title: Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound-Kinase Activities: A Way toward Selective Promiscuity by Design?
abstract: Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand-target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile.
authors.affiliation: Merck Serono , Chemin des Mines 9, 1202 Genève, Switzerland.
authors.forename: Serge
authors.initials: S
authors.lastname: Christmann-Franck
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus , Hinxton, Cambridgeshire CB10 1SD, U.K.
authors.forename: Gerard J P
authors.initials: GJ
authors.lastname: van Westen
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus , Hinxton, Cambridgeshire CB10 1SD, U.K.
authors.forename: George
authors.initials: G
authors.lastname: Papadatos
authors.affiliation: Merck Serono , Chemin des Mines 9, 1202 Genève, Switzerland.
authors.forename: Fanny
authors.initials: F
authors.lastname: Beltran Escudie
authors.affiliation: Merck Serono , Chemin des Mines 9, 1202 Genève, Switzerland.
authors.forename: Alexander
authors.initials: A
authors.lastname: Roberts
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus , Hinxton, Cambridgeshire CB10 1SD, U.K.
authors.forename: John P
authors.initials: JP
authors.lastname: Overington
authors.affiliation: Merck Serono , Chemin des Mines 9, 1202 Genève, Switzerland.
authors.forename: Daniel
authors.initials: D
authors.lastname: Domine
mesh_terms: D015195:Drug Design; D023281:Genomics; D008958:Models, Molecular; D011487:Protein Conformation; D047428:Protein Kinase Inhibitors / Q000378:metabolism / Q000494:pharmacology*; D011494:Protein Kinases / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D015203:Reproducibility of Results; D013379:Substrate Specificity
chemical_list: D047428:Protein Kinase Inhibitors; D011494:Protein Kinases