An in silico approach for screening non-synonymous SNPs in Mycobacterium tuberculosis PPE68 protein and impact on structure
Bibliographic record
Abstract
PPE68 has been implicated through computational methods in ethambutol (EMB) resistance, plausibly through gene-gene interactions with embA. The interest of the current study was to use in silico approaches to gain insights into the effects of mutations on the structure and function of PPE68. Drug-resistant clinical isolate sequences from the NCBI Database were studied. PPE68 missense nsSNPs were analysed using bioinformatics tools. Isolated sequences which harboured mutations that could be key in driving a drug-resistant phenotype were selected for structural modelling and molecular dynamics simulations- mutations likely occurring in conserved regions of the N-terminal domain, including A26T/Q92K, L163F/L167R, L167P, L167R and E44G. The 83 sequences that were aligned exhibited some clustering with an interest on the variants A26T/Q92K, L167P, L167R and L163F/L167R appearing to cluster together, with the exception of E44G. There was a significant shift in the conformation of the mutants with A26T/Q92K (9.99 ± 2.05 Å) and L167P (10.03 ± 2.14 Å), undergoing the greatest change. L163F/L167R and A26T/Q92K displayed the most stability, whilst being the most loosely packed. This is in contrast to the unstable E44G, L167P and L167R which were more compact than the wildtype. Considering that the PPE family of proteins are highly disordered in their native form and presumably in their most stable conformation, these outcomes are conceivable.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".