Affinity Crystallography: A New Approach to Extracting High-Affinity Enzyme Inhibitors from Natural Extracts
Bibliographic record
Abstract
Natural products are an important source of novel drug scaffolds. The highly variable and unpredictable timelines associated with isolating novel compounds and elucidating their structures have led to the demise of exploring natural product extract libraries in drug discovery programs. Here we introduce affinity crystallography as a new methodology that significantly shortens the time of the hit to active structure cycle in bioactive natural product discovery research. This affinity crystallography approach is illustrated by using semipure fractions of an actinomycetes culture extract to isolate and identify a cathepsin K inhibitor and to compare the outcome with the traditional assay-guided purification/structural analysis approach. The traditional approach resulted in the identification of the known inhibitor antipain (1) and its new but lower potency dehydration product 2, while the affinity crystallography approach led to the identification of a new high-affinity inhibitor named lichostatinal (3). The structure and potency of lichostatinal (3) was verified by total synthesis and kinetic characterization. To the best of our knowledge, this is the first example of isolating and characterizing a potent enzyme inhibitor from a partially purified crude natural product extract using a protein crystallographic approach.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".