About The Forum
Calendar 2017 Poster Sessions : Pathway and Mechanism of Antagonist Binding to Opioid Receptors
Abstract:
G protein-coupled receptors (GPCRs) are an important class of signaling protein of special interest for pharmaceutical development. Opioid receptors are a notable subset of GPCRs that are of interest in pharmaceutical development for new painkillers, ideally without the potential for addiction of current opioid drugs. Structure-based drug design is challenging at these receptors due to the difficulty of obtaining GPCR crystal structures. Subtle differences in molecules on the same scaffold can result in the molecule behaving as an agonist or antagonist, and minor alterations can produce large changes in selectivity among receptor subtypes.
Using a novel adaptive sampling method, we depart from the more traditional analysis of already bound ligands to computationally explore pathways and mechanisms of ligand binding in an unbiased manner that requires no prior knowledge of bound pose. A large number of short MD simulations are run in parallel, each exploring different regions of possible protein and ligand conformations. After each generation of simulations is complete, machine learning methods are used to identify those in which the ligand progresses along a possible binding pathway. No knowledge of the pathway or bound pose is required, making the method applicable to ligands where no similar crystal structure exists as well as to the identification of cryptic binding pockets and allosteric sites.
We apply the method to several opioid antagonists across with the goal of identifying differences in binding pathway across receptors that may in part be responsible for ligand functional selectivity. We find that the binding pathways of naloxone, naltrindole, and a multifunctional peptide at the mu- and delta-opioid receptors share a common entry pathway between transmembrane helices (TMs) 1 and 2 before proceeding deeper into the binding pocket. This is surprisingly different from the binding pathways of other GPCR ligands, such as dihydroalprenolol at the beta 2 adrenergic receptor, which enters between TMs 5 and 6.
Computationally determining time-resolved ligand binding pathways at all-atom resolution provides new insight into opioid antagonist selectivity and binding kinetics.
Bio:
Robin Betz is a PhD Candidate in Biophysics. She is a member of the Ron Dror group, and uses high performance computing, GPUs, and machine learning to enhance molecular dynamics simulations of proteins and drugs.
G protein-coupled receptors (GPCRs) are an important class of signaling protein of special interest for pharmaceutical development. Opioid receptors are a notable subset of GPCRs that are of interest in pharmaceutical development for new painkillers, ideally without the potential for addiction of current opioid drugs. Structure-based drug design is challenging at these receptors due to the difficulty of obtaining GPCR crystal structures. Subtle differences in molecules on the same scaffold can result in the molecule behaving as an agonist or antagonist, and minor alterations can produce large changes in selectivity among receptor subtypes.
Using a novel adaptive sampling method, we depart from the more traditional analysis of already bound ligands to computationally explore pathways and mechanisms of ligand binding in an unbiased manner that requires no prior knowledge of bound pose. A large number of short MD simulations are run in parallel, each exploring different regions of possible protein and ligand conformations. After each generation of simulations is complete, machine learning methods are used to identify those in which the ligand progresses along a possible binding pathway. No knowledge of the pathway or bound pose is required, making the method applicable to ligands where no similar crystal structure exists as well as to the identification of cryptic binding pockets and allosteric sites.
We apply the method to several opioid antagonists across with the goal of identifying differences in binding pathway across receptors that may in part be responsible for ligand functional selectivity. We find that the binding pathways of naloxone, naltrindole, and a multifunctional peptide at the mu- and delta-opioid receptors share a common entry pathway between transmembrane helices (TMs) 1 and 2 before proceeding deeper into the binding pocket. This is surprisingly different from the binding pathways of other GPCR ligands, such as dihydroalprenolol at the beta 2 adrenergic receptor, which enters between TMs 5 and 6.
Computationally determining time-resolved ligand binding pathways at all-atom resolution provides new insight into opioid antagonist selectivity and binding kinetics.
Bio:
Robin Betz is a PhD Candidate in Biophysics. She is a member of the Ron Dror group, and uses high performance computing, GPUs, and machine learning to enhance molecular dynamics simulations of proteins and drugs.