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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

Puni tekst: engleski, pdf (2 MB) str. 33-56 preuzimanja: 93* citiraj
APA 6th Edition
HALIM, S.A., UL-AQ, Z., KHAN, A., AL-RAWAHI, A. i AL-HARRASI, A. (2021). In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors. Acta Pharmaceutica, 71 (1), 33-56. https://doi.org/10.2478/acph-2021-0002
MLA 8th Edition
HALIM, SOBIA AHSAN, et al. "In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors." Acta Pharmaceutica, vol. 71, br. 1, 2021, str. 33-56. https://doi.org/10.2478/acph-2021-0002. Citirano 02.12.2020.
Chicago 17th Edition
HALIM, SOBIA AHSAN, ZAHEER UL-AQ, AJMAL KHAN, AHMED AL-RAWAHI i AHMED AL-HARRASI. "In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors." Acta Pharmaceutica 71, br. 1 (2021): 33-56. https://doi.org/10.2478/acph-2021-0002
Harvard
HALIM, S.A., et al. (2021). 'In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors', Acta Pharmaceutica, 71(1), str. 33-56. https://doi.org/10.2478/acph-2021-0002
Vancouver
HALIM SA, UL-AQ Z, KHAN A, AL-RAWAHI A, AL-HARRASI A. In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors. Acta Pharm. [Internet]. 2021 [pristupljeno 02.12.2020.];71(1):33-56. https://doi.org/10.2478/acph-2021-0002
IEEE
S.A. HALIM, Z. UL-AQ, A. KHAN, A. AL-RAWAHI i A. AL-HARRASI, "In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors", Acta Pharmaceutica, vol.71, br. 1, str. 33-56, 2021. [Online]. https://doi.org/10.2478/acph-2021-0002

Sažetak
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.

Ključne riječi
IL-2; virtual screening; FRED; MOE; GOLD; Surflex-Dock; ROC-curves

Hrčak ID: 236060

URI
https://hrcak.srce.hr/236060

Posjeta: 169 *