--- base_model: westlake-repl/SaProt_35M_AF2 library_name: peft --- # Base model: [westlake-repl/SaProt_35M_AF2](https://huggingface.co/westlake-repl/SaProt_35M_AF2) # Model Card for Model ID This model is trained on a sigle site deep mutation scanning dataset and 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). ## Protein Function 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. Interacts with the cytoplasmic tail of NMDA receptor subunits and shaker-type potassium channels. Required for synaptic plasticity associated with NMDA receptor signaling. Overexpression or depletion of DLG4 changes the ratio of excitatory to inhibitory synapses in hippocampal neurons. May reduce the amplitude of ASIC3 acid-evoked currents by retaining the channel intracellularly. May regulate the intracellular trafficking of ADR1B. ### Task type protein level regression ### Dataset description The dataset is from [Deep generative models of genetic variation capture the effects of mutations](https://www.nature.com/articles/s41592-018-0138-4). And can also be found on [SaprotHub dataset](https://huggingface.co/datasets/SaProtHub/DMS_DLG4_RAT). Label means fitness score of each mutant amino acid sequence. Ranging from negative infinity to positive infinity. If the effect larger than 0 represents high fitness, smaller than 0 represents low fitness. ### Model input type Amino acid sequence ### Performance 0.70 Spearman's ρ ### LoRA config lora_dropout: 0.0 lora_alpha: 16 target_modules: ["query", "key", "value", "intermediate.dense", "output.dense"] modules_to_save: ["classifier"] ### Training config class: AdamW betas: (0.9, 0.98) weight_decay: 0.01 learning rate: 1e-4 epoch: 50 batch size: 128 precision: 16-mixed