CaltechAUTHORS
  A Caltech Library Service

Computational Prediction of the Class a GPCR Active State Conformations

Dong, Sijia S. and Abrol, Ravinder and Goddard, William A., III (2014) Computational Prediction of the Class a GPCR Active State Conformations. Biophysical Journal, 106 (2). 308A. ISSN 0006-3495. http://resolver.caltech.edu/CaltechAUTHORS:20140710-092918920

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20140710-092918920

Abstract

There has been a great need for computational prediction of G-protein coupled receptor (GPCR) structures, especially those in their active states, in order to assist drug discovery. However, the active state conformation prediction is challenging not only because of the lack of homology templates derived from the existing active state X-ray structures, but also because of the high energy nature of the active conformations. In addition, experimental evidence suggests that a GPCR can have many different active states, but the existing several crystal structures usually only capture one of those states for each GPCR. Here we present a method to make the GPCR active state structural prediction possible, and our method can discover a number of active states for each GPCR. Instead of the traditional homology modeling, we used a template from mixed sources and sampled a discrete set of orientations of the seven transmembrane helices of the GPCR to locate structures that are likely to be in the active-state valley on the energy surface. Next, we did a local conformational sampling to find structures at the local minima of the active-state potential energy valleys. We have benchmarked the method with human β_2 adrenergic receptor, which has both its active and inactive state structures crystalized. Then we applied the method on a GPCR with unknown structure, the human somatostatin receptor subtype 5 (hSSTR5). Docking of agonists and antagonists to the predicted active and inactive state structures of hSSTR5 gave the expected result that antagonists favor the inactive state structures, while the agonists could not distinguish the inactive and active state structures without the presence of G proteins. In the end, we were able to build a model picture of hSSTR5 function consistent with experimental findings.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1016/j.bpj.2013.11.1786DOIArticle
http://www.sciencedirect.com/science/article/pii/S0006349513030440PublisherArticle
http://www.biophysics.org/2014meeting/Main/tabid/4177/Default.aspxOrganizationConference Website
ORCID:
AuthorORCID
Abrol, Ravinder0000-0001-7333-6793
Goddard, William A., III0000-0003-0097-5716
Additional Information:© 2014 Biophysical Society. Published by Elsevier Inc.
Record Number:CaltechAUTHORS:20140710-092918920
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20140710-092918920
Official Citation:Sijia S. Dong, Ravinder Abrol, William A. Goddard III, Computational Prediction of the Class a GPCR Active State Conformations, Biophysical Journal, Volume 106, Issue 2, Supplement 1, 28 January 2014, Page 308a, ISSN 0006-3495, http://dx.doi.org/10.1016/j.bpj.2013.11.1786. (http://www.sciencedirect.com/science/article/pii/S0006349513030440)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:47128
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:10 Jul 2014 19:37
Last Modified:15 Sep 2017 18:39

Repository Staff Only: item control page