2-Tier Bacterial and In Vitro Selection of Active and Methotrexate-Resistant Variants of Human Dihydrofolate Reductase
Bibliographic record
Abstract
We report a rapid and reliable 2-tier selection and screen for detection of activity as well as drug-resistance in mutated variants of a clinically-relevant drug-target enzyme. Human dihydrofolate reductase point-mutant libraries were subjected to a 1st-tier bacterial complementation assay, such that bacterial propagation served as an indicator of enzyme activity. Alternatively, when selection was performed in the presence of the inhibitor methotrexate (MTX), propagation indicated MTX resistance. The selected variants were then subjected to a 2nd-tier in vitro screen in 96-well plate format using crude bacterial lysate. Conditions were defined to establish a threshold for activity or for MTX resistance. The 2nd-tier assay allowed rapid detection of the best variants among the leads and provided reliable estimates of relative reactivity, (k(cat)) and IC(50)(MTX). Screening saturation libraries of active-site positions 7, 15, 24, 70, and 115 revealed a variety of novel mutations compatible with reactivity as well as 2 novel MTX-resistant variants: V115A and V115C. Both variants displayed K(i)(MTX)=20 nM, a 600-fold increase relative to the wild-type. We also present preliminary results from screening against further antifolates following simple modifications of the protocol. The flexibility and robustness of this method will provide new insights into interactions between ligands and active-site residues of this clinically relevant human enzyme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".