Mapping the druggable allosteric space of G-protein coupled receptors: a fragment-based molecular dynamics approach.
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TLDR
This work maps the surfaces of the β1 (β1AR) and β2 (β2AR) adrenergic receptor structures to detect a series of five potentially druggable allosteric sites and uses the FTMAP algorithm to identify ‘hot spots’ with affinity for a variety of organic probe molecules corresponding to drug fragments.Abstract:
To address the problem of specificity in G-protein coupled receptor (GPCR) drug discovery, there has been tremendous recent interest in allosteric drugs that bind at sites topographically distinct from the orthosteric site. Unfortunately, structure-based drug design of allosteric GPCR ligands has been frustrated by the paucity of structural data for allosteric binding sites, making a strong case for predictive computational methods. In this work, we map the surfaces of the beta1 (beta1AR) and beta2 (beta2AR) adrenergic receptor structures to detect a series of five potentially druggable allosteric sites. We employ the FTMAP algorithm to identify 'hot spots' with affinity for a variety of organic probe molecules corresponding to drug fragments. Our work is distinguished by an ensemble-based approach, whereby we map diverse receptor conformations taken from molecular dynamics (MD) simulations totaling approximately 0.5 micros. Our results reveal distinct pockets formed at both solvent-exposed and lipid-exposed cavities, which we interpret in light of experimental data and which may constitute novel targets for GPCR drug discovery. This mapping data can now serve to drive a combination of fragment-based and virtual screening approaches for the discovery of small molecules that bind at these sites and which may offer highly selective therapies.read more
Citations
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Molecular dynamics simulations and drug discovery
TL;DR: This review discusses the many roles atomistic computer simulations of macromolecular receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-Molecule binding energies.
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Principles in the design of ligand-targeted cancer therapeutics and imaging agents
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The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins.
Dima Kozakov,Laurie E. Grove,David R. Hall,Tanggis Bohnuud,Scott E. Mottarella,Lingqi Luo,Bing Xia,Dmitri Beglov,Sandor Vajda +8 more
TL;DR: Applications include determining the druggability of proteins, identifying ligand moieties that are most important for binding, finding the most bound-like conformation in ensembles of unliganded protein structures and providing input for fragment-based drug design.
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Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways
Kai Kohlhoff,Diwakar Shukla,Morgan Lawrenz,Gregory R. Bowman,David E. Konerding,Dan Belov,Russ B. Altman,Vijay S. Pande +7 more
TL;DR: This work used Google's Exacycle cloud computing platform to simulate 2 milliseconds of dynamics of the β2 adrenergic receptor — a major drug target G protein-coupled receptor (GPCR) and provides an atomistic description of the activation of a GPCR, revealing multiple activation pathways.
Journal ArticleDOI
Structural conservation of druggable hot spots in protein–protein interfaces
Dima Kozakov,David R. Hall,Gwo-Yu Chuang,Regina Cencic,Ryan Brenke,Laurie E. Grove,Dmitri Beglov,Jerry Pelletier,Adrian Whitty,Sandor Vajda +9 more
TL;DR: The structural and physicochemical signature of druggable sites at PPI interfaces is sufficiently robust to be detectable from the structure of the unliganded protein, even when substantial conformational adaptation is required for optimal ligand binding.
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