Towards the glycoproteome of <i>Mycobacterium tuberculosis</i>
Bibliographic record
Abstract
Tuberculosis continues to be a major threat to public health having one of the highest mortalities of any infectious disease despite the use of antibacterial therapy and a partially effective vaccine. Mycobacterium tuberculosis has a complex relationship with its host that is mediated in part by glycosylated proteins, but knowledge about the glycoproteome of tuberculosis is still lacking. In fact, the glycostructures are currently known only for two glycoproteins (alanine and proline rich secreted protein APA and superoxide dismutase SODC). To identify potentially glycosylated proteins in M. tuberculosis , we investigated mycobacterial subcellular fractions (cell wall, cell membrane and culture filtrate proteins) using the following lectins: Concanavalin A (ConA), Soybean Agglutinin (SBA), and Wheat Germ Agglutinin (WGA) followed by liquid chromatography‐mass spectrometry approaches and bioinformatic analyses. As expected, there was some overlap between the different lectins in their ability to bind potential glycoproteins. Interestingly, there was no general trend in that one lectin may be better than another. In fact, ConA identified the most glycoprotein candidates in CFP but the least in cell wall and membrane. SBA identified the most candidates in the cell wall whereas WGA identified the largest number of potential glycoproteins from the cell membrane.. To validate the presence of glycoproteins, several strategies were pursued including collision induced dissociation and electron transfer dissociation techniques, novel bioinformatics analyses involving a novel virtual neutral loss algorithm and statistical analysis of enrichment in cell fractions with and without lectin treatment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".