Selective targeting of indel‐inferred differences in spatial structures of highly homologous proteins
Bibliographic record
Abstract
Recent findings have shown that the protein elongation factor-1alpha (EF-1alpha) from the eukaryotic pathogen Leishmania donovani possesses virulence properties. This was unexpected, since it has greater than 80% sequence identity with its human homologue. Given that EF-1alpha is essential for cell survival, in principle, it can be considered an attractive drug target. However, the challenge is to be able to selectively target the protein so as not to affect function of the human homologue. While a limited number of discrete differences were scattered throughout the sequence, most of the difference between these 2 homologues could be attributed to a 12-amino acid insert present in human EF-1alpha and absent from the leishmania sequence. In the present study, we modeled the spatial differences in structures of human and L. donovani EF-1alpha's inferred by this insertion-deletion (or "indel"). The protein models were used to develop antibodies directed specifically toward the deletion region of the pathogen protein. The strategy described allowed successful selective targeting of this putative leishmania virulence factor while avoiding recognition of the highly similar human EF-1alpha homologue. These findings may establish a new strategy for the development of antagonists directed against certain pathogenic targets having close human homologues.
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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 | 0.000 |
| 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".