Dihydrofolate reductase (DHFR) Antibody

About the Target

Dihydrofolate reductase (DHFR) is a ubiquitously expressed NADPH-dependent oxidoreductase, a member of the reductase enzyme family that catalyzes the reduction of dihydrofolate (DHF) to tetrahydrofolate (THF), an essential cofactor for one-carbon transfer reactions in purine, thymidylate, and methionine synthesis, thereby supporting DNA synthesis, repair, and methylation. Structurally, DHFR adopts a conserved α/β-fold consisting of an eight-stranded β-sheet flanked by α-helices, with a dynamic Met20 loop that regulates substrate binding and product release at the active site, where NADPH donates a hydride to DHF. Depending on the literature source, DHFR may also be discussed as Dihydrofolate reductase (DHFR) and Dihydrofolate reductase.

Reported cellular context includes cytoplasm and mitochondrion, which can matter when signal is compared across treatments or changing cell states. Following DHFR across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.

Research Context

DHFR is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm and mitochondrion, a defined reference condition can make comparisons more interpretable across perturbations, passages, or replicate sets.

Consider these angles when interpreting target-level changes:

• apparent redistribution between cytoplasm and mitochondrion across matched conditions
• responses linked to nutrient status, mitochondrial state, or metabolic rewiring
• co-patterning with orthogonal markers and control conditions that clarify pathway state
• time-matched comparisons so changes reflect biology rather than handling or sampling drift

Variant Considerations

If your project spans exploratory questions, the regular version offers a balanced option for establishing baseline signal behavior for DHFR. This can help when protocols evolve over time and the goal is to compare experiments using a stable reference workflow.

Standardize sampling time, control choice, and downstream analysis thresholds so apparent differences in DHFR reflect biology rather than handling. When interpreting DHFR, it is often useful to decide early whether the main question is overall abundance, compartmental enrichment, or context-dependent redistribution.

For multi-run studies, a shared reference condition can keep DHFR trends easier to compare across datasets. That kind of consistency is especially helpful when follow-up work expands to new perturbations, model systems, or longitudinal collections.