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arXiv · 1804.08206

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

Amir Barati Farimani·Evan N. Feinberg·Vijay S. Pande
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Abstract

Many important analgesics relieve pain by binding to the $μ$-Opioid Receptor ($μ$OR), which makes the $μ$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 $μ$OR are largely unknown. We performed extensive molecular dynamics (MD) simulation and analysis to find the selective allosteric binding sites of the $μ$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.

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BibTeXRIS

Amir Barati Farimani, Evan N. Feinberg, Vijay S. Pande. 2018-04-23. Binding Pathway of Opiates to $μ$ Opioid Receptors Revealed by Unsupervised Machine Learning. https://doi.org/10.1016/j.bpj.2017.11.390

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