Department Of Pharmaceutical Chemistry, Madras Medical College, Chennai, Tamil Nadu, India
A potential therapeutic avenue for the control of diabetes mellitus has been identified in the inhibition of alpha-amylase and dipeptidyl peptidase-4 (DPP-4) enzymes to control postprandial glucose levels and stimulate beta cell activity. Out of 100 novel ligands computationally designed and evaluated for their binding affinities against both alpha amylase and DPP4 using molecular docking. AutoDock was used to optimize and dock ligands, with docking scores indicative of strong interactions and dual inhibition potential. The binding modes of high affinity ligands were visualized to confirm compatibility with receptor active sites, and key residues involved in binding were identified. Several ligands showed high binding energy values, potentially affording themselves as dual target inhibitors. The results from this study are of critical importance for understanding ligand-receptor interactions and serve as a solid basis for further experimental validation and drug development targeting diabetes associated enzymes.
Diabetes mellitus is a chronic metabolic disorder marked by excessively high blood sugar from insufficient insulin, either from insufficient secretion of the hormone or through defects in its action. Postprandial glucose level management is critical to prevent long term complications due to diabetes (1).It is one of various therapeutic strategies that come with the inhibition of alpha-amylase and DPP-4 enzymes. Carbohydrate digestion is partly performed by alpha amylase breaking down starches into glucose (2) and DPP-4 is responsible for regulation of incretin hormones, that appear to improve insulin secretion, and decrease glucagon secretion (3),(4).Simultaneous targeting of both alpha-amylase and DPP-4 together provides dual therapeutically advantage by hitting different pathways of glucose regulation. As computational approaches like molecular docking begin to advance the advances in computational approaches like molecular docking, advances in computational approaches like molecular docking are becoming a cost effective and time efficient approach for identifying and optimizing dual target inhibitors(5).In this work, 100 novel ligands were designed and tested for their binding affinities to alpha amylase and DPP-4. It seeks to identify potential dual inhibitors and a framework for subsequent experimental and clinical validation.
MATERIALS AND METHODS
Ligand preparation
ChemSketch (freeware version)(6) was used to draw and optimize 100 novel chemical structures to design a total of 100 novel ligands. The ligands were stored in .mol format and further energy minimized by Chem3D (7). We converted the minimized structures into .pdbqt format for molecular docking studies using AutoDockTools (ADT).
Ramachandran Plot Analysis
The Ramachandran plot analysis confirmed the structural reliability of 1HNY and 3HAC, with most residues positioned in the favored regions, ensuring accurate docking results(8).
Receptor preparation
The crystal structures of alpha-amylase (PDB ID: 1HNY) and DPP-4 (PDB ID: 3HAC) were downloaded from the Protein Data Bank (9). These structures were selected due to their high resolution (approximately 2 Å) and determination by the reliable X-ray diffraction method.Receptors were prepared by removing water molecules, adding polar hydrogens, and assigning Kollman charges using AutoDockTools.
ADMET Prediction
The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of the ligands were evaluated using SwissADME(10) and
OSIRIS Property Explorer(11)These tools predicted drug-likeness, bioavailability, and toxicity, ensuring the ligands adhered to Lipinski's rule of five and exhibited favorable pharmacokinetic profiles.
Molecular docking
Docking studies were performed using AutoDock Vina 1.5.6 (12). The active sites of alpha- amylase and DPP-4 were defined based on their co-crystallized ligands. The grid box dimensions and coordinates (x, y, z) for the docking simulations were determined using the CB-Dock server(13), which predicts the binding pockets and centers the grid box around the identified active site residues. This automated approach ensured optimal coverage of the ligand-binding site and included all key residues critical for Each ligand was docked against both receptors, and binding affinities were recorded as binding energies (kcal/mol).
Visualization and Interaction Analysis
Binding interactions of the docked ligands with the receptors were analyzed using Molegro Molecular Viewer (MMV)(14)Hydrogen bonds, hydrophobic interactions, and ?-? stacking interactions were identified.The top-scoring ligands were visualized and compared with standard drugs, correlating to the interaction pattern with the active site residues.
Results Ramachandran Plot
The Ramachandran plot analysis results indicate that the selected proteins, Alpha amylase with (PDB ID:1HNY and DPP-4 )with( PDB ID:3HAC), predominantly (more than 90%) have their amino-acid residues situated within the most favored region as represented in the figure no 1
Figure No 1: Ramchandran Plot For 1HNY & 3HAC
Novelty Assessment
The novelty of the designed ligands was checked by using the ZINC 15 and PUBCHEM database. The outcomes were shown below.
Table No 1: Ligands Showing Novelty
|
Novel ligands |
Already existing compounds |
|
DA6, DA7, DA9, DA11, DA12, DA13, DA14, DA15, DA16, DA17, DA18, DA19, DA20, DA21, DA23, DA24, DA25, DA26, DA27, DA28, DA29, DA30, DA31, DA32, DA33. DA34. DA35, DA36, DA37, DA38, DA39, DA42. DA43, DA44, DA45, DA47, DA48, DA49, DA50, DA51, DA52, DA53, DA57, DA58, DA60, DA66, DA67, DA69, DA70, DA72, DA73, DA74, DA76, DA77, DA78, DA79, DA80, DA82, DA84, DA85, DA86, DA87, DA88, DA89, DA90, DA91, DA92, DA93, DA94, DA95, DA96, DA97, DA98, DA99, , DA100 |
DA1, DA2, DA3, DA4, DA5, DA8, DA10, DA22, DA40, DA41, DA46, DA54, DA55, DA56, DA59, DA61, DA62, DA63, DA64, DA65, DA68, DA71, DA75, DA81, DA83 |
IN SILICO ADMET
Based on the novelty results, the developed ligands are evaluated for the drug-likeness and toxicity property by using the SwissADME Online Software tool and the Osiris Property Explorer. Mutagenicity, tumorigenicity, irritant, reproductive are presented by M, T, I and R respectively
Table No 2: ADMET Properities Of Novel Ligands
|
Lig No |
M |
T |
I |
R |
Log P |
Mol Wt. |
HBD |
HBA |
RULE OF 5 |
|
6 |
Yes |
Yes |
Yes |
Yes |
1.12 |
306.28 |
2 |
5 |
0 |
|
7 |
No |
No |
No |
Yes |
2.06 |
254.24 |
2 |
3 |
0 |
|
9 |
No |
Yes |
No |
Yes |
1.65 |
257.20 |
1 |
6 |
0 |
|
12 |
No |
No |
No |
Yes |
1.15 |
302.31 |
1 |
5 |
0 |
|
13 |
No |
No |
No |
Yes |
1.13 |
303.29 |
1 |
6 |
0 |
|
14 |
Yes |
Yes |
No |
Yes |
1.34 |
342.33 |
1 |
6 |
0 |
|
15 |
Yes |
No |
No |
Yes |
3.96 |
341.34 |
2 |
5 |
0 |
|
16 |
Yes |
Yes |
Yes |
Yes |
0.44 |
342.33 |
2 |
6 |
0 |
|
17 |
No |
No |
No |
Yes |
1.83 |
290.29 |
2 |
4 |
0 |
|
18 |
No |
Yes |
No |
Yes |
1.84 |
309.32 |
1 |
6 |
0 |
|
19 |
No |
Yes |
No |
Yes |
1.12 |
293.26 |
7 |
1 |
0 |
|
20 |
Yes |
Yes |
Yes |
Yes |
1.00 |
316.33 |
4 |
2 |
0 |
|
21 |
No |
No |
No |
Yes |
1.81 |
299.30 |
1 |
5 |
0 |
|
24 |
No |
No |
No |
Yes |
2.41 |
333.30 |
1 |
6 |
0 |
|
25 |
No |
No |
No |
Yes |
3.09 |
332.31 |
2 |
5 |
0 |
|
26 |
Yes |
Yes |
No |
Yes |
1.40 |
333.30 |
2 |
6 |
0 |
|
27 |
No |
No |
No |
Yes |
2.19 |
281.27 |
2 |
4 |
0 |
|
28 |
No |
No |
No |
Yes |
1.53 |
300.29 |
1 |
6 |
0 |
|
30 |
No |
No |
No |
Yes |
1.98 |
307.30 |
2 |
4 |
0 |
|
31 |
No |
No |
No |
No |
1.92 |
272.28 |
1 |
4 |
0 |
|
32 |
No |
No |
No |
No |
1.64 |
266.25 |
1 |
4 |
0 |
|
33 |
No |
No |
No |
No |
1.64 |
267.24 |
1 |
5 |
0 |
|
34 |
Yes |
No |
No |
No |
2.14 |
306.27 |
1 |
5 |
0 |
|
35 |
No |
No |
No |
No |
3.28 |
305.29 |
2 |
4 |
0 |
|
36 |
Yes |
Yes |
Yes |
Yes |
1.25 |
306.28 |
2 |
5 |
0 |
|
37 |
No |
No |
No |
No |
1.88 |
254.24 |
2 |
3 |
0 |
|
38 |
No |
No |
No |
Yes |
1.79 |
273.27 |
1 |
5 |
0 |
|
39 |
No |
Yes |
No |
No |
1.68 |
257.20 |
1 |
6 |
0 |
|
42 |
No |
No |
No |
No |
1.11 |
302.31 |
1 |
5 |
0 |
|
43 |
No |
No |
No |
No |
1.28 |
303.29 |
1 |
6 |
0 |
|
44 |
Yes |
Yes |
No |
Yes |
1.51 |
342.33 |
1 |
6 |
0 |
|
45 |
Yes |
No |
No |
No |
3.99 |
341.34 |
2 |
5 |
0 |
|
47 |
No |
No |
No |
No |
1.54 |
290.29 |
2 |
4 |
0 |
|
48 |
No |
No |
No |
No |
1.84 |
309.32 |
1 |
6 |
0 |
|
49 |
No |
Yes |
No |
No |
0.49 |
293.26 |
1 |
7 |
0 |
|
50 |
Yes |
Yes |
Yes |
Yes |
1.53 |
316.33 |
2 |
4 |
0 |
|
51 |
No |
No |
No |
No |
2.16 |
272.25 |
1 |
4 |
0 |
|
52 |
No |
No |
No |
No |
2.19 |
278.31 |
1 |
4 |
0 |
|
53 |
No |
No |
No |
No |
2.04 |
215.25 |
1 |
3 |
0 |
|
57 |
Yes |
No |
No |
No |
2.49 |
289.29 |
1 |
4 |
0 |
|
58 |
No |
No |
No |
No |
3.92 |
288.30 |
2 |
3 |
0 |
|
60 |
No |
No |
No |
No |
1.99 |
237.26 |
2 |
2 |
0 |
|
66 |
No |
No |
No |
No |
1.26 |
286.31 |
1 |
5 |
0 |
|
67 |
Yes |
Yes |
No |
Yes |
1.96 |
325.34 |
1 |
5 |
0 |
|
69 |
Yes |
Yes |
Yes |
Yes |
1.37 |
325.35 |
2 |
5 |
0 |
|
70 |
No |
No |
No |
No |
1.88 |
273.81 |
2 |
3 |
0 |
|
72 |
No |
Yes |
No |
No |
0.89 |
276.27 |
1 |
6 |
0 |
|
73 |
Yes |
Yes |
Yes |
Yes |
1.46 |
299.35 |
2 |
3 |
0 |
|
74 |
No |
No |
No |
No |
1.62 |
256.28 |
1 |
4 |
0 |
|
76 |
No |
No |
No |
No |
1.29 |
254.24 |
1 |
5 |
0 |
|
77 |
Yes |
No |
No |
No |
1.87 |
290.28 |
1 |
5 |
0 |
|
78 |
No |
No |
No |
No |
4.02 |
289.29 |
2 |
4 |
0 |
|
79 |
Yes |
Yes |
Yes |
Yes |
1.02 |
290.28 |
2 |
5 |
0 |
|
80 |
No |
No |
No |
No |
1.93 |
238.24 |
2 |
3 |
0 |
|
82 |
No |
Yes |
No |
No |
1.40 |
241.21 |
1 |
6 |
0 |
|
84 |
No |
No |
No |
No |
1.16 |
292.34 |
1 |
5 |
0 |
|
85 |
No |
No |
No |
No |
1.20 |
286.31 |
1 |
5 |
0 |
|
86 |
No |
No |
No |
No |
0.83 |
287.30 |
1 |
6 |
0 |
|
87 |
Yes |
Yes |
No |
Yes |
1.70 |
326.33 |
1 |
6 |
0 |
|
88 |
Yes |
No |
No |
No |
3.75 |
325.35 |
2 |
5 |
0 |
|
89 |
Yes |
Yes |
Yes |
Yes |
0.80 |
326.33 |
2 |
6 |
0 |
|
90 |
No |
No |
No |
No |
1.98 |
274.30 |
2 |
4 |
0 |
|
91 |
No |
Yes |
No |
Yes |
1.13 |
293.32 |
1 |
6 |
0 |
|
92 |
No |
Yes |
No |
No |
1.45 |
277.26 |
1 |
7 |
0 |
|
93 |
Yes |
Yes |
Yes |
Yes |
1.23 |
300.34 |
2 |
4 |
0 |
|
94 |
No |
No |
No |
No |
1.33 |
273.27 |
1 |
4 |
0 |
|
95 |
No |
No |
No |
No |
1.15 |
267.24 |
1 |
4 |
0 |
|
96 |
No |
No |
No |
No |
1.05 |
268.23 |
1 |
5 |
0 |
|
97 |
Yes |
No |
No |
No |
1.88 |
307.26 |
1 |
5 |
0 |
|
98 |
No |
No |
No |
No |
4.26 |
306.28 |
2 |
4 |
0 |
|
99 |
Yes |
Yes |
Yes |
Yes |
0.63 |
307.26 |
2 |
5 |
0 |
|
100 |
No |
No |
No |
No |
1.53 |
255.23 |
2 |
3 |
0 |
Molecular Docking
The ligands with good druglikeness properties and no toxicity were selected for molecular docking studies against DPP-4 inhibitors (3HAC) and alpha amylase (PDB ID 1HNY).
Table No 3 : Binding scores of ligands
|
S no |
Compound code |
Alpha Amylase (1HNY) |
DPP-4 (3HAC) |
|
1) |
Lig31 |
-6.96 |
-8.28 |
|
2) |
Lig32 |
-5.33 |
-8.02 |
|
3) |
Lig33 |
-6.86 |
-7.47 |
|
4) |
Lig35 |
-8.14 |
-8.52 |
|
5) |
Lig37 |
-7.18 |
-7.98 |
|
6) |
Lig42 |
-7.33 |
-7.87 |
|
7) |
Lig43 |
-7.07 |
-7.46 |
|
8) |
Lig46 |
-6.64 |
-7.97 |
|
9) |
Lig56 |
-6.60 |
-8.35 |
|
10) |
Lig58 |
-5.93 |
-8.65 |
|
11) |
Lig60 |
-6.56 |
-7.23 |
|
12) |
Lig66 |
-6.58 |
-6.87 |
|
13) |
Lig70 |
-6.88 |
-7.20 |
|
14) |
Lig74 |
-6.77 |
-7.50 |
|
15) |
Lig76 |
-7.33 |
-5.39 |
|
16) |
Lig78 |
-5.52 |
-6.27 |
|
17) |
Lig80 |
-6.15 |
-7.94 |
|
18) |
Lig84 |
-6.72 |
-7.17 |
|
19) |
Lig85 |
-6.30 |
-7.41 |
|
20) |
Lig86 |
-6.71 |
-7.82 |
|
21) |
Lig90 |
-6.11 |
-7.02 |
|
22) |
Lig94 |
-7.42 |
-7.97 |
|
23) |
Lig95 |
-7.06 |
-7.80 |
|
24) |
Lig96 |
-4.90 |
-8.53 |
|
25) |
Lig98 |
-7.99 |
-8.87 |
|
26) |
Lig100 |
-7.32 |
-8.00 |
|
27) |
Acarbose |
-2.1 |
- |
|
28) |
Sitagliptin |
- |
-7.23 |
Table No 4 : Chemical structures of top-performing ligands based on docking scores
Table No 6 : Ligand-Receptor Interactions
|
Lig code |
Hydrogen bonding Alpha amylase Dpp-4 |
|
|
|
|
|
|
Lig 37 |
His 185, Glu 76 |
Ile 102 , Phe 95 |
|
Lig 42 |
Lys 227 , Tyr 2, Pro 228 |
Phe 95 , Ile 102 |
|
Lig 43 |
Gln 63 |
Thr 156 ,Ser 212 |
|
Lig 94 |
Tyr 2 |
Pro 510 |
|
Lig 95 |
Lys 457 , Asp 456 , Lys 35 |
Pro 510 , Thr 565 |
|
Lig 98 |
Tyr 2 |
Lys 512 , Pro 510 |
|
Lig 100 |
His 185 |
Ile 102,Asn 92,Glu 91 |
|
Acarbose |
Phe 136 , Gly 139 , Thr 143 |
|
|
Sitagliptin |
Leu 410 , Phe 364, Lys 463 |
|
CONCLUSION
This study successfully identified several novel ligands with strong binding affinities for alpha- amylase and DPP-4, demonstrating their potential as dual-target inhibitors for diabetes mellitus management. High binding affinities towards both ?-amylase and DPP-4 were exhibited by various heterocyclic fused scaffolds, highlighting their potential as promising lead compounds. Significant scaffolds include chromone fused with benzimidazole, linked via a carbamide group; chromone-carbamide fused with pyridine and pyrrole; chromone-sulphamide fused with pyrrole and thiazole; and quinoxoline-sulphamide fused with thiazole, pyridine, and pyrimidine. These scaffolds achieved high docking scores due to their strong interactions with active site residues.These findings provide a robust framework for experimental validation and future drug development efforts.
ACKNOWLEDGEMENTS
We express our sincere thanks to the Department of Pharmaceutical Chemistry, College of Pharmacy, Madras Medical College (MMC), Chennai for providing necessary facilities for the research work.
Conflicts of Interest
The author declares there is no conflict of interest.
REFERENCES
Priyadharshini R., Gunasekaran P.*, Gishmi G., Novel Fused Heterocyclic Scaffolds Design and Molecular Docking of Targeting Alpha Amylase and DPP-4 for Diabetes Mellitus Management, Int. J. of Pharm. Sci., 2024, Vol 2, Issue 12, 1436-1446. https://doi.org/10.5281/zenodo.14394638
10.5281/zenodo.14394638
