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Strategies for Improved Modeling of GPCR-Drug Complexes: Blind Predictions of Serotonin Receptors Bound to Ergotamine
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center (SeRC), Sweden.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center (SeRC), Sweden.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center (SeRC), Sweden.
2014 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 54, no 7, 2004-2021 p.Article in journal (Refereed) Published
Abstract [en]

The recent increase in the number of atomic-resolution structures of G protein-coupled receptors (GPCRs) has contributed to a deeper understanding of ligand binding to several important drug targets. However, reliable modeling of GPCR-ligand complexes for the vast majority of receptors with unknown structure remains to be one of the most challenging goals for computer-aided drug design. The GPCR Dock 2013 assessment, in which researchers were challenged to predict the crystallographic structures of serotonin 5-HT1B and 5-HT2B receptors bound to ergotamine, provided an excellent opportunity to benchmark the current state of this field. Our contributions to GPCR Dock 2013 accurately predicted the binding mode of ergotamine with RMSDs below 1.8 angstrom for both receptors, which included the best submissions for the S-HT1B complex. Our models also had the most accurate description of the binding sites and receptor ligand contacts. These results were obtained using a ligand-guided homology modeling approach, which combines extensive molecular docking screening with incorporation of information from multiple crystal structures and experimentally derived restraints. In this work, we retrospectively analyzed thousands of structures that were generated during the assessment to evaluate our modeling strategies. Major contributors to accuracy were found to be improved modeling of extracellular loop two in combination with the use of molecular docking to optimize the binding site for ligand recognition. Our results suggest that modeling of GPCR-drug complexes has reached a level of accuracy at which structure-based drug design could be applied to a large number of pharmaceutically relevant targets.

Place, publisher, year, edition, pages
2014. Vol. 54, no 7, 2004-2021 p.
National Category
Biological Sciences Theoretical Chemistry
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-107045DOI: 10.1021/ci5002235ISI: 000339647000017OAI: oai:DiVA.org:su-107045DiVA: diva2:742999
Note

AuthorCount:3;

Available from: 2014-09-03 Created: 2014-09-02 Last updated: 2016-05-04Bibliographically approved
In thesis
1. The impact of GPCR structures on understanding receptor function and ligand binding
Open this publication in new window or tab >>The impact of GPCR structures on understanding receptor function and ligand binding
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

G protein-coupled receptors (GPCRs) form the largest superfamily of eukaryotic membrane proteins and are responsible for the action of nearly 30% of all marketed drugs. For a long period, efforts to study these receptors were limited by the paucity of atomic-resolution structural information. Numerous receptors spread across the GPCR superfamily have recently been crystallized, revealing crucial clues about receptor function and ligand recognition. The work in this thesis has primarily focused on using computational techniques to capitalize on this increasing amount of structural information. In papers I, II, and III protocols were developed to identify novel ligands for pharmaceutically important targets from in silico screens of large chemical libraries. In these papers, the fragment-based lead discovery (FBLD) approach was evaluated for GPCR targets using molecular docking screens. The high hit-rates obtained in these studies indicate promise for the use of computational approaches for fragment screening. In paper IV, molecular dynamics was used to identify a possible role for a conserved ionizable residue (Asp792.50) as a protonation switch during the activation process of the β2 adrenergic receptor. Analyses from this paper indicated that this residue could also perform a similar function in other class A GPCRs. Papers V and VI detail the modeling strategy followed during the GPCR Dock 2013 assessment to blindly predict the structure of two serotonin receptor subtypes (5-HT1B and 5-HT2B) bound to ergotamine. The developed ligand-steered homology modeling protocol was largely successful resulting in the best-ranked predictions for the 5-HT1B subtype. It is hoped that the work described in this thesis has highlighted the potential for structure-based computational approaches to identify novel ligands for important pharmaceutical targets and improve understanding of GPCR function.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2016. 56 p.
National Category
Biological Sciences Theoretical Chemistry
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-129879 (URN)978-91-7649-431-8 (ISBN)
Public defence
2016-06-09, William-Olssonsalen, Geovetenskapens hus, Svante Arrhenius väg 8, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.

Available from: 2016-05-17 Created: 2016-05-02 Last updated: 2016-05-19Bibliographically approved

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