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---
license: mit
---
### Model Overview
The **OphPred** model is a machine learning-based tool developed to predict the optimal pH of enzyme activity directly from protein sequences. Utilizing the ESM-2 protein language model combined with KNN (k-nearest neighbors) and XGBoost algorithms, OphPred provides robust and reliable predictions across various enzyme classes. The model has been rigorously validated using different train-validation splitting strategies, including random, homology-based, PFAM-based, and EC-based splits. OphPred is designed to be fast and efficient, making it suitable for high-throughput screening of large protein libraries.
### Key Features:
- **Input**: Protein sequences.
- **Output**: Predicted optimal pH range for enzyme activity.
- **Performance**: Demonstrated strong predictive accuracy with a mean absolute error (MAE) as low as 0.6 and Spearman correlation up to 0.77 when enriched with additional data.
- **Use Cases**: Useful for protein engineering, enzyme optimization in biotechnology, and exploring protein space for desired enzymatic properties.
### Citation
If you use this model, please cite the authors as follows:
Zaretckii, M.; Buslaev, P.; Kozlovskii, I.; Morozov, A.; Popov, P. Approaching Optimal pH Enzyme Prediction with Large Language Models. *ACS Synth. Biol.* **2024,** *10*, DOI: 10.1021/acssynbio.4c00465.
### Further Reading
You can read the full paper describing the development and validation of OphPred at this [link](https://doi.org/10.1021/acssynbio.4c00465). |