A Continuous Protein Design Model Using Artificial Power Law in Topology Optimization
Bibliographic record
Abstract
A continuous protein synthesis formulation based on the design principles applied to topology optimization problems is proposed in this paper. In contrast to conventional continuous protein design methods, the power law (PL) protein design formulation proposed in this paper can handle any number of residue types to accomplish the goal of protein synthesis, and hence provides a general continuous formulation for protein synthesis. Moreover, a discrete sequence with minimum energy can be determined by the PL design method as it inherits the feature of material penalization used in designing a structural topology. Since a continuous optimization method is implemented to solve the PL design formulation, the entire design process is more efficient and robust than conventional design methods employing stochastic or enumerative search methods. The performance of the proposed PL design formulation is explored by designing simple lattice protein models, for which an exhaustive search can be carried out to identify a sequence with minimum energy. We used residue probabilities as an initial guess for the design optimization to enhance the capability and efficiency of the PL design formulation. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".