What general conclusions can we draw from kinase profiling data sets?

Sutherland, Jeffrey J.; Gao, Canzhu; Cahya, Suntara; Vieth, Michał

doi:10.1016/j.bbapap.2012.12.023

Cited by 29 publications

(49 citation statements)

References 28 publications

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“…Furthermore, whether there even is a selectivity advantage of type II inhibitors over type I inhibitors has been questioned as well 29,41 . While the results of the new biochemical profiling presented in this study suggest that there is a selectivity advantage of type II inhibitors over type I inhibitors, a much larger set of type II inhibitors will need to be profiled to place this conclusion on firmer grounds.…”

Section: Resultsmentioning

confidence: 99%

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Conformational Analysis of the DFG-Out Kinase Motif and Biochemical Profiling of Structurally Validated Type II Inhibitors

et al. 2014

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Structural coverage of the human kinome has been steadily increasing over time. The structures provide valuable insights into the molecular basis of kinase function and also provide a foundation for understanding the mechanisms of kinase inhibitors. There are a large number of kinase structures in the PDB for which the Asp and Phe of the DFG motif on the activation loop swap positions, resulting in the formation of a new allosteric pocket. We refer to these structures as “classical DFG-out” conformations in order to distinguish them from conformations which have also been referred to as DFG-out in the literature but which do not have a fully formed allosteric pocket. We have completed a structural analysis of almost two hundred small molecule inhibitors bound to classical DFG-out conformations; we find that they are recognized by both type I and type II inhibitors. In contrast, we find that non-classical DFG-out conformations strongly select against type II inhibitors because these structures have not formed a large enough allosteric pocket to accommodate this type of binding mode. In the course of this study we discovered that the number of structurally validated type II inhibitors that can be found in the PDB and that are also represented in publicly available biochemical profiling studies of kinase inhibitors is very small. We have obtained new profiling results for several additional structurally validated type II inhibitors identified through our conformational analysis. Although the available profiling data for type II inhibitors is still much smaller than for type I inhibitors, a comparison of the two datasets supports the conclusion that type II inhibitors are more selective than type I. We comment on the possible contribution of the DFG-in to DFG-out conformational reorganization to the selectivity.

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Section: Resultsmentioning

confidence: 99%

“…41 With this in mind, we have not attempted to aggregate the results of different profiling studies.…”

Section: Results and Discussionmentioning

confidence: 99%

Conformational Analysis of the DFG-Out Kinase Motif and Biochemical Profiling of Structurally Validated Type II Inhibitors

et al. 2014

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“…11 We found a relatively low level of concordance between different profiling results that, in turn, affected the general kinase and inhibitor space conclusions drawn from the data. Here, we extend this work to evaluate the concordance of computational models and experimental data, and we compare it to the concordance between experimental results from different sources.…”

Section: Introductionmentioning

confidence: 86%

“…If the prefixes are not eliminated before the comparison (e.g., >1 μM is considered to be the same as >5 μM and therefore within 3-fold), then this value becomes 77, which was used in our previous work. 11 Similar to the two experimental data sets, the concordance between the predicted activities and experimental data was assessed for all 1466 collated kinase-inhibitor pairs common between the Lilly and Metz data sets. It is worth noting that certain compound activities contain qualifiers (i.e., experimental data that are reported to be below or above the threshold values).…”

Section: Theory and Methodsmentioning

confidence: 99%

Selectivity Data: Assessment, Predictions, Concordance, and Implications

et al. 2013

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Could high-quality in silico predictions in drug discovery eventually replace part or most of experimental testing? To evaluate the agreement of selectivity data from different experimental or predictive sources, we introduce the new metric concordance minimum significant ratio (cMSR). Empowered by cMSR, we find the overall level of agreement between predicted and experimental data to be comparable to that found between experimental results from different sources. However, for molecules that are either highly selective or potent, the concordance between different experimental sources is significantly higher than the concordance between experimental and predicted values. We also show that computational models built from one data set are less predictive for other data sources and highlight the importance of bias correction for assessing selectivity data. Finally, we show that small-molecule target space relationships derived from different data sources and predictive models share overall similarity but can significantly differ in details.

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“…This observation pointed to a need to dig deeper into whether the actives could indeed point to privileged chemotypes for these MTases, in a manner similar to the pan-gene family activity of some kinase inhibitors. 26 Thus, we turned our attention to the compounds that appeared to be specific for MTases and not the PDE or NHR targets.…”

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confidence: 99%

Beyond PAINs: Chemotype Sensitivity of Protein Methyltransferases in Screens

Gao

Margolis

Strelow

et al. 2015

ACS Med. Chem. Lett.

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Screening of the relatively new target class, the lysine and arginine methyltransferases (MTases), presents unique challenges in the identification and confirmation of active chemical matter. Examination of high throughput screening data generated using Scintillation Proximity Assay (SPA) format for a number of protein MTase targets reveals sensitivity to both the known pan assay interference compounds (PAINS) and also other scaffolds not currently precedented as assay interferers. We find that, in general, true actives show significant selectivity within the MTase family. With the exception of slight modifications of SAM-like compounds, scaffolds that are observed frequently in multiple MTase assays should be viewed with caution and should be carefully validated before following up.

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What general conclusions can we draw from kinase profiling data sets?

Cited by 29 publications

References 28 publications

Conformational Analysis of the DFG-Out Kinase Motif and Biochemical Profiling of Structurally Validated Type II Inhibitors

Conformational Analysis of the DFG-Out Kinase Motif and Biochemical Profiling of Structurally Validated Type II Inhibitors

Selectivity Data: Assessment, Predictions, Concordance, and Implications

Beyond PAINs: Chemotype Sensitivity of Protein Methyltransferases in Screens

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