Large‐scale survey for potentially targetable indels in bacterial and protozoan proteins
Bibliographic record
Abstract
Our previous results demonstrated that some essential, housekeeping proteins from pathogenic microorganisms may contain sizable insertions-deletions in their sequences (compared to close human homologs) that can be responsible for unexpected virulence properties. For example, we found that indel-bearing elongation factor-1alpha from several pathogenic protozoa can activate a human tyrosine phosphatase SHP-1 leading to deactivation of macrophages. On the one hand, these findings allowed development of a strategy for targeting some indel-containing pathogen proteins that have similar human counterparts. On the other hand, the results raised numerous questions regarding the nature and implications of sequence indels in pathogen proteins. In the present study, we conducted a large-scale survey of indels in proteins from 136 bacterial and protozoan genomes. It has been established that sizable insertions and deletions occur in approximately 5-10% of bacterial proteins with close human homologs, while proteins from the protozoan pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Leishmania donovani exhibit elevated indel content that can reach up to 25%. The finding suggested that the occurrence of sequence indels may be involved in the evolution of pathogenic mechanisms in these protozoa.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".