High-throughput enzymology reveals mutations throughout a phosphatase that decouple catalysis and transition state analog affinity
Bibliographic record
Abstract
Abstract Using High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), we measured over 9,000 inhibition curves detailing impacts of 1,004 single-site mutations throughout the Alkaline Phosphatase PafA on binding affinity for two transition state analogs (TSAs), vanadate and tungstate. As predicted by catalytic models invoking transition state complementary, mutations to active site and active site-contacting residues had highly similar impacts on catalysis and TSA binding. Unexpectedly, most mutations to more distal residues which reduced catalysis had little or no impact on TSA binding and many even increased affinity for tungstate. These disparate effects are accounted for by a model in which distal mutations alter the enzyme’s conformational landscape and increase occupancy of microstates that are catalytically less effective but better able to accommodate larger transition state analogs. In support of this model, glycine substitutions (rather than valine) were more likely to increase tungstate affinity, presumably due to increased conformational flexibility and increased occupancy of previously disfavored microstates. These results indicate that residues throughout an enzyme provide specificity for the transition state and discriminate against analogs that are larger only by tenths of an Ångström. Thus, engineering enzymes that rival the most powerful natural enzymes will likely require consideration not just of residues in and around the active site, but also of more distal residues that shape the enzyme’s conformational landscape and finetune the active site. In addition, the extensive functional communication between the active site and remote residues may provide interconnections needed for allostery and make allostery a highly evolvable trait. Significance Statement Transition state analogs (TSAs) resemble fleeting high-energy transition states and have been used to inhibit enzymes in nature and medicine, to learn about enzyme active site features, and to design and select new enzymes. While TSAs mimic transition states, they differ from actual TSs, and we exploit these differences here. Systematic TSA affinity measurements for 1,004 mutants of PafA (a model phosphatase enzyme) revealed effects in and around the active site that mirror their effects on catalysis, but TSA-binding and catalytic effects diverge more distally. These observations suggest that residues throughout an enzyme adjust its conformational landscape on the tenth-Ångström scale to optimize the active site for catalysis, rendering allostery more evolvable in nature but likely complicating enzyme design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".