Binding Pathway of Opiates to $\mu$ Opioid Receptors Revealed by Unsupervised Machine Learning

TL;DR

This study uses molecular dynamics, Markov models, and unsupervised machine learning to uncover the binding pathway and allosteric sites of opiates on the $fff$-Opioid Receptor, aiding analgesic drug design.

Contribution

It reveals the allosteric binding site and the pathway of opiates binding to the $fff$-Opioid Receptor using advanced simulation and machine learning techniques.

Findings

Identified the allosteric site responsible for opiate attraction.

Mapped the binding pathway of opiates to the orthosteric site.

Implications for designing novel analgesics.

Abstract

Many important analgesics relieve pain by binding to the $\mu$-Opioid Receptor ($\mu$OR), which makes the $\mu$OR among the most clinically relevant proteins of the G Protein Coupled Receptor (GPCR) family. Despite previous studies on the activation pathways of the GPCRs, the mechanism of opiate binding and the selectivity of $\mu$OR are largely unknown. We performed extensive molecular dynamics (MD) simulation and analysis to find the selective allosteric binding sites of the $\mu$OR and the path opiates take to bind to the orthosteric site. In this study, we predicted that the allosteric site is responsible for the attraction and selection of opiates. Using Markov state models and machine learning, we traced the pathway of opiates in binding to the orthosteric site, the main binding pocket. Our results have important implications in designing novel analgesics.