The Human Dental Pulp Proteome and N-Terminome: Levering the Unexplored Potential of Semitryptic Peptides Enriched by TAILS to Identify Missing Proteins in the Human Proteome Project in Underexplored Tissues
Bibliographic record
Abstract
An underexplored yet widespread feature of the human proteome is the proteolytic proteoforms of proteins. We used terminal amine isotopic labeling of substrates (TAILS), a high-content N-terminal positional proteomics technique, for in-depth characterization of the human dental pulp proteome from its N-terminome and to provide data for the Chromosome-centric Human Proteome Project (C-HPP). Dental pulp is a unique connective tissue maintaining tooth sensation and structure by supporting a single cell layer of odontoblasts that synthesize mineralization-competent dentine extracellular matrix. Therefore, we posited pulp to be a rich source of unique tissue-specific proteins and hence an abundant source of "missing" proteins as defined by neXtProt. From the identified 4332 proteins (false discovery rate (FDR) ≤ 0.7%), 21 528 unique peptides (FDR ≤ 1.0%) and 9079 unique N-termini, we analyzed N-terminal methionine excision, co- and posttranslational Nα-acetylation, protein maturation, and proteolytic processing. Apart from 227 candidate alternative translation initiation sites, most identified N-termini (78%) represented proteolytic processing and mechanism-informative internal neo-N-termini, confirming a pervasive amount of proteolytic-processing generated proteoforms in vivo. Furthermore, we identified 17 missing protein candidates for the C-HPP, highlighting the importance of using (i) less studied human specimens and (ii) orthogonal proteomic approaches such as TAILS to map the human proteome. The mass spectrometry raw data and metadata have been deposited to ProteomeXchange with the PXD identifier .
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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 | 0.001 |
| 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 | 0.001 |
| 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".