---
base_model: westlake-repl/SaProt_35M_AF2
library_name: peft
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
This model is trained on a sigle site deep mutation scanning dataset and can be used to predict fitness score of protein DLG4_RAT (Disks large homolog 4) mutations. 

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]()
### 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