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Fragment-Based Discovery of Subtype-Selective Adenosine Receptor Ligands from Homology Models
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Number of Authors: 4
2015 (English)In: Journal of Medicinal Chemistry, ISSN 0022-2623, E-ISSN 1520-4804, Vol. 58, no 24, 9578-9590 p.Article in journal (Refereed) Published
Abstract [en]

Fragment-based lead discovery (FBLD) holds great promise for drug discovery, but applications to G protein-coupled receptors (GPCRs) have been limited by a lack of sensitive screening techniques and scarce structural information. If virtual screening against homology models of GPCRs could be used to identify fragment ligands, FBLD could be extended to numerous important drug targets and contribute to efficient lead generation. Access to models of multiple receptors may further enable the discovery of fragments that bind specifically to the desired target. to investigate these questions, we used molecular docking, to screen >500 000 fragments against homology models. of the A(3) and A(1) adenosine receptors (ARs) with the goal to discover,A(3)AR-selective ligands. Twenty-one fragments with predicted A(3)AR-specific binding were evaluated in live-cell fluorescence-based assays; of eight verified ligands, six displayed A(3)/A(1), selectivity,, and three of these had high affinities ranging from 0.1 to 1.3 mu M. Subsequently, structure-guided fragment-to-lead optimization led to the identification of a >100-fold-selective antagonist with nanomolar affinity from commercial libraries. These results highlight that molecular docking screening can guide fragment-based discovery of selective ligands even if the Structures of both the target and antitarget receptors are unknown. The same approach can be readily extended to a large number of pharmaceutically important targets.

Place, publisher, year, edition, pages
2015. Vol. 58, no 24, 9578-9590 p.
National Category
Biochemistry and Molecular Biology Medicinal Chemistry
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-126393DOI: 10.1021/acs.jmedchem.5b01120ISI: 000367563100011PubMedID: 26592528OAI: oai:DiVA.org:su-126393DiVA: diva2:901841
Available from: 2016-02-09 Created: 2016-02-01 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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Ranganathan, Anirudh
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