Biochemical Characterization of FIKK Kinase from Cryptosporidium parvum and Discovery of Potent Inhibitors
Bibliographic record
Abstract
Cryptosporidium parasites cause serious human and animal diseases and affect millions of children worldwide. Drug discovery attempts against the parasites are insufficient and new drug targets are necessary. C. parvum harbors some unique protein kinases including one called FIKK kinase. FIKKs are parasite-specific protein kinases with distinctive sequence motifs and restricted to phylum Apicomplexa. The biochemistry and biology of the evolutionarily conserved members of the FIKK family have not been elucidated before this project. I explored the biochemical nature of the most conserved FIKK members in C. parvum and malaria causing P. falciparum, known as CpFIKK and PfFIKK8, respectively. I have identified the soluble domain boundary of the proteins and their substrate preferences, and characterized their activity in vitro. FIKKs need a ~40 residue extension to the predicted kinase domain to be soluble. They prefer Ser as phosphoacceptor residue flanked by Arg at the -3 and +3 positions in the substrate. Because their biological roles have not been completely elucidated, potent, selective and cell permeable inhibitors would be useful to understand the biological roles of FIKKs in parasites. Here, I report the first Cryptosporidium FIKK (CpFIKK) inhibitor and its selectivity profile. I systematically explored the structure activity relationship for CpFIKK inhibition and for selectivity against CpCDPK1. I identified 4b as a potent (IC50 = 0.2 nM) inhibitor of CpFIKK catalytic activity, and confirmed CpFIKK binding using a thermal melt assay. Minor variations of inhibitor structure led to significant change in selectivity profiles against CpCDPK1 and identified CpCDPK1 selective as well as dually acting C. parvum FIKK-CDPK1 inhibitors from the same structural class of compounds. I evaluated these CpFIKK inhibitors for inhibition of parasite growth in vitro. The observed effect in parasite growth did not correlate with CpFIKK inhibition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".