DISCRETE APPROACHES FOR SOLVING MOLECULAR DISTANCE GEOMETRY PROBLEMS USING NMR DATA
Bibliographic record
Abstract
The molecular distance geometry problem (MDGP) is the problem of finding the conformation of a molecule by exploiting known distances between some pairs of its atoms.Estimates of the distances between the atoms can be obtained through experiments of nuclear magnetic resonance (NMR) spectroscopy.The information on the distances, however, is usually limited, because only distances between hydrogens and shorter than 6 Å are usually available, and this makes the solution of the MDGP quite hard.In this paper, we focus our attention on protein backbones and we present a methodology for computing their full-atom conformations starting from NMR data.This task is performed by solving two MDGPs.First of all, only hydrogens are considered: we define an artificial backbone of hydrogens for which particular assumptions needed for the discretization of the problem are satisfied.This allows for solving the first MDGP with an ad hoc algorithm.Secondly, by exploiting the coordinates of the hydrogens and known bond lengths and bond angles, we compute the coordinates of the other atoms forming the protein backbone by using a polynomial-time algorithm.Computational experiments related to real proteins are presented.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".