RT Journal Article
SR Electronic
T1 Kinetic Machine Learning Unravels Ligand-Directed Conformational Change of μ Opioid Receptor
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 170886
DO 10.1101/170886
A1 Evan N. Feinberg
A1 Amir B. Farimani
A1 Carlos X. Hernandez
A1 Vijay S. Pande
YR 2017
UL http://biorxiv.org/content/early/2017/07/31/170886.abstract
AB The μ Opioid Receptor (μOR) is a G-Protein Coupled Receptor (GPCR) that mediates pain and is a key target for clinically administered analgesics. The current generation of prescribed opiates – drugs that bind to μOR – engender dangerous side effects such as respiratory depression and addiction in part by stabilizing off-target conformations of the receptor. To determine both the key conformations of μOR to atomic resolution as well as the transitions between them, long timescale molecular dynamics (MD) simulations were conducted and analyzed. These simulations predict new and potentially druggable metastable states that have not been observed by crystallography. We applied cutting edge algorithms (e.g., tICA and Transfer Entropy) to guide our analysis and distill the key events and conformations from simulation, presenting a transferrable and systematic analysis scheme. Our approach provides a complete, predictive model of the dynamics, structure of states, and structure–ligand relationships of μOR with broad applicability to GPCR biophysics and medicinal chemistry.