High tolerance to simultaneous active‐site mutations in TEM‐1 β‐lactamase: Distinct mutational paths provide more generalized β‐lactam recognition
Bibliographic record
Abstract
The diversity in substrate recognition spectra exhibited by various beta-lactamases can result from one or a few mutations in the active-site area. Using Escherichia coli TEM-1 beta-lactamase as a template that efficiently hydrolyses penicillins, we performed site-saturation mutagenesis simultaneously on two opposite faces of the active-site cavity. Residues 104 and 105 as well as 238, 240, and 244 were targeted to verify their combinatorial effects on substrate specificity and enzyme activity and to probe for cooperativity between these residues. Selection for hydrolysis of an extended-spectrum cephalosporin, cefotaxime (CTX), led to the identification of a variety of novel mutational combinations. In vivo survival assays and in vitro characterization demonstrated a general tendency toward increased CTX and decreased penicillin resistance. Although selection was undertaken with CTX, productive binding (K(M)) was improved for all substrates tested, including benzylpenicillin for which catalytic turnover (k(cat)) was reduced. This indicates broadened substrate specificity, resulting in more generalized (or less specialized) variants. In most variants, the G238S mutation largely accounted for the observed properties, with additional mutations acting in an additive fashion to enhance these properties. However, the most efficient variant did not harbor the mutation G238S but combined two neighboring mutations that acted synergistically, also providing a catalytic generalization. Our exploration of concurrent mutations illustrates the high tolerance of the TEM-1 active site to multiple simultaneous mutations and reveals two distinct mutational paths to substrate spectrum diversification.
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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 | 0.000 | 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) | 0.001 | 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".