Kinesin-2 heterodimerization alters entry into a processive run along the microtubule but not stepping within the run
Bibliographic record
Abstract
Heterodimeric KIF3AC and KIF3AB, two members of the mammalian kinesin-2 family, generate force for microtubule plus end–directed cargo transport. However, the advantage of heterodimeric kinesins over homodimeric ones is not well-understood. We showed previously that microtubule association for entry into a processive run was similar in rate for KIF3AC and KIF3AB at ∼7.0 μm −1 s −1 . Yet, for engineered homodimers of KIF3AA and KIF3BB, this constant is significantly faster at 11 μm −1 s −1 and much slower for KIF3CC at 2.1 μm −1 s −1 . These results led us to hypothesize that heterodimerization of KIF3A with KIF3C and KIF3A with KIF3B altered the intrinsic catalytic properties of each motor domain. Here, we tested this hypothesis by using presteady-state stopped-flow kinetics and mathematical modeling. Surprisingly, the modeling revealed that the catalytic properties of KIF3A and KIF3B/C were altered upon microtubule binding, yet each motor domain retained its relative intrinsic kinetics for ADP release and subsequent ATP binding and the nucleotide-promoted transitions thereafter. These results are consistent with the interpretation that for KIF3AB, each motor head is catalytically similar and therefore each step is approximately equivalent. In contrast, for KIF3AC the results predict that the processive steps will alternate between a fast step for KIF3A followed by a slow step for KIF3C resulting in asymmetric stepping during a processive run. This study reveals the impact of heterodimerization of the motor polypeptides for microtubule association to start the processive run and the fundamental differences in the motile properties of KIF3C compared with KIF3A and KIF3B.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".