A Target Class Ligandability Evaluation of WD40 Repeat-Containing Proteins

Target class-focused drug discovery has a strong track record in pharmaceutical research, yet public domain data indicate that many members of protein families remain unliganded. Here we present a systematic approach to scale up the discovery and characterization of small molecule ligands for the WD40 repeat (WDR) protein family. We developed a comprehensive suite of protocols for protein production, crystallography, and biophysical, biochemical, and cellular assays. A pilot hit-finding campaign using DNA-encoded chemical library selection followed by machine learning (DEL-ML) to predict ligands from virtual libraries yielded first-in-class, drug-like ligands for 7 of the 16 WDR domains screened, thus demonstrating the broader ligandability of WDRs. This study establishes a template for evaluation of protein family wide ligandability and provides an extensive resource of WDR protein biochemical and chemical tools, knowledge, and protocols to discover potential therapeutics for this highly disease-relevant, but underexplored target class.

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Publication Details

Journal
Journal of Medicinal Chemistry
Published
2024-11-04
DOI
https://doi.org/10.1021/acs.jmedchem.4c02010
Citations
18
Primary Topic
Signaling Pathways in Disease
Type
article
Field-Weighted Citation Impact
4.21

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A Target Class Ligandability Evaluation of WD40 Repeat-Containing Proteins

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A Target Class Ligandability Evaluation of WD40 Repeat-Containing Proteins

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article en
18 citations

Abstract

Target class-focused drug discovery has a strong track record in pharmaceutical research, yet public domain data indicate that many members of protein families remain unliganded. Here we present a systematic approach to scale up the discovery and characterization of small molecule ligands for the WD40 repeat (WDR) protein family. We developed a comprehensive suite of protocols for protein production, crystallography, and biophysical, biochemical, and cellular assays. A pilot hit-finding campaign using DNA-encoded chemical library selection followed by machine learning (DEL-ML) to predict ligands from virtual libraries yielded first-in-class, drug-like ligands for 7 of the 16 WDR domains screened, thus demonstrating the broader ligandability of WDRs. This study establishes a template for evaluation of protein family wide ligandability and provides an extensive resource of WDR protein biochemical and chemical tools, knowledge, and protocols to discover potential therapeutics for this highly disease-relevant, but underexplored target class.

Journal of Medicinal ChemistryVol. 68(2)
Google (United States) (US), University of Toronto (CA), York University (CA), Princess Margaret Cancer Centre (CA), Structural Genomics Consortium (CA), Relay Therapeutics (United States) (US), Cara Therapeutics (United States) (US)
U.S. Department of Energy, Boehringer Ingelheim, Bristol-Myers Squibb, Pfizer, Bayer, McGill University, Genome Canada, Merck KGaA, Ontario Genomics, European Federation of Pharmaceutical Industries and Associations, Ontario Genomics Institute, University of Toronto, Innovative Medicines Initiative, National Institutes of Health, Genentech, Office of Science, Bristol-Myers Squibb Canada, Takeda Canada, National Institute of General Medical Sciences, Janssen Canada, Office of Research Infrastructure Programs, National Institutes of Health, Argonne National Laboratory
Openalex Percentile: Top 5%
Signaling Pathways in Disease
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