Cell wall proteome analysis of Mycobacterium smegmatis strain MC2 155
Bibliographic record
Abstract
BACKGROUND: The usually non-pathogenic soil bacterium Mycobacterium smegmatis is commonly used as a model mycobacterial organism because it is fast growing and shares many features with pathogenic mycobacteria. Proteomic studies of M. smegmatis can shed light on mechanisms of mycobacterial growth, complex lipid metabolism, interactions with the bacterial environment and provide a tractable system for antimycobacterial drug development. The cell wall proteins are particularly interesting in this respect. The aim of this study was to construct a reference protein map for these proteins in M. smegmatis. RESULTS: A proteomic analysis approach, based on one dimensional polyacrylamide gel electrophoresis and LC-MS/MS, was used to identify and characterize the cell wall associated proteins of M. smegmatis. An enzymatic cell surface shaving method was used to determine the surface-exposed proteins. As a result, a total of 390 cell wall proteins and 63 surface-exposed proteins were identified. Further analysis of the 390 cell wall proteins provided the theoretical molecular mass and pI distributions and determined that 26 proteins are shared with the surface-exposed proteome. Detailed information about functional classification, signal peptides and number of transmembrane domains are given next to discussing the identified transcriptional regulators, transport proteins and the proteins involved in lipid metabolism and cell division. CONCLUSION: In short, a comprehensive profile of the M. smegmatis cell wall subproteome is reported. The current research may help the identification of some valuable vaccine and drug target candidates and provide foundation for the future design of preventive, diagnostic, and therapeutic strategies against mycobacterial diseases.
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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) | 0.001 | 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".