Utility of formaldehyde cross‐linking and mass spectrometry in the study of protein–protein interactions
Bibliographic record
Abstract
For decades, formaldehyde has been routinely used to cross-link proteins in cells, tissue, and in some instances, even entire organisms. Due to its small size, formaldehyde can readily permeate cell walls and membranes, resulting in efficient cross-linking, i.e. the formation of covalent bonds between proteins, DNA, and other reactive molecules. Indeed, formaldehyde cross-linking is an instrumental component of many mainstream analytical/cell biology techniques including chromatin immunoprecipitation (ChIP) of protein-DNA complexes found in nuclei; immunohistological analysis of protein expression and localization within cells, tissues, and organs; and mass spectrometry (MS)-compatible silver-staining methodologies used to visualize low abundance proteins in polyacrylamide gels. However, despite its exquisite suitability for use in the analysis of protein environments within cells, formaldehyde has yet to be commonly employed in the directed analysis of protein-protein interactions and cellular networks. The general purpose of this article is to discuss recent advancements in the use of formaldehyde cross-linking in combination with MS-based methodologies. Key advantages and limitations to the use of formaldehyde over other cross-linkers and technologies currently used to study protein-protein interactions are highlighted, and formaldehyde-based experimental approaches that are proving very promising in their ability to accurately and efficiently identify novel protein-protein and multiprotein interaction complexes are presented.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".