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Article

A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs

1
Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10065, USA
2
ICRM, Consiglio Nazionale delle Ricerche, 20131 Milano, Italy
3
Department of Physiology and Biophysics & Institute for Computational Biomedicine, Weill Cornell Medical College, NY 10065, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Irina S. Moreira
Molecules 2019, 24(11), 2097; https://doi.org/10.3390/molecules24112097
Received: 19 April 2019 / Revised: 23 May 2019 / Accepted: 30 May 2019 / Published: 2 June 2019
(This article belongs to the Special Issue GPCR Mechanism and Drug Design)
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity. View Full-Text
Keywords: functional selectivity; biased ligands; molecular dynamics; deep neural networks; sensitivity analysis; pharmacological efficacy functional selectivity; biased ligands; molecular dynamics; deep neural networks; sensitivity analysis; pharmacological efficacy
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MDPI and ACS Style

Plante, A.; Shore, D.M.; Morra, G.; Khelashvili, G.; Weinstein, H. A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs. Molecules 2019, 24, 2097. https://doi.org/10.3390/molecules24112097

AMA Style

Plante A, Shore DM, Morra G, Khelashvili G, Weinstein H. A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs. Molecules. 2019; 24(11):2097. https://doi.org/10.3390/molecules24112097

Chicago/Turabian Style

Plante, Ambrose, Derek M. Shore, Giulia Morra, George Khelashvili, and Harel Weinstein. 2019. "A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs" Molecules 24, no. 11: 2097. https://doi.org/10.3390/molecules24112097

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