Determinants and Prediction of Esterase Substrate Promiscuity Patterns
Bibliographic record
Abstract
Esterases receive special attention because of their wide distribution in biological systems and environments and their importance for physiology and chemical synthesis. The prediction of esterases' substrate promiscuity level from sequence data and the molecular reasons why certain such enzymes are more promiscuous than others remain to be elucidated. This limits the surveillance of the sequence space for esterases potentially leading to new versatile biocatalysts and new insights into their role in cellular function. Here, we performed an extensive analysis of the substrate spectra of 145 phylogenetically and environmentally diverse microbial esterases, when tested with 96 diverse esters. We determined the primary factors shaping their substrate range by analyzing substrate range patterns in combination with structural analysis and protein-ligand simulations. We found a structural parameter that helps rank (classify) the promiscuity level of esterases from sequence data at 94% accuracy. This parameter, the active site effective volume, exemplifies the topology of the catalytic environment by measuring the active site cavity volume corrected by the relative solvent accessible surface area (SASA) of the catalytic triad. Sequences encoding esterases with active site effective volumes (cavity volume/SASA) above a threshold show greater substrate spectra, which can be further extended in combination with phylogenetic data. This measure provides also a valuable tool for interrogating substrates capable of being converted. This measure, found to be transferred to phosphatases of the haloalkanoic acid dehalogenase superfamily and possibly other enzymatic systems, represents a powerful tool for low-cost bioprospecting for esterases with broad substrate ranges, in large scale sequence data sets.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".