2018
Kennedy, Amanda J.; Ballante, Flavio; Johansson, Johan R.; Milligan, Graeme; Sundström, Linda; Nordqvist, Anneli; Carlsson, Jens
Structural characterization of agonist binding to protease-activated receptor 2 through mutagenesis and computational modeling Journal Article
In: ACS Pharmacology & Translational Science, 1 (2), pp. 119-133, 2018, ISSN: 2575-9108.
Abstract | Links | BibTeX | Tags: Biochemistry, Clusterizer, Homology Modeling, Molecular Docking
@article{Kennedy2018,
title = {Structural characterization of agonist binding to protease-activated receptor 2 through mutagenesis and computational modeling},
author = {Amanda J. Kennedy and Flavio Ballante and Johan R. Johansson and Graeme Milligan and Linda Sundström and Anneli Nordqvist and Jens Carlsson},
url = {https://pubs.acs.org/doi/10.1021/acsptsci.8b00019},
doi = {10.1021/acsptsci.8b00019},
issn = {2575-9108},
year = {2018},
date = {2018-10-16},
journal = {ACS Pharmacology & Translational Science},
volume = {1},
number = {2},
pages = {119-133},
abstract = {Protease-activated receptor 2 (PAR2) is a G protein-coupled receptor that is activated by proteolytic cleavage of its N-terminus. The unmasked N-terminal peptide then binds to the transmembrane bundle, leading to activation of intracellular signaling pathways associated with inflammation and cancer. Recently determined crystal structures have revealed binding sites of PAR2 antagonists, but the binding mode of the peptide agonist remains unknown. In order to generate a model of PAR2 in complex with peptide SLIGKV, corresponding to the trypsin-exposed tethered ligand, the orthosteric binding site was probed by iterative combinations of receptor mutagenesis, agonist ligand modifications and data-driven structural modeling. Flexible-receptor docking identified a conserved binding mode for agonists related to the endogenous ligand that was consistent with the experimental data and allowed synthesis of a novel peptide (1-benzyl-1H[1,2,3]triazole-4-yl-LIGKV) with higher functional potency than SLIGKV. The final model may be used to understand the structural basis of PAR2 activation and in virtual screens to identify novel PAR2 agonist and competitive antagonists. The combined experimental and computational approach to characterize agonist binding to PAR2 can be extended to study the many other G protein-coupled receptors that recognize peptides or proteins.},
keywords = {Biochemistry, Clusterizer, Homology Modeling, Molecular Docking},
pubstate = {published},
tppubtype = {article}
}
2015
Flavio, Ballante; Marshall, Garland R.
An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design Journal Article
In: Journal of Chemical Information and Modeling, 56 (1), pp. 54-72, 2015.
Abstract | Links | BibTeX | Tags: Chemoinformatics, Clusterizer, DockAccessor, Docking Assessment, Molecular Docking, Molecular Modeling
@article{Ballante2015,
title = {An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design},
author = {Flavio, Ballante and Garland R. Marshall },
editor = {American Chemical Society},
url = {http://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00603},
doi = {10.1021/acs.jcim.5b00603},
year = {2015},
date = {2015-12-18},
urldate = {2015-12-18},
journal = {Journal of Chemical Information and Modeling},
volume = {56},
number = {1},
pages = {54-72},
abstract = {Molecular docking is a widely used technique in drug design to predict the binding pose of a candidate compound in a defined therapeutic target. Numerous docking protocols are available, each characterized by different search methods and scoring functions, thus providing variable predictive capability on a same ligand-protein system. To validate a docking protocol, it is necessary to determine a priori the ability to reproduce the experimental binding pose (i.e. by determining the Docking Accuracy, DA) to select the most appropriate docking procedure, and thus estimate the rate of success in docking novel compounds. As common docking programs use generally different RMSD formulas, scoring functions and format results, it is both difficult and time consuming to: consistently determine and compare their predictive capability to identify the best protocol to be used for the target of interest; extrapolate the binding poses (i.e. Best- docked (BD), Best-cluster (BC) and Best-fit (BF) poses) when applying a given docking program over thousands/millions of molecules during virtual screening. To reduce this difficulty, two new procedures, called Clusterizer and DockAccessor have been developed and implemented for use with some common and “free-for-academics” programs, such as: AutoDock4, AutoDock4(Zn), AutoDock Vina, DOCK, MpSDockZn, PLANTS, and Surflex-Dock to automatically extrapolate BD, BC and BF poses, as well as perform consistent cluster and docking accuracy (DA) analyses. Clusterizer and DockAccessor represent two novel tools, (code available over the internet) to collect computationally determined poses as well as detect the most predictive docking approach. Herein, an application to lysine deacetylase (KDAC) inhibitors is illustrated.},
keywords = {Chemoinformatics, Clusterizer, DockAccessor, Docking Assessment, Molecular Docking, Molecular Modeling},
pubstate = {published},
tppubtype = {article}
}