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SuperBiHelix method for predicting the pleiotropic ensemble of G-protein–coupled receptor conformations

Bray, Jenelle K. and Abrol, Ravinder and Goddard, William A., III and Trzaskowski, Bartosz and Scott, Caitlin E. (2014) SuperBiHelix method for predicting the pleiotropic ensemble of G-protein–coupled receptor conformations. Proceedings of the National Academy of Sciences of the United States of America, 111 (1). E72-E78. ISSN 0027-8424. PMCID PMC3890820.

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There is overwhelming evidence that G-protein–coupled receptors (GPCRs) exhibit several distinct low-energy conformations, each of which might favor binding to different ligands and/or lead to different downstream functions. Understanding the function of such proteins requires knowledge of the ensemble of low-energy configurations that might play a role in this pleiotropic functionality. We earlier reported the BiHelix method for efficiently sampling the (12)^7 = 35 million conformations resulting from 30° rotations about the axis (η) of all seven transmembrane helices (TMHs), showing that the experimental structure is reliably selected as the best conformation from this ensemble. However, various GPCRs differ sufficiently in the tilts of the TMHs that this method need not predict the optimum conformation starting from any other template. In this paper, we introduce the SuperBiHelix method in which the tilt angles (θ, φ) are optimized simultaneously with rotations (η) efficiently enough that it is practical and sufficient to sample (5 × 3 × 5)^7 = 13 trillion configurations. This method can correctly identify the optimum structure of a GPCR starting with the template from a different GPCR. We have validated this method by predicting known crystal structure conformations starting from the template of a different protein structure. We find that the SuperBiHelix conformational ensemble includes the higher energy conformations associated with the active protein in addition to those associated with the more stable inactive protein. This methodology was then applied to design and experimentally confirm structures of three mutants of the CB1 cannabinoid receptor associated with different functions.

Item Type:Article
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URLURL TypeDescription CentralArticle
Abrol, Ravinder0000-0001-7333-6793
Goddard, William A., III0000-0003-0097-5716
Additional Information:© 2013 National Academy of Sciences. Contributed by William A. Goddard III, November 18, 2013 (sent for review July 9, 2013). This work was financially supported by funds donated to the Materials and Process Simulation Center. J.K.B. acknowledges the Department of Energy Computational Science Graduate Fellowship. The computers used were funded by grants from Defense University Research Instrumentation Program and by National Science Foundation (equipment part of Materials Research Science and Engineering Center). Author contributions: J.K.B., R.A., and W.A.G. designed research; J.K.B., R.A., and C.E.S. performed research; B.T. contributed new reagents/analytic tools; J.K.B., R.A., W.A.G., B.T., and C.E.S. analyzed data; and J.K.B., R.A., W.A.G., and C.E.S. wrote the paper. The authors declare no conflict of interest.
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Caltech Beckman InstituteUNSPECIFIED
Department of Energy (DOE)UNSPECIFIED
Army Research Office (ARO)UNSPECIFIED
Office of Naval Research (ONR)UNSPECIFIED
Subject Keywords:protein structure prediction; A2A adenosine receptor; β2-adrenergic receptor; constitutive activity; functional selectivity
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Issue or Number:1
PubMed Central ID:PMC3890820
Record Number:CaltechAUTHORS:20140109-105957725
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:43294
Deposited By: Tony Diaz
Deposited On:09 Jan 2014 22:15
Last Modified:03 Oct 2019 06:06

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