Publications by authors named "Brandon J Bongers"

4 Publications

  • Page 1 of 1

Characterization of cancer-related somatic mutations in the adenosine A receptor.

Eur J Pharmacol 2020 Aug 26;880:173126. Epub 2020 Apr 26.

Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, 2333 CC, Leiden, the Netherlands. Electronic address:

In cancer, G protein-coupled receptors (GPCRs) are involved in tumor progression and metastasis. In this study we particularly examined one GPCR, the adenosine A receptor. This receptor is activated by high concentrations of its endogenous ligand adenosine, which suppresses the immune response to fight tumor progression. A series of adenosine A receptor mutations were retrieved from the Cancer Genome Atlas harboring data from patient samples with different cancer types. The main goal of this work was to investigate the pharmacology of these mutant receptors using a 'single-GPCR-one-G protein' yeast assay technology. Concentration-growth curves were obtained with the full agonist NECA for the wild-type receptor and 15 mutants. Compared to wild-type receptor, the constitutive activity levels in mutant receptors F141L, Y202C and L310P were high, while the potency and efficacy of NECA and BAY 60-6583 on Y202C was lower. A 33- and 26-fold higher constitutive activity on F141L and L310P was reduced to wild-type levels in response to the inverse agonist ZM241385. These constitutively active mutants may thus be tumor promoting. Mutant receptors F259S and Y113F showed a more than one log-unit decrease in potency. A complete loss of activation was observed in mutant receptors C29R, W130C and P249L. All mutations were characterized at the structural level, generating hypotheses of their roles on modulating the receptor conformational equilibrium. Taken together, this study is the first to investigate the nature of adenosine A receptor cancer mutations and may thus provide insights in mutant receptor function in cancer.
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http://dx.doi.org/10.1016/j.ejphar.2020.173126DOI Listing
August 2020

Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors.

J Chem Inf Model 2020 09 12;60(9):4283-4295. Epub 2020 May 12.

Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.

Kinases are frequently studied in the context of anticancer drugs. Their involvement in cell responses, such as proliferation, differentiation, and apoptosis, makes them interesting subjects in multitarget drug design. In this study, a workflow is presented that models the bioactivity spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC, and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based models were included, which were thoroughly benchmarked and optimized. A virtual screening was performed to test the workflow for one of the main targets, RET kinase. This resulted in 5 novel and chemically dissimilar RET inhibitors with remaining RET activity of <60% (at a concentration of 10 μM) and similarities with known RET inhibitors from 0.18 to 0.29 (Tanimoto, ECFP6). The four more potent inhibitors were assessed in a concentration range and proved to be modestly active with a pIC value of 5.1 for the most active compound. The experimental validation of inhibitors for RET strongly indicates that the multitarget workflow is able to detect novel inhibitors for kinases, and hence, this workflow can potentially be applied in polypharmacology modeling. We conclude that this approach can identify new chemical matter for existing targets. Moreover, this workflow can easily be applied to other targets as well.
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http://dx.doi.org/10.1021/acs.jcim.9b01204DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7525794PMC
September 2020

The RESOLUTE consortium: unlocking SLC transporters for drug discovery.

Authors:
Giulio Superti-Furga Daniel Lackner Tabea Wiedmer Alvaro Ingles-Prieto Barbara Barbosa Enrico Girardi Ulrich Goldmann Bettina Gürtl Kristaps Klavins Christoph Klimek Sabrina Lindinger Eva Liñeiro-Retes André C Müller Svenja Onstein Gregor Redinger Daniela Reil Vitaly Sedlyarov Gernot Wolf Matthew Crawford Robert Everley David Hepworth Shenping Liu Stephen Noell Mary Piotrowski Robert Stanton Hui Zhang Salvatore Corallino Andrea Faedo Maria Insidioso Giovanna Maresca Loredana Redaelli Francesca Sassone Lia Scarabottolo Michela Stucchi Paola Tarroni Sara Tremolada Helena Batoulis Andreas Becker Eckhard Bender Yung-Ning Chang Alexander Ehrmann Anke Müller-Fahrnow Vera Pütter Diana Zindel Bradford Hamilton Martin Lenter Diana Santacruz Coralie Viollet Charles Whitehurst Kai Johnsson Philipp Leippe Birgit Baumgarten Lena Chang Yvonne Ibig Martin Pfeifer Jürgen Reinhardt Julian Schönbett Paul Selzer Klaus Seuwen Charles Bettembourg Bruno Biton Jörg Czech Hélène de Foucauld Michel Didier Thomas Licher Vincent Mikol Antje Pommereau Frédéric Puech Veeranagouda Yaligara Aled Edwards Brandon J Bongers Laura H Heitman Ad P IJzerman Huub J Sijben Gerard J P van Westen Justine Grixti Douglas B Kell Farah Mughal Neil Swainston Marina Wright-Muelas Tina Bohstedt Nicola Burgess-Brown Liz Carpenter Katharina Dürr Jesper Hansen Andreea Scacioc Giulia Banci Claire Colas Daniela Digles Gerhard Ecker Barbara Füzi Viktoria Gamsjäger Melanie Grandits Riccardo Martini Florentina Troger Patrick Altermatt Cédric Doucerain Franz Dürrenberger Vania Manolova Anna-Lena Steck Hanna Sundström Maria Wilhelm Claire M Steppan

Nat Rev Drug Discov 2020 07;19(7):429-430

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http://dx.doi.org/10.1038/d41573-020-00056-6DOI Listing
July 2020

Proteochemometrics - recent developments in bioactivity and selectivity modeling.

Drug Discov Today Technol 2019 Dec 20;32-33:89-98. Epub 2020 Sep 20.

Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands. Electronic address:

Proteochemometrics is a machine learning based modeling approach relying on a combination of ligand and protein descriptors. With ongoing developments in machine learning and increases in public data the technique is more frequently applied in early drug discovery, typically in ligand-target binding prediction. Common applications include improvements to single target quantitative structure-activity relationship models, protein selectivity and promiscuity modeling, and large-scale deep learning approaches. The increase in predictive power using proteochemometrics is observed in multi-target bioactivity modeling, opening the door to more extensive studies covering whole protein families. On top of that, with deep learning fueling more complex and larger scale models, proteochemometrics allows faster and higher quality computational models supporting the design, make, test cycle.
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http://dx.doi.org/10.1016/j.ddtec.2020.08.003DOI Listing
December 2019