CISA: Combined NMR Resonance Connectivity Information Determination and Sequential Assignment
Bibliographic record
Abstract
A nearly complete sequential resonance assignment is a key factor leading to successful protein structure determination via NMR spectroscopy. Assuming the availability of a set of NMR spectral peak lists, most of the existing assignment algorithms first use the differences between chemical shift values for common nuclei across multiple spectra to provide the evidence that some pairs of peaks should be assigned to sequentially adjacent amino acid residues in the target protein. They then use these connectivities as constraints to produce a sequential assignment. At various levels of success, these algorithms typically generate a large number of potential connectivity constraints, and it grows exponentially as the quality of spectral data decreases. A key observation used in our sequential assignment program, CISA, is that chemical shift residual signature information can be used to improve the connectivity determination, and thus to dramatically decrease the number of predicted connectivity constraints. Fewer connectivity constraints lead to less ambiguities in the sequential assignment. Extensive simulation studies on several large test datasets demonstrated that CISA is efficient and effective, compared to three most recently proposed sequential resonance assignment programs RANDOM, PACES, and MARS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".