Biophysical Techniques and Assay Design for the Development of Small Molecule Inhibitors
Bibliographic record
Abstract
One of the most important pillars in preclinical research involves the biophysical characterization of protein-compound interactions. This characterization, describing the in vitro potency and binding properties of the interaction, is measured through techniques such as fluorescence polarization assays, thermal shift assays, isothermal titration calorimetry experiments, and activity-based assays. A broad biophysical profile of each compound against the target of interest allows for fine-tuning of the molecule through structure-activity relationship-guided design. Biophysical profiling can enable accurate prediction of the interaction within a biological setting and thus provide insight into the mechanism-of-action observed in a cellular setting. This thesis details the recombinant protein expression and purification as well as the biophysical assay development of several attractive therapeutic targets for cancer treatment including STAT3, STAT5B, UBA5, HDAC6, and HDAC8. To aid in STAT inhibitor discovery, exploration of assay development to confirm STAT-phosphorylation inhibition was described. This thesis reports a rapid fluorescence-based screening method to identify SH2 domain binders followed by a phosphorylation-inhibition assay to validate mechanism of action for molecules determined from the high-throughput screening assay. Additionally, the development and optimization of a fluorescence polarization (FP) assay for binding affinity determination of DNA binding domain inhibitors for STAT3 was also described. Similarly, FP assays and thermal shift assays were also validated for drug discovery program geared towards identification of highly selective and potent HDAC6 and HDAC8 inhibitors. Fluorescence-based activity assays and in cellulo nanoBRET assays were also established for HDAC proteins to confirm the results obtained from preliminary biophysical assays. Exploration of high-throughput virtual screening was also employed to identify novel UBA5 inhibitor scaffolds from a diverse natural product and natural product derivative libraries. This experiment yielded a lead candidate with potential for refinement through structure-activity relationship (SAR) to improve drug-like qualities of the molecule. Lastly, the hijacking of the upregulated prenylation pathway in cancer for artificial protein-membrane anchorage was investigated for utility as an alternative protein inhibition mechanism. Collectively, these studies demonstrate the progress in the assay development area for the targeting of multiple proteins to aid the drug discovery process.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".