A Continuous Model for Protein Synthesis Using Artificial Power Law in Topology Optimization
Bibliographic record
Abstract
A continuous protein synthesis formulation based on the design principles developed for structural topology optimization is proposed in this paper. Unlike conventional continuous protein design methods, the Power Law-based (PL) design formulation proposed in this paper enables using any number of residue types to accomplish the goal of protein synthesis and hence provides a continuous protein design formulation applicable to any general protein design problems. Moreover, a discrete sequence with minimum energy can be synthesized by the PL design method as it inherits the feature of material penalization used for the topology optimization. Since a continuous optimization method is implemented to solve the PL design formulation, the entire design process is more efficient and robust than the conventional design methods employing a stochastic or enumerative search process. The performance of the PL design formulation is demonstrated by designing simple lattice protein models for which an exhaustive search can be carried out to identify the sequence with minimum energy. The comparison with the exchange replica method indicates that the PL design method is millions of times more efficient than the conventional stochastic protein design method.
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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".