Impact of Cancer-Associated PKM2 Mutations on Enzyme Activity and Allosteric Regulation: Structural and Functional Insights into Metabolic Reprogramming
Bibliographic record
Abstract
Mammalian pyruvate kinase M2 (PKM2) is a key regulator of glycolysis and is highly expressed in proliferative tissues including tumors. Mutations in PKM2 have been identified in various cancers, but their effects on enzyme activity and regulation are not fully understood. This study investigates the structural and functional effects of cancer-associated PKM2 mutations on enzyme kinetics, allosteric regulation, and oligomerization. Using computational modeling, X-ray crystallography, and biochemical assays, we demonstrated how these mutations impact PKM2 activity, substrate binding, and allosteric activation via fructose-1,6-bisphosphate (FBP), contributing to altered enzyme function. In this study, we characterized four cancer-associated PKM2 mutations (P403A, C474S, R516C, and L144P) using computational, structural, and biochemical approaches. Computational modeling revealed disruptions in allosteric signaling pathways, particularly affecting the communication between regulatory sites and the active site. X-ray crystallography demonstrated local conformational changes in the hinge and FBP-binding regions, leading to a shift from the active tetrameric state to a less active dimeric state, particularly in the C474S and R516C mutants. The mutants exhibited reduced maximal velocity, reduced substrate affinity, and altered activation by the allosteric activator fructose-1,6-bisphosphate (FBP). Under alkaline pH conditions, mimicking the tumor microenvironment, these mutations further destabilized the PKM2 oligomeric state, favoring the formation of lower-order species. Our findings suggest that PKM2 is highly sensitive to mutations, and these alterations may contribute to metabolic reprogramming in cancer cells by impairing its enzymatic regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".