Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics

Figures & data

Figure 1 Illustration to show the superimposed conformations obtained by docking ragaglitazar and the ten derivative compounds (Comp#1–#10) to PPARα and PPARγ receptors, respectively. (A) Ragaglitazar and Comp#1–#10 to PPARα (1k7l). (B) Ragaglitazar and Comp#1–#10 to PPARγ (1k74). (C) Ragaglitazar and Comp#1 to PPARα (1k7l). (D) Ragaglitazar and Comp#1 to PPARγ (1k74).

Notes: The binding pocket is defined by those residues that have at least one heavy atom within a distance of 5Å from the ligand.⁹² The carbon atoms of ragaglitazar are in black, while the carbon atoms for Comp#1 are in gray. For the overlapping part between ragaglitazar and Comp#1, part of the Comp#1 was covered by ragaglitazar. The blue dotted lines indicate the H-bond interactions of the receptor with its ligands. The purple helix is a part of the AF2 function domain. See the text for further explanation.
Abbreviations: PPARα, peroxisome proliferator-activated receptor-alpha; PPARγ, peroxisome proliferator-activated receptor-gamma.

Figure 2 Illustration to show how to generate the best ten compounds from the ZINC36728034 structure through the core hopping method.

Notes: The top hit compound, ZINC36728034, screened out from the lead-now database was selected as the most potential lead compound for further modification, according to the vital importance of the acidic head of the ligand. Based on ZINC36728034, the new molecule Comp#0 was designed, as shown on the left bottom. Subsequently, the core hopping method was used to search the fragment database for replacing the amide group (ie, the R₀ group) by the best ten R groups (ie, R₁ to R₁₀), as shown on the right side of the figure.

Table 1 The compound ragaglitazar was used as a positive control, and the ten compounds (Comp#1–#10) were ranked roughly according to their docking scores to the receptors PPARα and PPARγ

Compound	Docking scores (Kcal/mol)		ADME properties predicted
	PPARα (1k71)	PPARγ (1k74)	PSAa	logPo/wb	logSc	PPCacod	Human oral absorptione
Ragaglitazar	−11.49	−12.29	70.42	6.07	−6.38	364.38	95.35
Comp#0f	−10.33	−11.79	126.98	3.27	−4.65	258.17	71.34
Comp#1	−13.65	−14.98	180.80	5.61	−5.76	106.26	48.14
Comp#2	−13.64	−14.58	185.78	4.16	−6.05	114.87	37.69
Comp#3	−13.31	−14.55	146.67	4.23	−4.60	111.24	57.53
Comp#4	−13.22	−14.41	121.81	6.42	−6.59	176.39	73.58
Comp#5	−13.18	−13.85	130.62	5.96	−6.40	141.02	64.80
Comp#6	−13.01	−13.68	183.70	4.55	−6.47	106.99	55.75
Comp#7	−12.95	−14.11	118.33	6.23	−6.25	175.59	71.11
Comp#8	−13.85	−13.07	152.69	5.01	−6.15	105.27	43.31
Comp#9	−13.36	−13.45	146.77	5.83	−6.30	134.45	62.69
Comp#10	−13.04	−14.89	145.04	4.58	−5.00	112.41	60.36

Notes: Listed are also the corresponding physiochemical descriptors calculated with QP simulations.^41,72,73

a The van der Waals surface area of the polar nitrogen and oxygen atoms; the accepted region is (7.0 to 200.0);

b the predicted octanol/water partition coefficient; the accepted region is (−2.0 to 6.5);

c the predicted aqueous solubility, where S (mol dm⁻³) is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid; the accepted region is (−6.5 to 0.5);

d predicted apparent Caco-2 cell permeability in nm/second. Caco-2 cells are a model for the gut–blood barrier. QikProp predictions are for nonactive transport. The result of <25 is poor;

e predicted percent of human oral absorption on a scale from 0% to 100%. The prediction is based on a quantitative multiple linear regression model. This property usually correlates well with human oral absorption. The result of <25% is poor;

f Comp#0 was an initial structure for core hopping that was designed with the intention to make it have stronger affinity than ZINC36728034. See the bottom left of Figure 2 for its structure.

Abbreviations: PPARα, peroxisome proliferator-activated receptor-alpha; PPARγ, peroxisome proliferator-activated receptor-gamma; ADME, absorption, distribution, metabolism, and excretion; PSA, polar surface area; QP, QikProp.

Figure 3 Illustration to show the outcomes of molecular dynamic simulations for the interactions of the receptors with Comp#1 – the best derivative found in this study, as shown in Table 1. (A) The RMSD of all backbone atoms for the receptor PPARα. (B) The RMSD of all backbone atoms for the receptor PPARγ. (C) The RMSF of the side-chain atoms for the receptor PPARα. (D) The RMSF of the side-chain atoms for the receptor PPARγ.

Notes: The blue line indicates the outcome for the system of the receptor alone without any ligand; the red line indicates the outcome for the system of the receptor with the ligand Comp#1; and the green line indicates the outcome for the system of the receptor with the ligand Comp#1. The curves involved with the AF2 helix region are framed with the grey box.
Abbreviations: RMSD, root mean square deviation; PPARα, peroxisome proliferator-activated receptor-alpha; PPARγ, peroxisome proliferator-activated receptor-gamma; RMSF, root mean square fluctuation.

Table 2 The residues involved in forming the binding pocket of PPARα and PPARγ for the ligand Comp#1a

PPARα (1k7l)			PPARγ (1k74)
Leu-254	Glu-269	Ile-272	Pro-269	Ala-278	Arg-280
Phe-273	Cys-275	Cys-276	Ile-281	Phe-282	Gly-284
Gln-277	Cys278	Thr-279	Cys-285	Gln-286	Phe-287
*Ser-280	*Tyr-314	Ile-317	Arg-288	*Ser-289	*His-323
Phe-318	Leu-321	Val-324	Ile-326	Tyr-327	Leu-330
Met-330	Leu-331	Val-332	Leu-333	Val-339	Leu-340
*Ala-333	Tyr-334	Leu-344	Ile-341	Ser-342	Met348
Leu-347	Phe-351	Ile-354	Leu-353	Phe-360	Phe-363
Met-355	Lys-358	*His-440	Met-364	*His-449	Leu-453
Val-444	Ile-447	Leu-456	Leu-465	Leu-469	*Tyr-473
Leu-460	*Tyr-464

Notes:

a See the text or the study by Chakrabarti et al⁸³ for the definition of binding pockets used in this study. Those residues marked with an asterisk are the key residues for forming the H-bonding network, as shown in Figure 1.

Abbreviations: PPARα, peroxisome proliferator-activated receptor-alpha; PPARγ, peroxisome proliferator-activated receptor-gamma.

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