IV. RESULTS
4.2 Molecular Docking
There were eighteen ligands in total after screening by QSAR. Ertiprotafib, a PTP1B inhibitor, was used as standard, and PTP1B (PDB ID: 2NT7) was used as the protein target.
Molecular docking was conducted using Autodock Vina wizard in PyRx, using blind docking method and exhaustiveness of 8 models.
Table 7. Molecular docking results in binding energy between PTP1B and ligands
No. Compounds Class
Binding Energy (kcal/mol)
1 Ertiprotafib (standard) - -7.4
2 Caffeic acid
Phenolic acids
-6.7
3 Ferulic acid -5.5
4 Gallic acid -6.2
5 3,4-O-Dimethylgallic acid Hydroxybenzoic acids
-6.5 6 p-Coumaroyl glycolic acid
Hydroxyphenyl-propanoic acids
-6.9 7 3,4-Dihydroxyphenylacetic acid
Hydroxyphenyl-acetic acids
-6.5
8 Urolithin A
Hydroxycoumarins
-8.8
9 Scopoletin -7.2
10 Umbelliferone -6.3
11 Chrysin
Flavones
-8.8
12 Gardenin B -6.4
13 Cirsilineol -7.6
14 Hesperetin 3'-O-glucuronide
Flavanones -8.4
15 Naringenin -8.5
16 3-Methoxysinensetin Flavonols -6.2
17
5,6,7,3',4'-Pentahydroxyisoflavone Isoflavonoids -7.6
18 Carnosic acid Phenolic terpenes -7.5
19 Salvianolic acid B Other polyphenols -7.9
Eight phenolic compounds, namely urolithin A, chrysin, salvianolic acid B, cirsilineol, hesperetin 3'-O-glucuronide, naringenin, 5,6,7,3',4'-pentahydroxyisoflavone, and carnosic acid
,
were found to have lower binding energy compared to the standard (Ertiprotafib). Thebinding site of those compounds were of same location, hence they shared similar amino acid residues interaction. The interaction between the ligand and amino acid residues can be seen visually in Figure 3 and it is summarized in Table 7.
Table 8. Summary of the interaction between ligands and PTP1B amino acid residues
No Compounds Interaction with Amino Acid Residues
1 Ertiprotafib (standard) Hydrogen bond Arg56
Pi-alkyl bond Ile57, Lys58, His60, Leu71, Pro210
2 Urolithin A
Hydrogen bond Arg221
Pi-Sigma bond Ala217
Pi-Pi bond Tyr46, Phe182
Pi-alkyl bond Val49
Unfavorable donor-donor Gln266
3 Chrysin
Hydrogen bond Gly220, Arg221
Pi-Sigma bond Ala217
Pi-alkyl bond Val49
Unfavorable donor-donor Phe182
Unfavorable acceptor acceptor Tyr46
4 Cirsilineol
Hydrogen bond Phe182, Arg221
Pi-Pi bond Phe182
Pi-alkyl bond Tyr46, Arg47, Lys120, Ala217
5 Hesperetin 3'-O-glucuronide
Hydrogen bond Arg47, Asp48, Ser216, Arg221
Pi-Pi bond Phe182
Pi-alkyl bond Val49, Ala217
Unfavorable acceptor acceptor Tyr46
6 Naringenin
Hydrogen bond Asp48, Ser216
Pi-Sigma bond Tyr46
Pi-Pi bond Tyr46, Phe182
Pi-alkyl bond Val49, Ala217
Unfavorable acceptor acceptor Tyr46
7 5,6,7,3',4'-Pentahydroxyisoflavone
Hydrogen bond Asp48, Ser216, Arg221, Gln262
Pi-Sigma bond Ala217
Pi-alkyl bond Ala217
8 Carnosic Acid
van der Waals Asp48, Val49, Asp181, Ser216, Ile219, Arg221, Gln262
Pi-Sigma bond Phe182
Pi-alkyl bond Tyr46, Phe182, Ala217
Unfavorable donor-donor Gly220
9 Salvianolic Acid B Hydrogen bond Arg56, Lys58, His60, Lys73, Ser80, Glu101, Gln102, Pro206
Pi-alkyl bond Lys103, Pro210
Figure 3. Interactions between ligands and amino acid residues in PTP1B. Interaction with ertiprotafib (A), urolithin A (B), chrysin (C), Cirsilineol (D), hesperetin 3'-O-glucuronide (E), naringenin (F), 5,6,7,3',4'-pentahydroxyisoflavone (G), carnosic acid (H), and salvianolic acid (I).
A B C
D E F
G H
H I
4.3. Drug-likeness and ADMET prediction
Drug-likeness according to Lipinski’s Ro5 and other ADMET parameters were predicted using ADMETlab 2.0 online software. Their summary and annotations are shown in Table 8.
The prediction of probability is divided into six categories, according to Xiong et al. (2021).
ADMET parameters included in this study were HIA, BBB penetration, Ames toxicity, carcinogenicity, skin sensitization, eye irritation, LC50 of fathead minnow, and LC50 of Daphnia magna.
Table 9. Drug-likeness and ADMET prediction
Compounds
Lipinski's Rule Violationa
HIAb BBB Penetrationc
Ames Toxicityd
Carcino-genicitye
Skin Sensiti-zationf
Eye
Irritationg LC50FMh LC50DMi
Ertiprotafib 2 --- --- --- - +++ -- 6.490 6.261
Urolithin A 0 --- --- --- + +++ +++ 4.890 5.129
Chrysin 0 --- --- + - +++ +++ 5.066 5.220
Cirsilineol 0 --- --- - --- + ++ 5.120 6.587
Hesperetin 3'-O-glucuronide
2 + --- -- -- -- --- 4.790 5.404
Naringenin 0 --- --- -- + +++ +++ 6.692 6.410
5,6,7,3',4'-Pentahydroxyisoflavone
0 -- --- + -- +++ +++ 5.033 6.006
Carnosic acid 0 --- - --- --- +++ ++ 4.573 5.604
Salvianolic acid B 1 --- --- --- - -- - 7.044 7.223
The prediction probability values are depicted into 6 symbols: 0 – 0.1 (---), 0.1 – 0.3 (--), 0.3 – 0.5 (-), 0.5 – 0.7 (+), 0.7 – 0.9 (++), and 0.9 – 1 (+++). HIA: Human Intestinal Absorption, BBB: Blood-Brain Barrier. a : accepted if no more than 1 violation exist; b: HIA+ = <30%, HIA- ≥30% absorption, value is probability being HIA+; c : probability of BBB+; d : probability of being toxic; e : probability of being toxic; f : probability of being sensitizer; g: probability of being irritants; h : 96-h lethal concentration to kill 50% of fathead minnow in -log[(mg/L)/(1000 x MW)]; I : 46-h lethal concentration to kill 50% Daphnia magna in -log[(mg/L)/(1000 x MW)].
4.4. Molecular Dynamic Simulation
Figure 4 depicts the PTP1B and ligand complexes with their fluctuation plot. The fluctuation plot describes the RMSF value of each residue of PTP1B. The x-axis indicates the amino acid residues in
PTP1B, while the y-axis indicates the RMSF value in terms of Å. Depending on the binding conformation, the amino acid residues can deviate from their initial position, thus change the shape of the protein.
Figure 4. Molecular dynamic simulation results. Ensemble of protein models are depicted on the left and fluctuation plot on the right for PTP1B complexes with (A) ertiprotafib (control), (B) urolithin A, and (C) chrysin.
From the fluctuation plot, it can be seen that PTP1B complex with control, urolithin A, and chrysin were within the range of stable peptide conformation, that is 1 – 3 Å. However, some residues deviated exceeding 3 Å, specifically Glu62 and Asp63 in PTP1B-Control, Arg43, Glu62, Asp63, Asn90, and Glu207 in PTP1B-Urolithin A, and Asp298 in PTP1B-Chrysin complex.
A
B
C
V. DISCUSSION
4.1. QSAR Analysis
QSAR analysis is a bioinformatic tool to obtain the probability of biological activities of a compound based on their chemical structure (Ishikawa, 2012). To do this analysis, PASS Server was deployed in this study as an online software to conduct QSAR analysis. Antidiabetic activity and PTP1B inhibitor activity were targeted and screened during this phase and their probabilities is annotated by Table 6. Antidiabetic activity reflects the general abilities to control high blood glucose that be done through any means, including insulin sensitizing and stimulating, as well as promoting or inhibiting proteins related to diabetes. Because of this broad range of mechanism in diabetes, it is narrowed down into the inhibitory effect of PTP1B as the novel protein target in the development of antidiabetic agent.
From the total of 43 phenolic compounds tested, the results showed that 18 compounds were indicated as having Pa > Pi for their antidiabetic activity and PTP1B inhibitor activity. The results of Pa > Pi indicates that the activity is. However, all of the Pa of PTP1B inhibitory activity were < 0.3. This does not necessarily mean that those compounds will have low inhibitory activity of PTP1B, but it indicates the probability of finding this activity experimentally is rather low since their structure is dissimilar with the experimentally known PTP1B inhibitor. On the other hand, it also indicates a higher chance of obtaining a new structurally active compound possible (Goel at al., 2011 as cited in Hussain et al., 2016). With this, therefore, they are worth studied and analyzed further through molecular docking analysis to predict their binding affinity towards PTP1B with the hope of being suitable as a new drug candidate.
In some other studies, QSAR analysis was omitted and the ligands was directly analyzed using molecular docking and molecular dynamics. For example, a study from Damián-Medina et al. (2020) did not incorporate QSAR analysis to screen the ligand prior to molecular docking.
This was only true when the sample size of the ligand was limited already, thus the importance
has shifted to finding the strength of their affinity during binding. Since in this current study, there were 43 phenolic compounds to be analyzed, therefore it is sensible to screen them first using QSAR
4.2. Molecular Docking
The aim of molecular docking simulation is to predict the structural conformation of the ligand in order to bind with the protein target as well as how strong they can bind as reflected by their binding energy (Roy et al., 2015). The protein used in the docking simulation, in this case PTP1B, needed to be sterilized from water molecules and other hetero atoms as they can interfere the binding with the ligand of interest (Forli et al., 2016). The binding energy between the ligand and protein is integral to the inhibition as the inhibitory effect, i.e., the suppression of the biological activity, is the result of the ligand binding (as inhibitor) to the target protein (Lopina, 2017). Binding energy of the ligand was then compared with the binding energy of the standard (ertiprotafib) and those who have lower binding energy means that their binding is more energetically feasible (Odoemelam et al., 2022).
As shown in the Table 7, 8 compounds had a lower binding energy compared to the standard. Lower binding energy as calculated by Autodock Vina means a better binding affinity of the ligand towards PTP1B. The lowest binding energy was achieved by two compounds, namely urolithin A and chrysin with both having -8.8 kcal/mol. Other compounds, cirsilineol (-7.6 kcal/mol), hesperetin 3'-O-glucuronide (-8.4 kcal/mol), naringenin (-8.5 kcal/mol), 5,6,7,3',4'-pentahydroxyisoflavone (-7.6 kcal/mol), carnosic acid (-7.5 kcal/mol), and salvioanolic acid B (-7.9 kcal/mol), had their binding energy lower than ertiprotafib (-7.4 kcal/mol). This suggests that those compounds would tightly bind to the binding site of PTP1B.
The interaction between the ligand and amino acid residues in PTP1B is depicted in Figure 3 and summarized in Table 8. Urolithin A was indicated to have interaction with Tyr46, Val49, Phe182, Ala217, and Arg221. Meanwhile, chrysin interacted with Val49, Ala217, Gly220, and Arg221. However, as seen on the visualization (Figure 3), they also had unfavorable bond with
Gln266, and Phe182 and Tyr46, respectively. These unfavorable bonds might have an effect on the complex’s stability as they reduce the stability by repulsive interaction (Dhorajiwala et al., 2019). Because of that, the interaction analysis was conducted also to all 8 aforementioned ligands to compare and contrast their interactions. Some of the interactions were found to involve unfavorable bonds. Nonetheless, cirsilineol, 5,6,7,3',4'-pentahydroxyisoflavone, and salvianolic acid did not involve any unfavorable bonds, albeit having a slightly higher binding energy than urolithin A and chrysin. In conclusion, the fact that the majority of the ligands with unfavorable bond interactions actually had better binding affinity, it is possible that the repulsion effect was negated by other interactions. However, the stability might still be impacted, therefore, the interpretation must include molecular dynamics results.
Related to the interactions towards amino acid residues, it is also important to note that all the 8 aforementioned ligands were positioned in the same binding site and sharing some residues during interactions. The interactions were mainly involving catalytic site of PTP1B specifically the A and D site (Liu et al., 2022). A site is the most accessible pocket and the main catalytic site in PTP1B, mainly involving the catalytic Cys215, Tyr46, Asp48, Val49, Phe182, Ala217, Ile219, and Gln262 (Ala et al., 2006 as cited in Liu et al., 2022). Designing an inhibitor that targets this site is of interest due to its primary catalytic role. However, sole binding to A site lacks of specificity as they are similar to other PTPs. Meanwhile, D site is a small and narrow pocket near A site. Targeting this site impacts greatly on the potency and specificity, albeit does not have biological implication towards insulin signaling pathway (Liu et al., 2022). According to Zhang et al. (2018) who conducted in silico structure-based analysis, inhibitor targeting these two sites along with C site was the most promising PTP1B inhibitor among the other types of catalytic inhibitor.
Urolithin A has two hydroxide groups which act as good hydrogen donors, moreover the aromatic rings help stabilize the conjugated base, making it a better donor. Nevertheless, in this study, it clashed with Gln266 residue that act as a hydrogen donor as well (Figure 3). Still, one
oxygen atom showed to be able to bind with Arg221 through hydrogen bond interaction. In the chrysin, clash of hydrogen appeared in one the hydroxide group with Phe182, and repulsion by being hydrogen acceptors with Tyr46 was apparent in the ketone group. The other interactions of were in virtue of interaction of π-π and π-σ orbitals of the amino acid residues and ligands, moreover due to the presence of aromatic ring of the phenols.
Binding with PTP1B and inhibiting its action will positively impact the management of T2DM as it inhibits the prevention of insulin signaling cascade during its internalization (Tautz et al., 2022; Liu et al., 2022. The results of this study suggest that some of the phenolic compounds in banana peel are able to bind to PTP1B and feasibly could have inhibitory effect.
Inhibitory effect of those compound towards PTP1B has not been well evidenced. However, some in silico studies have indeed predicted the ability of some phenolic compounds to bind with PTP1B (Damián-Medina, 2020; Mechate et al., 2021; Rath et al., 2022). Interestingly, this study showed that the majority if the compounds that had a good binding affinity falls into polyphenol class, namely flavones, flavanones, and isoflavonoids. In line with that, Rath et al.
(2022) concluded that in their study indeed that some of the polyphenols, mainly silydianin, could be a promising PTP1B inhibitor. Similarly, in vivo study by Kim et al. (2016) showed that polyphenol-rich Cudrania tricuspidate leaves extract exhibited a strong inhibitory activity towards PTP1B.