Polyphenol Inhibition of Human Pancreatic Lipase: An In-Silico Study Towards Obesity Control

doi:10.21203/rs.3.rs-5360869/v1

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Polyphenol Inhibition of Human Pancreatic Lipase: An In-Silico Study Towards Obesity Control

https://doi.org/10.21203/rs.3.rs-5360869/v1

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Background: Inhibiting human pancreatic lipase (EC3.1.1.3), a key enzyme in dietary fat breakdown and absorption, is an effective therapeutic approach for obesity control. Polyphenols, due to their multifaceted structure, enhance insulin sensitivity, reduce inflammation, and modulate gut microbiota, offering synergistic effects in controlling obesity.

Methods: Considering the adverse side effects associated with current anti-obesity therapeutics, we explored a library of polyphenols known for their antiobesity properties to explicitly potent HPL inhibitors through extensive in-silicostudy including molecular docking, DFT, MD simulation, PCA, DCCM-based conformational analysis and pharmacokinetic analysis.

Results: Significant binding affinity and interactions with catalytic triad (SER 152, HIS 263, and ASP 176) of HPL through molecular docking, alongside higher MM/GBSA values of -53.29, -52.76, and -53.37 kcal/mol, identified (-)-epigallocatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3'-O-methyl gallate), respectively, as potent leads. The DFT study and molecular dynamics simulation affirmed the strong reactivity of these compounds in the catalytic site of HPL and stable protein-ligand complex over 100 ns. FEL, PCA, and DCCM analysis also demonstrated these protein-ligand complexes' stable dynamic behavior and conformational changes. Moreover, post-simulation MMPBSA analysis indicated higher binding free energy and favorable ADMET and drug-likeness pharmacokinetic properties asserted these lead potentials as explicit HPL inhibitors with potential for obesity control.

Conclusion: To sum up, (-)-epigallocatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3'-O-methyl gallate) are identified as promising HPL inhibitors, with potential application in managing obesity due to their stable interaction with the enzyme and favorable pharmacokinetic characteristics.

Computational Biology

HPL

secondary metabolites

molecular docking

MD simulations

DFT

Human pancreatic lipase (triacylglycerol acyl hydrolase EC3.1.1.3), secreted by the acinar cells of the pancreas, exhibits a strong preference for triacylglycerides and collaborates with bile salts in the gastrointestinal tract to hydrolyze them into fatty acids and glycerol [1, 2]. HPL is pivotal in regulating lipid metabolism by hydrolyzing 50–70% of dietary fat [3] essential for lipid absorption. Inhibiting HPL effectively reduces triglyceride intake, offering a potential therapeutic strategy for obesity [4]—a chronic medical condition characterized by excess adiposity, which affects approximately 650 million individuals globally. Lifestyle interventions, encompassing dietary adjustments, physical activity, and behavioral modifications, entail the primary approach to managing obesity [5]. Despite their implementation, these interventions typically yield an average weight loss of ~ 10%. Maintaining this weight loss is challenging, with about 80% of the weight normally regained within five years [6]. Moreover, Obesity is linked with a spectrum of negative health consequences, encompassing metabolic disorders such as stroke, diabetes, atherosclerosis, cardiovascular diseases, hyperlipidemia, hypertension, chronic kidney disease, nonalcoholic fatty liver disease, and cancer alongside depression and osteoarthritis [7, 8]. Its profound impact on health necessitates effective preventive and therapeutic interventions. Orlistat, the primary FDA-approved lipase inhibitor for managing obesity, functions by covalently binding to the active serine site of lipases, thereby inhibiting their ability to hydrolyze dietary fat. However, its use leads to gastrointestinal side effects and potential reductions in the absorption of fat-soluble vitamins [9]. Despite these drawbacks, it is chosen as a standard compound in obesity studies due to its unique mechanism and established efficacy, providing a reliable baseline for evaluating new treatments while ongoing research aims to develop alternatives that minimize side effects, improve patient adherence, and offer full health benefits.

Human pancreatic lipase, located at10Q26 of the human chromosome [10] is composed of 449 amino acids, with SER 152, HIS 263, and ASP 176 forming the conserved catalytic triad [11]. Its activity relies on the presence of colipase, and lipase inhibitors have shown efficacy in both preclinical and clinical obesity models [12]. Modulating the activity of human pancreatic lipase may offer new therapeutic approaches to inhibit fat absorption in the body, potentially aiding in treating obesity and associated metabolic disorders [13]. Natural products have high safety profiles, effectively inhibit pancreatic lipase, and have significant gastrointestinal exposure, making them advantageous in treating metabolic disorders associated with obesity( X.-D. Hou et al., 2022). Phytochemicals offer a promising alternative to current medication, addressing major issues related to gut microbiota and nutrient deficiencies, underscoring the necessity of managing side effects while targeting specific enzymes [15, 16]. Key pharmacological actions that aid in obesity control include their effects on lipid metabolism and adipogenesis [17]. Polyphenols, in particular, demonstrate modest effects through multiple metabolic targets and pathways, such as suppression of food intake, inhibition of lipogenesis, enhancement of lipolysis, prevention of fatty acid oxidation, and inhibition of adipogenesis and apoptosis [18, 19]. The chemical structure, substitutions, and number of hydroxyl groups in polyphenols significantly affect their bioavailability and absorption. Consequently, these factors have a major influence on their biological activities, including their antioxidant characteristics, free radical scavenging, enhanced insulin sensitivity, reduced inflammation, and modulation of gut microbiota, providing synergistic benefits for obesity control [20].

Breakthroughs in pharmaceutical sciences, like computer-aided screening (CAS), molecular docking, molecular dynamics (MD) simulations, and computer-aided drug design (CADD), are key to rapidly discovering potent drug compounds and refining methodologies in drug discovery and development [21, 22]. To explore potential new target sites, we developed a robust computational framework that encompasses docking, molecular mechanics using generalized Born and surface area solvation (MM/GBSA), molecular dynamics (MD) simulations, density functional theory (DFT), along with pharmacokinetic and toxicity assessments. This framework focuses on predicting the effectiveness of polyphenols as inhibitors of HPL. It emphasizes inhibitors that demonstrate strong binding affinities and maintain stable interaction poses with the catalytic sites. The analysis includes key stability indicators like root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), the radius of gyration (Rg), solvent-accessible surface area (SASA), and hydrogen bond interactions. These inhibitors are expected to bind efficiently to the target enzyme, exhibiting potent reactivity and kinetic stability, thereby effectively suppressing obesity.

2.1 Library of Compounds and Ligand Preparation

A library of polyphenols, recognized for their reported in vitro antiobesity properties, was prepared to identify potential HPL inhibitors in context to their therapeutic purposes. The library of included metabolites in the library are presented in Table S1 and Figure S1. The 3D conformer of each ligand sourced from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) was imported into the Flare workspace through their canonical SMILES. Utilizing a ligand prep module, each ligand was prepared by minimizing energy while maintaining a pH of 7.

2.2 Protein preparation and validation

A lipase protein (PDB ID: 1LPB), with no mutation and 2.46 Å resolution, was retrieved from the protein data bank (https://www.rcsb.org/) and imported into the Flare workspace using the input protonation state. The protein prep module was then employed to prepare protein by; removing complexed ligands, capping residues, maintaining a pH of 7, allowing slight side chain movements, eliminating atoms from residues with incomplete backbones, discarding water molecules and unwanted chains, and filling gaps in residues.

2.3 Molecular Docking

Utilizing Cresset Flare Flare Pro⁺ software (http://www.cresset-group.com/flare/) [23], a molecular docking study of selected metabolites was performed. The grid size was created by enclosing the catalytic triad residues. The docking calculation was performed in normal mode, with the evaluation of the results based on their interaction and binding affinity. Lead Finder™, which features a specific algorithm and scoring functions for virtual screening, was applied in docking analysis. Validation of the procedure was achieved through redocking and superimposition, ensuring reliable and consistent outcomes [24].

2.4 MM/GBSA binding energy calculations

The MM/GBSA calculations were performed for the docked protein-ligand complex utilizing Cresset Flare Pro⁺ software in normal mode with the default settings (https://www.cresset-group.com/flare) [25].

2.5 Electrostatic complementarity

An electrostatic complementarity analysis was carried out with the Flare Pro⁺ module of Cresset software to assess the electrostatic compatibility between the selected ligands and the protein binding pocket [26]. Non-covalent interactions between small molecules and their receptors significantly impact the binding affinity of the protein-ligand complex. This evaluation provides details regarding the binding interactions of these ligands with the HPL targets.

2.6 Density Functional Theory (DFT)

DFT analysis utilizing GuassView 6 and Guassian 09 software ([27] with the Becke, 3-parameter, Lee-Yang-Parr (B3LYP) functional and the 6-31G (d,p) basis set was performed to assess the reactivity of listed phytochemicals and orlistat within the protein's catalytic domain [28]. The band gap energy (ΔE) was calculated by computing the energies of the frontier molecular orbitals, specifically the highest occupied (E_HOMO) and lowest unoccupied molecular orbital (E_LUMO) [29, 30]. The formulae below were used to compute the ionization potential (I), electron affinity (A), chemical hardness (η), chemical softness (S), electronegativity (χ), electrophilicity index (⍵), and chemical potential (µ) [29–31].

Energy difference of HOMO and LUMO (ΔE_gap ) = E_LUMO − E_HOMO ……………..(Eq. 1)

Ionization potential (I) = − E_HOMO ……....................................................................(Eq. 2)

Electron affinity (A) = − E_LUMO ……………………………………………………(Eq. 3)

Chemical (Global) hardness (η) = ΔE/2 ……………………………………….…..(Eq. 4)

Chemical softness (S) = 1/η ………………………………………………………..(Eq. 5)

Electronegativity (χ) = (I + A) / 2 ≈ − µ (chemical potential) ………………..……(Eq. 6)

Electrophilicity index (ω) = µ²/2η ……………………………………………...….(Eq. 7)

2.7 Molecular Dynamics Simulation

A 100 ns molecular dynamics (MD) simulation was performed using Gromacs software to evaluate the conformational dynamics and stability of the biomolecules [32, 33]. MD simulation focused on complexes with binding affinities < − 9.0 kcal/mol. The ligand topology file was initially developed using the Swissparam website [34], while protein topology files were created using the CHARMM27 force field with a simple point charge (SPC) water model. Then the protein.gro and ligand.gro files were combined to create the complex.gro files. The complex was enclosed in a triclinic box under periodic boundary conditions and neutralized with Cl⁻ or Na⁺ ions. The steepest descent algorithm was employed to perform energy minimization for 5,000 steps. The system was then equilibrated under NVT conditions at 300 k temperature for 50,000 steps, followed by NTP conditions at 1 atm pressure for 50,000 steps (100 ps). The last step was to run a 100 ns MD simulation. The simulation results were analyzed using RMSD, RMSF, Rg, SASA, hydrogen bonding, and the free energy landscape (FEL) [35].

2.8 Principal Component Analysis (PCA) and Dynamic Cross-Correlation Matrix (DCCM) Analysis

Principal component analysis (PCA) was used using the Bio3d package in R Studio to evaluate the dynamic behavior and conformational space of biomolecules [36]. The analysis involved evaluating the eigenvectors and eigenvalues, as well as their projections onto the initial three principal components [37]. The variable dynamic behavior of complexes essential for biological activities was extracted through PCA [38]. It was expected that stable protein-ligand complexes would exhibit reduced dynamic fluctuations due to the increased rigidity of the ligand-binding sites. Additionally, dynamic cross-correlation matrix (DCCM) analysis was conducted utilizing the Bio3D package [36] to understand and compare the dynamic and conformational changes occurring in apo-protein and protein-ligand complexes.

2.9 Molecular Mechanics Poisson-Boltzman Surface Area (MM/PBSA)

Protein-ligand complex binding energies, which are necessary for MD simulations and thermodynamic assessments, were assessed using the gmx_MMPBSA module of the GROMACS software. The binding energy (ΔGbind) of protein and ligand complexes was determined using the well-known MM-PBSA technique in conjunction with MD simulations, as shown by the equation [39]:

ΔG_bind = G_complex - (G_receptor + G_ligand)

2.10 Pharmacokinetic Analysis

The physicochemical properties of the compounds were evaluated using the SwissADME drug-likeness online platform [40]. Likewise, the ADMET and toxicity profile were accessed using pkCSM [41] and ProTox-II web servers [42], respectively.

3.1 Molecular Docking and MM/GBSA Calculation

Molecular docking, a key approach in drug discovery, advances the prediction of enzyme inhibitors by offering a detailed insight into their interactions and effectiveness [43]. Initially, the chosen polyphenols were evaluated through molecular docking, which involved analyzing binding scores and competitive interactions with catalytic residues to determine the most effective HPA inhibitor. To ensure the accuracy and reliability of the molecular docking process, redocking was performed, showing an RMSD value of under 2 Å for the ligand [44]. After successful validation, the selected compound library was docked into the catalytic diad of HPL, and the results were analyzed regarding interaction with the active site, binding affinity, and MM/GBSA value. Docking results were expressed as LF (LigandFit) rank score, LF dG score, LF Vscore, and LF LE (LigandFit Ligand Efficiency) score. LF rank and dG here signify the exact ranking of each docked ligand pose by energy and the binding affinity, respectively, while VSscore and LE illustrate the correct ranking of compounds as active or inactive in virtual screening and the estimated efficiency of the ligand [45]. These compounds showed LF Rank scores ranging from − 2.255 to -11.09, LF dG score from − 0.462 to -19.516, LF Vscore from − 4.377 to -12.689, LF LE score from − 0.015 to -0.61, and MM/GBSA from − 24.47 to -53.37 kcal/mol, compared to reference ligand orlistat, which exhibited an LF Rank score of 0.293, an LF dG of -8.343, an LF Vscore of -8.864, an LF LE of -0.238, and an MM/GBSA of -49.4 kcal/mol.

Using MM/GBSA, which provides a prominent binding score [46], three compounds—(-)-epigallocatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3'-O-methyl gallate)—exhibited higher MM/GBSA of -53.29, -52.76, and − 53.37 kcal/mol, respectively, compared to orlistat (-49.4 kcal/mol). Their interactions with catalytic residues, as detailed in Table S2, underscore their significance in inhibiting HPL. (-)-epigallocatechin-3-O-p-coumarate showed hydrogen bonds interaction with SER 152 (Fig. 1A and Table 1), a catalytic residue accountable for the hydrolysis of triglycerides into smaller absorbable forms, thereby managing obesity. The active site residues SER 152 and HIS 263 formed strong hydrogen bonds with (+)-catechin-3-O-gallate (Fig. 1B). Similarly, (-)-epicatchein-3-O-(3'-O-methyl gallate) exhibited catalytic inhibition via hydrogen bonds with SER 152 (Fig. 1C). These compounds demonstrated potency for competitive inhibition through interactions with catalytic site residues and higher MM/GBSA values (Table 1), indicating a need for further investigation with additional in-silico analyses.

Table 1

Molecular docking and MM/GBSA results
Compound	LF Rank score	LF dG	LF Vscore	LF LE	Interactions	MM/GBSA
(-)-Epigallocatechin-3-O-p-coumarate	-5.601	-8.13	-9.928	-0.246	H-bond: GLY76, ASP79, TYR114, HIS151, SER152 Aromatic-Aromatic: TYR114 Cation-pi: HIS263	-53.29
(+)-catechin-3-O-gallate	-10.381	-10.166	-10.821	-0.318	H-bond: GLY76, PHE77, ASP79, TYR114, SER152, PHE215, HIS263 Aromatic-Aromatic: PHE77, HIS151, PHE215, HIS263	-52.76
(-)-Epicatechin-3-O-(3'-O-methyl gallate)	-10.377	-10.751	-11.617	-0.326	H-bond: GLY76, ASP79, HIS151, SER152, PHE215, ARG256, HIS263 Aromatic-Aromatic: PHE215, and HIS263	-53.37
Orlistat	0.293	-8.343	-8.864	-0.238	H-bond: PHE77, SER152, HIS263	-49.4

3.2 Electrostatic complementarity

The three screened ligands—(-)-epigallocatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3'-O-methyl gallate) were subjected for electrostatic complementarity analysis in context to EC, EC r and EC rho. EC is a normalized complementarity score surface integral that effectively provides the average score throughout the surface of the ligand, while EC r and EC rho represent the Spearman rank correlation coefficient and Pearson's correlation coefficient, respectively. The electrostatic potential of proteins and ligands sampled on the surface vertices is often computed using these scores [45]. These scores' metrics vary, but they often fall between 1 (perfect complementarity) and − 1 (complete clash). As shown in Table 2, all three ligands were found to manifest better electrostatic complementarity scores with no indication of steric clashes, thereby indicating their potential for HPL inhibition.

Table 2

Electrostatic complementarity results of (-)-epigallocatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3'-O-methyl gallate).
Compounds	EC	EC r	EC rho
(-)-Epigallocatechin-3-O-p-coumarate	0.198	0.118	0.128
(+)-catechin-3-O-gallate	0.192	0.097	0.057
(-)-Epicatechin-3-O-(3'-O-methyl gallate)	0.23	0.17	0.081

3.3 DFT

The reactivity of ligands in catalytic site of HPL was evaluated using a DFT study. The band-gap energy, which is the difference between the LUMO and HOMO energies (E_LUMO − E_HOMO), serves as an indicator of molecular reactivity. Ligands with a smaller band-gap energy are more polarizable and exhibit higher chemical reactivity [47]. As displayed in Fig. 2, the hit compounds showed higher reactivity with lower band gap energy compared to the reference compound orlistat. Furthermore, their ionization energy and electron affinity indicate greater reactivity, indicating their capabilities to donate and attract electrons [48].

To better comprehend the protein-ligand interaction and the effectiveness of the drug, quantum mechanical parameters of chemical hardness (η), softness (S), electronegativity (χ), electrophilic index (ω), and chemical potential (µ) play crucial roles. These parameters characterize the ligand's reactivity and chemical stability. The χ indicates a molecule's ability to attract electrons, η, and S denotes the presence of barriers to electron flow, ω represents the energy lost as a result of the electrons flowing between them, and µ marks the electron transfer path [49–51]. When compared to the orlistat, all of the previously described metrics, as displayed in Table S3, the hit compounds showed excellent efficacy of reactivity in the binding pocket of HPL.

3.4 Molecular Dynamic Simulation

In computational-aided drug design (CADD), MD simulations are pivotal by revealing complex molecular behaviors at atomic and molecular levels. They also contribute to a deeper understanding of protein or enzyme interactions with drug targets, thereby facilitating the optimization of drug candidates [52].

3.4.1 Root mean square deviation (RMSD)

Through RMSD analysis, dynamic behaviors were explored, uncovering shifts in conformation and structural modifications in the backbone of both apoprotein and protein-ligand complexes [53]. Additionally, it facilitates the comparison of the native folded protein structure with its partly or completely unfolded counterparts, serving as a valuable reaction coordinate in protein folding studies [54]. The RMSD plot of all complexes is displayed in Fig. 3. The RMSD graph revealed that all selected complexes along with apo-proprotein gain remarkable stability within a 20 ns simulation trajectory. Although the RMSD value of the orlistat complex is lower than that of the selected candidates, it achieves stability only after 60 ns of simulation trajectory. The apo-protein has the lowest RMSD value with an average of 0.18 nm, followed by the orlistat-protein complex with an average of 0.24 nm. The (-)-epigallocatechin-3-O-p-coumarate complex has an average RMSD value of 0.33 nm, (+)-catechin-3-O-gallate has 0.36 nm, and (-)-epicatechin-3-O-(3’-O-methyl gallate) complex has 0.41 nm. The average RMSD values for each complex, including the apo-protein, are presented in Table 3.

Table 3

MD simulation results spanning 100 ns for (-)-epigallocatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3'-O-methyl gallate)
Complex	Average RMSD (nm)	Average RMSF (nm)	Average Rg (nm)	Average SASA (nm²)
(+)-Catechin-3-O-gallate	0.36	0.13	2.63	238.99
(-)-Epigallocatechin-3-O-p-coumarate	0.33	0.14	2.63	237.49
(-)-Epicatechin-3-O-(3’-O-methyl gallate)	0.41	0.13	2.65	236.50
Apo-protein	0.18	0.11	2.59	234.54
Orlistat (reference)	0.24	0.09	2.62	237.09

3.4.2 Root mean square fluctuations (RMSF)

RMSF assesses the displacement of particular atoms or groups of atoms from the reference structure, averaged across all atoms. It is employed to investigate the dynamic behavior of specific amino acids within the protein-ligand complexes' backbone. Higher RMSF values suggest increased flexibility and mobility in specific protein regions, providing insights into the characteristics of protein loops and protease-labile segments [55]. Likewise, secondary protein structures like helices and sheets are stable when their RMSF value is lower. All complexes along with the apo-protein exhibited fluctuations in similar regions. The current study revealed orlistat complex showed the lowest fluctuations with an average fluctuation of 0.09 nm, followed by apo-protein with an average RMSF of 0.11 nm, (+)-catechin-3-O-gallate complex and (-)-epicatechin-3-O-(3’-O-methyl gallate) complex have an average RMSF of 0.13 nm (Table 3). In addition, (-)-epigallocatechin-3-O-p-coumarate complex exhibited the highest RMSF with an average of 0.14 nm. Figure 4 shows that high levels of fluctuation were observed in VAL210, PRO211, ASN240 to THR255, GLN233, GLY348 to HIS354, LYS 363, THR375 to ASP379, and TYR403 to ARG414 amino acid residues, none of which are part of the catalytic site.

3.4.3 Radius of gyration (Rg)

The stability of the selected complexes and apo-protein was evaluated using the radius of gyration (Rg). This measure was analyzed over a 100 ns simulation trajectory to assess the structural compactness, stability, and conformational states of the complexes and the apo-protein [56]. The MD simulation results showed that all protein-ligand complexes exhibited Rg values nearly comparable to those of the apo-protein, indicating all ligands bind effectively in the binding pocket of the protein. (+)-catechin-3-O-gallate-protein and (-)-epicatechin-3-O-p-coumarate-protein complexes exhibited equal Rg value of 2.63 nm, suggesting both complexes have comparable stability. The (-)-epicatechin-3-O-(3’-O-methyl gallate)-protein complex displayed the highest average Rg value of 2.65 nm, while the orlistat (reference)-protein complex exhibited a slightly lower average Rg value of 2.61 nm (Table 3). The Rg values obtained during the 100 ns simulation trajectory indicate that all the protein-ligand complexes achieved a relatively stable folded conformation. In summary, the Rg graph (Fig. 5) demonstrated that all the selected compounds, along with the reference, bind effectively with the protein and form more compact structures.

3.4.4 Solvent accessible surface area (SASA)

The solvent-accessible surface area (SASA) parameter quantifies the area of the protein and protein-ligand complex that is exposed to organic solvents and water. SASA is crucial for assessing the extent of conformational changes during interactions. Typically, the bound conformation of biomolecules exhibits a higher SASA value in comparison to their unbound state [57]. A lower SASA value suggests a more compact structure with less surface area available for solvent interactions. The apo-protein, in its unbound conformation, has the lowest SASA value, averaging 234.54 nm², while the protein-ligand complexes have slightly higher SASA values. The (-)-epicatechin-3-O-(3’-O-methyl gallate) complex has the lowest average SASA value compared to the other protein-ligand complex (Fig. 6), suggesting (-)-epicatechin-3-O-(3’-O-methyl gallate) bind strongly in the binding pocket of the protein, making the complex more compact. Similarly, orlistat complex has an average SASA value of 237.09 nm², (+)-catechin-3-O-gallate complex has an average of 238.99 nm², and (-)-epicatechin-3-O-p-coumarate complex has 237.49 nm² (Table 3). Overall, all the protein-ligand complexes have lower SASA values, which suggests all the ligands bind effectively in the binding site of the protein and make the complex more compact.

3.4.5 Hydrogen bonding

Given the importance of hydrogen bonds in ligand binding and their influence on biological functions like as metabolism, adsorption, drug affinity, and specificity, analyzing hydrogen-bonding patterns during the MD simulation is crucial. Assessing hydrogen bond interactions is a key step in elucidating and characterizing the molecular interaction patterns of protein-ligand complexes through molecular dynamics simulations [58, 59]. In the current investigation, we analyzed hydrogen bonds from MD trajectory to assess the stability between lipase and the potent ligands. It was observed that all the complexes constantly retained hydrogen bonds across the 100 ns simulation trajectory, as shown in Fig. 7. (-)-epigallocatechin-3-O-p-coumarate and (-)-epicatechin-3-O-(3’-O-methyl gallate) complex exhibited the highest of five hydrogen bonds during 100 ns simulation trajectory. The Orlistat complex was found to have up to four hydrogen bonds and the (+)-catechin-3-O-gallate complex exhibited a maximum of two hydrogen bonds. These hydrogen-bond observations revealed that all ligands were effectively and securely bound to the enzymes through hydrogen bonding.

3.5 Free energy landscape

Proper folding and confirmation are crucial for biomolecules to function correctly. FEL aids in evaluating the conformational variability and free energy of protein structures. Conformational diversity assesses how diverse the sampled conformations are, while the free energy parameter provides insight into their relative stability and accessibility [60, 61]. These insights into conformational dynamics provide valuable perspectives for understanding protein biological functions and guiding the development of inhibitors or drugs [62]. The Gibbs free energy landscape was determined utilizing gmx_covar, gmx_anaeig, and gmx_sham, based on the projections of their first (PC1) and second (PC2) eigenvectors, respectively. In the FEL plot, a red spot indicates a folded macromolecular structure with low Gibbs free energy, whereas a blue spot signifies an unfolded structure with high Gibbs free energy [63]. A yellow spot denotes intermediate energy, while green represents the metastable frame duration of biomolecules. The figure of Gibbs free energy is displayed in Fig. 8. As shown in Fig. 8 (B), lipase gained a more folded structure after forming a complex with (-)-epigallocatechin-3-O-p-coumarate, while orlistat-lipase resulted in an unfolded structure. Overall, all three selected ligands gained folded structure after binding with protein. In conclusion, the free energy landscape plot, which reflects conformational changes, demonstrated the overall stability of biomolecules when complexed with (+)-catechin-3-O-gallate, (-)-epigallocatechin-3-O-p-coumarate, (-)-epicatechin-3-O-(3’-O-methyl gallate), and orlistat.

3.6 Principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM) analysis

Principal component analysis (PCA) was conducted on the Cα atoms for both the apo-protein and the protein-ligand complex trajectories. PCA revealed the initial seven eigenvectors of apo-protein as well as (+)-catechin-3-O-gallate and (-)-epigallocatechin-3-O-p-coumarate complexes accounted more than 90% of movement, whereas, (-)-epicatechin-3-O-(3’-O-methyl gallate) complex account around 50% of movement and orlistat (reference) complex accounted only around 38% of movement. (+)-catechin-3-O-gallate and (-)-epigallocatechin-3-O-p-coumarate explored significantly larger conformational space (Fig. 9). PCA assessment of the root mean square fluctuation (RMSF) showed heightened fluctuations in the loop region of the apo-protein after ligand binding. This is because loops typically have more fluctuations compared to helices. Such increased fluctuations are indicative of conformational changes in the apo-protein. Comparable flexibility in PC1 and PC2 was observed for (-)-epicatechin-3-O-(3’-O-methyl gallate) when compared to the apo-protein. Whereas, (+)-catechin-3-O-gallate and (-)-epigallocatechin-3-O-p-coumarate exhibited increased flexibility in the binding site of apo-protein, on the other hand, orlistat showed reduced flexibility.

Additionally, the evaluation of the dynamic cross-correlation matrix was evaluated using the Cα atom coordinates from the 100 ns molecular dynamics trajectories. The result of the DCCM analysis is displayed in Fig. 10. The DCCM plot of (-)-epicatechin-3-O-(3’-O-methyl gallate)-complex indicates a slight overall increase in negatively correlated motion upon ligand binding, whereas (+)-catechin-3-O-gallate showed a much greater increase in anti-correlated motion. In contrast, (-)-epigallocatechin-3-O-p-coumarate exhibited overall positively correlated motions. The highest negatively correlated motion was observed for (+)-catechin-3-O-gallate complex. Orlistat (reference) complex showed reduced correlation in motion. In brief, compared to the apo-protein, negatively correlated motions in the binding pocket of protein increase upon the binding of potent ligands. Therefore, it is likely to be concluded that the binding of selected phytochemicals creates a more stable environment compared to the orlistat.

3.7 Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA)

The MM/PBSA approach, which combines molecular mechanics with Poisson-Boltzmann surface area continuum solvation, is a utilized method technique for calculating the binding free energy of protein-ligand complexes [64]. Thus, the MMPBSA analysis combined with MD simulations demonstrated significant differences in the free-binding energies of the studied compounds, as illustrated in Table 4. Particularly, (+)-catechin-3-O-gallate complex exhibited significant binding energy of -59.46 kcal/mol, (-)-epigallocatechin-3-O-p-coumarate complex unveiled a binding affinity of -56.54 kcal/mol, and the (-)-epicatechin-3-O-(3’-O-methyl gallate) complex demonstrated the binding energy of -48.48 kcal/mol. In contrast, the orlistat complex displayed a comparatively low binding energy of 40.26 kcal/mol. These results highlight the strong binding properties of (+)-catechin-3-O-gallate, (-)-epigallocatechin-3-O-p-coumarate complex, and (-)-epicatechin-3-O-(3’-O-methyl gallate compared to the reference ligand orlistat.

Table 4

Free binding energy of protein-ligand complex through MMPBSA
Complex	ΔG_complex (kcal mol^− 1)	ΔG_protein (kcal mol^− 1)	ΔG_ligand (kcal mol^− 1)	ΔG_bind (MM/PBSA) (kcal mol^− 1)
(+)-Catechin-3-O-gallate complex	-1604.26	-1639.46	24.26	-59.46
(-)-Epigallocatechin-3-O-p-coumarate complex	-1612.72	-1646.54	22.72	-56.54
(-)-Epicatechin-3-O-(3’-O-methyl gallate) complex	-1607.28	-1629.64	26.12	-48.48
Orlistat complex	-1608.89	-1642.45	6.70	40.26

3.8 In Silico pharmacokinetic study

A compound must exhibit high selectivity and minimal adverse effects to be considered a potential drug candidate [65]. The effectiveness and eventual appearance of side effects attributed mostly to the compound's ADMET properties lead to a high attrition rate in the final phase of drug development [66]. However, the likelihood of failure for a promising candidate can be reduced by using in-silico techniques. Hence, the drug-likeness and ADMET parameters of the potent candidates, (-)-epigallocatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3'-O-methyl gallate), were thoroughly evaluated and displayed in Table S4 and Table S5 respectively.

For a successful drug candidate, solubility is a crucial physicochemical property[67]. SwissADME’s ESOL, Ali, and SILICOS-IT models, a solubility parameter[40], predicted (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3'-O-methyl gallate) as soluble and (-)-epigallocatechin-3-O-p-coumarate as moderate soluble (Table S4). All these three hit compounds surpass the minimal intestinal absorption criteria of 30%. Similarly, these compounds were found to be safer based on blood-brain barrier (BBB) permeability and log VDss, which indicate absorption into the brain that results in neurotoxicity and distribution in the tissues, respectively [68]. Drug metabolism and the detoxification of foreign substances are greatly aided by cytochrome P450 enzymes, particularly CYP3A4 [69], the hit compounds don't inhibit CYP3A4, and therefore possess no negative effects and are readily metabolized in the liver. AMES toxicity and hepatotoxicity are crucial factors in assessing drug-induced liver injury and mutagenicity [70]. The hit compounds showed no AMES toxicity and hepatotoxicity, thereby indicating non–mutagenicity and non-drug-induced liver injury (Table S5). Moreover, the hit compounds were further subjected to drug-likeness Lipinski’s rule and Veber's rule to assess whether the hit compounds have drug-like properties or not. As per the Lipinski's rule of five criteria [71]—molecular weight ≤ 500 Da, log P ≤ 5, hydrogen bond donors ≤ 5, and hydrogen bond acceptors ≤ 10—and Veber's rule [72]—TPSA > 140 Å² and rotatable bonds ≤ 10—the hit compounds exhibited better drug-like properties compared to the reference compound, orlistat.

The hit compounds—(-)-epigallocatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3'-O-methyl gallate), therefore, demonstrated favorable pharmacokinetic properties, including significant intestinal absorption, acceptable drug-like behavior, good bioavailability, non-inhibition of cytochrome P450, and minimal toxicity (Table S6).

Tea polyphenols have been effective in controlling obesity by inhibiting human pancreatic lipase, thereby reducing fat absorption [19, 73]. Our potent compounds through in silico analysis—(+)-catechin-3-O-gallate, (-)-epigallocatechin-3-O-p-coumarate, and (-)-epicatechin-3-O-(3'-O-methyl gallate) found to demonstrate significant in vitro inhibition assay with IC₅₀ value of 4.57 [74], 0.835, and 0.68 µM [73], respectively. These results indicate their potential effectiveness in controlling obesity.

Encompassing a diverse range of plant species and dietary sources, polyphenols are a heterogeneous group of secondary metabolites that have gained remarkable concentration in the present days for their potential health benefits, especially the ability to control obesity. Our extensive in-silico study on a library of polyphenols has identified (-)-epigallocatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3'-O-methyl gallate) as potent inhibitors of HPL. These polyphenols demonstrated significant binding affinity and interactions with the catalytic residues of HPL, supported by high MM/GBSA values. Further validation through DFT studies and MDs simulations in regards to RMSD, RsMSF, Rg, SASA, and H-bonding confirmed the strong reactivity and stability of these compounds in the catalytic region of HPL. Similarly, the FEL plot revealed all selected protein-ligand complexes gained folded conformation, and the PCA and DCCM plots showed stable dynamic behavior of complexes after 100 ns simulation trajectory. Additionally, post-simulation MM/PBSA analysis indicated favorable binding free energy and ADMET and drug-likeness assessments affirmed their potential as effective and safe therapeutic agents for obesity management. These findings indicate that the identified polyphenols could be promising candidates for the development of anti-obesity therapeutics. However, future research should be carried out to experimentally validate these findings through pharmacokinetics studies.

Acknowledgment

We would like to acknowledge Cresset-group for providing the license of Flare and Spark software.

Funding Statement

There was no funding for this research.

Conflict of Interest Statement

The authors report there are no competing interests to declare.

Author contributions

S.R.U. performed computational work, analyzed data, and wrote the manuscript; J.B. did a literature review, analyzed data, and drafted the manuscript; B.K.R. performed in silico and drafted the manuscript and N.P. supervised the manuscript.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Ethical Approval: Ethical approval was not necessary for this computational study since it did not involve animals, human participants, or identifiable data.

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The authors declare no competing interests.

LipaseSupplementary.docx
Supporting Information Table S1: List of the selected metabolites. Table S2: Molecular docking results of the selected metabolites, Table S3: DFT results of (-)-epigallocatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, and (-)-epicatechin-3-O-(3’-O-methyl gallate), Table S4: Drug-likeness of (-)-epicatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, (-)-Epicatechin-3-O-(3’-O-methyl gallate), and orlistat., Table S4: ADMET properties of selected metabolites, Table S5: ADMET properties of of (-)-epicatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, (-)-Epicatechin-3-O-(3'-O-methyl gallate), and orlistat, Table S6: Toxicity analysis of (-)-epicatechin-3-O-p-coumarate, (+)-catechin-3-O-gallate, (-)-Epicatechin-3-O-(3’-O-methyl gallate), and orlistat, Figure S1: Structure of selected metabolites.
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Polyphenol Inhibition of Human Pancreatic Lipase: An In-Silico Study Towards Obesity Control

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Library of Compounds and Ligand Preparation

2.2 Protein preparation and validation

2.3 Molecular Docking

2.4 MM/GBSA binding energy calculations

2.5 Electrostatic complementarity

2.6 Density Functional Theory (DFT)

2.7 Molecular Dynamics Simulation

2.8 Principal Component Analysis (PCA) and Dynamic Cross-Correlation Matrix (DCCM) Analysis

2.9 Molecular Mechanics Poisson-Boltzman Surface Area (MM/PBSA)

2.10 Pharmacokinetic Analysis

3. Results and Discussions

3.1 Molecular Docking and MM/GBSA Calculation

3.2 Electrostatic complementarity

3.3 DFT

3.4 Molecular Dynamic Simulation

3.4.1 Root mean square deviation (RMSD)

3.4.2 Root mean square fluctuations (RMSF)

3.4.3 Radius of gyration (Rg)

3.4.4 Solvent accessible surface area (SASA)

3.4.5 Hydrogen bonding

3.5 Free energy landscape

3.6 Principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM) analysis

3.7 Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA)

3.8 In Silico pharmacokinetic study

4. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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