Funding: This research was funded by European Union's Horizon2020 Marie Skłodowska-Curie Actions (MSCA) Program under Grant Agreement 641833 (ONCORNET) and European Cooperation in Science and Technology (COST) Action CA18133 European Research Network on Signal Transduction (ERNEST). A. Inoue was funded by the Leading Advanced Projects for Medical Innovation (LEAP) JP19gm0010004 from the Japan Agency for Medical Research and Development. ; Although class A G protein−coupled receptors (GPCRs) can function as monomers, many of them form dimers and oligomers, but the mechanisms and functional relevance of such oligomerization is ill understood. Here, we investigate this problem for the CXC chemokine receptor 4 (CXCR4), a GPCR that regulates immune and hematopoietic cell trafficking, and a major drug target in cancer therapy. We combine single-molecule microscopy and fluorescence fluctuation spectroscopy to investigate CXCR4 membrane organization in living cells at densities ranging from a few molecules to hundreds of molecules per square micrometer of the plasma membrane. We observe that CXCR4 forms dynamic, transient homodimers, and that the monomer−dimer equilibrium is governed by receptor density. CXCR4 inverse agonists that bind to the receptor minor pocket inhibit CXCR4 constitutive activity and abolish receptor dimerization. A mutation in the minor binding pocket reduced the dimer-disrupting ability of these ligands. In addition, mutating critical residues in the sixth transmembrane helix of CXCR4 markedly diminished both basal activity and dimerization, supporting the notion that CXCR4 basal activity is required for dimer formation. Together, these results link CXCR4 dimerization to its density and to its activity. They further suggest that inverse agonists binding to the minor pocket suppress both dimerization and constitutive activity and may represent a specific strategy to target CXCR4. ; Publisher PDF ; Peer reviewed
G protein-coupled receptors (GPCRs) are intensively studied due to their therapeutic potential as drug targets. Members of this large family of transmembrane receptor proteins mediate signal transduction in diverse cell types and play key roles in human physiology and health. In 2013 the research consortium GLISTEN (COST Action CM1207) was founded with the goal of harnessing the substantial growth in knowledge of GPCR structure and dynamics to push forward the development of molecular modulators of GPCR function. The success of GLISTEN, coupled with new findings and paradigm shifts in the field, led in 2019 to the creation of a related consortium called ERNEST (COST Action CA18133). ERNEST broadens focus to entire signaling cascades, based on emerging ideas of how complexity and specificity in signal transduction are not determined by receptor-ligand interactions alone. A holistic approach that unites the diverse data and perspectives of the research community into a single multidimensional map holds great promise for improved drug design and therapeutic targeting. ; The authors are grateful for the continued support of the European Cooperation in Science and Technology (COST) through Actions CM1207 GLISTEN and CA18133 ERNEST. On behalf of ERNEST, M.E.S. thanks the Max Delbrück Center for Molecular Medicine Berlin for support in managing the Action. M.E.S. is supported by the Deutsche Forschungsgemeinschaft (DFG) (SO1037/1-3) and the Berlin Institute of Health (Delbrück Fellowship BIH_PRO_314). J.S. acknowledges support from the Instituto de Salud Carlos III FEDER (PI18/00094) and the ERA-NET NEURON & Ministry of Economy, Industry and Competitiveness (AC18/00030). J.C. receives funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 715052). D.E.G. is supported by the Lundbeck Foundation (R313- 2019-526) and Novo Nordisk Foundation (NNF17OC0031226). G.M.K. is funded by the National Brain Research Program (2017-1.2.1-NKP-2017-00002). M.K. acknowledges support from the Israel Science Foundation (Grants 1454/13 and 3512/19) and the DS Research Center at the University of Haifa. S.M. is supported by the Alfred Benzon Foundation (ABF-0-0-312) and Polish National Science Center (HARMONIA 2015/18/M/NZ2/00423). M.M.R. acknowledges support from the European Research Council: VIREX Grant agreement 682549, Call ERC-2105- CoG, the Independent Research Fund Denmark, the NovoNordisk Foundation (NNF17OC0029222:) and the Lundbeck Foundation (R268-2017-409). E.S. thanks the Xunta de Galicia (Centro singular de Investigacion de Galicia ́ acreditacion 2019-2022, ED431G 2019/03 and GI-1597 2017- ́ 2019 ED431B2017/70) and the European Union (European Regional Development Fund - ERDF) for financial support. J.K.S.T. acknowledges support from the DFG (HI1502/1-2) and the Novo Nordisk Foundation (Challenge Grant PRISM). N.V. is funded by grants from the Slovenian Research Agency (P3-310, J3-7605, BI-DE/18-19-015). P.K. is supported by the DFG (KO4095/4-1 and Heisenberg professorship KO4095/5- 1). All coauthors thank the stellar organizers of the eight GLISTEN meetings for their vital contributions and their associated institutes and companies for support, including the University of Warsaw (Poland), Pompeu Fabra University and Autonomous University of Barcelona (Spain), Research Centre for Natural Sciences of the Hungarian Academy of Sciences (Budapest, Hungary), Actelion Pharmaceuticals (Allschwil, Switerland), Vrije Universiteit (Amsterdam, The Netherlands), Friedrich Alexander University Erlangen and Philipps-University Marburg (Germany), University of Chemistry and Technology Prague (Czech Republic), the University of Porto (Portugal), and Sosei Heptares (Cambridge, UK). Parts of this paper are derived from the Memorandum of Understanding for the implementation of the COST Action "European Research Network on Signal Transduction" (ERNEST) CA18133.
We present a robust protocol based on iterations of free energy perturbation (FEP) calculations, chemical synthesis, biophysical mapping and X‐ray crystallography to reveal the binding mode of an antagonist series to the A2A adenosine receptor (AR). Eight A2AAR binding site mutations from biophysical mapping experiments were initially analyzed with sidechain FEP simulations, performed on alternate binding modes. The results distinctively supported one binding mode, which was subsequently used to design new chromone derivatives. Their affinities for the A2AAR were experimentally determined and investigated through a cycle of ligand‐FEP calculations, validating the binding orientation of the different chemical substituents proposed. Subsequent X‐ray crystallography of the A2AAR with a low and a high affinity chromone derivative confirmed the predicted binding orientation. The new molecules and structures here reported were driven by free energy calculations, and provide new insights on antagonist binding to the A2AAR, an emerging target in immuno‐oncology ; This work was financially supported by the Swedish Research Council (Grant 521‐2014‐2118); Consellería de Cultura, Educación e Ordenación Universitaria of the Galician Government (Grant ED431B2017/70); Centro Singular de Investigación de Galicia accreditation 2016–2019 (Grant ED431G/09), and the European Regional Development Fund (ERDF). Additional support from the Swedish strategic research program eSSENCE is acknowledged. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC). This research program has been developed in the frame of the European COST action ERNEST (Grant CA 18133) and GLISTEN (Grant CA 1207) ; SI
WOS: 000471758500010 ; PubMed ID: 31209238 ; The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; AstraZenecaAstraZeneca; European Union Horizon 2020 research [668858 PrECISE]; Joint Research Center for Computational Biomedicine (Bayer AG); National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences; Wellcome TrustWellcome Trust [102696, 206194] ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; AstraZeneca ; European Union Horizon 2020 research [668858 PrECISE] ; Joint Research Center for Computational Biomedicine (Bayer AG) ; National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences ; Wellcome Trust [102696, 206194] ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).