Screening of Microbial Extracts for Anticancer Compounds Using <i>Streptomyces</i> Kinase Inhibitor Assay
Bibliographic record
Abstract
Eukaryotic kinases are known to play an important role in signal transduction pathways by phosphorylating their respective substrates. Abnormal phosphorylations by these kinases have resulted in diseases. Hence inhibitors of kinases are of considerable pharmaceutical interest for a wide variety of disease targets, especially cancers. A number of reports have been published which indicate that eukaryotic-like kinases may complement two-component kinase systems in several bacteria. In Streptomyces sp. such kinases have been found to have a role in formation of aerial hyphae, spores, pigmentation & even in antibiotic production in some strains. Eukaryotic kinase inhibitors are seen to inhibit formation of aerial mycelia in Streptomyces without inhibiting vegetative mycelia. This property has been used to design an assay to screen for eukaryotic kinase inhibitors. The assay involves testing of compounds against Streptomyces 85E ATCC 55824 using agar well diffusion method. Inhibitors of kinases give rise to "bald" colonies where aerial mycelia and sporulation inhibition is seen. The assay has been standardized using known eukaryotic protein kinase inhibiting anticancer agents like AG-490, AG-1295, AG-1478, Flavopiridol and Imatinib as positive controls, at a concentration ranging from 10 μg/well to 100 μg/well. Anti-infective compounds which are not reported to inhibit eukaryotic protein kinases were used as negative controls. A number of microbial cultures have been screened for novel eukaryotic protein kinase inhibitors. Further these microbial extracts were tested in various cancer cell lines like Panel, HCT116, Calul, ACHN and H460 at a concentration of 10 μg/mL/ well. The anticancer data was seen correlating well with the Streptomyces kinase assay thus validating the assay.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".