Calibrative approaches to protein solubility modeling of a mutant series using physicochemical descriptors
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Bibliographic record
Abstract
A set of physicochemical properties describing a protein of known structure is employed for a calibrative approach to protein solubility. Common hydrodynamic and electrophoretic properties routinely measured in the bio-analytical laboratory such as zeta potential, dipole moment, the second osmotic virial coefficient are first estimated in silico as a function a pH and solution ionic strength starting with the protein crystal structure. The utility of these descriptors in understanding the solubility of a series of ribonuclease Sa mutants is investigated. A simple two parameter model was trained using solubility data of the wild type protein measured at a restricted number of solution pHs. Solubility estimates of the mutants demonstrate that zeta potential and dipole moment may be used to rationalize solubility trends over a wide pH range. Additionally a calibrative model based on the protein's second osmotic virial coefficient, B₂₂ was developed. A modified DVLO type potential along with a simplified representation of the protein allowed for efficient computation of the second viral coefficient. The standard error of prediction for both models was on the order of 0.3 log S units. These results are very encouraging and demonstrate that these models may be trained with a small number of samples and employed extrapolatively for estimating mutant solubilities.
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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) | 0.000 | 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 it