Customized Approaches for Inhibition and Degradation of Drug Targets
Bibliographic record
Abstract
Diseases are often caused by increase or decrease of specific proteins, and changes in the level of their activities in cell. Many cancers have already been directly or indirectly linked to such changes, for example. This necessitates development of therapeutics for diseases caused by such imbalances and dysregulations. Therefore, the dysregulated proteins are attractive drug targets, and small molecules are needed to modulate their function or antagonize their interactions with partner proteins. However, some protein targets may not be druggable, and finding small molecule modulators of their function may not be practical. Alternative approach would be to selectively target these proteins for degradation. This approach utilizes protein degradation pathway to specifically ubiquitinate the protein of interest by E3 ligases and mark it for degradation. My research takes advantage of all available options for modulating the level of activities of drug targets. Major step in the discovery of small molecule modulators of drug targets is to characterize their function and develop high throughput methods for screening. In the first chapter of my thesis, I focused on better understanding of the mechanism of function of protein arginine methyltransferase (PRMT) 1 in nucleosome setting by investigating its crosstalk with other methyltransferases. This study resulted in discovery of the activating effect of monomethylation of H4K20 on nucleosome by SETD8 on H4R3 methylation by PRMT1. In the second chapter, I focused on discovery of ligands for DDB1 Cul4-associated factor 1 (DCAF1) to employ the protein degradation approach. This project resulted in discovery of a potent antagonist of DCAF1 with a Kd value of 38 nM, a great step in the development for a chemical probe or PROTAC. Utilizing the two different approaches in targeting proteins of interest, will result in new opportunities that can facilitate early-stage drug discovery.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".