Use of a Combination of Isotopically Coded Cross-Linkers and Isotopically Coded N-Terminal Modification Reagents for Selective Identification of Inter-peptide Crosslinks
Bibliographic record
Abstract
Cross-linking combined with mass spectrometry has great potential for determining three-dimensional structures of proteins and protein assemblies. One of the main analytical challenges of this method is the specific detection and identification of the inter-peptide crosslinks in the peptide mixture after enzymatic digestion of the cross-linked protein complex. These inter-peptide crosslinks are important because they provide the critical distance information needed for structural proteomics studies. In this paper, we demonstrate the use of isotopically coded N-terminal modification (ICNTM) in combination with isotopically coded cross-linkers (ICCL) for specific detection of inter-peptide crosslinks. Inter-peptide crosslinks contain two amino termini, compared to one in the case of free peptides, dead-end crosslinks, or intra-peptide crosslinks. Therefore, N-terminal modification with a 1:1 mixture of heavy and light isotopically coded reagents produces inter-peptide crosslinks with a distinct isotopic signature (a 1:2:1 ratio). Modification also occurs at the epsilon-amino groups of non-cross-linked lysine residues, resulting in two modifications per free lysine-containing peptide. However, if ICCL and ICNTM are used together, inter-peptide crosslinks can be distinguished from free lysine-containing peptides. Specialized software has also been developed for the analysis of ICCL + ICNTM experimental data. This procedure, combined with software for data analysis, provides a simple and rapid method for specific detection of inter-peptide crosslinks.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".