Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound-Kinase Activities: A Way toward Selective Promiscuity by Design?
| العنوان: | Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound-Kinase Activities: A Way toward Selective Promiscuity by Design? |
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| المؤلفون: | John P. Overington, Daniel Domine, Fanny Beltran Escudie, Alexander Roberts, Gerard J. P. van Westen, Serge Christmann-Franck, George Papadatos |
| المصدر: | Journal of Chemical Information and Modeling, 56(9), 1654-1675 Journal of Chemical Information and Modeling |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | Models, Molecular, 0301 basic medicine, Protein Conformation, General Chemical Engineering, Library and Information Sciences, Biology, computer.software_genre, Article, Substrate Specificity, 03 medical and health sciences, 0302 clinical medicine, Protein structure, Kinome, Kinase activity, Protein kinase A, Protein Kinase Inhibitors, Kinase, Drug discovery, Reproducibility of Results, Genomics, General Chemistry, Small molecule, Computer Science Applications, Cancer treatment, 030104 developmental biology, Drug Design, 030220 oncology & carcinogenesis, Data mining, Protein Kinases, computer |
| الوصف: | 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. |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c7fa043270ab809590de16d717965a8 http://hdl.handle.net/1887/133513 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....4c7fa043270ab809590de16d717965a8 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |