Perspectives on protein evolution from simple exact models.
Bibliographic record
Abstract
Understanding the evolution of biopolymers is important to rationalise the directed and undirected design of functional molecules. Large scale experiments or detailed computational studies are often impractical. Therefore, simple model systems, such as RNA secondary structure and lattice proteins have been adapted to study general statistical and topological features of genotype (sequence) to phenotype (structure) maps. We review findings from such models that address aspects of thermodynamic and mutational robustness, neutral evolution and recombination of proteins. We compare various modelling approaches, and discuss their generality, parameter dependency and experimental verifications of their predictions. The most striking observation is the universal emergence of neutral nets--sets of phenotypically identical genotypes that are interconnected by series of point mutations. However, fast adaptation by point mutations appears to be problematic for proteins. This may explain why proteins appear to be more specific while RNA is rather versatile. This could even be the reason why RNA had to evolve before proteins. Similar principles of biological organisation are reflected in sequence and structure databases of real proteins. Insights gained from modelling are useful for designing more efficient database organisation and search strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".