Literature DB >> 29364664

Binding-Site Compatible Fragment Growing Applied to the Design of β2-Adrenergic Receptor Ligands.

Florent Chevillard1, Helena Rimmer2, Cecilia Betti3, Els Pardon4,5, Steven Ballet3, Niek van Hilten1, Jan Steyaert4,5, Wibke E Diederich2, Peter Kolb1.   

Abstract

Fragment-based drug discovery is intimately linked to fragment extension approaches that can be accelerated using software for de novo design. Although computers allow for the facile generation of millions of suggestions, synthetic feasibility is however often neglected. In this study we computationally extended, chemically synthesized, and experimentally assayed new ligands for the β2-adrenergic receptor (β2AR) by growing fragment-sized ligands. In order to address the synthetic tractability issue, our in silico workflow aims at derivatized products based on robust organic reactions. The study started from the predicted binding modes of five fragments. We suggested a total of eight diverse extensions that were easily synthesized, and further assays showed that four products had an improved affinity (up to 40-fold) compared to their respective initial fragment. The described workflow, which we call "growing via merging" and for which the key tools are available online, can improve early fragment-based drug discovery projects, making it a useful creative tool for medicinal chemists during structure-activity relationship (SAR) studies.

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Year:  2018        PMID: 29364664     DOI: 10.1021/acs.jmedchem.7b01558

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  7 in total

1.  Interrogating dense ligand chemical space with a forward-synthetic library.

Authors:  Florent Chevillard; Silvia Stotani; Anna Karawajczyk; Stanimira Hristeva; Els Pardon; Jan Steyaert; Dimitrios Tzalis; Peter Kolb
Journal:  Proc Natl Acad Sci U S A       Date:  2019-05-21       Impact factor: 11.205

2.  Structure-based development of a subtype-selective orexin 1 receptor antagonist.

Authors:  Jan Hellmann; Matthäus Drabek; Jie Yin; Jakub Gunera; Theresa Pröll; Frank Kraus; Christopher J Langmead; Harald Hübner; Dorothee Weikert; Peter Kolb; Daniel M Rosenbaum; Peter Gmeiner
Journal:  Proc Natl Acad Sci U S A       Date:  2020-07-15       Impact factor: 11.205

Review 3.  Finding the Perfect Fit: Conformational Biosensors to Determine the Efficacy of GPCR Ligands.

Authors:  Keith M Olson; Andra Campbell; Andrew Alt; John R Traynor
Journal:  ACS Pharmacol Transl Sci       Date:  2022-08-14

Review 4.  Machine Learning and Computational Chemistry for the Endocannabinoid System.

Authors:  Kenneth Atz; Wolfgang Guba; Uwe Grether; Gisbert Schneider
Journal:  Methods Mol Biol       Date:  2023

5.  Turning Nonselective Inhibitors of Type I Protein Arginine Methyltransferases into Potent and Selective Inhibitors of Protein Arginine Methyltransferase 4 through a Deconstruction-Reconstruction and Fragment-Growing Approach.

Authors:  Giulia Iannelli; Ciro Milite; Nils Marechal; Vincent Cura; Luc Bonnefond; Nathalie Troffer-Charlier; Alessandra Feoli; Donatella Rescigno; Yalong Wang; Alessandra Cipriano; Monica Viviano; Mark T Bedford; Jean Cavarelli; Sabrina Castellano; Gianluca Sbardella
Journal:  J Med Chem       Date:  2022-04-28       Impact factor: 8.039

Review 6.  In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Authors:  Lauro Ribeiro de Souza Neto; José Teófilo Moreira-Filho; Bruno Junior Neves; Rocío Lucía Beatriz Riveros Maidana; Ana Carolina Ramos Guimarães; Nicholas Furnham; Carolina Horta Andrade; Floriano Paes Silva
Journal:  Front Chem       Date:  2020-02-18       Impact factor: 5.221

7.  Selective targeting of ligand-dependent and -independent signaling by GPCR conformation-specific anti-US28 intrabodies.

Authors:  Timo W M De Groof; Nick D Bergkamp; Raimond Heukers; Truc Giap; Maarten P Bebelman; Richard Goeij-de Haas; Sander R Piersma; Connie R Jimenez; K Christopher Garcia; Hidde L Ploegh; Marco Siderius; Martine J Smit
Journal:  Nat Commun       Date:  2021-07-16       Impact factor: 17.694

  7 in total

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