Progress Towards a Public Chemogenomic Set for Protein Kinases and a Call for Contributions

Drewry, David H.; Wells, Carrow; Andrews, David; Angell, Richard; Al-Ali, Hassan; Axtman, Alison D.; Capuzzi, Stephen J.; Elkins, J.M.; Ettmayer, Peter; Frederiksen, Mathias; Gileadi, O.; Gray, Nathanael S.; Hooper, Alice; Knapp, S.; Laufer, Stefan; Luecking, Ulrich; Müller, Susanne; Muratov, Eugene; Denny, R. Aldrin; Saikatendu, Kumar Singh; Treiber, Daniel K.; Zuercher, William J.; Willson, Timothy M.

doi:10.1101/104711

Cited by 25 publications

(49 citation statements)

References 64 publications

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“…Generation of the test data for Round 1 was based on a single-dose kinome scan of a library of multi-targeted compounds. 5,11 This was followed by a dose-response determination of the dissociation constant (K d ) values for 430 compound-kinase pairs between 70 inhibitors and 199 kinases that were not available in the public domain (see Methods). An additional set of completely new K d data was generated for Round 2, consisting of 394 multi-dose assays between 25 inhibitors and 207 kinases with single-dose inhibition >80%.…”

Section: Challenge Test Datasetsmentioning

confidence: 99%

“…To explore the compound-kinase pairs across the full spectrum of single-dose inhibition levels, we experimentally profiled 81 additional pairs, which were not part of Round 1 or 2 datasets, based solely on the pK d predictions from the three top-performing models. These follow-up experiments were carried out in an unbiased manner, regardless of the compound class or selectivity, kinase target families, or inhibition levels of the pairs, to investigate whether it is possible to use predictive models to identify potent inhibitors of kinases showing less than 80% single-dose inhibition; this activity cut-off is often used when selecting pairs for multi-dose K d testing 5,11,13 . Most of the measured pK d values of these 81 pairs were distributed as expected, according to the expected single-dose inhibition function (Fig.…”

Section: Model-based Target Predictionsmentioning

confidence: 99%

“…The compounds were supplied as 10 mM stocks in DMSO, and the top screening concentration was 10 mM. 25 of the axitinib-kinase pairs generated for Round 2 were already profiled in previous published studies, 5,11 and were therefore excluded from the Round 2 dataset. The Spearman correlation between these newly-measured pK d 's and those available from DTC was 0.701 (Suppl.…”

Section: Bioactivity Data For Model Testingmentioning

confidence: 99%

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Crowdsourced mapping extends the target space of kinase inhibitors

Cichońska

Ravikumar

Allaway

et al. 2020

Preprint

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Despite decades of intensive search for compounds that modulate the activity of particular proteins, there are currently small-molecule probes available only for a small proportion of the human proteome. Effective approaches are therefore required to map the massive space of unexplored compound-target interactions for novel and potent activities. Here, we carried out a crowdsourced benchmarking of the accuracy of machine learning (ML) algorithms at predicting kinase inhibitor potencies across multiple kinase families. A total of 268 ML predictions were scored in unpublished bioactivity data sets. Top-performing algorithms used kernel learning, gradient boosting and deep learning, with predictive accuracy exceeding that of target activity assays. Subsequent experiments carried out based on the the top-performing model predictions demonstrated that these models and their ensemble can improve the accuracy of experimental mapping efforts, especially for so far under-studied kinases. The open-source ML algorithms together with the novel dose-response data for 905 bioactivities between 95 compounds and 295 kinases provide a unique resource for extending the druggable kinome.AI approaches, that can leverage the information extracted from similar kinases and compounds to predict the activity of so far unexplored interactions.The Challenge was implemented in a screening-based, pre-competitive drug discovery project in collaboration with the NIH-supported Illuminating the Druggable Genome (IDG) program ( https://commonfund.nih.gov/idg ), with the common aim to establish kinome-wide target profiles of small-molecule agents, and thereby to extend the druggability of the human kinome space by providing activity information on under-studied proteins. The specific questions this Challenge sought to address were: (i) What are the best computational modelling approaches for predicting quantitative compound-target activity profiles?; (ii) What are the optimal molecular and chemical descriptors for maximal prediction accuracy?; and (iii) What are the most predictive bioactivity assays and publicly available datasets? The Challenge attracted 212 active participants, and a total of 268 predictions were scored, covering a wide range of ML approaches, including deep and kernel learning and gradient boosting decision trees. Here, we describe the benchmarking results from the Challenge, and the use of top-performing models for identifying novel kinase inhibitor activities.

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Section: Challenge Test Datasetsmentioning

confidence: 99%

Section: Model-based Target Predictionsmentioning

confidence: 99%

Section: Bioactivity Data For Model Testingmentioning

confidence: 99%

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Crowdsourced mapping extends the target space of kinase inhibitors

Cichońska

Ravikumar

Allaway

et al. 2020

Preprint

Self Cite

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“…To identify weaknesses of cells lacking cyclin F, we conducted a kinase inhibitor drug screen to assess differences in viability between CCNF K/O and parental cell lines (HeLa; Fig 1A). To this end, we used the KCGS, a set whose origins can be traced to the well-utilised kinase inhibitor collections PKIS and PKIS2 (Elkins et al, 2016;Drewry et al, 2017). The simple premise of KCGS is that screening a publicly available, well annotated set of potent and selective kinase inhibitors in disease-relevant phenotypic assays is an efficient way to elucidate biology and uncover dependencies (Jones & Bunnage, 2017).…”

Section: Kcgs Screen Identifies Synthetic Lethality Between Chk1 Inhimentioning

confidence: 99%

E2F1 proteolysis via SCF ‐cyclin F underlies synthetic lethality between cyclin F loss and Chk1 inhibition

et al. 2019

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Cyclins are central engines of cell cycle progression in conjunction with cyclin‐dependent kinases (CDKs). Among the different cyclins controlling cell cycle progression, cyclin F does not partner with a CDK, but instead forms via its F‐box domain an SCF (Skp1‐Cul1‐F‐box)‐type E3 ubiquitin ligase module. Although various substrates of cyclin F have been identified, the vulnerabilities of cells lacking cyclin F are not known. Thus, we assessed viability of cells lacking cyclin F upon challenging them with more than 180 different kinase inhibitors. The screen revealed a striking synthetic lethality between Chk1 inhibition and cyclin F loss. Chk1 inhibition in cells lacking cyclin F leads to DNA replication catastrophe. Replication catastrophe depends on accumulation of the transcription factor E2F1 in cyclin F‐depleted cells. We find that SCF‐cyclin F controls E2F1 ubiquitylation and degradation during the G2/M phase of the cell cycle and upon challenging cells with Chk1 inhibitors. Thus, Cyclin F restricts E2F1 activity during the cell cycle and upon checkpoint inhibition to prevent DNA replication stress. Our findings pave the way for patient selection in the clinical use of checkpoint inhibitors.

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“…However, most of these kinase-specific inhibitors target a relatively small fraction of the human kinome with only about 20% avidly being explored as primary targets for drug therapy (5,6). Thus, the majority of the kinome remains untargeted for cancer therapy and about 50% of the kinome is largely uncharacterized with respect to the function and role of these kinases in cancer, representing the understudied or 'dark' cancer kinome (6)(7)(8). Notably, several CRISPR/cas9 and/or RNAi loss-of-function studies have shown that many dark kinases are essential for cancer cell viability highlighting the therapeutic potential of the dark kinome for the treatment of cancer (9,10).…”

Section: Introductionmentioning

confidence: 99%

Kinome Profiling of Primary Endometrial Tumors Using Multiplexed Inhibitor Beads and Mass Spectrometry Identifies SRPK1 As Candidate Therapeutic Target

Johnson

Kumar

Kurimchak

et al. 2020

Preprint

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Protein kinases (collectively, termed the kinome) represent one of the most tractable drug targets in the pursuit of new and effective cancer treatments. However, less than 20% of the kinome is currently being explored as primary targets for cancer therapy, leaving the majority of the kinome untargeted for drug therapy. Chemical proteomics approaches such as Multiplexed Inhibitor Beads and Mass Spectrometry (MIB-MS) have been developed that measure the

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Progress Towards a Public Chemogenomic Set for Protein Kinases and a Call for Contributions

Cited by 25 publications

References 64 publications

Crowdsourced mapping extends the target space of kinase inhibitors

Crowdsourced mapping extends the target space of kinase inhibitors

E2F1 proteolysis via SCF ‐cyclin F underlies synthetic lethality between cyclin F loss and Chk1 inhibition

Kinome Profiling of Primary Endometrial Tumors Using Multiplexed Inhibitor Beads and Mass Spectrometry Identifies SRPK1 As Candidate Therapeutic Target

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