Targeting the NF-κB/IκBα complex via fragment-based E-Pharmacophore virtual screening and binary QSAR models.


Journal

Journal of molecular graphics & modelling
ISSN: 1873-4243
Titre abrégé: J Mol Graph Model
Pays: United States
ID NLM: 9716237

Informations de publication

Date de publication:
01 2019
Historique:
received: 20 08 2018
revised: 27 09 2018
accepted: 30 09 2018
pubmed: 12 11 2018
medline: 20 2 2020
entrez: 12 11 2018
Statut: ppublish

Résumé

Nuclear factor-κB (NF-κB) transcription factors represent a conserved family of proteins that regulate not only immune cells, but also heart cells, glial cells and neurons, playing a fundamental role in various cellular processes. Due to its dysregulation in certain cancer types as well as in chronic inflammation and autoimmune diseases, it has recently been appreciated as an important therapeutic target. The aim of this study was to investigate the binding pocket of NF-κB (p50/p65) heterodimer complex in association with NF-κB inhibitor IκBα to identify potent ligands via fragment-based e-pharmacophore screening. The ZINC Clean Fragments (∼2 million) and the Schrodinger's medically relevant Glide fragments library (∼670) were used to create the e-pharmacophore models at the potential binding site which was validated by site mapping. Glide/HTVS docking was conducted followed by re-docking of the top 20% fragments by Glide/SP and Glide/XP protocols. The top-85000 Glide XP-docked fragments were used to generate the e-pharmacophore hypotheses. The Otava small molecule library (∼260000 drug-like molecules) and 85 known NF-κB inhibitors were additionally screened against the derived e-pharmacophore models. The top-1000 high-scored molecules, which were well aligned to the e-pharmacophore models, from the Otava small molecule library, were then docked into the binding pocket. Finally, the selected 88 hit molecules and the 85 known inhibitors were analyzed by the MetaCore/MetaDrug™ platform, which uses developed binary QSAR models for therapeutic activity prediction as well as pharmacokinetic and toxicity profile predictions of screening molecules. Ligand selection criteria led to the refinement of 3 potent hit molecules using molecular dynamics (MD) simulations to better investigate their structural and dynamical profiles. The selected hit molecules had a low toxicity and a significant therapeutic potential for heart failure, antiviral activity, asthma and depression, all conditions in which NF-κB plays a critical role. These hit ligands were also structurally stable at the NF-κB/IκBα complex as per the MD simulations and MM/GBSA analysis. Two of these ligands (Otava IDs: 1426436 and 6248112) showed stronger binding and therefore are hypothesized to be more potent. The identification of new potent NF-κB/IκBα inhibitors may thus present a novel therapy for inflammation-mediated conditions as well as cancer, facilitating more efficient research, and leading the way to future drug development efforts.

Identifiants

pubmed: 30415122
pii: S1093-3263(18)30632-6
doi: 10.1016/j.jmgm.2018.09.014
pii:
doi:

Substances chimiques

Ligands 0
NF-kappa B 0
NF-KappaB Inhibitor alpha 139874-52-5

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

264-277

Informations de copyright

Copyright © 2018 Elsevier Inc. All rights reserved.

Auteurs

Tarek Kanan (T)

School of Medicine, Bahcesehir University, Istanbul, Turkey; Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey.

Duaa Kanan (D)

School of Medicine, Bahcesehir University, Istanbul, Turkey; Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey.

Ismail Erol (I)

Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey; Department of Chemistry, Gebze Technical University, Kocaeli, Turkey.

Samira Yazdi (S)

Molecular Simulations and Design Group, Max Planck Institute for Dynamics and Complex Technical Systems, Magdeburg, Germany.

Matthias Stein (M)

Molecular Simulations and Design Group, Max Planck Institute for Dynamics and Complex Technical Systems, Magdeburg, Germany. Electronic address: matthias.stein@mpi-magdeburg.mpg.de.

Serdar Durdagi (S)

Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University, Istanbul, Turkey; Neuroscience Program, Institute of Health Sciences, Bahcesehir University, Istanbul, Turkey. Electronic address: serdar.durdagi@med.bau.edu.tr.

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Classifications MeSH