Substrate Deconstruction and the Nonadditivity of Enzyme Recognition
Bibliographic record
Abstract
Predicting substrates for enzymes of unknown function is a major postgenomic challenge. Substrate discovery, like inhibitor discovery, is constrained by our ability to explore chemotypes; it would be expanded by orders of magnitude if reactive sites could be probed with fragments rather than fully elaborated substrates, as is done for inhibitor discovery. To explore the feasibility of this approach, substrates of six enzymes from three different superfamilies were deconstructed into 41 overlapping fragments that were tested for activity or binding. Surprisingly, even those fragments containing the key reactive group had little activity, and most fragments did not bind measurably, until they captured most of the substrate features. Removing a single atom from a recognized substrate could often reduce catalytic recognition by 6 log-orders. To explore recognition at atomic resolution, the structures of three fragment complexes of the β-lactamase substrate cephalothin were determined by X-ray crystallography. Substrate discovery may be difficult to reduce to the fragment level, with implications for function discovery and for the tolerance of enzymes to metabolite promiscuity. Pragmatically, this study supports the development of libraries of fully elaborated metabolites as probes for enzyme function, which currently do not exist.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".