| license: mit |
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| base_model: westlake-repl/SaProt_35M_AF2 |
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| Model Description |
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| This model is fine-tuned to predict the mutation effects of *Bacillus subtilis* α-amylase. It takes amino acid sequences as input and performs protein-level regression to predict quantitative enzyme activity values, enabling accurate assessment of how mutations alter enzyme function. |
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| Task type: protein-level regression |
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| Model input type: Amino acid sequence |
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| Dataset |
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| The dataset is sourced from van der Flier et al. (2024), available at https://www.sciencedirect.com/science/article/pii/S2001037024002940. It contains a total of 3706 rows, with each sample carrying 1 to 8 mutation sites. The full dataset is randomly split into training, validation, and test sets following an 8:1:1 ratio. The target label is absorbance, ranging from -0.001 to 0.211. A higher absorbance value indicates greater starch degradation, corresponding to stronger detergent activity of the amylase enzyme. |
| Performance (on test set) |
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| Spearman correlation: 0.76 |
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| LoRA config |
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| r: 8 |
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| lora_dropout: 0.1 |
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| lora_alpha: 16 |
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| target_modules: ["query", "intermediate.dense", "value", "output.dense", "key"] |
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| modules_to_save: ["classifier"] |
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| Training config |
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| optimizer: |
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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: 0.0005 |
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| epoch: 30 |
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| batch size: 64 |
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| precision: 16-mixed |
