition also has a high mortality burden, with approximately 6.7 million deaths reported in 2021 alone [3]. T2D increases the risk of several serious complications. It raises the chance of cardiovascular disease by two to four times [4]. Around 30 to 40 percent of patients develop chronic kidney disease [5]. Retinopathy affects nearly 35 percent of patients within ten years of diagnosis, and many also develop neuropathy [6]. Although several treatment methods exist, less than half of the global patient population achieves proper glycemic control [7]. This highlights the urgent need for more effective, safe, and affordable treatment strategies.
The development of T2D involves a complex network of proteins that regulate glucose metabolism, insulin secretion, and energy balance. These proteins originate from different physiological systems but often work together or influence one another. When they become dysfunctional, they contribute to rising blood glucose levels and long-term metabolic imbalance [8]. In the digestive system, enzymes like alpha-amylase (AA) and alpha-glucosidase (AG) help break down complex carbohydrates into simpler sugars. Alpha-amylase is produced in the pancreas, while alpha-glucosidase is located in the intestinal brush border [7, 9]. Both play a major role in determining how quickly and how much glucose enters the bloodstream after meals. Another key protein, Sodium-Glucose Cotransporter-2 (SGLT-2), is found in the kidney and is responsible for reabsorbing most of the glucose that passes through the renal tubules. In diabetes, the increased expression of SGLT-2 reduces glucose excretion in the urine, thereby worsening hyperglycemia [10]. In the liver, the glucagon receptor (GCGR) mediates the effects of glucagon, a hormone that stimulates hepatic glucose production during fasting states. Overactivity of GCGR signaling exacerbates hyperglycemia in T2D by promoting excessive gluconeogenesis and glycogenolysis [11].
Proteins in the pancreas and gut also help control insulin secretion in response to food intake. The incretin system, which includes hormones such as GLP-1 and GIP, relies on proteins like Dipeptidyl Peptidase-4 (DPP-4), GLP-1 receptors (GLP-1R), and GIP receptors (GIPR) to stimulate insulin release after meals. These proteins play a crucial role in regulating blood sugar levels in a glucose-dependent manner [12]. Inside pancreatic β-cells, proteins such as the KATP subunit (Kir6.2) and Glucokinase (GK) function as glucose sensors. They help trigger insulin release by responding to changes in glucose levels through membrane depolarization or metabolic activity [13]. Impaired function of these proteins is a key feature of impaired insulin secretion in many patients with type 2 diabetes (T2D).
Other proteins influence how the body responds to insulin and manages glucose production in the liver. For example, Protein Tyrosine Phosphatase 1B (PTP1B) and Glycogen Synthase Kinase-3 Beta (GSK-3β) interfere with insulin receptor signaling and glycogen storage when they are overactive, which contributes to insulin resistance [13, 14]. The liver also produces glucose through the processes of gluconeogenesis and glycogen breakdown. Enzymes such as Phosphoenolpyruvate Carboxykinase (PEPCK), Fructose-1,6-Bisphosphatase (FBPase), and glycogen phosphorylase (GP) are responsible for these processes and are often upregulated in diabetes, contributing to elevated fasting blood sugar levels [15, 16]. Aldose reductase (AR) also plays a role in diabetic complications by converting excess glucose into sorbitol in the polyol pathway, leading to oxidative stress and cellular damage in nerves, kidneys, and the retina [18]. Moreover, proteins such as AMP-activated Protein Kinase (AMPK), Peroxisome Proliferator-Activated Receptor Gamma (PPARγ), Sirtuin1 (SIRT1), and Adiponectin Receptor 1 (AdipoR1) regulate how the body uses and stores energy. When these proteins are disrupted, cells struggle to process glucose and fatty acids properly, particularly in muscle, fat, and liver tissues [19-22]. Together, these 19 proteins form an interconnected network that underlies nearly every major metabolic abnormality seen in T2D.
Prodigiosin is a red pigment produced mainly by Serratia, though it is also found in other genera such as Pseudoalteromonas, Hahella, and Streptomyces [23]. The structure of prodigiosin consists of three connected pyrrole rings, and it has a molecular weight of 323.43 g/mol, which endows it with excellent physicochemical and drug-likeness properties. Our recent study demonstrated that prodigiosin met the key criteria of Lipinski's rule of five, Ghose's rule, Veber's rule, and Muegge's rule, indicating its potential as an orally available drug candidate [24]. Furthermore, prodigiosin has been proven to have antimicrobial activity against a range of pathogens, including bacteria, fungi, and viruses [24–26]. Other studies have also shown that it can selectively target cancer cells while exhibiting limited toxicity toward normal cells, indicating a favorable safety profile [27]. The ability to interact with multiple targets and cross cell membranes adds to its value as a drug candidate. These features make it a strong contender for further investigation in the treatment of complex diseases, including T2D.
Over the past two decades, a range of drugs has been developed to target the proteins involved in T2D. These include alpha-glucosidase inhibitors like acarbose, SGLT-2 inhibitors such as empagliflozin, DPP-4 inhibitors like sitagliptin, and GLP-1R agonists including semaglutide [28]. Other agents such as sulfonylureas, glucokinase activators, and modulators of PPARγ and GSK-3β have also been used to improve glycemic control [29–32]. While many of these drugs have proven effective, their long-term use is often limited by safety concerns, side effects, or decreasing efficacy over time. For instance, sulfonylureas and glucokinase activators can lead to hypoglycemia and β-cell exhaustion when used over extended periods. AG inhibitors are associated with gastrointestinal side effects, including bloating, diarrhea, and discomfort, which can often reduce patient adherence [33]. Though SGLT-2 inhibitors have shown benefits in heart and kidney health, they may raise the risk of urinary tract infections and diabetic ketoacidosis in some individuals [34]. PPARγ agonists can lead to edema, weight gain, macular edema, and heart failure [33]. The safety of DPP-4 inhibitors has also been debated due to reports of pancreatitis and immune-related effects [35]. Another key limitation of many current drugs is that they act on single targets, despite T2D being a disease driven by multiple pathways and systems. Challenges such as drug resistance, declining efficacy, and complications from multiple medications are still common. This highlights the need for safer and more versatile therapies. In light of these, natural compounds offer a promising alternative due to their structural complexity, low toxicity, and potential to act on multiple targets simultaneously [36,37]. Recent technological advances, including genomics, metagenomics, and other omics-based approaches, are increasingly being used to identify versatile natural products with therapeutic potential [38–41]. Nevertheless, while numerous studies focus on targeting a single protein associated with T2D, very few have demonstrated the potential of antidiabetic agents that act on multiple proteins [42,43].
In silico methods rationalize and accelerate multiple stages of the drug discovery pipeline by exploiting molecular structural information and statistical/machine-learning models, which is also known as Computer-Aided Drug Design (CADD) [44]. Compared with traditional methods, CADD enables the rapid use of computational tools to screen vast chemical libraries, select targets, optimize potency, and assess developability, thereby reducing the number of compounds that must be synthesized and assayed experimentally. Therefore, the aim of this study is to investigate the interaction potential of prodigiosin with a panel of 19 proteins known to play critical roles in the onset and progression of T2D using advanced computational tools, such as Molecular Docking and Molecular Dynamics simulation. By systematically mapping these molecular interactions, this study could provide foundational insights for the repositioning of prodigiosin as a lead scaffold in the development of novel antidiabetic agents.
In this study, the three-dimensional structures of 19 target proteins and the ligand prodigiosin were obtained for molecular docking and dynamics simulations. These protein structures were downloaded from the RCSB Protein Data Bank (https://www.rcsb.org/). The specific PDB IDs for each protein are listed in Table 1. These structures were selected based on high resolution and quality among other structures of the same proteins in the database. All structures were retrieved in PDB format and prepared as receptors against prodigiosin. Alternatively, the 3D structure of prodigiosin was retrieved in SDF format from the PubChem database, one of the largest free and public chemical databases in the world (https://pubchem.ncbi.nlm.nih.gov/). The PubChem CID of prodigiosin was 135455579.
[Table 1. All the PDB IDs of the selected proteins]
All selected proteins and the ligand were prepared prior to molecular docking. For the preparation process, various software applications, including PyMol v2.5.8 (https://www.pymol.org/) [45], AutoDockTools v1.5.6 (https://autodocksuite.scripps.edu/adt/) [46], and Biovia Discovery Studio 2021 [47], were utilized for different purposes. The process began by converting the SDF file of prodigiosin to PDB format using PyMol v2.5.8, and then further processing it to PDBQT format with AutoDockTools v1.5.6, which incorporated Gasteiger charges and non-polar hydrogens for the ligand. Consequently, the PDB files of all proteins were also turned into PDBQT files using both AutoDockTools v1.5.6 and Biovia Discovery Studio 2021, applying distinct steps from the ligand preparation process. The steps included removing water molecules and other heteromolecules, merging polar hydrogen atoms, and assigning Kollman charges. Subsequently, docking grid boxes for all the proteins were prepared separately using AutoDockTools v1.5.6. While the grid boxes had the same dimensions (x: 40, y: 40, and z: 40), the center axes varied for each protein. The center axes of the docking grid boxes of all proteins were summarized in Table 2.
[Table 2. The value of X, Y, and Z center axes of the docking grid box of each protein.]
Molecular docking is a fundamental computational technique in structural biology and drug discovery that aims to predict the preferred orientation of a ligand when it is bound to a target receptor. The core premise is based on the lock-and-key model, where the ligand (key) must complement the receptor's binding site (lock) both geometrically and chemically. The process is driven by a search algorithm that systematically explores countless possible conformations and orientations of the ligand within the binding site, and a scoring function that quantitatively evaluates each predicted pose by estimating the strength of the binding affinity [48]. In our study, docking of prodigiosin to each selected protein was performed separately using AutoDock Vina v1.1.2 [49]. The binding affinity and molecular orientation of the ligands were predicted using the Lamarckian Genetic Algorithm within AutoDock Vina v1.1.2. To optimize the docking accuracy, the search space was defined as a volume of 27,000 ų, and an exhaustiveness parameter of 8 was applied [50]. Following the docking simulation, the complexes were analyzed with Biovia Discovery Studio 2021 to characterize the specific bonds and interactions formed between the ligand and each protein receptor. Afterwards, these interactions were validated by matching the amino acid residues in the binding pockets of each protein.
The binding pockets of all proteins were identified using the CASTpFOLD server to validate the interaction between prodigiosin and selected proteins. CASTpFOLD is a free server that applies theoretical and algorithmic results of computational geometry to identify pockets and cavities of proteins [51]. Specifically, the server analyzes the best binding pocket using the CASTp (Computed Atlas of Surface Topography of Proteins) algorithm. The CASTp algorithm mathematically defines and measures surface-accessible pockets and interior, inaccessible cavities, providing detailed information such as volume, area, and the specific amino acids that line the pocket. We used the PDB ID of the top-docked proteins to search for their binding pockets within CASTpFOLD in our study.
Molecular Mechanics with Generalized Born and Surface Area (MM-GBSA) is a valuable and efficient computational tool for calculating binding free energy (ΔG_bind) and for gaining mechanistic insights into receptor-ligand complexes. MM-GBSA estimates ΔG_bind between a ligand and a receptor based on a thermodynamic cycle that subtracts the sum of the free energies of the unbound receptor and unbound ligand from the free energy of the receptor-ligand complex [52,53]. This tool provides greater accuracy than molecular docking in calculating the binding affinity of a ligand to a target receptor, as it considers critical factors such as flexibility and solvation that docking often overlooks [54]. Therefore, after analyzing the molecular docking results, we conducted MM-GBSA by employing the Prime MM-GBSA v3.0 model within the Schrödinger suite 2023-1. We calculated the relative binding free energy (ΔG_bind) of prodigiosin with eight proteins separately, which were selected based on their best molecular docking results among other proteins.
Molecular dynamics (MD) simulation is a powerful computational technique that models the physical movements of atoms and molecules over time, providing a dynamic view of biological processes that static experimental structures cannot capture. The simulations are governed by a force field that approximates the potential energy of the system based on factors like bond stretching, angle bending, van der Waals forces, and electrostatic interactions [55]. In our study, we subjected complexes with a binding affinity of ≤ -8.0 kcal/mol to MD simulations to determine their stability. [56]. We utilized the Desmond package within the Schrödinger suite 2023-1 for 100 ns to perform the MD simulation of each complex. For observing stable interactions and key features of a receptor-ligand complex, an MD simulation for 100 ns was well-justified in various studies [57–59].
Before the simulation began, we prepared the protein-ligand complexes and optimized the system to achieve better results. The protein-ligand complexes were pre-processed by employing the Protein Preparation Wizard of Maestro v13.5 [60]. The complex preparation process involved assigning bond order, adding polar hydrogens and missing side chains, and forming disulfide bonds. To optimize the system, we designated an orthorhombic-shaped boundary box measuring 10 × 10 × 10 ų in dimension and filled it with a TIP3P water model for each complex. Random placement of Na⁺ and Cl⁻ ions throughout the solvation box was used to achieve a salt concentration of 0.15 M. The system was then minimized and equilibrated using the OPLS3e force field [61]. The simulations were conducted in the NPT ensemble (constant Number of particles, Pressure, and Temperature) at 300.0 K and 1.01325 bar [62,63]. Each system was energy-minimized to a convergence threshold of 1.2 kcal/mol/Å to relieve steric clashes before the production run [64].
Finally, a 100 ns MD simulation was performed with a 100 ps output frequency, generating a trajectory of 1,000 frames. Furthermore, an energy threshold of -1.2 kcal/mol was applied to filter and identify the most stable and significant intermolecular interactions [65,66]. After the MD simulation procedures, we evaluated the stability and flexibility of the protein-ligand complexes by using several metrics derived from the trajectory, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent-accessible surface area (SASA), radius of gyration (Rg), and protein-ligand contacts.
Molecular docking was conducted for prodigiosin against 19 key proteins involved in T2D. This method was applied to determine the general interaction profiles of prodigiosin against selected proteins and to select the best-docked proteins for further analysis. The molecular docking results for each protein against prodigiosin are presented in Table 3, where the lowest binding energy is considered to indicate the strongest binding affinity between prodigiosin and the specific protein [67]. The results showed that SGLT-2 had the most favorable binding energy of -9.4 kcal/mol, whereas AMPK had the least favorable binding energy of -5.2 kcal/mol. Notably, prodigiosin exhibited strong binding affinity (≤ -7.0 kcal/mol) for 15 out of the 19 protein targets investigated. This broad-scale interaction suggests its potential to modulate multiple pathways implicated in T2D. However, we utilized molecular docking as a filtering tool to identify the best interacting proteins with the highest binding affinity, and filtered out the proteins that had a binding energy of > -8.0 kcal/mol. This strategy mirrors established workflows reported in the literature, which apply binding energy thresholds to enhance the identification of stable protein–ligand complexes [67–69]. We identified 8 proteins that met our requirements and selected them for further analysis. The docking results and details about the interactions of these 8 proteins with prodigiosin are listed in Table 4.
[Table 3. Molecular docking results between prodigiosin and 19 key proteins involved in T2D.]
The molecular docking results demonstrated that prodigiosin bound most strongly to eight proteins: SGLT-2, SIRT1, FBPase, GSK-3β, AG, PPARγ, AR, and Kir6.2. As shown in Table 4, SGLT-2 exhibited the strongest binding affinity with the most negative energy of -9.4 kcal/mol, while Kir6.2 exhibited the weakest binding affinity among these eight, with a value of -8.0 kcal/mol. The specific interactions stabilizing the prodigiosin-protein complexes are illustrated in Figures 1 and 2.
Prodigiosin engaged binding sites through a combination of conventional hydrogen bonds and hydrophobic interactions, including pi-pi stacking, pi-sigma, pi-alkyl, and pi-sulfur interactions. Specifically, persistent hydrogen bonds were observed in several complexes, including those with SGLT-2 (GLU99), FBPase (PRO188), AG (ASP326), PPARγ (CYS285, ARG288, ILE326), AR (TRP111), and Kir6.2 (GLY295 of chain A), further indicating the stability of the complexes.
Pi-sulfur interactions were observed in various protein complexes with prodigiosin, particularly with CYS199 in GSK-3β, MET365 in PPARγ, and CYS298 in AR, suggesting their supportive role in stabilizing prodigiosin within the active or regulatory pockets. Additionally, a pi-anion interaction occurred between prodigiosin and Glu256 of AG, while three carbon–hydrogen bonds were observed in Kir6.2 (MET169, Chain B; THR293, THR294, Chain A) and one in PPARγ (LEU340). Although these bonds are individually weaker than conventional hydrogen bonds, they collectively contribute to the stabilization of prodigiosin within the binding site. Crucially, all these interacting residues were located within the predicted binding pockets for each protein (Table 4), as identified by the CASTpFOLD server. Since these pockets contain amino acids essential for protein function, binding at these sites suggests a high potential for functional modulation, like inhibition or activation [70].
[Table 4. Prodigiosin's binding affinity, interaction details, and CASTpFOLD-predicted binding pockets for the top eight protein targets.]
[Figure 1. Molecular docking interactions (3D and 2D) of prodigiosin with type 2 diabetes-associated protein SGLT‑2 (A), SIRT1 (B), FBPase (C), GSK‑3β (D).]
[Figure 2. 3D and 2D interactions between prodigiosin and other proteins, including AG (A), PPARγ (B), AR (C), and Kir6.2 (D).]
The MM-GBSA method provides a robust approach for calculating the binding free energy (ΔG_bind) of a protein–ligand complex, offering a reliable estimate of binding affinity in a flexible and solvated environment. In this study, the ΔG_bind was calculated for the initial (0 ns) and final (100 ns) frames of the molecular dynamics trajectory to assess changes in binding stability (Table 5). The results revealed that the Kir6.2-prodigiosin complex had the most negative final ΔG_bind (-58.14 kcal/mol), indicating the strongest binding affinity among the eight targets. Conversely, the FBPase-prodigiosin complex had the least negative final ΔG_bind (-20.05 kcal/mol), suggesting the weakest affinity. Meanwhile, the AG-prodigiosin complex exhibited the most significant stabilization, with its ΔG_bind changing from -1.84 kcal/mol to -25.30 kcal/mol. Interestingly, the SIRT1 and AR complexes showed a notable decrease in binding affinity over the simulation. Despite these variations, all complexes yielded favourable binding energies (≤ -20.05 kcal/mol), demonstrating prodigiosin's strong potential for multi-target interactions.
[Table 5. MM-GBSA binding free energy (ΔG_bind) analysis of the top 8 protein-prodigiosin complexes at the initial (0 ns) and final (100 ns) stages of the simulation.]
To assess the stability, flexibility, and other dynamic properties of the selected top eight protein-prodigiosin complexes, molecular dynamics (MD) simulations were conducted in a solvated, ionized, and neutralized environment to mimic physiological conditions. We evaluated key dynamic properties and interactions from the MD trajectories, including the root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent-accessible surface area (SASA), radius of gyration (Rg), and intermolecular contacts (Table 6). In this study, we categorized the selected eight proteins into regulatory (SGLT-2, SIRT1, PPARγ, and Kir6.2) and metabolic (FBPase, GSK-3β, AG, and AR) groups to analyze global dynamic properties, including RMSD, Rg, SASA, and intermolecular contacts. However, for the residue-specific root mean square fluctuation (RMSF) analysis, proteins were grouped by size (small, medium, and large) to facilitate clear and meaningful visualization of flexibility across the polypeptide chain.
[Table 6. Summary of MD simulation parameters, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg), showing their highest, lowest and average values.]
The evaluation of the extent of structural deviation in the protein backbone of a protein-ligand complex compared to its reference conformation is called root mean square deviation (RMSD). RMSD is an important parameter in MD simulation that provides details of the protein's stability and conformational changes by assessing the displacement of structural components such as heavy atoms, Cα atoms, side chains, and the backbone over a 100 ns timescale [71,72]. Figure 3 represents the RMSD analysis of our chosen proteins in complex with prodigiosin. Regulatory proteins are presented in Figure 3A, while Figure 3B illustrates the metabolic protein group. Most complexes demonstrated an initial rise in RMSD followed by stabilization, indicating convergence toward equilibrium.
Among the proteins, AR exhibited the lowest average RMSD (1.39 Å), indicating high structural stability, whereas SGLT-2 (5.27 Å) and SIRT1 (5.73 Å) displayed relatively higher RMSD values. The average RMSD values for the other regulatory proteins were 3.07 Å (PPARγ) and 3.73 Å (Kir6.2). For the metabolic proteins, the RMSD values were 2.32 Å (FBPase), 2.01 Å (GSK-3β), and 2.08 Å (AG). Overall, the RMSD analysis suggested that metabolic proteins exhibited tighter structural stability, while regulatory proteins, such as SGLT-2 and SIRT1, showed greater conformational flexibility. However, SGLT-2 and SIRT1 reached stable plateaus over the course of the simulation, indicating that the cause of elevated deviations was more likely associated with the intrinsic flexibility or conformational adaptability of these proteins rather than a loss of structural integrity. Therefore, RMSD values above 5 Å may still represent equilibrated systems if the trajectories stabilize [73–75].
The measurement of the fluctuations of amino acid residues in a protein-ligand complex, when the ligand binds to a specific region of the protein, is called the root mean square fluctuation (RMSF). RMSF is important for understanding the local variations and stability of individual amino acid residues upon ligand binding, which contributes to the overall stability of the protein-ligand complex. Figure 4 depicts the RMSF profiles of the eight proteins in complex with prodigiosin. Figure 4A represents the smaller proteins (<350 amino acids), including SIRT1, GSK-3β, PPARγ, and AR, while Figures 4B and 4C illustrate medium-sized proteins (350 to 650 amino acids) SGLT-2 and AG, and larger proteins (> 1000 amino acids) FBPase and Kir6.2, respectively. The average RMSF values for the smaller proteins were 2.31 Å, 0.97 Å, 1.44 Å, and 0.75 Å for SIRT1, GSK-3β, PPARγ, and AR, respectively. Alternatively, the average RMSF values were 1.37 Å and 1.05 Å for SGLT-2 and AG, respectively, and 1.05 Å and 1.39 Å for FBPase and Kir6.2, respectively.
Among all the prodigiosin complexes, SIRT1 demonstrated the highest overall fluctuation with the RMSF value of 2.31 Å, indicating substantial local flexibility. Figure 4A reveals that SIRT1 had the largest peaks at the N-terminal residues, reflecting the intrinsically disordered nature of this regulatory region, whereas the catalytic domain of SIRT1 is relatively stable. In contrast, AR displayed the lowest average RMSF (0.75 Å), suggesting a more rigid backbone and a stable ligand-binding conformation [76]. Overall, prodigiosin binding restricted backbone mobility, with average RMSF values remaining below 1.5 Å in most proteins, which reflects enhanced conformational stability while preserving essential conformational plasticity required for function [77,78].
[Figure 3. Root mean square deviation (RMSD) analysis from Molecular Dynamics Simulation of Prodigiosin Complexes with Type 2 Diabetes-Associated Proteins. (A) RMSD values of Regulatory proteins (SGLT-2, SIRT1, PPARγ, and Kir6.2) in complex with prodigiosin. (B) RMSD values Metabolic proteins (FBPase, GSK-3β, AG, and AR) in complex with prodigiosin. Here, the complexes are indicated as SGLT-2 (red), SIRT1 (purple), PPARγ (green), Kir6.2 (black), FBPase (blue), GSK-3β (orange), AG (grey), and AR (gold).]
[Figure 4. Root mean square fluctuation (RMSF) profiles of prodigiosin–protein complexes.
(A) Smaller proteins (<350 amino acids), including SIRT1, GSK-3β, PPARγ, and AR. (B) Medium-sized proteins (350 to 650 amino acids), SGLT-2 and AG. (C) Larger proteins (>1000 amino acids), FBPase and Kir6.2. Here, the complexes are indicated as SGLT-2 (red), SIRT1 (purple), PPARγ (green), Kir6.2 (black), FBPase (blue), GSK-3β (orange), AG (grey), and AR (gold).]
The radius of gyration (Rg) measures the root mean square distance of the protein's atoms from its center of mass in a protein–ligand complex. In molecular dynamics simulations, Rg is used to evaluate the compactness and structural stability of protein–ligand complexes. Stable Rg values suggest that protein folding is preserved when binding to the ligand, whereas fluctuating values indicate conformational changes [79]. In our study, we found stable Rg across all proteins bound to prodigiosin, reflecting the compactness and stability of these complexes (Figure 5). Among these, GSK-3β showed the lowest average Rg (4.11 Å), demonstrating a highly compact structure, whereas SIRT1 exhibited the highest Rg (4.69 Å), indicating comparatively greater conformational flexibility. SGLT-2 (4.14 Å) and Kir6.2 (4.32 Å) displayed similarly compact folds, while FBPase (4.28 Å) and AR (4.48 Å) maintained moderately higher values. PPARγ (4.61 Å) and AG (4.42 Å) demonstrated intermediate compactness.
Proteins with lower Rg values can be considered structurally more compact and stable, whereas slightly higher Rg values may reflect the presence of flexible domains or solvent-exposed regions that are important for their biological activity [78,79]. In our study, the stable Rg values with small fluctuations for all the complexes suggested that prodigiosin binding did not induce any large-scale structural rearrangements but instead preserved the compactness. These findings align with previous studies, which showed that stable Rg values are indicative of preserved protein folding during ligand binding, whereas significant deviations typically correspond to conformational rearrangements [80–82].
Solvent-accessible surface area (SASA) represents the protein surface in a protein–ligand complex that is accessible to solvent molecules. SASA analysis in MD simulation calculates the protein's free surface area upon binding with the ligand, and provides information about the compactness and conformational stability of the complex. Variations in SASA values over the simulation timeframe reflect conformational transitions, such as open and closed states, as well as the differential exposure of hydrophobic and hydrophilic residues [83]. In this study, we analyzed the SASA of the eight proteins bound with prodigiosin to identify their conformational stability. We found that all the regulatory proteins have very low SASA values (Figure 6A) compared to the metabolic proteins (Figure 6B).
The lowest average SASA value of 25.58 Å2 was seen for the PPARγ-prodigiosin complex. SGLT-2 and SIRT1 exhibited similar SASA values of around 42 Ų, whereas Kir6.2 and FBPase displayed slightly higher average values of 69.24 Ų and 82.35 Ų, respectively. Complexes with lower SASA values generally reflect increased compactness and reduced solvent accessibility, often associated with enhanced stability of the protein–ligand interface [83]. In contrast, GSK-3β, AR, and AG exhibited substantially higher SASA values of 126.81 Ų, 223.97 Ų, and 303.45 Ų, respectively. These elevated values suggested greater conformational flexibility and solvent accessibility in their prodigiosin-bound states.
[Figure 5. Radius of gyration (Rg) analysis of prodigiosin–protein complexes.
(A) Regulatory proteins (SGLT-2, SIRT1, PPARγ, and Kir6.2). (B) Metabolic proteins (FBPase, GSK-3β, AG, and AR). Here, the prodigiosin complexes are indicated as SGLT-2 (red), SIRT1 (purple), PPARγ (green), Kir6.2 (black), FBPase (blue), GSK-3β (orange), AG (grey), and AR (gold).]
[Figure 6. Solvent-accessible surface area (SASA) analysis of prodigiosin–protein complexes. (A) Regulatory proteins (SGLT-2, SIRT1, PPARγ, and Kir6.2). (B) Metabolic proteins (FBPase, GSK-3β, AG, and AR). Here, the complexes are indicated as SGLT-2 (red), SIRT1 (purple), PPARγ (green), Kir6.2 (black), FBPase (blue), GSK-3β (orange), AG (grey), and AR (gold).]
In this study, the Simulation Interaction Diagram (SID) was employed to analyze the binding stability and interaction profiles of prodigiosin with the selected protein targets, thereby highlighting the intermolecular contacts within the protein–prodigiosin complexes. These interactions consisted of hydrogen bonds, hydrophobic interactions, ionic bonds, and water bridges. In regulatory proteins (Figure 7), prodigiosin binding was dominated by hydrophobic contacts, particularly with residues VAL95, PHE98, ALA102, LEU283, VAL286, TYR290, and PHE453 in SGLT-2 (Figure 7A), ALA262, PHE273, PH3297, HIS363, and PHE414 in SIRT1 (Figure 7B), and PHE282, TYR327, MET329, LEU330, LEU333, ILE341, and MET364, in PPARγ (Figure 7C), For Kir6.2 (Figure 7D), hydrophobic stabilization was consistently observed across all four chains, involving residues such as PHE168, ALA172, and ILE296. Notably, multiple hydrogen bonds in most regulatory proteins, such as those involving GLU99 and GLN457 in SGLT-2, SER289 and ILE326 in PPARγ, and ALA172 and THR294 in chain A of Kir6.2, further contributed to ligand stability.
Similarly, metabolic proteins exhibited diverse binding patterns (Figure 8). Prodigiosin formed multiple hydrogen bonds with key residues, including PRO188 and ALA189, and ILE190 in multiple chains of FBPase (Figure 8A), VAL135 in GSK-3β (Figure 8B), GLU141, SER142, and ASP326 in AG (Figure 8C), and CYS298, LEU300, SER302 in AR (Figure 8D). The presence of ionic bonds was suggested as a particularly strong interaction in two of the complexes, such as FBPase-prodigiosin and AR-prodigiosin. Notably, three distinct ionic interactions occurred at ALA299, LEU301, and SER302 in AR. Collectively, these results demonstrate that prodigiosin achieves stable binding through a combination of hydrophobic contacts, hydrogen bonds, and water bridges. The prevalence of conserved hydrophobic interactions across diverse targets highlights this as a key mechanism for its multi-target activity.
[Figure 7. Simulation Interaction Diagram (SID) plots showing intermolecular contacts in regulatory protein–prodigiosin complexes during 100 ns MD simulations: (A) SGLT-2, (B) SIRT1, (C) PPARγ, and (D) Kir6.2.]
[Figure 8. SID plots of intermolecular contacts in metabolic protein–prodigiosin complexes over 100 ns MD simulations: (A) FBPase, (B) GSK-3β, (C) AG, and (D) AR.]
Prodigiosin, a natural tripyrrole pigment, has earned considerable interest due to its diverse pharmacological effects, including antimicrobial, anticancer, and immunomodulatory properties. Recent studies have suggested its potential in regulating metabolism and redox signaling pathways [84,85]. These properties of prodigiosin raise the possibility of its repurposing as a drug in metabolic disorders such as type 2 diabetes (T2D). In this study, we systematically explored the interaction of prodigiosin with 19 proteins (8 of them were chosen) implicated in the onset and progression of T2D, using molecular docking, MM-GBSA binding free energy calculations, and molecular dynamics simulations to construct an interaction map of its binding potential, which may help to develop this compound as a novel antidiabetic agent.
Molecular docking and the post-docking interaction analysis revealed that prodigiosin docked with the best score of −9.4 kcal/mol into a pocket of Sodium–Glucose Cotransporter-2 (SGLT-2) lined by HIS80, LEU84, PHE98, GLU99, VAL286, TRP289, TYR290, TRP291, and GLN457 amino acids. Subsequently, the improved free binding energy in the MM-GBSA analysis (from -41.89 to -52.05 kcal/mol) indicated that prodigiosin is a strong binder of SGLT-2. Although the average RMSD (5.27 Å) was on the higher end, which is generally expected for a large membrane protein with mobile loops [86,87]. Moreover, the stable Rg value (average 4.14 Å) and low SASA value (average 42.1 Ų) exhibited compactness and stability of the complex over the trajectory. Furthermore, the presence of interacting amino acids (TYR290/TRP291) in the functionally key region of the SGLT-2 predicted an inhibitor-like binding that overlaps the canonical gliflozin (and glucose) site [88]. This inhibition of SGLT-2 by prodigiosin could result in lowering glycemia in T2D [10]. Similarly, prodigiosin interacted with Sirtuin 1 (SIRT1), with a good docking score (-8.7 kcal/mol), strong MM-GBSA (−56.7 kcal/mol), and consistent MD results, demonstrating a strong and stable SIRT1-prodigiosin complex. In addition, prodigiosin's interaction with the catalytic histidine (HIS363) and key residues of the peptide/NAD⁺ channel (PHE273/PHE297) [89,90] suggests inhibition of SIRT1, as most reported small-molecule activators bind allosterically and do not occupy the His363 region [91,92]. Therapeutically, inhibition of SIRT1 is undesirable because it generally opposes insulin-sensitizing benefits seen with SIRT1 activation in metabolic diseases, such as T2D [93,94].
The prodigiosin-FBPase (Fructose-1,6-Bisphosphatase) complex engaged amino acid residues LEU186 and PRO188 of different chains, with an initial docking binding affinity of -8.6 kcal/mol; however, MM-GBSA analysis revealed a decrease in free binding energy of -20.05 kcal/mol. Functionally, potent FBPase inhibitors either bind to the catalytic metal site (GLU97, ASP118, ASP121, GLU280) [95,96] or the allosteric AMP site (THR31, TYR113, ARG140, LYS112, VAL117) [97]. In contrast, prodigiosin primarily associates with residues around position 188 of FBPase, and the less favorable MM-GBSA results suggest weak or uncertain inhibitory activity. Although the complex exhibited stability with a low RMSD (2.32 Å average) and a modest SASA value, its predicted inhibitory potential remains limited. Alternatively, prodigiosin interacted with PHE67, VAL70, ALA83, VAL110, LEU132, LEU188, and CYS199 of Glycogen Synthase Kinase-3 Beta (GSK-3β), yielding a robust docking score and a modest MM-GBSA result. Particularly, CYS199 at the lip of the active site is a known sentinel residue modulating inhibitor engagement in multiple studies [98,99]. The MD metrics also advocate that the GSK-3β-prodigiosin forms a well-packed, buried complex. Based on these results, it can be concluded that prodigiosin has the potential to inhibit glycogen synthase kinase-3β (GSK-3β). In glucose homeostasis, the inhibition of GSK-3β can enhance insulin signaling, which is directionally favorable in T2D if selectivity and safety are addressed [100,101].
Our study also revealed that prodigiosin can inhibit both Alpha-Glucosidase (AG) and Aldose Reductase (AR), although the effectiveness of inhibition differs. Prodigiosin interacted with the catalytic apparatus of AG, specifically with GLU256 and ASP326, which are located within the catalytic motif [102]. However, MD simulation showed a fairly mobile, solvent-exposed pocket (average RMSD 2.08 Å and SASA 303 Ų) for the complex. Overall, these results support a moderate inhibition effect of prodigiosin against AG. In contrast, prodigiosin formed persistent hydrophobic and polar contacts with TRP111, PHE122, TRP219, CYS298, and LEU300/LEU301, which are the residues known to define the AR specificity pocket [103,104]. These interactions support prodigiosin's strong inhibitory binding, as also represented by the MD data, which showed the most rigid complex with an average RMSD of 1.39 Å and the lowest RMSF. Therapeutically, concurrent inhibition of alpha-glucosidase (AG) and aldose reductase (AR) could benefit T2D by reducing postprandial glucose excursions (AG) and limiting polyol–pathway–mediated neuropathic damage (AR), although clinical safety needs to be proven [105,106].
Subsequently, the interacting set of prodigiosin in Peroxisome proliferator-activated receptor gamma (PPARγ), such as HE282, CYS285, ARG288, LEU330, LEU340, PHE383, MET363, and HIS449, overlaps both agonist-favored residues (CYS285 and HIS449) and an allosteric antagonists-favored residue (ARG288) of PPARγ [107,108]. Therefore, prodigiosin likely modulates PPARγ, based on the strong and stable MM-GBSA and MD results; however, the direction of modulation (agonism vs. antagonism vs. partial) is uncertain from these data alone. In another complex, prodigiosin contacted two well-characterized Kir6.2 constriction sites: the helix-bundle crossing (HBC; PHE168/MET169/ALA172) and the cytoplasmic G-loop gate (THR293–GLY295–ILE296), as observed across chains A, B and D in our complex [109]. Furthermore, MD stability (RMSD 3.73 Å; Rg 4.32 Å) and persistent hydrophobic contacts indicate a channel-blocking effect of prodigiosin against Kir6.2, which would increase insulin secretion in β cells but requires functional verification.
Taken together, our findings support the inhibitory engagement of prodigiosin against SGLT-2, GSK-3β, AG, AR, and Kir6.2, whereas FBPase binding is weak, and PPARγ is modulated, though the agonism/antagonism balance remains uncertain. Alternatively, SIRT1 is likely inhibited by prodigiosin, which would be undesirable in the context of T2D. However, these findings are derived solely from computational predictions and therefore require biochemical, biophysical and cellular validation. Nonetheless, the interaction map presented here provides a practical foundation and a head start for future experimental work to assess and optimize prodigiosin as a potential antidiabetic agent.
Type 2 diabetes (T2D) is a complex metabolic disease caused by insulin resistance and dysfunction of the pancreatic β-cells. Many proteins are involved in the initiation and progression of the disease. Therefore, we docked prodigiosin against 19 T2D-relevant proteins and selected eight (SGLT-2, SIRT1, FBPase, GSK-3β, AG, PPARγ, AR, Kir6.2) for detailed follow-up to establish an intermolecular network of prodigiosin. Using a workflow of molecular docking, MM-GBSA analysis and 100-ns molecular dynamics, we analyzed and evaluated the strength, stability and flexibility of the interactions between prodigiosin and these selected proteins. Our MM-GBSA results indicated that Kir6.2, SIRT1, SGLT-2, and PPARγ were strong binders, with free binding energies of -58.14 kcal/mol, -56.70 kcal/mol, -52.05 kcal/mol, and -49.99 kcal/mol, respectively. Alternatively, GSK-3β (-38.61 kcal/mol) and AR (-39.18 kcal/mol) were found to be moderate binders, whereas AG (-25.30 kcal/mol) and FBPase (-20.05 kcal/mol) were identified as weak binders with prodigiosin. However, subsequent MD simulation and the presence of interacting amino acid residues in the functional motifs of these proteins predicted that prodigiosin displays a multi-target inhibitory profile, most promising for SGLT-2, GSK-3β, AR, Kir6.2, and AG. Furthermore, while prodigiosin could modulate the function of PPARγ, the direction of modulation could not be confirmed. In contrast, the interaction of prodigiosin with FBPase is weak, with no effect. However, prodigiosin could inhibit SIRT1, which raises a concern, as this is an undesirable off-target in T2D. Although this interaction map would provide targets for prodigiosin in the development of a new treatment strategy for T2D, biochemical (IC₅₀/Kᵢ), electrophysiological, and cellular validation are required to confirm the potency, selectivity, and safety of prodigiosin.
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Conceptualization, T.J.H.; Methodology/Planning (protein selection), T.J.H. and T.R.; Data curation, T.R.; Formal analysis (docking and simulation), T.R.; Investigation, T.R.; Validation, T.R. and M.S.H.B.; Resources, T.R. and T.J.H.; Writing—original draft preparation, T.R. and M.S.H.B.; Writing—review & editing (including title/structure refinement), T.R., M.S.H.B., and T.J.H.; Visualization, T.R.; Funding acquisition, T.J.H.
The authors declare no conflict of interest.
This study was supported by the Ministry of Science and Technology, Government of the People’s Republic of Bangladesh.
All the data used is contained within the article.
The authors gratefully acknowledge the Ministry of Science and Technology, Government of the People’s Republic of Bangladesh, for financial support. The authors also extend their sincere thanks to their family members for their invaluable support.
This study did not involve human or vertebrate animal subjects requiring ethical approval.
Figure 1. Molecular docking interactions (3D and 2D) of prodigiosin with type 2 diabetes-associated protein SGLT‑2 (A), SIRT1 (B), FBPase (C), GSK‑3β (D).
Figure 2. 3D and 2D interactions between prodigiosin and other proteins, including AG (A), PPARγ (B), AR (C), and Kir6.2 (D).
Figure 3. Root mean square deviation (RMSD) analysis from Molecular Dynamics Simulation of Prodigiosin Complexes with Type 2 Diabetes-Associated Proteins. (A) RMSD values of Regulatory proteins (SGLT-2, SIRT1, PPARγ, and Kir6.2) in complex with prodigiosin. (B) RMSD values Metabolic proteins (FBPase, GSK-3β, AG, and AR) in complex with prodigiosin. Here, the complexes are indicated as SGLT-2 (red), SIRT1 (purple), PPARγ (green), Kir6.2 (black), FBPase (blue), GSK-3β (orange), AG (grey), and AR (gold).
Figure 4. Root mean square fluctuation (RMSF) profiles of prodigiosin–protein complexes. (A) Smaller proteins (<350 amino acids), including SIRT1, GSK-3β, PPARγ, and AR. (B) Medium-sized proteins (350 to 650 amino acids), SGLT-2 and AG. (C) Larger proteins (>1000 amino acids), FBPase and Kir6.2. Here, the complexes are indicated as SGLT-2 (red), SIRT1 (purple), PPARγ (green), Kir6.2 (black), FBPase (blue), GSK-3β (orange), AG (grey), and AR (gold).
Figure 5. Radius of gyration (Rg) analysis of prodigiosin–protein complexes.
(A) Regulatory proteins (SGLT-2, SIRT1, PPARγ, and Kir6.2). (B) Metabolic proteins (FBPase, GSK-3β, AG, and AR). Here, the prodigiosin complexes are indicated as SGLT-2 (red), SIRT1 (purple), PPARγ (green), Kir6.2 (black), FBPase (blue), GSK-3β (orange), AG (grey), and AR (gold).
Figure 6. Solvent-accessible surface area (SASA) analysis of prodigiosin–protein complexes. (A) Regulatory proteins (SGLT-2, SIRT1, PPARγ, and Kir6.2). (B) Metabolic proteins (FBPase, GSK-3β, AG, and AR). Here, the complexes are indicated as SGLT-2 (red), SIRT1 (purple), PPARγ (green), Kir6.2 (black), FBPase (blue), GSK-3β (orange), AG (grey), and AR (gold).
Figure 7. Simulation Interaction Diagram (SID) plots showing intermolecular contacts in regulatory protein–prodigiosin complexes during 100 ns MD simulations: (A) SGLT-2, (B) SIRT1, (C) PPARγ, and (D) Kir6.2.
Figure 8. SID plots of intermolecular contacts in metabolic protein–prodigiosin complexes over 100 ns MD simulations: (A) FBPase, (B) GSK-3β, (C) AG, and (D) AR.
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Prodigiosin interactions with type 2 diabetes proteins in an in silico docking and simulation study.pdfRahman, T., Hossain Bappi, M. S., & Hossain, T. J. (2025). Prodigiosin as a Potential Multi-Target Antidiabetic Agent: Molecular Docking and Dynamics Simulations with Type 2 Diabetes-Related Proteins [Preprint].
