Characterization of the <i>Mycobacterium tuberculosis</i> Proteome by Liquid Chromatography Mass Spectrometry-based Proteomics Techniques: A Comprehensive Resource for Tuberculosis Research
Bibliographic record
Abstract
Approximately, one-third of the world's population is infected with Mycobacterium tuberculosis, the causative agent of tuberculosis. Secreted and membrane proteins that interact with the host play important roles for the pathogenicity of the bacteria and are potential drug targets or components of vaccines. In this present study, subcellular fractionation in combination with membrane enrichment was used to comprehensively analyze the M. tuberculosis proteome. The proteome of the M. tuberculosis cell wall, membrane, cytosol, lysate, and culture filtrate was defined with a high coverage. Exceptional enrichment for membrane proteins was achieved using wheat germ agglutinin (WGA)-affinity two-phase partitioning, a technique that has to date not yet been exploited for the enrichment of mycobacterial membranes. Overall, 1051 M. tuberculosis protein groups including 183 transmembrane proteins have been identified by LC-MS/MS analysis using stringent database search criteria with a minimum of two peptides and an estimated FDR of less than 1%. With many mycobacterial antigens and lipoglycoproteins identified, the results from this study suggest that many of the newly discovered proteins could represent potential candidates mediating host-pathogen interactions. In addition, this data set provides experimental information about protein localization and thus serves as a valuable resource for M. tuberculosis proteome research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".