<i>ABACUS</i>, a direct method for protein NMR structure computation via assembly of fragments
Bibliographic record
Abstract
The ABACUS algorithm obtains the protein NMR structure from unassigned NOESY distance restraints. ABACUS works as an integrated approach that uses the complete set of available NMR experimental information in parallel and yields spin system typing, NOE spin pair identities, sequence specific resonance assignments, and protein structure, all at once. The protocol starts from unassigned molecular fragments (including single amino acid spin systems) derived from triple-resonance (1)H/(13)C/(15)N NMR experiments. Identifications of connected spin systems and NOEs precede the full sequence specific resonance assignments. The latter are obtained iteratively via Monte Carlo-Metropolis and/or probabilistic sequence selections, molecular dynamics structure computation and BACUS filtering (A. Grishaev and M. Llinás, J Biomol NMR 2004;28:1-10). ABACUS starts from scratch, without the requirement of an initial approximate structure, and improves iteratively the NOE identities in a self-consistent fashion. The procedure was run as a blind test on data recorded on mth1743, a 70-amino acid genomic protein from M. thermoautotrophicum. It converges to a structure in ca. 15 cycles of computation on a 3-GHz processor PC. The calculated structures are very similar to the ones obtained via conventional methods (1.22 A backbone RMSD). The success of ABACUS on mth1743 further validates BACUS as a NOESY identification protocol.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.015 |
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".