In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors

Halim, Sobia Ahsan; Zaheer-Ul-Haq; Khan, Ajmal; Al-Rawahi, Ahmed; Al-Harrasi, Ahmed

doi:10.2478/acph-2021-0002

Abstract

Interleukin-2 (IL-2) is involved in the activation and differentiation of T-helper cells. Uncontrolled activated T cells play a key role in the pathophysiology by stimulating inflammation and autoimmune diseases like arthritis, psoriasis and Crohn’s disease. T cells activation can be suppressed either by preventing IL-2 production or blocking the IL-2 interaction with its receptor. Hence, IL-2 is now emerging as a target for novel therapeutic approaches in several autoimmune disorders. This study was carried out to set up an effective virtual screening (VS) pipeline for IL-2. Four docking/scoring approaches (FRED, MOE, GOLD and Surflex-Dock) were compared in the re-docking process to test their performance in producing correct binding modes of IL-2 inhibitors. Surflex-Dock and FRED were the best in predicting the native pose in its top-ranking position. Shapegauss and CGO scoring functions identified the known inhibitors of IL-2 in top 1, 5 and 10 % of library and differentiated binders from non-binders efficiently with average AUC of > 0.9 and > 0.7, resp. The applied docking protocol served as a basis for the VS of a large database that will lead to the identification of more active compounds against IL-2.

References

1. A. C. Church, Clinical advances in therapies targeting the interleukin-2 receptor, QJM: Int. J. Med.96 (2003) 91–102; https://doi.org/10.1093/qjmed/hcg01410.1093/qjmed/hcg01412589007
Search in Google Scholar Back to article
2. A. L. Moine, M. Goldman and D. Abramowicz, Multiple pathways to allograft rejection, Transplantation73 (2002) 1373–1381; https://doi.org/10.1097/00007890-200205150-0000110.1097/00007890-200205150-0000112023610
Search in Google Scholar Back to article
3. S. L. Gaffen and K. D. Liu, Overview of interleukin-2 function, production and clinical applications, Cytokine 28 (2004) 109–123; https://doi.org/10.1016/j.cyto.2004.06.01010.1016/j.cyto.2004.06.01015473953
Search in Google Scholar Back to article
4. M. R. Arkin and J. A. Wells, Small-molecule inhibitors of protein-protein interactions: progressing towards the dream, Nat. Rev. Drug Dis. 3 (2004) 301–317; https://doi.org/10.1038/nrd134310.1038/nrd134315060526
Search in Google Scholar Back to article
5. T. Dasgupta, P. Chitnumsub, S. Kamchonwongpaisan, C. Maneeruttanarungroj, S. E. Nichols, T. M. Lyons, J. Tirado-Rives, W. L. Jorgensen, Y. Yuthavong and K. S. Anderson, Exploiting structural analysis, in silico screening, and serendipity to identify novel inhibitors of drug-resistant falciparum malaria, ACS Chem. Biol.4 (2009) 29–40; https://doi.org/10.1038/nrd134310.1038/nrd1343
Search in Google Scholar Back to article
6. C. Mc Innesa, Virtual screening strategies in drug discovery, Curr. Opin. Chem. Biol.11 (2007) 494–502; https://doi.org/10.1016/j.cbpa.2007.08.03310.1016/j.cbpa.2007.08.03317936059
Search in Google Scholar Back to article
7. S. A. Halim and Z. Ul-Haq, Structure based 3D-QSAR studies of interleukin-2 inhibitors: Comparing the quality and predictivity of 3D-QSAR models obtained from different alignment methods and charge calculations, Chem. Biol. Int. 238 (2015) 9–24; https://doi.org/10.1016/j.cbi.2015.05.01810.1016/j.cbi.2015.05.01826051521
Search in Google Scholar Back to article
8. S. A. Halim, O. M. Abdalla, M. A. Mesaik, A. Wadood, Z. Ul-Haq and M. Kontoyianni, Identification of novel interleukin-2 inhibitors through computational approaches, Mol. Div.17 (2013) 345–355; https://doi.org/10.1007/s11030-013-9431-410.1007/s11030-013-9431-423494734
Search in Google Scholar Back to article
9. S. A. Halim, M. Jawad, M. Ilyas, Z. Mir, A. A. Mirza and T. Husnain, In silico identification of novel IL-1β inhibitors to target protein–protein interfaces, Comp. Biol. Chem.58 (2015) 158–166; https://doi.org/10.1016/j.compbiolchem.2015.06.00410.1016/j.compbiolchem.2015.06.00426253030
Search in Google Scholar Back to article
10. E. S. H. E. Ashry, M. R. Amer, O. M. Abdalla, A. A. Aly, S. Soomro, A. Jabeen, S. A. Halim, M. A. Mesaik and Z. Ul-Haq, Synthesis, biological evaluation, and molecular docking studies of benzyl, alkyl and glycosyl [2-(arylamino)-4,4-dimethyl-6-oxocyclohex-1-ene]carbodithioates, as potential immunomodulatory and immunosuppressive agents, Bioorg. Med. Chem.20 (2012) 3000–3008; https://doi.org/10.1016/j.bmc.2012.03.00310.1016/j.bmc.2012.03.00322480848
Search in Google Scholar Back to article
11. M. A. Mesaik, S. A. Halim, Z. Ul-Haq, M. I. Choudhary, S. Shahnaz, S. A. Ayatollahi, S. Murad and A. Ahmad, Immunosuppresive activity of buxidin and buxenone from Buxus hyrcana, Chem. Biol. Drug Des.75 (2010) 310–317; https://doi.org/10.1111/j.1747-0285.2009.00906.x10.1111/j.1747-0285.2009.00906.x20659112
Search in Google Scholar Back to article
12. M. A. Mesaik, A. Jabeen, S. A. Halim, A. Begum, A. S. Khalid, M. Asif, B. Fatima, Z. Ul-Haq and M. I. Choudhary, In silico and in vitro immunomodulatory studies on compounds of Lindelofia stylosa, Chem. Biol. Drug Des.79 (2012) 290–299; https://doi.org/10.1111/j.1747-0285.2011.01310.x10.1111/j.1747-0285.2011.01310.x22181857
Search in Google Scholar Back to article
13. Saifullah, S. Khan, Azizudin, S. A. Halim, M. Kashif, A. Jabeen, M. Asif, M. A. Mesaik, Z. Ul-Haq, A. Dar and M. I. Choudhary, In-vitro immunomodulatory and anti-cancerous activities of biotransformed products of dianabol through Azadirachta indica and its molecular docking studies, Chem. Cent. J.7 (2013) Article ID 163 (11 pages); https://doi.org/10.1186/1752-153X-7-16310.1186/1752-153X-7-163387477824764465
Search in Google Scholar Back to article
14. G. B. McGaughey, R. P. Sheridan, C. I. Bayly, J. C. Culberson, C. Kreatsoulas, S. Lindsley, V. Maiorov, J. F. Truchon and W. D. Cornell, Comparison of topological, shape, and docking methods in virtual screening, J. Chem. Inf. Model.47 (2007) 1504–1519; https://doi.org/10.1021/ci700052x10.1021/ci700052x17591764
Search in Google Scholar Back to article
15. M. R. McGann, H. R. Almond, A. Nicholls, J. A. Grant and F. K. Brown, Gaussian docking functions, Biopolymers68 (2003) 76–90; https://doi.org/10.1002/bip.1020710.1002/bip.1020712579581
Search in Google Scholar Back to article
16. G. Jones, P. Willett, R. C. Glen, A. R. Leach and R. J. Taylor, Development and validation of a genetic algorithm for flexible docking, Mol. Biol.267 (1997) 727-748; https://doi.org/10.1006/jmbi.1996.089710.1006/jmbi.1996.08979126849
Search in Google Scholar Back to article
17. A. N. Jain, Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine, J. Med. Chem.46 (2003) 499–511; https://doi.org/10.1021/jm020406h10.1021/jm020406h12570372
Search in Google Scholar Back to article
18. MOE v2006.08 (2006) Chemical Computing Group Inc., 910-1010 Sherbrooke St. W. Montreal, QC H3A 2R7 Canada; https://www.chemcomp.com/
Search in Google Scholar Back to article
19. H. Edelsbrunner, Weighted Alpha Shapes, Technical Report UIUCDCS-R-92-1760, Department of Computer Science, University of Illinois at Urbana-Champaign (IL) USA, 1992.
Search in Google Scholar Back to article
20. J. J. Irwin and B. K. Shoichet, ZINC-a free database of commercially available compounds for virtual screening, J. Chem. Inf. Model.45 (2005) 177–182; https://doi.org/10.1021/ci049714+10.1021/ci049714+136065615667143
Search in Google Scholar Back to article
21. SYBYL molecular modeling program version 7.3 (2007) Tripos Associates Inc., St. Louis (MO) USA; http://www.tripos.com/sybyl/
Search in Google Scholar Back to article
22. GOLD Protein Ligand Docking Software version 3.2 (2008) Cambridge Crystallographic Data Center, Cambridge, UK; https://www.ccdc.cam.ac.uk/solutions/csd-discovery/components/gold/
Search in Google Scholar Back to article
23. M. D. Eldridge, C. W. Murray, T. R. Auton, G. V. Paolini and R. P. Mee, Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes, J. Comp. Aid. Mol. Des.11 (1997) 425–445; https://doi.org/10.1023/A:100799612454510.1023/A:1007996124545
Search in Google Scholar Back to article
24. G. M. Verkhivker, D. Bouzida, D. K. Gehlhaar, P. A. Rejto, S. Arthurs, A. B. Colson, S. T. Freer, V. Larson, B. A. Luty, T. Marrone and P. W. Rose, Deciphering common failures in molecular docking of ligand-protein complexes, J. Comp. Aid. Mol. Des.14 (2000) 731–751; https://doi.org/10.1023/A:100815823155810.1023/A:1008158231558
Search in Google Scholar Back to article
25. M. Stahl and M. Rarey, Detailed analysis of scoring functions for virtual screening, J. Med. Chem.44 (2001) 1035–1042; https://doi.org/10.1021/jm000399210.1021/jm0003992
Search in Google Scholar Back to article
26. W. Welch, J. Ruppert and A. N. Jain, Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites, Chem. Biol.3 (1996) 449–462.
Search in Google Scholar Back to article
27. M. R. Arkin, M. Randal, W. L. DeLano, J. Hyde, T. N. Luong, J. D. Oslob, D. R. Raphael, L. Taylor, J. Wang, R. S. McDowell, J. A. Wells and A. C. Braisted, Binding of small molecules to an adaptive protein–protein interface, Proc. Nat. Acad. Sci. USA100 (2003) 1603–1608; https://doi.org/10.1073/pnas.25275629910.1073/pnas.252756299
Search in Google Scholar Back to article
28. C. D. Thanos, M. Randal and J. A. Wells, Potent small-molecule binding to a dynamic hot spot on IL-2, J. Am. Chem. Soc.25 (2003) 15280–15281; https://doi.org/10.1021/ja038261710.1021/ja0382617
Search in Google Scholar Back to article
29. M. Clark, R. D. Cramer III and N. Van Opdenbosch, Validation of the general purpose tripos 5.2 force field, J. Comp. Chem.10 (1989) 982–1012; https://doi.org/10.1002/jcc.54010080410.1002/jcc.540100804
Search in Google Scholar Back to article
30. T. A. Halgren, Merck molecular force field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular interactions, J. Comp. Chem.17 (1996) 520–552; https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<520::AID-JCC2>3.0.CO;2-W10.1002/(SICI)1096-987X(199604)17:5/6<520::AID-JCC2>3.0.CO;2-W
Search in Google Scholar Back to article
31. T. A. Halgren, MMFF VI. MMFF94s option for energy minimization studies, J. Comp. Chem.20 (1999) 720–729; https://doi.org/10.1002/(SICI)1096-987X(199905)20:7<720::AID-JCC7>3.0.CO;2-X10.1002/(SICI)1096-987X(199905)20:7<720::AID-JCC7>3.0.CO;2-X
Search in Google Scholar Back to article
32. J. Boström, J. R. Greenwood and J. Gottfries, Assessing the performance of OMEGA with respect to retrieving bioactive conformations, J. Mol. Graph. Model.21 (2003) 449–462; https://doi.org/10.1016/S1093-3263(02)00204-810.1016/S1093-3263(02)00204-8
Search in Google Scholar Back to article
33. C. A. Lipinski, F. Lombardo, B. W. Dominy and P. J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Del. Rev.23 (1997) 3–25; https://doi.org/10.1016/S0169-409X(00)00129-010.1016/S0169-409X(00)00129-0
Search in Google Scholar Back to article
34. R. Wang, Y. Fu and L. Lai, A new atom-additive method for calculating partition coefficients, J. Chem. Inf. Model.37 (1997) 615–621; https://doi.org/10.1021/ci960169p10.1021/ci960169p
Search in Google Scholar Back to article
35. Y. C. Martin, A bioavailability score, J. Med. Chem.48 (2005) 3164–3170; https://doi.org/10.1021/jm049200210.1021/jm0492002
Search in Google Scholar Back to article
36. A. C. Braisted, J. D. Oslob, W. L. Delano, J. Hyde, R. S. McDowell, N. Waal, C. Yu, M. R. Arkin and B. C. Raimundo, Discovery of a potent small molecule IL-2 inhibitor through fragment assembly, J. Am. Chem. Soc.125 (2003) 3714–3715; https://doi.org/10.1021/ja034247i10.1021/ja034247i
Search in Google Scholar Back to article
37. B. Geng, P. R. Fleming, S. Umlauf, A. Lin and P. V. Pallaia, The synthesis and selective IL-2 inhibitory activity of bis piperazine–phenol Mannich adducts, Bioorg. Med. Chem. Lett.12 (2002) 775–778; https://doi.org/10.1016/S0960-894X(02)00011-210.1016/S0960-894X(02)00011-2
Search in Google Scholar Back to article
38. B. C. Raimundo, J. D. Oslob, A. C. Braisted, J. Hyde, R. S. McDowell, M. Randal, N. D. Waal, J. Wilkinson, C. H. Yu and M. R. Arkin, Integrating fragment assembly and biophysical methods in the chemical advancement of small-molecule antagonists of IL-2: an approach for inhibiting protein-protein interactions, J. Med. Chem.47 (2004) 3111–3130; https://doi.org/10.1021/jm049967u10.1021/jm049967u15163192
Search in Google Scholar Back to article
39. J. W. Tilley, L. Chen, D. C. Fry, S. D. Emerson, G. D. Powers, D. Biondi, T. Varnell, R. Trilles, R. Guthrie, F. Mennona, G. Kaplan, R. A. LeMahieu, M. Carson, R. J. Han, C. M. Liu, R. Palermo and G. Ju, Identification of a small molecule inhibitor of the IL-2/IL-2Rr receptor interaction which binds to IL-2, J. Am. Chem. Soc.119 (1997) 7589–7590; https://doi.org/10.1021/ja970702x10.1021/ja970702x
Search in Google Scholar Back to article
40. N. D. Waal, W. Yang, J. D. Oslob, M. R. Arkin, J. Hyde, W. Lu, R. S. McDowell, C. H. Yu and B. C. Raimundo, Identification of non-peptidic small-molecule inhibitors of interleukin-2, Bioorg. Med. Chem. Lett.15 (2005) 983–987; https://doi.org/10.1016/j.bmcl.2004.12.04510.1016/j.bmcl.2004.12.04515686897
Search in Google Scholar Back to article
41. N. Triballeau, F. Acher, I. Brabet, J. P. Pin and H. O. Bertrand, Virtual screening workflow development guided by the “Receiver Operating Characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4, J. Med. Chem.48 (2005) 2534–2547; https://doi.org/10.1021/jm049092j10.1021/jm049092j15801843
Search in Google Scholar Back to article
42. I. Halperin, B. Ma, H. Wolfson and R. Nussinov, Principles of docking: an overview of search algorithms and a guide to scoring functions, Proteins47 (2002) 409–443; https://doi.org/10.1002/prot.1011510.1002/prot.1011512001221
Search in Google Scholar Back to article
43. Z. Ul-Haq, S. A. Halim, R. Uddin and J. D. Madura, Benchmarking docking and scoring protocol for the identification of potential acetylcholinesterase inhibitors, J. Mol. Graph. Model.28 (2010) 870–882; https://doi.org/10.1016/j.jmgm.2010.03.00710.1016/j.jmgm.2010.03.00720447848
Search in Google Scholar Back to article

In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors

Abstract

Paradigm

My account