Mass Spectrometric Methods for Generation of Protein Mass Database Used for Bacterial Identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The availability of a suitable database is critical in a proteomic approach for bacterial identification by mass spectrometry (MS). The major limitation of the present public proteome database is the lack of extensive low-mass bacterial protein entries with masses experimentally verified for most bacteria. Here, we present a method based on mass spectrometry to create protein mass tables specifically tailored for bacterial identification. Several issues related to the detection of bacterial proteins for the purpose of database creation are addressed. Three species of bacteria, namely, Escherichia coli, Bacillus megaterium, and Citrobacter freundii, which can be found in the ambient environment, were chosen for this study. Direct matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis of each bacterial extract reveals 20-29 protein components in the mass range from 2000 to 20,000 Da. HPLC fractionation of bacterial extracts followed by off-line MALDI-TOF analysis of individual fractions detects 156-423 components. Analysis of the extracts by HPLC/electrospray ionization MS shows the number of detectable proteins in the range of 46-59. Although a number of components were common to the three detection schemes employed, some unique components were found using each of these techniques. In addition, for E. coli where a large proteome database exists in the public domain, a number of masses detected by the mass spectrometric methods do not match with the proteome database. Compared to the public proteome database, the mass tables generated in this work are demonstrated to be more useful for bacterial identification in an application where the bacteria of interest have limited protein entries in the public database. The implication of this work for future development of a comprehensive mass database is discussed.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it