Abstract
Literature data suggests that Dipeptidyl peptidase-4 (DPP-4) is a potential target for type 2 Diabetes Mellitus. Therefore, it is of interest to identify new DPP-4 inhibitors using molecular docking analysis. We document compounds such as STOCK1N-98884, STOCK1N-98881, and STOCK1N-98866 with optimal binding features with DPP-4 from the ligand database at https://www.ibscreen.com/ for further consideration.
Keywords: DPP-4, GLP-1, diabetes, docking analysis, inhibitor
Background
Insulin resistance in type 2 diabetes and related issues are known [1]. Symptoms associated with the disease include retinopathy, edema, micro aneurysms, nephropathy outlines, symmetrical fringe neuropathy influencing engine and tactile nerves of the smaller attachments [2-4]. Several models of treatments using insulin, secretagogues (sulfonylureas and incretins) and hypoglycemias (biguanides, thiazolidinediones and a-glucosidase inhibitors) are currently available [5-10]. Inhibitors of the dipeptidyl peptidase-4 (DPP-4) are linked with the activities of GLP-1 and gastric inhibitory polypeptide (GIP) [7,8]. Description of the structural models for DPP-4 is known [15-17]. Therefore, it is of interest to identify molecules to inhibit DPP-4 using molecular docking analysis.
Materials and Methods:
Sequence to structure modeling and docking analysis of DPP-4:
The DPP-4 protein sequence downloaded from GenBank was analyzed in a comprehensive using tools such as Clustal Omega, Pfam, Prosite, SMART, PANTHER, PHYLIP, STRING and InterProScan, molecular docking and ligand-protein analysis tools to glean valuable insights [11-21].
Results and discussion:
A comprehensive analysis of DDP-4 using sequence and structure information is highly relevant in the fight against T2DM with reference to known data in the literature. The Multiple Sequence Analysis (MSA) of DDP-4 from different organisms such as Homo sapiens (DPP4, 766 amino acid), Rattus norvegicus (DPP4, 767 amino acid), Mus musculus (DPP4, 760 amino acid), Danio rerio (DPP4, 750 amino acid) and Gallus gallus (DPP4, 760 amino acid) is given in Figure 1. Secondary structure information of DDP-4 is also shown in Figure 1. Domain and phylogeny analysis of DDP-4 in different organisms is given in Figure 2.The Secondary structure analysis of human DDP-4 along with small nonpolar, hydrophobic, polar, and aromatic plus cysteine residues in human DDP-4 is shown in Figure 3. Protein-protein interaction network linked to DDP-4 is shown in Figure 4. We further show the DDP-4 associated pathways in Figure 5. The molecular docking interaction of DPP4 with STOCK1N-98884 is given in Figure 6 and Table 1-Table 4. This information gleaned from the analysis of DDP-4 is relevant in the design and development of novel compounds in combating the disease.
Figure 1.
(A) MSA of DDP-4 from different organisms (Homo sapiens, Rattus norvegicus, Mus musculus, Danio rerio and Gallus gallus) (B) Secondary structure information on DDP-4
Figure 2.
Domain and phylogeny analysis of DDP-4 in different organisms
Figure 3.
(A) Secondary structure analysis of human DDP-4 (Homo sapiens). (B) Small non-polar, hydrophobic, polar and aromatic plus cysteine residues in human DDP-4.
Figure 4.
(A) Interacting proteins with DPP4 using STRING v10.database. (B) Explanation of interactions shown.
Figure 5.
DDP-4 linked pathways.
Figure 6.
(A) Molecular docking interaction of DPP4 with STOCK1N-98884. (B) Cartoon interpretation of DPP4 with compound STOCK1N-98884. (C) Boiled-egg plot.
Table 1. Lowest binding energy for the Ligands-Protein interaction, along with scores for various interaction types, as detected by GLIDE GScore; Glide extra precision scores (kcal/mol) Lipophilic E Vdw; Chemscore lipophilic pair term and fraction of the total protein–ligand vdw energy HBond; Hydrogen-bonding term .
| Compounds ID | Binding Energy MM-GBSA (kcal/mol) | GScore | Lipophilic E vdw | H-bond | Electro | Protein ligands interaction |
| STOCK1N-98884 | -72.7837 | -11.56 | -2.91 | -6.87 | -2.01 | Glu:205, Glu:206, Try :547, Ser:630 and Asn710 |
| STOCK1N-98881 | -61.2792 | -10.2 | -3.37 | -4.44 | -2.41 | Arg:125, Glu:205, Glu:206, Lys:554, Trp:629 and Ser:630 |
| STOCK1N-98866 | -59.2571 | -9.58 | -2.46 | -3.65 | -3.19 | Arg:125, Try :547, Lys:554 and Trp:629 |
| Known Inhibitor | ||||||
| Linagliptin | -44.1282 | -6.79 | -2.22 | -2.61 | -0.34 | Try :547, Ser:630 and Asn710 |
| Electro; Electrostatic rewards Protein ligands interaction; p–p stacking, p–cat interaction and hydrogen bond between the ligands and protein |
Table 4. Biological activity spectrum of compounds (Pa – Active; Pi – Inactive).
| Molecule | Pa | Pi | Activity |
| STOCK1N-98884 | 1.219 | 0.449 | Anti-diabetic |
| STOCK1N-98881 | 1.812 | 0.642 | Anti-diabetic |
| STOCK1N-98866 | 1.121 | 0.318 | Anti-diabetic |
Conclusions:
We document compounds STOCK1N-98884, STOCK1N-98881, and STOCK1N-98866 from the IBS ligand database with optimal binding features with DPP-4 towards combating T2DM.
Table 2. Evaluation of drug-like properties of the lead molecules by Qikprop Maestro 10.5 molecular docking suite.
| Molecule | QPlog Po/w (-2.0 to 6.5) | Q P log HERG (acceptable ange:above -5.0) | QPP Caco (nm/s)<25—poor>500—great | Q P log,BB(-3 to 1.2) | QPP MDCK (nm/s) | Q Plog Kp (-8.0 to -0.1) |
| STOCK1N-98884 | -0.3 | -1.056 | 131.328 | -0.94 | 70.119 | -2.798 |
| STOCK1N-98881 | 3.376 | -0.015 | 283.926 | -0.628 | 485.3 | -2.406 |
| STOCK1N-98866 | 2.219 | -3.804 | 143.431 | -1.641 | 60.643 | -3.179 |
| Predicted IC50 value for blockage of HERG K+ channels; (acceptable range above -5.0) Molecule STOCK, InterBioScreen’s library (IBS), Q P log Poct; was predicted partition coefficient of octanol/gas, (8.0 to 35.0); QPP Caco, predicted apparent Caco-2 cell permeability in nm/s. Caco-2 cells is a model for the gut blood barrier (nm/s) <25-poor, >500-great. Q P log BB, predicted brain/blood partition coefficient; QPP MDCK, predicted apparent MDCK cell permeability in nm/s. MDCK cells are considered to be a good mimic for the blood–brain barrier; (nm/s) <25-poor, >500-great; Q P log KP, Predicted skin permeability; Q P log Khsa Prediction of binding to human serum albumin; (acceptable range -1.5 to 1.5) |
Table 3. Boiled egg parameters.
| Molecule | MW | TPSA | XLOGP3 | MLOGP | GI absorption | BBB permeant |
| STOCK1N-98884 | 430.88 | 159.85 | -0.3 | -0.66 | High | No |
| STOCK1N-98881 | 624.04 | 158.3 | 2.99 | 0.23 | Low | No |
| STOCK1N-98866 | 421.4 | 127.08 | 2.81 | 0.93 | High | No |
Acknowledgments
The authors sincerely thank Almanac Life Science India Pvt. Ltd. for analysis and support.
Edited by P Kangueane
Citation: Alsamghan et al. Bioinformation 16(6):444-451 (2020)
References
- 1.Care D. Diabetes Care . 2019;42:S81. doi: 10.2337/dc19-S008. [DOI] [PubMed] [Google Scholar]
- 2.Nilsson E, et al. Diabetes . 2014;63:2962. doi: 10.2337/db13-1459. [DOI] [PubMed] [Google Scholar]
- 3.Monami M, et al. Obesity and Metabolism . 2013;15:112. doi: 10.1111/dom.12000. [DOI] [PubMed] [Google Scholar]
- 4.Dunkley AJ, et al. Diabetes Care . 2014;37:922. doi: 10.2337/dc13-2195. [DOI] [PubMed] [Google Scholar]
- 5.Guido RV, et al. estudos avançados . 2010;24:81. [Google Scholar]
- 6.Gautier J, et al. Diabetes and metabolism . 2005;31:233. doi: 10.1016/s1262-3636(07)70190-8. [DOI] [PubMed] [Google Scholar]
- 7.Elrishi M, et al. Practical Diabetes International . 2007;24:474. [Google Scholar]
- 8.Bjelke JR, et al. Journal of Biological Chemistry, . 2004;279:34691. doi: 10.1074/jbc.M405400200. [DOI] [PubMed] [Google Scholar]
- 9.Gross JL, et al. Arquivos Brasileiros de Endocrinologia and Metabologia . 2002;46:16. [Google Scholar]
- 10.Quevillon E, et al. Nucleic acids research . 2005;33:W116. doi: 10.1093/nar/gki442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Tarique M, et al. Frontiers in microbiology . 2017;8:130. doi: 10.3389/fmicb.2017.00130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Castresana J. Molecular biology and evolution . 2000;17:540. doi: 10.1093/oxfordjournals.molbev.a026334. [DOI] [PubMed] [Google Scholar]
- 13.Malik A, et al. Bioinformation . 2020;16:223. doi: 10.6026/97320630016223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Afaq S, et al. Bioinformation . 2020;16:229. doi: 10.6026/97320630016229. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Nugent T, Jones DT. BMC bioinformatics . 2013;14:276. doi: 10.1186/1471-2105-14-276. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Anisimova M, Gascuel O. Systematic biology . 2006;55:539. doi: 10.1080/10635150600755453. [DOI] [PubMed] [Google Scholar]
- 17.Mishra J, Bhalla US. Biophysical Journal . 2002;83:1298. doi: 10.1016/S0006-3495(02)73901-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Bhardwaj SK, et al. Brain research bulletin . 1999;49:155. doi: 10.1016/s0361-9230(99)00047-7. [DOI] [PubMed] [Google Scholar]
- 19.Johnson TO, et al. Nature Reviews Drug Discovery . 2002;1:696. doi: 10.1038/nrd895. [DOI] [PubMed] [Google Scholar]
- 20.Friesner RA, et al. Journal of medicinal chemistry . 2004;47:1739. doi: 10.1021/jm0306430. [DOI] [PubMed] [Google Scholar]
- 21.Tripathi SK, et al. Journal of theoretical biology . 2013;334:87. doi: 10.1016/j.jtbi.2013.05.014. [DOI] [PubMed] [Google Scholar]