MANTI: Automated Annotation of Protein N-Termini for Rapid Interpretation of N-Terminome Data Sets
Bibliographic record
Abstract
Site-specific proteolytic processing is an important, irreversible post-translational protein modification with implications in many diseases. Enrichment of protein N-terminal peptides followed by mass spectrometry-based identification and quantification enables proteome-wide characterization of proteolytic processes and protease substrates but is challenged by the lack of specific annotation tools. A common problem is, for example, ambiguous matches of identified peptides to multiple protein entries in the databases used for identification. We developed MaxQuant Advanced N-termini Interpreter (MANTI), a standalone Perl software with an optional graphical user interface that validates and annotates N-terminal peptides identified by database searches with the popular MaxQuant software package by integrating information from multiple data sources. MANTI utilizes diverse annotation information in a multistep decision process to assign a conservative preferred protein entry for each N-terminal peptide, enabling automated classification according to the likely origin and determines significant changes in N-terminal peptide abundance. Auxiliary R scripts included in the software package summarize and visualize key aspects of the data. To showcase the utility of MANTI, we generated two large-scale TAILS N-terminome data sets from two different animal models of chemically and genetically induced kidney disease, puromycin adenonucleoside-treated rats (PAN), and heterozygous Wilms Tumor protein 1 mice (WT1). MANTI enabled rapid validation and autonomous annotation of >10 000 identified terminal peptides, revealing novel proteolytic proteoforms in 905 and 644 proteins, respectively. Quantitative analysis indicated that proteolytic activities with similar sequence specificity are involved in the pathogenesis of kidney injury and proteinuria in both models, whereas coagulation processes and complement activation were specifically induced after chemical injury.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".