Stable Structure-Approximating Inverse Protein Folding in 2D Hydrophobic-Polar-Cysteine (HPC) Model
Bibliographic record
Abstract
The inverse protein folding problem is that of designing an amino acid sequence which folds into a prescribed conformation/structure. This problem arises in drug design where a particular structure is necessary to ensure proper protein-protein interactions. Gupta et al. (2005) introduced a design in the two-dimensional (2D) hydrophobic-polar (HP) model of Dill that can be used to approximate any given (2D) shape. They conjectured that the protein sequences of their design are stable but only proved the stability for an infinite class of very basic structures. We introduce a refinement of the HP model, in which the cysteine and non-cysteine hydrophobic monomers are distinguished and SS-bridges, which two cysteines can form, are taken into account in the energy function. We call this model the HPC model. We consider a subclass of linear structures designed in Gupta et al. (2005) which is rich enough to approximate (although more coarsely) any given structure. We refine these structures for the HPC model by setting approximately a half of H amino acids to cysteine ones and call them snake structures. We first prove that the proteins of the snake structures are stable under the strong HPC model in which we make an additional assumption that non-cysteine amino acids act as cysteine ones, i.e., they can form their own bridges to reduce the energy. Then we consider a subclass of snake structures called wave structures that can still approximate any given shape and prove that their proteins are stable under the proper HPC model. This partially confirms the conjecture stated in Gupta et al. (2005). To prove the above results we developed a computational tool, called 2DHPSolver, which we used to perform large case analysis required for the proofs. We conjecture that the proteins of snake structures are stable under the proper HPC model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".