Genome-wide Analysis of Substrate Specificities of the Escherichia coli Haloacid Dehalogenase-like Phosphatase Family
Bibliographic record
Abstract
Haloacid dehalogenase (HAD)-like hydrolases are a vast superfamily of largely uncharacterized enzymes, with a few members shown to possess phosphatase, beta-phosphoglucomutase, phosphonatase, and dehalogenase activities. Using a representative set of 80 phosphorylated substrates, we characterized the substrate specificities of 23 soluble HADs encoded in the Escherichia coli genome. We identified small molecule phosphatase activity in 21 HADs and beta-phosphoglucomutase activity in one protein. The E. coli HAD phosphatases show high catalytic efficiency and affinity to a wide range of phosphorylated metabolites that are intermediates of various metabolic reactions. Rather than following the classical "one enzyme-one substrate" model, most of the E. coli HADs show remarkably broad and overlapping substrate spectra. At least 12 reactions catalyzed by HADs currently have no EC numbers assigned in Enzyme Nomenclature. Surprisingly, most HADs hydrolyzed small phosphodonors (acetyl phosphate, carbamoyl phosphate, and phosphoramidate), which also serve as substrates for autophosphorylation of the receiver domains of the two-component signal transduction systems. The physiological relevance of the phosphatase activity with the preferred substrate was validated in vivo for one of the HADs, YniC. Many of the secondary activities of HADs might have no immediate physiological function but could comprise a reservoir for evolution of novel phosphatases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".