Pharmacoinformatics Profiling and Dynamic Studies of Selected Compounds Acting as Potential Inhibitors against DPP4 Enzyme

 Introduction

Diabetes is a worldwide metabolic disorder that has become a widespread epidemic disease in the last few decades, owing especially to the increasing frequency and incidence of Type 2 diabetes mellitus (T2DM), which affects 90–95 percent of people with the disease and is difficult to cure. The ultimate treatment goals for T2DM include the long-term regulation of blood sugar levels and the treatment of diabetes complications. Exercise plays an important role in the prevention and control of insulin resistance, prediabetes, gestational diabetes mellitus, type 2 diabetes, and diabetes-related health problems. Exercise must be done regularly to reap long-term benefits, and it must comprise a variety of regular pieces of training. Aerobic and resistance training both increase insulin action, at least immediately, and aid in the management of blood glucose levels, lipids, blood pressure, cardiovascular risk, mortality, and quality of life. For all of the reasons described above, physical exercise is an important component in the prevention and control of type 2 diabetes and can be regarded as a cornerstone of diabetes management, along with proper food and medicine. Current oral therapy options include sulfonylureas, metformin, thiazolidinedione derivatives, glycosidase inhibitors, and newly developed dipeptidyl peptidase-4 (DPP-4) inhibitors and glucagon-like peptide-1 (GLP-1) analogs (1,2).

Glucagon-like peptide-1 (GLP-1) is an incretin hormone. Which is secreted by intestinal L-cells in response to food intake. The active form of GLP-1 is a 30-amino acid long peptide, which stimulates insulin release, inhibits glucagons release, and slows down gastric emptying. Each of these phenomena benefits in the control of glucose homeostasis in patients with type II diabetes. The active form of GLP-1 is rapidly inactivated (t1/2 = 1 min) by the plasma DPP-IV enzyme, which cleaves a dipeptide from the N-terminus (14,15). Thus inhibition of DPP-IV extends the half-life of endogenously secreted GLP-1, which in turn enhances insulin secretion and improves glucose tolerance. (3–5).

The main enzyme of interest considered in this study is the DPP-4 inhibitors. DPP IV (dipeptidyl peptidase IV) is a versatile protein involved in a variety of physiological functions. It acts as a binding protein, a receptor, and a proteolytic enzyme. It’s a serine peptidase from the S9b protein family. DPP IV occurs as a soluble homodimer and as a ubiquitous type II integral plasma membrane glycoprotein. It has a strong link to several disorders, including diabetes, obesity, and tumor growth, making it an appealing target for drug discovery research (2,6,7).

DPP-IV is a serine protease that deactivates the glucagon-like peptide 1 (GLP-1) and the glucose-dependent insulinotropic peptide (GIP), both of which stimulate insulin production. GLP-1 is a substrate of DPP-IV, a major incretin hormone that regulates glucose activity in a glucose-dependent manner, inhibits glucagon release, reduces stomach emptying, and promotes islet-cell regeneration and differentiation. DPP-IV inhibitors raise the concentration of active GLP-1 in the blood and promote insulin release in response to an increase in blood glucose levels. In-silico approaches appear to be a potent practice in the area of technology for creating new and optimized pharmacological agents with superior therapeutic intervention. The quantitative association between structural characteristics of substances and their biological activity is studied using statistical correlation analysis in QSAR. It is a key method in the design of ligand-based drugs. In this study, we presented a 2D QSAR methodology for generating and optimizing new medicines against the DPP-IV enzyme from the existing literature. We used computational methods such as 2D QSAR modeling, molecular docking, virtual screening, and molecular dynamics simulation to develop a new, selective, and powerful DPP-IV inhibitor for diabetes treatment. The findings of this study could be crucial in the future development of effective Type II anti-diabetic medicines based on prospective DPP-IV inhibitors.

Materials and Methods

2D QSAR modeling

However, because of the extremely diverse structure, no promising 2d QSAR model emerged. Only those compounds with structurally similar scaffolds, such as pyrimidine, xanthene, quinazolinone, and imidazoquinoline, were chosen, and they were divided into the training and test sets. All of the compounds were chosen using the following criteria: (i) Both training and test sets should cover the broadest range of molecular bioactivities (IC50); (ii) both low-active and high-active compounds should be included; and (iii) both training and test sets should include varied architectures. The training set consists of 34 compounds with high structural diversity and a broad range of molecular bioactivities with PIC50 values ranging from 4.6 to 9 nM (PIC50 = 9-log IC50) [Table 2, 3].

The first step was the calculation of the 2d descriptors of both the training and test sets using the DRAGON software (8). DRAGON provides over 900 2d characteristics for any molecule. In our test set of 7 molecules, out of 900 2d descriptors only those variable were chosen that has a high correlation with the experimental PIC50 value but has less correlation with each other. Those are the descriptors Har2, BELm4, BEHv3, and VEe1. The main characteristic of these four columns of descriptors (or variable) is very less correlation with each other. But each of these 4 columns has a high correlation with the experimental PIC50 column. Har2 descriptors are topological descriptors that relate to the square reciprocal distance sum index. BELm4 corresponds to the burden matrix’s lowest eigenvalue n. 4 weighted by atomic masses. BEHv3 is the highest eigenvalue n. 3 of the burden matrix/weighted by the atomic van der Waals volume. The eigenvector coefficient sum from the electronegativity weighted distance matrix corresponds to VEe1 (Refer to the Dragon software) [Table 1].

Out of 40 molecules 7 molecules were randomly chosen as the test set and the remaining 33 molecules were considered as the training set. The next step was forecasting the predicted PIC50 value of test set molecules using the SYSTAT program (total 7 molecules with mole ID 60,62,147,152,171,172, 179). The column of experimental PIC50 value of the test set was chosen as the dependent variable, while the other four columns of the test set were chosen as the independent variables. As a result, we obtained the following chart [Table-1] in SYSTAT software (9). Then by using this chart we generate a formula to predict the PIC50 value (explain in result section). The same formula is also used to predict the PIC50 value of the training set.

Table 1: The figure represents Regression coefficient data obtained from the test set. Here variables 2, 3, 4, 5 are the variables Har2, BELm4, BEHv3, VEe1 Respectively.

Effect	Coefficient	Standard Error	Standard Coefficient	Tolerance	T	P-Value
CONSTANT	35.225	21.112	0	.	1.668	0.237
VAR(2)	0.395	0.16	6.799	0.001	2.475	0.132
VAR(3)	-5.682	5.572	-0.51	0.029	-1.02	0.415
VAR(4)	15.811	6.56	1.4	0.021	2.41	0.138
VAR(5)	-22.893	9.757	-6.633	0.001	-2.246	0.144

Table 2: This represents test set analysis for 2D QSAR (without docking approach)

Mol ID	PIC50 experimental	Har2	BELm4	BEHv3	VEe1	PIC50 predicted
60	4.040100412	78.008	1.372	3.259	4.619	4.027738
62	4.460798401	68.396	1.268	3.183	4.415	4.290462
147	5.677780705	84.942	1.524	3.369	4.688	6.062597
152	9	152.052	1.725	3.559	5.804	8.884467
171	7.356547324	110.05	1.605	3.581	5.188	7.425447
172	8.151810883	133.488	1.688	3.597	5.556	8.040203
179	6.673664139	78.505	1.599	3.451	4.602	6.359132

 

Table 3: This represents training set analysis for 2D QSAR (without docking approach)

Mol ID	Experimental PIC50	Har2	BELm4	BEHv3	VEe1	Predicted PIC50
2	4.616184634	66.487	1.505	3.182	4.393	2.677608
15	6.482804102	108.44	1.46	3.493	5.227	5.329192
23	9	154.956	1.677	3.558	5.815	10.036649
28	8.522878745	141.096	1.719	3.558	5.633	8.489831
34	7.677780705	115.022	1.394	3.556	5.321	7.148245
35	7.107905397	143.723	1.664	3.662	5.838	6.791285
54	7.050609993	111.897	1.62	3.461	5.18	6.355606
61	4.485585079	72.097	1.372	3.253	4.512	4.047578
63	5.826813732	108.44	1.46	3.475	5.228	5.021701
64	6.301899454	113.587	1.459	3.505	5.316	5.520194
72	7.102372909	123.146	1.449	3.54	5.404	7.89162
74	6.872895202	122.849	1.447	3.548	5.4	8.003729
95	6.443697499	137.347	1.663	3.657	5.751	6.191083
96	6.943095149	149.725	1.663	3.658	5.923	7.158608
97	6.906578315	137.897	1.663	3.657	5.755	6.316761
98	5.907981529	132.262	1.663	3.655	5.669	6.028112
99	7.974694135	132.596	1.576	3.577	5.468	10.022611
100	7.869666232	144.926	1.662	3.58	5.64	10.514146
120	8.958607315	132.612	1.576	3.61	5.479	10.298871
122	8.397940009	154.892	1.658	3.701	5.915	10.091
123	8.15490196	130.712	1.568	3.631	5.581	7.590772
148	6.397940009	139.474	1.72	3.461	5.634	6.286899
149	7.638272164	139.474	1.677	3.461	5.64	6.393867
151	7.602059991	145.813	1.722	3.558	5.717	8.412988
153	8.823908741	161.508	1.738	3.559	5.884	10.714281
154	8.698970004	161.508	1.699	3.559	5.89	10.798521
157	8.045757491	95.824	1.548	3.51	4.899	7.623547
158	8	142.477	1.639	3.573	5.63	9.79573
174	7.838631998	139.144	1.678	3.597	5.641	8.385238
175	8.330683119	139.341	1.678	3.597	5.644	8.394374
176	8.809668302	140.124	1.671	3.596	5.644	8.727622
177	6.065501549	78.511	1.453	3.451	4.603	7.168181
178	6.340273905	72.996	1.546	3.448	4.499	6.794769

 

The fundamental issue with 2d QSAR is that it fails when the structure of the molecules and their scaffold is extremely diverse. For a highly diverse structure, even 3d QSAR fails. The key reason for the failure is that only ligand-based QSAR was used. No DRAGON characteristics are related to receptor-ligand interactions. The descriptors of DRAGON only give an idea about the ligand structure and morphology. But the IC50 value of a drug is highly correlated with receptor-ligand interaction-related values such as Docking Score, Ligand Efficiency, Glide Ligand Efficiency, Glide Gscore, Glide Lipo, Glide Hbond, Glide Rewards, Glide Evdw, Glide Ecoul, Glide Erotb, Glide Esite, Glide Emodel, Glide Energy, Glide Einternal, Glide Confnum, Glide Posenum, and so on.

Molecular Docking

A new dataset consisting of 74 compounds was established from the list of 179 original sets of compounds. These 74 compounds were docked against the DPP-IV receptor.

Protein Preparation

The crystal structure of protein Human DPP-4 (Dipeptidyl peptidase-4) (PDB ID: 6B1E) was downloaded from the Protein Data Bank (PDB) and modeled in this study using Protein preparation wizard (Maestro version 11.4) at 2.2 Å (10,11). Protein structures must be processed before they can be employed as a receptor for docking. Some common activities are I hydrogen atom addition, (ii) atomic charge assignment, (iii) removal of water molecules that are not involved in ligand binding, and (iv) selenocysteine replacement with cysteines.

Site Map Generation

The site map generation tool was used to identify the probable binding site of the protein. The binding site containing the native ligand was chosen for the docking study.

Ligand Preparation

The ligand structures were created in the CDX format using the application Chem Draw extreme version 8.0. These ligands were then translated to the mol2 3d format and run using the Maestro LigPrep module in the Schrodinger suite, version 11.4 (12). They were transformed to a stable form by minimizing energy and optimizing missing hydrogen atoms. These ligands’ bond orders were set, and the charged groups were neutralized. The ionization and tautomeric states were created using the Epik module at pH levels ranging from 6.8 to 7.2. Compounds were minimized in the last stage of LigPrep.

Receptor Grid Generation

For the selected binding site, the grid was generated taking the binding site as the centroid.

Glide Ligand Docking

The proposed compounds were glide docked using the previously created receptor grid and ligand molecules. 74 molecules were docked for the chosen grid. The Glide ligand docking program was used to score the favorable contacts between ligand molecules and the receptor (13). All docking calculations were done in standard precession (SP) mode with the OPLS-2005 force field.

2D QSAR modeling with help of docked scores

There is no good association between the docking score and the experimental PIC50 result. Because, in addition to docking score, other factors such as Ligand Efficiency, Glide Ligand Efficiency, Glide Gscore, Glide Lipo, Glide Hbond, Glide Rewards, Glide Evdw, Glide Ecoul, Glide Erotb, Glide Esite, Glide Emodel, Glide Energy, Glide Einternal, Glide Confnum, Glide Posenum, and so on are responsible for a molecule’s activity (PIC50). As a result, an attempt was made to identify the best descriptors among them that are highly connected with the experimental PIC50 value. Then, a test set of 9 random molecules was created. The remaining molecules (65 molecules) are used as the training set.

All compounds in the training and test sets were chosen using the following criteria: (i) Both training and test sets should cover the broadest range of molecular bioactivities (IC50); (ii) both low-active and high-active compounds should be included; and (iii) both training and test sets should include varied architectures. The training set contains 65 compounds exhibiting high structural diversity and a broad range of molecular bioactivities with PIC50 values ranging from 4.6 to 9 nM (PIC50=9-log IC50) [Table 5, 6].

In SYSTAT software, the experimental PIC50 is used as the dependent variable for the test set (9 molecules), and a couple of the above-mentioned descriptors (which are highly correlated with the experimental PIC50 value) are used as the independent variable. As a result, we were able to gather the following information. Then, using a test set, an attempt was made to construct a regression curve and produce an activity forecasting equation [Table 4].

Table 4: Data obtained from SYSTAT software using docking score as an additional descriptor Regression Coefficients B=(X’X)^-1X’Y

Effect	Coefficient	Standard Error	Standard Coefficient	Tolerance	T	P-Value
CONSTANT	5.796	0	0	.	.	.
VAR(2)	0.002	0	0.449	0.112	.	.
VAR(3)	0.021	0	0.447	0.148	.	.
VAR(4)	0.005	0	0.485	0.096	.	.
VAR(5)	-146.025	0	-121.132	0	.	.
VAR(6)	-128.175	0	-6.327	0	.	.
VAR(7)	-640.812	0	-81.364	0	.	.
VAR(8)	951.967	0	203.829	0	.	.
VAR(9)	0.038	0	0.249	0.135	.	.

 

Table 5: This represents test set analysis for 2D QSAR with docking approach

Source File	Experimental Pic50	Potential Energy-Opls-2005	Bend Energy-Opls-2005	Solvation Energy-Opls-2005	Docking Score	Glide Ligand Efficiency	Glide Ligand Efficiency Sa	Glide Ligand Efficiency Ln	Glide Evdw	Predicted Pic50
14.mol2	7.37675071	54.022	67.719	-72.561	-4.539	-0.175	-0.517	-1.066	-33.386	7.435752
64.mol2	6.301899454	-285.357	51.051	-94.059	-5.785	-0.199	-0.613	-1.325	-33.606	6.272965
93.mol2	7	208.983	32.347	-65.123	-4.229	-0.136	-0.429	-0.954	-30.885	7.097363
113.mol2	6.928117993	-101.134	47.523	-96.796	-5.879	-0.226	-0.67	-1.381	-33.726	6.952285
168.mol2	6.638272164	12.662	24.868	-93.219	-4.365	-0.175	-0.511	-1.035	-31.178	6.69153
131.mol2	5.756961951	-552.935	16.842	-331.248	-6.637	-0.277	-0.798	-1.589	-26.468	5.746601
149.mol2	7.638272164	166.818	66.749	-74.468	-5.246	-0.159	-0.51	-1.167	-45.287	7.733725
122.mol2	8.397940009	234.34	36.574	-43.334	-4.734	-0.132	-0.434	-1.033	-32.994	8.494239
153.mol2	8.823908741	11.492	77.109	-59.095	-4.879	-0.136	-0.447	-1.064	-45.03	8.869509

 

Table 6: This represents training set analysis for 2D QSAR with docking approach

Source File	Experimental Pic50	Potential Energy-Opls-2005	Bend Energy-Opls-2005	Solvation Energy-Opls-2005	Docking Score	Glide Ligand Efficiency	Glide Ligand Efficiency Sa	Glide Ligand Efficiency Ln	Glide Evdw	Pic50 Predicted
6.mol2	6.028260409	-45.829	7.913	-73.095	-4.37	-0.19	-0.54	-1.057	-28.481	6.714623
10.mol2	6.806875402	-207.516	105.777	-70.324	-5.438	-0.175	-0.551	-1.227	-33.508	7.515839
11.mol2	7.301029996	104.69	20.663	-48.302	-6.088	-0.21	-0.645	-1.394	-32.374	7.166273
15.mol2	6.482804102	-461.713	50.557	-108.291	-6.357	-0.227	-0.689	-1.467	-30.2	6.605745
16.mol2	6.721246399	62.519	58.121	-80.765	-5.481	-0.189	-0.581	-1.255	-36.042	7.549445
17.mol2	8.070581074	102.324	66.779	-60.297	-5.071	-0.181	-0.55	-1.171	-32.347	7.258029
18.mol2	7.744727495	105.909	25.4	-80.73	-5.51	-0.197	-0.598	-1.272	-25.703	7.312631
19.mol2	8	170.587	46.787	-42.01	-5.235	-0.209	-0.612	-1.241	-27.999	7.861036
27.mol2	7.552841969	235.677	38.133	-67.92	-4.449	-0.139	-0.441	-0.996	-25.33	7.686517
28.mol2	8.522878745	-176.249	68.958	-51.88	-5.638	-0.171	-0.548	-1.254	-33.581	7.961375
30.mol2	8.886056648	-160.73	54.193	-129.824	-5.519	-0.162	-0.526	-1.219	-35.785	7.899307
31.mol2	8.769551079	-170.578	110.554	-79.606	-5.44	-0.17	-0.54	-1.218	-36.102	8.714996
32.mol2	8.22184875	121.467	64.803	-101.989	-6.372	-0.22	-0.675	-1.459	-34.229	7.887197
33.mol2	7.585026652	3.315	63.369	-89.694	-4.912	-0.196	-0.574	-1.164	-28.32	7.742349
34.mol2	7.677780705	295.438	54.042	-48.409	-5.405	-0.186	-0.573	-1.238	-30.901	7.86128
36.mol2	8.795880017	119.458	120.885	-81.891	-4.78	-0.177	-0.531	-1.113	-33.775	8.298972
37.mol2	8.207608311	-243.117	39.403	-125.774	-4.875	-0.212	-0.603	-1.179	-26.913	7.571183
39.mol2	7.657577319	105.246	38.681	-70.501	-3.862	-0.143	-0.429	-0.899	-28.118	6.765394
41.mol2	8.167491087	222.721	75.119	-77.165	-4.159	-0.154	-0.462	-0.968	-27.426	7.998941
42.mol2	7.823908741	129.887	96.584	-76.902	-4.583	-0.176	-0.522	-1.076	-33.275	8.413825
44.mol2	8.15490196	40.866	35.747	-53.202	-4.933	-0.197	-0.577	-1.169	-30.945	7.6774
47.mol2	6.562249437	195.947	20.948	-45.87	-4.287	-0.148	-0.454	-0.982	-28.201	6.402943
48.mol2	7.657577319	-177.949	37.283	-96.12	-3.093	-0.141	-0.394	-0.756	-29.442	7.144525
49.mol2	8.793174124	38.219	61.579	-82.72	-5.693	-0.248	-0.704	-1.377	-35.479	8.784609
53.mol2	8.107905397	-154.517	34.293	-78.718	-6.297	-0.191	-0.612	-1.4	-33.131	7.978545
54.mol2	7.050609993	-502.929	114.181	-60.711	-5	-0.179	-0.542	-1.154	-36.139	7.329617
55.mol2	7.602059991	206.313	28.037	-48.478	-4.977	-0.172	-0.527	-1.14	-29.031	6.729904
63.mol2	5.826813732	-818.795	49.613	-115.121	-6.123	-0.219	-0.664	-1.413	-38.047	5.730089
66.mol2	6.924453039	77.327	21.107	-38.915	-4.47	-0.179	-0.523	-1.06	-27.939	6.872375
67.mol2	7.075720714	-80.769	21.968	-58.715	-5.017	-0.201	-0.587	-1.189	-24.148	7.523072
69.mol2	6.465973894	180.764	24.4	-35.033	-5.602	-0.207	-0.622	-1.304	-30.919	7.104212
70.mol2	6.353596274	48.567	20.055	-42.259	-5.881	-0.218	-0.653	-1.369	-30.864	6.85275
71.mol2	6.040958608	103.539	22.038	-31.925	-3.577	-0.128	-0.388	-0.826	-28.954	6.254138
72.mol2	7.102372909	190.373	29.694	-62.651	-4.358	-0.145	-0.451	-0.99	-26.698	6.993748
73.mol2	7.721246399	114.119	66.778	-57.594	-5.456	-0.188	-0.578	-1.249	-28.472	8.248523
74.mol2	6.872895202	193.187	30.469	-77.072	-4.26	-0.142	-0.441	-0.968	-34.885	6.472619
90.mol2	4.620150821	40.215	27.695	-375.123	-6.008	-0.215	-0.652	-1.387	-29.198	5.779906
91.mol2	5.496209317	52.448	40.907	-381.371	-5.439	-0.201	-0.604	-1.266	-32.284	5.479672
99.mol2	7.974694135	-158.819	110.561	-80.836	-5.597	-0.181	-0.567	-1.262	-35.628	8.501749
100.mol2	7.869666232	-132.876	111.266	-80.334	-5.018	-0.152	-0.488	-1.116	-36.301	8.64286
104.mol2	6.924453039	46.027	56.393	-77.703	-3.349	-0.134	-0.392	-0.794	-29.159	7.125431
105.mol2	6.363512104	-225.56	40.995	-118.021	-4.284	-0.171	-0.501	-1.015	-31.904	6.69265
108.mol2	6.841637508	49.81	55.946	-71.556	-6.144	-0.236	-0.7	-1.443	-33.221	7.757227
110.mol2	6.742321425	42.183	44.993	-81.032	-4.243	-0.152	-0.46	-0.98	-30.76	6.163714
115.mol2	8.795880017	38.332	61.513	-82.659	-5.693	-0.248	-0.704	-1.377	-35.479	8.783754
118.mol2	9	-127.831	36.877	-104.876	-4.643	-0.122	-0.411	-1.001	-41.512	8.299109
121.mol2	7.229147988	164.876	21.165	-56.395	-5.166	-0.184	-0.56	-1.192	-27.35	7.308348
123.mol2	8.15490196	283.582	38.794	-44.869	-3.827	-0.12	-0.38	-0.857	-30.1	7.701209
124.mol2	6.306273051	84.785	35.075	-87.765	-5.297	-0.183	-0.561	-1.213	-38.28	6.518691
125.mol2	6.739928612	37.021	36.339	-74.576	-5.255	-0.181	-0.557	-1.203	-36.584	7.147122
126.mol2	6.573488739	64.481	35.078	-78.557	-5.074	-0.181	-0.55	-1.171	-36.437	6.707977
127.mol2	6.876148359	38.002	42.078	-95.733	-5.783	-0.193	-0.599	-1.314	-37.118	7.028593
128.mol2	6.806875402	17.673	41.791	-86.744	-5.184	-0.179	-0.549	-1.187	-32.888	6.783377
129.mol2	7.036212173	3.441	33.476	-91.381	-5.332	-0.178	-0.552	-1.211	-34.325	7.06126
130.mol2	7.107905397	35.358	41.869	-85.485	-4.975	-0.172	-0.527	-1.139	-37.222	6.84209
152.mol2	9	28.731	67.594	-65.337	-4.866	-0.139	-0.455	-1.068	-42.048	8.591106
156.mol2	7.698970004	-535.627	75.267	-76.175	-5.427	-0.147	-0.489	-1.177	-42.362	8.526031
158.mol2	8	-17.474	105.914	-65.788	-4.676	-0.142	-0.454	-1.04	-33.64	8.274704
161.mol2	7.318758763	120.932	62.889	-85.212	-5.978	-0.221	-0.664	-1.392	-31.652	7.354926
162.mol2	8.22184875	183.408	46.684	-52.57	-5.76	-0.199	-0.61	-1.319	-27.937	7.680396
164.mol2	8	-321.034	13.919	-58.837	-5.28	-0.23	-0.653	-1.277	-27.351	7.393335
166.mol2	7.193820026	133.048	33.53	-43.478	-6.053	-0.242	-0.708	-1.435	-29.962	7.940206
167.mol2	8.055517328	153.943	58.48	-122.856	-5.339	-0.167	-0.53	-1.195	-34.934	8.452689
170.mol2	6.301029996	6.384	33.708	-64.147	-5.513	-0.184	-0.571	-1.253	-36.787	6.507021
173.mol2	6.856360765	-89.683	43.502	-67.308	-4.834	-0.146	-0.47	-1.075	-44.489	6.918569

 

Molecular Dynamics Simulations Study

The molecule with the best binding affinity was further subjected to a molecular dynamics simulation study. Molecular Dynamics Simulation is a computer-based simulation approach used to analyze the physical motions of atoms or molecules. MD simulations can identify a few critical hydrogen bond interactions. MD simulations assist in protein docking and virtual screening advances. The iMODS server was utilized in this work to simulate molecular dynamics. The iMODS service aids in the exploration of normal mode analysis and generates accessible information about routes that may involve macromolecules or homologous structures (14–16).

For the hit chemical receptor complex, molecular dynamics simulations were also run using the Desmond program (17). Individually, the complex was solvated in an explicit water box of size 10 with a single-point charge (SPC) water model TIP3P with periodic boundary condition (PBC). The protein and ligand were modeled using the OPLS3e force field, and Na and Cl- ions were added to make the total charge of the system neutral. Following that, the system was energy reduced for 2000 steps before a 50 ns production run. Following minimization, the complex was subjected to manufacturing run at the NPT ensemble. Using the Nose-Hoover thermostatic algorithm and the Martina-Tobias-Klein approach, the system was gently heated to maintain a temperature of 300 K and pressure. To simulate long-range electrostatic interactions, the Particle- Mesh Ewald (PME) approach was used with a grid spacing of 0.8. The Desmond package’s Simulation Interaction Diagram tool was used to investigate the precise interactions between the ligand and protein. The data was examined in terms of protein and ligand RMSD and root mean square fluctuation (RMSF).

Results

2D QSAR Result analysis (Without docking approach)

The PIC50 value of the test set molecules (total of 7 molecules with mole IDs 60,62,147,152,171,172,179) was predicted using the formula given below and the regression curve was constructed using MICROSOFT EXCEL. The following formula was used to calculate the expected value of PIC50 for both the training and test sets (Total 40 molecules):

PREDICTED PIC50=Constant+ Coefficient of VAR (2) ×Har2 value+ Coefficient of VAR (3) × BELm4 value +Coefficient of VAR (4) ×BEHv3 Value+ Coefficient of VAR (5) ×VEe1 Value.

Or PREDICTED PIC50=35.225+ 0.395×Har2 value+ (-5.682) × BELm4 value +15.811×BEHv3 Value + (-22.893) ×VEe1 Value.

Here we performed 2d QSAR because it is more robust than 3d QSAR. And we got R² =0.7644 which is a decent value for 2d QSAR  [Figure 1]. For 2D QSAR modeling, the value of the regression coefficient, R² should be greater than 0.7 to build a decent model and also predict the activity of all molecules with reasonable precision. (27-29).

Figure 1: Correlation curve of training and test set for 2D QSAR (without docking approach) Click here to View figure

2D QSAR Result Analysis (With Docking Approach)

A total of 74 molecules have been docked to the active site. Potential Energy-OPLS-2005(VAR2), Bend Energy-OPLS-2005 (VAR3), Solvation Energy-OPLS-2005(VAR4), Docking Score(VAR5), Glide Ligand Efficiency (VAR6), Glide Ligand Efficiency Sa (VAR7), Glide Ligand Efficiency Ln (VAR8), and Glide Evdw are docking descriptors that are highly correlated with Experimental PIC50 (VAR9). With the help of SYSTAT software taking those descriptors as independent variables and the Experimental PIC50 value as the dependent variable for the TEST set, we obtained the following data (Table 4).

We used the following formula to predict the PIC50 value of any molecule.

PREDICTED PIC50=Constant+ Coefficient of VAR (2) × Potential Energy + Coefficient of VAR (3) × Bend Energy +Coefficient of VAR (4) ×Solvation Energy + Coefficient of VAR (5) ×docking score + Coefficient of VAR (6) × glide ligand efficiency +Coefficient of VAR (7) × glide ligand efficiency sa + Coefficient of VAR (8) × glide ligand efficiency ln +Coefficient of VAR (9) × glide evdw

Or, PREDICTED PIC50 =5.796 + 0.002× Potential Energy +0.021× Bend Energy +0.005×Solvation Energy + (-146.025) ×docking score + (-128.175) × glide ligand efficiency +(-640.812) × glide ligand efficiency sa +951.967× glide ligand efficiency ln + 0.038× glide evdw

With the help of the above-mentioned formula, we have predicted the PIC50 value of Both test (9 molecules) and training set (65 molecules).

We classified our 74 molecules into three groups: (i) those with a PIC50 value less than 5.5 are inactive, those with a PIC50 value greater than 5.5 are active, and those with a PIC50 value less than 5.5 are inactive; (ii) those with a PIC50 score between 5.5 and 7.5 are moderately active; (iii) while those with a PIC50 value greater than 7.5 are active.

However, we discovered that a few compounds produce erroneous active results, which are referred to as outliers, in which the projected PIC50 value differs from the experimental PIC50 value. However, because the R² value is 0.7098, or greater than 70%, the activity of the majority of the molecules may be predicted with reasonable precision [Figure 2].

Figure 2: Correlation curve of training and test set for 2D QSAR (using dock score as descriptors) Click here to View figure

Molecular Docking Result analysis

The 40 compounds used for 2D QSAR modeling using the docking approach were analyzed. The best docking score was demonstrated by compound 131.mol2. The compound 131.mol2 demonstrated a docking score of -6.637. The structural analysis of this compound was done on Biovia Discovery Studio Visualizer (18). Compound 131.mol2 showed two hydrogen bond interactions at SER209, VAL207. It also 2 pi-alkyl interactions at ARG356 and one pi-alkyl interaction at PHE357. Furthermore, the native co-crystallized ligand, vildagliptin was also separately docked with the DPP IV receptor. To validate the docking procedure, the docked pose of the hit compound 131 was superimposed with the docked pose of the co-crystallized ligand, vildagliptin [Figure 3]. It was observed that both the docked poses of the two compounds superimposed with each other thus validating the docking protocol [Figure 4].

Figure 3: Biovia Discovery Studio structural analysis of our hit compound 131 (red color) with the receptor dipeptidyl peptidase IV (light violet color) Click here to View figure

Figure 4: The left-sided figure represents docked pose of native ligand, vildagliptin (blue color) with the hit compound 131 (blue color). The right-sided figure represents the superimposed structural analysis of native ligand, vildagliptin (blue color) with the hit compound 131 (blue color) at the active site of the receptor dipeptidyl peptidase IV. Click here to View figure

Molecular Dynamics Simulation Results

Compound 131 was identified as the best hit and was subjected to molecular dynamics simulation analysis. Here the docked complex of the compound 131 with receptor dipeptidyl peptidase IV was considered for MD simulation. Normal mode analysis mobility allows us to analyze the large-scale B-factor and mobility as well as the stability of the molecules [Figure 5]. The IMOD server exposed the internal coordinates analysis depending on the protein-ligand structural interactions.

Figure 5: Normal Mode Analysis of hit compound 131 with target receptor dipeptidyl peptidase IV using iMODS software Click here to View figure

IMODs also measure the B-factor and structural deformity and calculate the eigenvalue. Image 1  represents the docked complex of our protein and ligand. Image 2 of the Figure represents the deformability graph. The deformity graph illustrated peaks in the graph which represent regions in the protein with deformability. Image 3 represents the B-Factor graph. The main-chain deformability, also known as the B-Factor, is a measure of a molecule’s ability to deform at each of its residues. Image 4 represents the eigenvalue of the complex. The motion stiffness is represented by the eigenvalue associated with each normal mode. Its value is proportional to the amount of energy required to distort the structure. The simpler the deformation, the lower the eigenvalue. Our docked complex demonstrated an eigenvalue of 1.145917e-04 which eventually suggested that our protein-ligand complex can be deformed easily. Image 5 represents the variance plot. The variance plot demonstrates individual variances in red color whereas cumulative variance in green color. Image 6 represents the covariance map. This map demonstrates the correlation motion between a pair of residues in red color, uncorrelated motion in white color, and anti-correlated motion in blue color. Image 7 represents the elastic map of our docked complex. Each dot in the graph represents one spring inside the atoms’ pair. The dots are colored dependent on stiffness, with darker grey dots indicating stiffer springs and lighter grey dots indicating softer springs. From the molecular dynamics study, it was evident that our complex showed a good amount of deformability. Furthermore, it also showed a moderately low eigenvalue, suggesting that it could be deformed easily. The variance map exhibited a higher degree of cumulative variances than an individual variance. The elastic network map also produced satisfactory results.

For MD Simulation using the Desmond program for our hit compound-receptor complex, the protein RMSD exhibited a stable trajectory throughout the entire 50 ns simulation process. The ligand RMSD exhibited fluctuations until 34 ns but then showed a stable trajectory throughout the rest of the simulation process [Figure 6]. Regarding the Protein RMSF analysis, the highest fluctuations were observed at 4.8 Å and 4.2 Å. Overall, the Protein and Ligand RMSF trajectories were found to be stable [Figure 7, Figure 8]. Other than these, the amino acid interactions of our protein-ligand complex were also analyzed. The notable hydrogen interactions were observed at PHE208, GLU361, ASP302, ILE405, CYS551, TYR585. The notable hydrophobic interactions were observed at ARG429, TYR585, CYS551, MET591. Water bridges were observed in VAL207, CYS301, ASP302, GLY355, ARG356, PRO359, GLU361, ILE405, CYS551, TYR585. Ionic interactions were found in ASP302 and GLU361. Among these residues, only GLU361 and TYR585 exhibited strong interactions for more than 30% of the entire simulation process [Figure 9, Figure 10, Figure 11]. Other than this, other ligand properties of the hit compound such as radius of gyration, molecular surface area, intramolecular hydrogen bonds, solvent accessible surface area, and polar surface area were monitored throughout the 50 ns simulation process [Figure 12]. The radius of gyration, molecular surface area, and polar surface area plots of the hit compound demonstrated stable trajectories throughout the entire 50 ns simulation study. The solvent-accessible surface area plot showed slight fluctuations between 16-28 ns but still showed a stable trajectory throughout the rest of the simulation process. Furthermore, the intramolecular hydrogen bond plot of the hit compounds demonstrated zero hydrogen bonds. The 50ns simulation process gave a detailed analysis of the structural stability of our protein-ligand complex.

Figure 6: Protein-Ligand RMSD plot. Click here to View figure

Figure 7: Protein RMSF plot Click here to View figure

Figure 8: Ligand RMSF plot Click here to View figure

Figure 9: Protein-Ligand contacts plot detailing amino acid interactions concerning interaction fraction Click here to View figure

Figure 10: Protein-Ligand Contacts plot detailing amino acid interactions with respect to time Click here to View figure

Figure 11: ligand-protein contacts detailing best prominent amino acid interactions during the simulation process Click here to View figure

Figure 12: Root Mean Square Deviation, Radius of Gyration, Intramolecular Hydrogen Bonds, Molecular Surface Area, Solvent Accessible Surface Area, Polar Surface Area plots of the hit compound. Click here to View figure

Discussion

Designing a drug that targets DPP4 is not a new area of research. In previous studies, for designing a potent DPP4 inhibitor most of the researchers have chosen 3 to 4 well-known marketed DPP4 inhibitors and then tried to build a scaffold virtually by pharmacophore modeling i.e combining the important pharmacophore. Then they have synthesized several derivatives and measured their IC50 Value(19,20) or performed virtual screening from a large database (20–23). In most of the cases they have used a structurally similar scaffold for QSAR modeling and that’s too in less number (24–26). But in this cumulative study, we have collected 180 highly diversified molecules with their experimental PIC50 value. All of them are either marketed drugs or passing through the clinical trial phase. Still, we are getting a good correlation curve, so this model is quite reliable. Contributing a good model along with a reliable activity predicting formula to the current research community was our motto, which we have achieved successfully. The practical application of this work is that we can predict the PIC50 value of any molecule by using the above-mentioned formula, just we need to dock this molecule with the DPP4 receptor and have to collect the descriptors obtained after docking. If the predicted PIC50 value comes out to be less than 5 then the molecule would be non-potent as a DPP4 inhibitor. So we can skip the synthesis process of that molecule. That will save our time as well as expenditure.

With the help of this study, we were able to generate two different 2D QSAR models with the help of two different approaches. The first approach was to generate 2D descriptors for a given set of molecules using the DRAGON software and then eventually generating regression curves with the aid of SYSTAT software. The first approach generated a QSAR model with regression coefficient, R²=0.7644 (training set) and R²=0.9857 (test set). The second approach was to perform docking for another set of molecules and eventually use docking results as an additional set of descriptors. Finally, the descriptors data were used to generate regression curves using SYSTAT software. The second approach also gave a satisfactory regression coefficient value, R²=0.7098 (training set) and R²=0.9987 (test set). The proposed modeling process and computer-aided drug creation were founded on computational trials using statistically stable descriptor values. This method can be used to find new potential DPP-4 inhibitors.

Acknowledgement

The products used in this study are products that are commonly and primarily used in our field of study and country.There is no conflict of interest between the authors and producers of the products because we do not intend to use these products as a means of litigation but rather to advance knowledge. Furthermore, the research was not funded by the production company, but rather by the authors’ own personal efforts.

Conflict of Interest

There is no conflict of interest

Funding Source

No funding source

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