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--- |
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license: mit |
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tags: |
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- biology |
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- protein |
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--- |
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# PLTNUM-ESM2-HeLa |
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PLTNUM is a protein language model trained to predict protein half-lives based on their sequences. |
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This model was created based on [facebook/esm2_t33_650M_UR50D](https://huggingface.co./facebook/esm2_t33_650M_UR50D) and trained on protein half-life dataset of HeLa human cell line ([paper link](https://pubmed.ncbi.nlm.nih.gov/29414762/)). |
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### Model Sources |
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- **Repository:** https://github.com/sagawatatsuya/PLTNUM |
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- **Paper:** [Prediction of Protein Half-lives from Amino Acid Sequences by Protein Language Models](https://www.biorxiv.org/content/10.1101/2024.09.10.612367v1) |
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- **Demo:** https://huggingface.co./spaces/sagawa/PLTNUM |
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## Uses |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from torch import sigmoid |
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import torch.nn as nn |
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from transformers import AutoModel, AutoConfig, PreTrainedModel, AutoTokenizer |
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class PLTNUM_PreTrainedModel(PreTrainedModel): |
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config_class = AutoConfig |
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def __init__(self, config): |
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super(PLTNUM_PreTrainedModel, self).__init__(config) |
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self.model = AutoModel.from_pretrained(self.config._name_or_path) |
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self.fc_dropout1 = nn.Dropout(0.8) |
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self.fc_dropout2 = nn.Dropout(0.4) |
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self.fc = nn.Linear(self.config.hidden_size, 1) |
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self._init_weights(self.fc) |
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def _init_weights(self, module): |
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if isinstance(module, nn.Linear): |
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nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) |
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if module.bias is not None: |
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nn.init.constant_(module.bias, 0) |
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elif isinstance(module, nn.Embedding): |
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nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) |
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if module.padding_idx is not None: |
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nn.init.constant_(module.weight[module.padding_idx], 0.0) |
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elif isinstance(module, nn.LayerNorm): |
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nn.init.constant_(module.bias, 0) |
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nn.init.constant_(module.weight, 1.0) |
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def forward(self, inputs): |
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outputs = self.model(**inputs) |
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last_hidden_state = outputs.last_hidden_state[:, 0] |
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output = ( |
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self.fc(self.fc_dropout1(last_hidden_state)) |
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+ self.fc(self.fc_dropout2(last_hidden_state)) |
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) / 2 |
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return output |
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def create_embedding(self, inputs): |
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outputs = self.model(**inputs) |
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last_hidden_state = outputs.last_hidden_state[:, 0] |
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return last_hidden_state |
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model = PLTNUM_PreTrainedModel.from_pretrained("sagawa/PLTNUM-ESM2-HeLa") |
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tokenizer = AutoTokenizer.from_pretrained("sagawa/PLTNUM-ESM2-HeLa") |
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seq = "MSGRGKQGGKARAKAKTRSSRAGLQFPVGRVHRLLRKGNYSERVGAGAPVYLAAVLEYLTAEILELAGNAARDNKKTRIIPRHLQLAIRNDEELNKLLGRVTIAQGGVLPNIQAVLLPKKTESHHKPKGK" |
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input = tokenizer( |
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[seq], |
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add_special_tokens=True, |
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max_length=512, |
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padding="max_length", |
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truncation=True, |
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return_offsets_mapping=False, |
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return_attention_mask=True, |
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return_tensors="pt", |
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) |
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print(sigmoid(model(input))) |
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``` |
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## Citation |
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Prediction of Protein Half-lives from Amino Acid Sequences by Protein Language Models |
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Tatsuya Sagawa, Eisuke Kanao, Kosuke Ogata, Koshi Imami, Yasushi Ishihama |
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bioRxiv 2024.09.10.612367; doi: https://doi.org/10.1101/2024.09.10.612367 |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
