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This repository provides a fine-tuned version of the [ESM-1b]([https://website-name.com](https://huggingface.co/facebook/esm1b_t33_650M_UR50S)) model, trained to classify phosphosites using unlabeled phosphosites(ie, which kinases phosphorylate those phosphosites is unknown) from [PhosphoSitePlus](https://www.phosphosite.org/staticDownloads). The model is designed for binary classification, distinguishing phosphosites from non-phosphorylated peptid sequences [(Musite, a Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites)](https://www.sciencedirect.com/science/article/pii/S1535947620311518)
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### **Dataset & Labeling Strategy**
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The dataset was constructed using phosphosite information from **PhosphoSitePlus**, with the following assumptions:
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This repository provides a fine-tuned version of the [ESM-1b]([https://website-name.com](https://huggingface.co/facebook/esm1b_t33_650M_UR50S)) model, trained to classify phosphosites using unlabeled phosphosites(ie, which kinases phosphorylate those phosphosites is unknown) from [PhosphoSitePlus](https://www.phosphosite.org/staticDownloads). The model is designed for binary classification, distinguishing phosphosites from non-phosphorylated peptid sequences [(Musite, a Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites)](https://www.sciencedirect.com/science/article/pii/S1535947620311518)
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### **Developed by:**
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Zeynep Işık (MSc, Sabanci University)
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### **Dataset & Labeling Strategy**
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The dataset was constructed using phosphosite information from **PhosphoSitePlus**, with the following assumptions:
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