Assessing functional divergence in EF-1 and its paralogs in eukaryotes and archaebacteria
Bibliographic record
Abstract
A number of methods have recently been published that use phylogenetic information extracted from large multiple sequence alignments to detect sites that have changed properties in related protein families. In this study we use such methods to assess functional divergence between eukaryotic EF-1alpha (eEF-1alpha), archaebacterial EF-1alpha (aEF-1alpha) and two eukaryote-specific EF-1alpha paralogs-eukaryotic release factor 3 (eRF3) and Hsp70 subfamily B suppressor 1 (HBS1). Overall, the evolutionary modes of aEF-1alpha, HBS1 and eRF3 appear to significantly differ from that of eEF-1alpha. However, functionally divergent (FD) sites detected between aEF-1alpha and eEF-1alpha only weakly overlap with sites implicated as putative EF-1beta or aminoacyl-tRNA (aa-tRNA) binding residues in EF-1alpha, as expected based on the shared ancestral primary translational functions of these two orthologs. In contrast, FD sites detected between eEF-1alpha and its paralogs significantly overlap with the putative EF-1beta and/or aa-tRNA binding sites in EF-1alpha. In eRF3 and HBS1, these sites appear to be released from functional constraints, indicating that they bind neither eEF-1beta nor aa-tRNA. These results are consistent with experimental observations that eRF3 does not bind to aa-tRNA, but do not support the 'EF-1alpha-like' function recently proposed for HBS1. We re-assess the available genetic data for HBS1 in light of our analyses, and propose that this protein may function in stop codon-independent peptide release.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".