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README.md

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-license: mit
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+license: mit
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+### 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).