A GRAPH-BASED AUTOMATED NMR BACKBONE RESONANCE SEQUENTIAL ASSIGNMENT
Bibliographic record
Abstract
The success in backbone resonance sequential assignment is fundamental to protein three dimensional structure determination via NMR spectroscopy. Such a sequential assignment can roughly be partitioned into three separate steps, which are grouping resonance peaks in multiple spectra into spin systems, chaining the resultant spin systems into strings, and assigning strings of spin systems to non-overlapping consecutive amino acid residues in the target protein. Separately dealing with these three steps has been adopted in many existing assignment programs, and it works well on protein NMR data that is close to ideal quality, while only moderately or even poorly on most real protein datasets, where noises as well as data degeneracy occur frequently. We propose in this work to partition the sequential assignment not into physical steps, but only virtual steps, and use their outputs to cross validate each other. The novelty lies in the places where the ambiguities in the grouping step will be resolved in finding the highly confident strings in the chaining step, and the ambiguities in the chaining step will be resolved by examining the mappings of strings in the assignment step. In such a way, all ambiguities in the sequential assignment will be resolved globally and optimally. The resultant assignment program is called GASA, which was compared to several recent similar developments RIBRA, MARS, PACES and a random graph approach. The performance comparisons with these works demonstrated that GASA might be more promising for practical use.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".