Acta Pharmaceutica, Vol. 71 No. 1, 2021.
Original scientific paper
https://doi.org/10.2478/acph-2021-0002
In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors
SOBIA AHSAN HALIM
; Natural and Medical Science Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman
ZAHEER UL-AQ
; Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, 75270 Karachi, Pakistan
AJMAL KHAN
; Natural and Medical Science Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman
AHMED AL-RAWAHI
; Natural and Medical Science Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman
AHMED AL-HARRASI
; Natural and Medical Science Research Center, University of Nizwa, Birkat-ul-Mouz 616, Nizwa, Sultanate of Oman
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.
Keywords
IL-2; virtual screening; FRED; MOE; GOLD; Surflex-Dock; ROC-curves
Hrčak ID:
236060
URI
Publication date:
31.3.2021.
Visits: 1.763 *