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Strategies for Improved Modeling of GPCR-Drug Complexes: Blind Predictions of Serotonin Receptors Bound to Ergotamine
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center (SeRC), Sweden.
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center (SeRC), Sweden.
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center (SeRC), Sweden.
2014 (engelsk)Inngår i: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 54, nr 7, s. 2004-2021Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2014. Vol. 54, nr 7, s. 2004-2021
HSV kategori
Forskningsprogram
biokemi med inriktning mot bioinformatik
Identifikatorer
URN: urn:nbn:se:su:diva-107045DOI: 10.1021/ci5002235ISI: 000339647000017OAI: oai:DiVA.org:su-107045DiVA, id: diva2:742999
Merknad

AuthorCount:3;

Tilgjengelig fra: 2014-09-03 Laget: 2014-09-02 Sist oppdatert: 2017-12-05bibliografisk kontrollert
Inngår i avhandling
1. The impact of GPCR structures on understanding receptor function and ligand binding
Åpne denne publikasjonen i ny fane eller vindu >>The impact of GPCR structures on understanding receptor function and ligand binding
2016 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2016. s. 56
HSV kategori
Forskningsprogram
biokemi med inriktning mot bioinformatik
Identifikatorer
urn:nbn:se:su:diva-129879 (URN)978-91-7649-431-8 (ISBN)
Disputas
2016-06-09, William-Olssonsalen, Geovetenskapens hus, Svante Arrhenius väg 8, Stockholm, 10:00 (engelsk)
Opponent
Veileder
Merknad

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.

Tilgjengelig fra: 2016-05-17 Laget: 2016-05-02 Sist oppdatert: 2017-02-24bibliografisk kontrollert

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