Site-specific independent double labeling of proteins with reporter atoms
Bibliographic record
Abstract
Many types of physical, spectroscopic, and biological studies of proteins and other macromolecules are facilitated by the incorporation of reporter groups. In many cases these are single atom substitutes, for example isotopes (13C for C), or light (F for H) and heavy (Se for S) atom homologs. In some circumstances the incorporation of two different labels in the same molecule would be greatly desirable. Commonly used protein engineering methods for incorporating them can rarely cope with differential double labeling, and have other limitations such as universal, non-specific, or random incorporation. Although de novo peptide synthesis has the power to achieve highly specific labeling, the difficulties inherent in creating long sequences lead us to propose protein semisynthesis as the most practical approach. By ligating combinations of natural and labeled synthetic fragments to reform holoproteins, we can overcome any of the limitations discussed. Using cytochrome c as a model protein we show that two reporter atoms, selenium and bromine, can be simultaneously and site-specifically incorporated without significant consequences to structure and (or) function. This capability opens up the prospect of advances in a number of areas in structural biology. Key words: semisynthesis, peptide synthesis, reporter groups, cytochrome c, structural biology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".