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Complementarity between in Silico and Biophysical Screening Approaches in Fragment-Based Lead Discovery against the A(2A) Adenosine Receptor
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
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2013 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 53, no 10, 2701-2714 p.Article in journal (Refereed) Published
Abstract [en]

Fragment-based lead discovery (FBLD) is becoming an increasingly important method in drug development. We have explored the potential to complement NMR-based biophysical screening of chemical libraries with molecular docking in FBLD against the A(2A) adenosine receptor (A(2A)AR), a drug target for inflammation and Parkinson's disease. Prior to an NMR-based screen of a fragment library against the A(2A)AR, molecular docking against a crystal structure was used to rank the same set of molecules by their predicted affinities. Molecular docking was able to predict four out of the five orthosteric ligands discovered by NMR among the top 5% of the ranked library, suggesting that structure-based methods could be used to prioritize among primary hits from biophysical screens. In addition, three fragments that were top-ranked by molecular docking, but had not been picked up by the NMR-based method, were demonstrated to be A2AAR ligands. While biophysical approaches for fragment screening are typically limited to a few thousand compounds, the docking screen was extended to include 328,000 commercially available fragments. Twenty-two top-ranked compounds were tested in radioligand binding assays, and 14 of these were A(2A)AR ligands with K-i values ranging from 2 to 240 mu M. Optimization of fragments was guided by molecular dynamics simulations and free energy calculations. The results illuminate strengths and weaknesses of molecular docking and demonstrate that this method can serve as a valuable complementary tool to biophysical screening in FBLD.

Place, publisher, year, edition, pages
2013. Vol. 53, no 10, 2701-2714 p.
National Category
Biological Sciences Theoretical Chemistry
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-97041DOI: 10.1021/ci4003156ISI: 000326480000019OAI: oai:DiVA.org:su-97041DiVA: diva2:669014
Note

AuthorCount:5;

Available from: 2013-12-02 Created: 2013-12-02 Last updated: 2017-12-06Bibliographically 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)
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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: 2017-02-24Bibliographically approved

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Ranganathan, AnirudhCarlsson, Jens
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