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

Plante, Ambrose; Shore, Derek M.; Morra, Giulia; Khelashvili, George; Weinstein, Harel

doi:10.3390/molecules24112097

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

Journal Article · Sat Jun 01 00:00:00 EDT 2019 · Molecules

DOI:https://doi.org/10.3390/molecules24112097· OSTI ID:1628488

Plante, Ambrose ^[1]; ^[2]; Morra, Giulia ^[3]; ^[4]; ^[4]

Cornell Univ., Ithaca, NY (United States). Weill Medical College; DOE/OSTI
Cornell Univ., Ithaca, NY (United States). Weill Medical College
Cornell Univ., Ithaca, NY (United States). Weill Medical College; Consiglio Nazionale dell Ricerche, Milano (Italy). ICRM
Cornell Univ., Ithaca, NY (United States). Weill Medical College; Cornell Univ., Ithaca, NY (United States). Weill Medical College. Dept. of Physiology and Biophysics. Inst. for Computational Biomedicine

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-HT_2A 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 Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1628488

Journal Information:: Molecules, Journal Name: Molecules Journal Issue: 11 Vol. 24; ISSN 1420-3049

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs

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