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An integrated in silico screening strategy for identifying promising disruptors of p53-MDM2 interaction.
Comput Biol Chem. 2019 Dec; 83:107105.CB

Abstract

The p53 protein, also called guardian of the genome, plays a critical role in the cell cycle regulation and apoptosis. This protein is frequently inactivated in several types of human cancer by abnormally high levels of its negative regulator, mouse double minute 2 (MDM2). As a result, restoration of p53 function by inhibiting p53-MDM2 protein-protein interaction has been pursued as a compelling strategy for cancer therapy. To date, a limited number of small-molecules have been reported as effective p53-MDM2 inhibitors. X-ray structures of MDM2 in complex with some ligands are available in Protein Data Bank and herein, these data have been exploited to efficiently identify new p53-MDM2 interaction antagonists through a hierarchical virtual screening strategy. For this purpose, the first step was aimed at compiling a focused library of 686,630 structurally suitable compounds, from PubChem database, similar to two known effective inhibitors, Nutlin-3a and DP222669. These compounds were subjected to the subsequent structure-based approaches (quantum polarized ligand docking and molecular dynamics simulation) to select potential compounds with highest binding affinity for MDM2 protein. Additionally, ligand binding energy, ADMET properties and PAINS analysis were also considered as filtering criteria for selecting the most promising drug-like molecules. On the basis of these analyses, three top-ranked hit molecules, CID_118439641, CID_60452010 and CID_3106907, were found to have acceptable pharmacokinetics properties along with superior in silico inhibitory ability towards the p53-MDM2 interaction compared to known inhibitors. Molecular docking and molecular dynamics results well confirmed the interactions of the final selected compounds with critical residues within p53 binding site on the MDM2 hydrophobic clefts with satisfactory thermodynamics stability. Consequently, the new final scaffolds identified by the presented computational approach could offer a set of guidelines for designing promising anti-cancer agents targeting p53-MDM2 interaction.

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Publisher Full Text (DOI)

Authors+Show Affiliations

Sirous H
Bioinformatics Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, 81746-73461 Isfahan, Iran. Electronic address: h_sirous@pharm.mui.ac.ir.
Chemi G
Department of Excellence of Biotechnology, Chemistry and Pharmacy, 2018-2022, University of Siena, Via Aldo Moro 2, 53100, Siena, Italy.
Campiani G
Department of Excellence of Biotechnology, Chemistry and Pharmacy, 2018-2022, University of Siena, Via Aldo Moro 2, 53100, Siena, Italy.
Brogi S
Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126, Pisa, Italy.

MeSH

AnimalsComputer SimulationCrystallography, X-RayDrug Evaluation, PreclinicalLigandsMiceModels, MolecularMolecular StructureProtein BindingProto-Oncogene Proteins c-mdm2Quantum TheorySmall Molecule LibrariesTumor Suppressor Protein p53

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31473433

Citation

Sirous, Hajar, et al. "An Integrated in Silico Screening Strategy for Identifying Promising Disruptors of p53-MDM2 Interaction." Computational Biology and Chemistry, vol. 83, 2019, p. 107105.
Sirous H, Chemi G, Campiani G, et al. An integrated in silico screening strategy for identifying promising disruptors of p53-MDM2 interaction. Comput Biol Chem. 2019;83:107105.
Sirous, H., Chemi, G., Campiani, G., & Brogi, S. (2019). An integrated in silico screening strategy for identifying promising disruptors of p53-MDM2 interaction. Computational Biology and Chemistry, 83, 107105. https://doi.org/10.1016/j.compbiolchem.2019.107105
Sirous H, et al. An Integrated in Silico Screening Strategy for Identifying Promising Disruptors of p53-MDM2 Interaction. Comput Biol Chem. 2019;83:107105. PubMed PMID: 31473433.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - An integrated in silico screening strategy for identifying promising disruptors of p53-MDM2 interaction. AU - Sirous,Hajar, AU - Chemi,Giulia, AU - Campiani,Giuseppe, AU - Brogi,Simone, Y1 - 2019/08/16/ PY - 2019/06/13/received PY - 2019/08/05/revised PY - 2019/08/12/accepted PY - 2019/9/2/pubmed PY - 2019/12/20/medline PY - 2019/9/2/entrez KW - Cancer KW - Drug design KW - Molecular dynamics simulation KW - Virtual screening KW - p53-MDM2 inhibitors SP - 107105 EP - 107105 JF - Computational biology and chemistry JO - Comput Biol Chem VL - 83 N2 - The p53 protein, also called guardian of the genome, plays a critical role in the cell cycle regulation and apoptosis. This protein is frequently inactivated in several types of human cancer by abnormally high levels of its negative regulator, mouse double minute 2 (MDM2). As a result, restoration of p53 function by inhibiting p53-MDM2 protein-protein interaction has been pursued as a compelling strategy for cancer therapy. To date, a limited number of small-molecules have been reported as effective p53-MDM2 inhibitors. X-ray structures of MDM2 in complex with some ligands are available in Protein Data Bank and herein, these data have been exploited to efficiently identify new p53-MDM2 interaction antagonists through a hierarchical virtual screening strategy. For this purpose, the first step was aimed at compiling a focused library of 686,630 structurally suitable compounds, from PubChem database, similar to two known effective inhibitors, Nutlin-3a and DP222669. These compounds were subjected to the subsequent structure-based approaches (quantum polarized ligand docking and molecular dynamics simulation) to select potential compounds with highest binding affinity for MDM2 protein. Additionally, ligand binding energy, ADMET properties and PAINS analysis were also considered as filtering criteria for selecting the most promising drug-like molecules. On the basis of these analyses, three top-ranked hit molecules, CID_118439641, CID_60452010 and CID_3106907, were found to have acceptable pharmacokinetics properties along with superior in silico inhibitory ability towards the p53-MDM2 interaction compared to known inhibitors. Molecular docking and molecular dynamics results well confirmed the interactions of the final selected compounds with critical residues within p53 binding site on the MDM2 hydrophobic clefts with satisfactory thermodynamics stability. Consequently, the new final scaffolds identified by the presented computational approach could offer a set of guidelines for designing promising anti-cancer agents targeting p53-MDM2 interaction. SN - 1476-928X UR - https://www.unboundmedicine.com/medline/citation/31473433/An_integrated_in_silico_screening_strategy_for_identifying_promising_disruptors_of_p53_MDM2_interaction_ DB - PRIME DP - Unbound Medicine ER -