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--- |
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base_model: westlake-repl/SaProt_35M_AF2 |
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library_name: peft |
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--- |
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# Base model: [westlake-repl/SaProt_35M_AF2](https://huggingface.co/westlake-repl/SaProt_35M_AF2) |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is trained on a sigle site deep mutation scanning dataset and |
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can be used to predict fitness score of mutant amino acid sequence of protein [PTEN_HUMAN](https://www.uniprot.org/uniprotkb/P60484/entry) (Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase). |
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## Protein Function |
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Dual-specificity protein phosphatase, dephosphorylating tyrosine-, serine- and threonine-phosphorylated proteins. |
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Also functions as a lipid phosphatase, removing the phosphate in the D3 position of the inositol ring of PtdIns(3,4,5)P3/phosphatidylinositol 3,4,5-trisphosphate, PtdIns(3,4)P2/phosphatidylinositol 3,4-diphosphate and PtdIns3P/phosphatidylinositol 3-phosphate with a preference for PtdIns(3,4,5)P3. |
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Furthermore, this enzyme can also act as a cytosolic inositol 3-phosphatase acting on Ins(1,3,4,5,6)P5/inositol 1,3,4,5,6 pentakisphosphate and possibly Ins(1,3,4,5)P4/1D-myo-inositol 1,3,4,5-tetrakisphosphate. |
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### Task type |
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protein level regression |
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### Dataset description |
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The dataset is from [Deep generative models of genetic variation capture the effects of mutations](https://www.nature.com/articles/s41592-018-0138-4). |
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And can also be found on [SaprotHub dataset](https://huggingface.co/datasets/SaProtHub/DMS_PTEN_HUMAN). |
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Label means fitness score of each mutant amino acid sequence, ranging from minus infinity to positive infinity, smaller means more stable. |
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### Model input type |
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Amino acid sequence |
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### Performance |
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0.62 Spearman's ρ |
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### LoRA config |
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lora_dropout: 0.0 |
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lora_alpha: 16 |
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target_modules: ["query", "key", "value", "intermediate.dense", "output.dense"] |
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modules_to_save: ["classifier"] |
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### Training config |
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class: AdamW |
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betas: (0.9, 0.98) |
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weight_decay: 0.01 |
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learning rate: 1e-4 |
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epoch: 50 |
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batch size: 64 |
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precision: 16-mixed |
