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The impact of GPCR structures on understanding receptor function and ligand binding
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
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: urn:nbn:se:su:diva-129879ISBN: 978-91-7649-431-8 (print)OAI: oai:DiVA.org:su-129879DiVA: diva2:925618
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: 2017-02-24Bibliographically approved
List of papers
1. Complementarity between in Silico and Biophysical Screening Approaches in Fragment-Based Lead Discovery against the A(2A) Adenosine Receptor
Open this publication in new window or tab >>Complementarity between in Silico and Biophysical Screening Approaches in Fragment-Based Lead Discovery against the A(2A) Adenosine Receptor
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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.

National Category
Biological Sciences Theoretical Chemistry
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-97041 (URN)10.1021/ci4003156 (DOI)000326480000019 ()
Note

AuthorCount:5;

Available from: 2013-12-02 Created: 2013-12-02 Last updated: 2017-12-06Bibliographically approved
2. Insights into the Role of Asp79(2.50) in beta(2) Adrenergic Receptor Activation from Molecular Dynamics Simulations
Open this publication in new window or tab >>Insights into the Role of Asp79(2.50) in beta(2) Adrenergic Receptor Activation from Molecular Dynamics Simulations
2014 (English)In: Biochemistry, ISSN 0006-2960, E-ISSN 1520-4995, Vol. 53, no 46, 7283-7296 p.Article in journal (Refereed) Published
Abstract [en]

Achieving a molecular-level understanding of G-protein-coupled receptor (GPCR) activation has been a long-standing goal in biology and could be important for the development of novel drugs. Recent breakthroughs in structural biology have led to the determination of high-resolution crystal structures for the beta(2) adrenergic receptor (beta(2)AR) in inactive and active states, which provided an unprecedented opportunity to understand receptor signaling at the atomic level. We used molecular dynamics (MD) simulations to explore the potential roles of ionizable residues in beta(2)AR activation. One such residue is the strongly conserved Asp79(2.50), which is buried in a transmembrane cavity and becomes dehydrated upon beta(2)AR activation. MD free energy calculations based on beta(2)AR crystal structures suggested an increase in the population of the protonated state of Asp79(2.50) upon activation, which may contribute to the experimentally observed pH-dependent activation of this receptor. Analysis of MD simulations (in total >100 mu s) with two different protonation states further supported the conclusion that the protonated Asp79(2.50) shifts the conformation of the beta(2)AR toward more active-like states. On the basis of our calculations and analysis of other GPCR crystal structures, we suggest that the protonation state of Asp(2.50) may act as a functionally important microswitch in the activation of the beta(2)AR and other class A receptors.

National Category
Biological Sciences Theoretical Chemistry
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-111913 (URN)10.1021/bi5008723 (DOI)000345551800013 ()
Note

AuthorCount:3;

Available from: 2015-01-12 Created: 2015-01-08 Last updated: 2017-12-05Bibliographically approved
3. Fragment-Based Discovery of Subtype-Selective Adenosine Receptor Ligands from Homology Models
Open this publication in new window or tab >>Fragment-Based Discovery of Subtype-Selective Adenosine Receptor Ligands from Homology Models
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.

National Category
Biochemistry and Molecular Biology Medicinal Chemistry
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-126393 (URN)10.1021/acs.jmedchem.5b01120 (DOI)000367563100011 ()26592528 (PubMedID)
Available from: 2016-02-09 Created: 2016-02-01 Last updated: 2017-11-30Bibliographically approved
4. Strategies for Improved Modeling of GPCR-Drug Complexes: Blind Predictions of Serotonin Receptors Bound to Ergotamine
Open this publication in new window or tab >>Strategies for Improved Modeling of GPCR-Drug Complexes: Blind Predictions of Serotonin Receptors Bound to Ergotamine
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.

National Category
Biological Sciences Theoretical Chemistry
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-107045 (URN)10.1021/ci5002235 (DOI)000339647000017 ()
Note

AuthorCount:3;

Available from: 2014-09-03 Created: 2014-09-02 Last updated: 2017-12-05Bibliographically approved

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