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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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# 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 [DLG4_RAT](https://www.uniprot.org/uniprotkb/P31016/entry) (Disks large homolog 4). |
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## Function |
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Postsynaptic scaffolding protein that plays a critical role in synaptogenesis and synaptic plasticity by providing a platform for the postsynaptic clustering of crucial synaptic proteins. |
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Interacts with the cytoplasmic tail of NMDA receptor subunits and shaker-type potassium channels. |
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Required for synaptic plasticity associated with NMDA receptor signaling. Overexpression or depletion of DLG4 changes the ratio of excitatory to inhibitory synapses |
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in hippocampal neurons. May reduce the amplitude of ASIC3 acid-evoked currents by retaining the channel intracellularly. |
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May regulate the intracellular trafficking of ADR1B. |
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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_DLG4_RAT). |
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Label means fitness score of each mutant amino acid sequence. |
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### Model input type |
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Amino acid sequence |
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### Performance |
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0.70 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: 128 |
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precision: 16-mixed |
