Approximation Algorithms for Three Dimensional Protein Folding
Bibliographic record
Abstract
Predicting the secondary structure of a protein using a lattice model is one of the most studied computational problems in bioinformatics. Here the secondary structure or three dimensional structure of a protein is predicted from its amino acid sequence. The secondary structure refers to the local sub-structures of a protein. Simplified energy models have been proposed in the literature on the basis of interaction of amino acid residues in proteins. We focus on a well researched model known as the Hydrophobic-Polar (HP) energy model. In this paper, we propose the hexagonal prism lattice with diagonals that can overcome the problems of other lattice structures, e.g., parity problem. We give two approximation algorithms for protein folding on this lattice using HP model. Our first algorithm leads us to a similar structure of helix structure that is commonly found in a protein structure. This motivates us to propose the next algorithm with a better approximation ratio. Finally, we analyze the algorithms on the basis of intensity of the chemical forces along the different types of edges of hexagonal prism lattice with diagonals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".