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license: mit |
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### Model Overview |
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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. |
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### Key Features: |
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- **Input**: Protein sequences. |
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- **Output**: Predicted optimal pH range for enzyme activity. |
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- **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. |
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- **Use Cases**: Useful for protein engineering, enzyme optimization in biotechnology, and exploring protein space for desired enzymatic properties. |
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### Citation |
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If you use this model, please cite the authors as follows: |
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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. |
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### Further Reading |
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You can read the full paper describing the development and validation of OphPred at this [link](https://doi.org/10.1021/acssynbio.4c00465). |
