Functional significance of evolving protein sequence in dihydrofolate reductase from bacteria to humans
Bibliographic record
Abstract
With the rapidly growing wealth of genomic data, experimental inquiries on the functional significance of important divergence sites in protein evolution are becoming more accessible. Here we trace the evolution of dihydrofolate reductase (DHFR) and identify multiple key divergence sites among 233 species between humans and bacteria. We connect these sites, experimentally and computationally, to changes in the enzyme's binding properties and catalytic efficiency. One of the identified evolutionarily important sites is the N23PP modification (∼mid-Devonian, 415-385 Mya), which alters the conformational states of the active site loop in Escherichia coli dihydrofolate reductase and negatively impacts catalysis. This enzyme activity was restored with the inclusion of an evolutionarily significant lid domain (G51PEKN in E. coli enzyme; ∼2.4 Gya). Guided by this evolutionary genomic analysis, we generated a human-like E. coli dihydrofolate reductase variant through three simple mutations despite only 26% sequence identity between native human and E. coli DHFRs. Molecular dynamics simulations indicate that the overall conformational motions of the protein within a common scaffold are retained throughout evolution, although subtle changes to the equilibrium conformational sampling altered the free energy barrier of the enzymatic reaction in some cases. The data presented here provide a glimpse into the evolutionary trajectory of functional DHFR through its protein sequence space that lead to the diverged binding and catalytic properties of the E. coli and human enzymes.
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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".