Multiple Sequence Alignment as a Guideline for Protein Engineering Strategies
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Bibliographic record
Abstract
Many proteins lack the thermodynamic stability and/or solubility that is required for their use in a desired application. For this reason, it can be advantageous to improve these qualities through rational protein engineering. An effective means for achieving this goal is to use sequence alignment analysis to select amino acid substitutions that are likely to increase the thermodynamic stability or solubility of a protein. Advantages of using this approach are that generally only a small number of substitutions need to be tested, these substitutions are rarely debilitating to protein function, and knowledge of the three-dimensional structure of the protein of interest is not required. This chapter will describe approaches that have been used to exploit the information contained in sequence alignments for the engineering of improved protein properties.
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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) | 0.001 | 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 it