Chemical screening methods to identify ligands that promote protein stability, protein crystallization, and structure determination
Bibliographic record
Abstract
The 3D structures of human therapeutic targets are enabling for drug discovery. However, their purification and crystallization remain rate determining. In individual cases, ligands have been used to increase the success rate of protein purification and crystallization, but the broad applicability of this approach is unknown. We implemented two screening platforms, based on either fluorimetry or static light scattering, to measure the increase in protein thermal stability upon binding of a ligand without the need to monitor enzyme activity. In total, 221 different proteins from humans and human parasites were screened against one or both of two sorts of small-molecule libraries. The first library comprised different salts, pH conditions, and commonly found small molecules and was applicable to all proteins. The second comprised compounds specific for protein families of particular interest (e.g., protein kinases). In 20 cases, including nine unique human protein kinases, a small molecule was identified that stabilized the proteins and promoted structure determination. The methods are cost-effective, can be implemented in any laboratory, promise to increase the success rates of purifying and crystallizing human proteins significantly, and identify new ligands for these proteins.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".