A “Holistic” Kinesin Phylogeny Reveals New Kinesin Families and Predicts Protein Functions
Bibliographic record
Abstract
Kinesin superfamily proteins are ubiquitous to all eukaryotes and essential for several key cellular processes. With the establishment of genome sequence data for a substantial number of eukaryotes, it is now possible for the first time to analyze the complete kinesin repertoires of a diversity of organisms from most eukaryotic kingdoms. Such a "holistic" approach using 486 kinesin-like sequences from 19 eukaryotes and analyzed by Bayesian techniques, identifies three new kinesin families, two new phylum-specific groups, and unites two previously identified families. The paralogue distribution suggests that the eukaryotic cenancestor possessed nearly all kinesin families. However, multiple losses in individual lineages mean that no family is ubiquitous to all organisms and that the present day distribution reflects common biology more than it does common ancestry. In particular, the distribution of four families--Kinesin-2, -9, and the proposed new families Kinesin-16 and -17--correlates with the possession of cilia/flagella, and this can be used to predict a flagellar function for two new kinesin families. Finally, we present a set of hidden Markov models that can reliably place most new kinesin sequences into families, even when from an organism at a great evolutionary distance from those in the analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".