2-s2.0-85053437358

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[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 Elsevier LtdProteasomal system plays an important role in maintaining cell homeostatis. Overexpression of proteasomes leads to several major diseases, such as cancer and autoimmune disorder. The β5 subunit of proteasome is a crucial active site in proteolysis, and targeting proteasome β5 subunit is essential for proteasome inhibition. In the present study, a pharmacophore-based virtual screening and molecular docking were employed to identify ligands as inhibitors of β5 subunit of proteasome. The pharmacophore features were built with one hydrogen bond donor, two hydrogen bond acceptors, and one hydrophobic feature using native ligand of proteasome (HU10), which was then used to screen ZINC database using ZINCPharmer. The retrieved virtual hits were subjected to molecular docking analysis using iDock. The best six hits were subjected to molecular dynamics (MD) simulation and each complex was stable during 40 ns MD simulation as indicated by root-mean-square-deviation (RMSD) and root-mean-square-fluctuation (RMSF) values. The current study identifies 5 best hits having better binding potentials than HU10 as predicted by molecular mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method, i.e. Lig1540/ZINC33356240, Lig1546/ZINC33356235, Lig1522/ZINC20854878, Lig980/ZINC12391945, and Lig1119/ZINC19865241, which can be used in the development of new proteasome inhibitors.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]MM-PBSA,Molecular docking,Molecular dynamics simulations,Pharmacophore modeling,Proteasomes,Virtual Screening,Drug Evaluation, Preclinical,Humans,Ligands,Models, Molecular,Molecular Structure,Proteasome Endopeptidase Complex,Proteasome Inhibitors,Structure-Activity Relationship,Thermodynamics[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]MM-PBSA,Molecular docking,Molecular dynamics simulation,Pharmacophore model,Proteasome,Virtual screening[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This work is supported in part by the Hibah Penelitian Pasca Doktor 2018 from Ministy of Research, Technology, and Higher Education, Republic of Indonesia. The authors wish to thank Dr. Sandra Megantara for helping in the analysis of pharmacophore model.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1016/j.compbiolchem.2018.08.009[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]