A tree-decomposition approach to protein structure prediction
Bibliographic record
Abstract
This paper proposes a tree decomposition of protein structures, which can be used to efficiently solve two key subproblems of protein structure prediction: protein threading for backbone prediction and protein side-chain prediction. To develop a unified tree-decomposition based approach to these two subproblems, we model them as a geometric neighborhood graph labeling problem. Theoretically, we can have a low-degree polynomial time algorithm to decompose a geometric neighborhood graph G = (V, E) into components with size O(|V|((2/3))log|V|). The computational complexity of the tree-decomposition based graph labeling algorithms is O(|V|Delta(tw+1)) where Delta is the average number of possible labels for each vertex and tw( = O(|V|((2/3))log|V|)) the tree width of G. Empirically, tw is very small and the tree-decomposition method can solve these two problems very efficiently. This paper also compares the computational efficiency of the tree-decomposition approach with the linear programming approach to these two problems and identifies the condition under which the tree-decomposition approach is more efficient than the linear programming approach. Experimental result indicates that the tree-decomposition approach is more efficient most of the time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".