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--- |
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license: mit |
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metrics: |
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- accuracy |
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tags: |
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- biology |
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pipeline_tag: text-classification |
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--- |
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# Model description |
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In biology, "targeting peptides" typically refer to "targeting signal peptides" or "targeting sequences," also known as "signal peptides" or "signal sequences." These are short amino acid sequences located at the N-terminal or C-terminal of a protein that direct the protein to specific locations within the cell, such as the mitochondria, chloroplasts, plastids, endoplasmic reticulum, and more. Targeting peptides play a crucial signaling role during protein synthesis, ensuring that the protein is correctly localized to its intended cellular destination. |
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**TarPepSubLoc-ESM2** (TarPepSubLoc, Targeting Peptide Subcellular Localization) is a protein language model fine-tuned from [**ESM2**](https://github.com/facebookresearch/esm) pretrained model [(***facebook/esm2_t36_3B_UR50D***)](https://huggingface.co/facebook/esm2_t36_3B_UR50D) on a trageting peptides subcelluar localization dataset with five classes. |
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**TarPepSubLoc-ESM2** achieved the following results: |
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Train Loss: 0.0385 |
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Train Accuracy: 0.9881 |
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Validation Loss: 0.0566 |
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Validation Accuracy: 0.9812 |
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Epoch: 20 |
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# The dataset for training **TarPepSubLoc-ESM2** |
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The full dataset contains 13,005 protein sequences, including SP (2,697), MT (499), CH (227), TH (45), and Other (9,537). |
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The highly imbalanced sample sizes across the six categories in this dataset pose a significant challenge for classification. |
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- "SP" for signal peptide, |
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- "MT" for mitochondrial transit peptide (mTP), |
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- "CH" for chloroplast transit peptide (cTP), |
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- "TH" for thylakoidal lumen composite transit peptide (lTP), |
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- "Other" for no targeting peptide (in this case, the length is given as 0). |
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The dataset was downloaded from the website at [**TargetP - 2.0**](https://services.healthtech.dtu.dk/services/TargetP-2.0/). |
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# Model training code at GitHub |
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https://github.com/pengsihua2023/TarPepSubLoc-ESM2 |
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# How to use **TarPepSubLoc-ESM2** |
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### An example |
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Pytorch and transformers libraries should be installed in your system. |
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### Install pytorch |
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``` |
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pip install torch torchvision torchaudio |
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``` |
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### Install transformers |
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``` |
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pip install transformers |
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``` |
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### Run the following code |
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``` |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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# Load the fine-tuned model and tokenizer from Hugging Face |
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model_name = "sihuapeng/TarPepSubLoc-ESM2" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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# Define the amino acid sequence |
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sequence = "MNSLLMITACLALVGTVWAKEGYLVNSYTGCKFECFKLGDNDYCLRECRQQYGKGSGGYCYAFGCWCTHLYEQAVVWPLPNKTCNGK" |
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# Tokenize the sequence |
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inputs = tokenizer(sequence, return_tensors="pt") |
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# Make the prediction |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class_id = logits.argmax().item() |
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# Define the ID to Label mapping |
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id2label = {0: 'CH', 1: 'MT', 2: 'Other', 3: 'SP', 4: 'TH'} |
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# Get the predicted label |
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predicted_label = id2label[predicted_class_id] |
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print(f"The predicted class for the sequence is: {predicted_label}") |
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``` |
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## Funding |
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This project was funded by the CDC to Justin Bahl (BAA 75D301-21-R-71738). |
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### Model architecture, coding and implementation |
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Sihua Peng |
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## Group, Department and Institution |
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### Lab: [Justin Bahl](https://bahl-lab.github.io/) |
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### Department: [College of Veterinary Medicine Department of Infectious Diseases](https://vet.uga.edu/education/academic-departments/infectious-diseases/) |
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### Institution: [The University of Georgia](https://www.uga.edu/) |
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