Using Chemical Crosslinking and Mass Spectrometry for \nProtein Model Validation and Fold Recognition
Bibliographic record
Abstract
The 3D structures of proteins may provide important clues to their functions and roles in complex biological pathways. Traditional methods such as X-ray crystallography and NMR are not feasible for all proteins, while theoretical models are typically not validated by experimental data. This project investigates the use of chemical crosslinkers as an experimental means of validating these models. Five target proteins were successfully purified from yeast whole cell extract: Transketolase (TKL1), inorganic pyrophosphatase (IPP1), amidotransferase/cyclase HIS7, phosphoglycerate kinase (PGK1) and enolase (ENO1). These TAP-tagged target proteins from yeast Saccharomyces cerevisiae allowed the protein to be isolated in two affinity purification steps. Subsequent structural analysis used the homobifunctional chemical crosslinker BS3 to join pairs of lysine residues on the surface of the purified protein via a flexible spacer arm. Mass spectrometry (MS) analysis of the crosslinked protein generated a set of mass values for crosslinked and non-crosslinked peptides, which was used to identify surface lysine residues in close proximity. The Automatic Spectrum Assignment Program was used to assign sequence information to the crosslinked peptides. This data provided inter-residue distance constraints that can be used to validate or refute theoretical protein structure models generated by structure prediction software such as SWISS-MODEL and RAPTOR. This approach was able to validate the structure models for four of the target proteins, TKL1, IPP1, HIS7 and ENO1. It also successfully selected the correct models for TKL1 and IPP1 from a protein model library and provided weak support for the HIS7, PGK1 and ENO1 models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".