Prediction of quantitative function of artificially-designed protein from structural information
Bibliographic record
Abstract
Abstract Artificially designed proteins are widely used in applications such as optogenetics and biosensing. While experimental optimization of these proteins is effective, it is also costly and labor-intensive. To address this challenge, computational approaches have been developed, primarily relying on sequence-based features. However, protein function is inherently tied to its three-dimensional (3D) structure, and incorporating structural information could enable more accurate predictions and provide deeper biological interpretability. Here, we proposed a structure-based analysis framework called ‘Foldinsight’ for predicting protein functionalities. In our framework, we first predict protein structures from sequences using AlphaFold2 and then utilize these structures to predict protein properties. Since proteins vary in the number of atoms and lack direct atomic correspondence, we applied molecular field mapping, which captures the energy states surrounding a protein and converts them into fixed-length numerical vectors. This transformation enables the application of machine learning, allowing protein properties to be predicted from structure-derived features. Applying this framework to channelrhodopsin mutants, we achieved predictive performance comparable to sequence-based models. Additionally, our structure-based analysis successfully identified key structural regions contributing to functional differences, highlighting the advantage of incorporating structural data into predictive modeling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".