The TransHLA model is a pioneering tool in the field of bioinformatics, specifically designed to determine whether a peptide will be recognized by Human Leukocyte Antigen (HLA) as an epitope. By utilizing a hybrid transformer model, it sets itself apart as the first of its kind to predict epitopes without requiring HLA allele input. In this guide, we will walk you through the process of using the TransHLA model, troubleshoot common issues, and provide insights into its powerful functionality.

Understanding TransHLA

TransHLA consists of two distinct models:

• TransHLA_I: Designed for shorter peptides, typically ranging from 8 to 14 amino acids.
• TransHLA_II: Aimed at longer peptides, which can be between 13 to 21 amino acids.

Imagine the TransHLA model as a skilled chef in a kitchen, where the kitchen represents the biological landscape of epitopes. Just like a chef has special tools for various recipes, the TransHLA_I and TransHLA_II models act as tools tailored for different peptide sizes, ensuring that no flavor is overlooked in the epitope-cooking process.

Getting Started with TransHLA

Before diving into peptide predictions, you need to ensure that you have the right packages installed. Here’s what you need:

• PyTorch
• fair-esm
• Transformers

Additionally, verify that your CUDA version is 11.8 or higher to run the model efficiently; otherwise, it will default to CPU execution. You can install the required packages using the following commands:

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install transformers
pip install fair-esm

Using the TransHLA_I Model

To predict whether a peptide is an epitope using the TransHLA_I model, follow these steps:

from transformers import AutoTokenizer
from transformers import AutoModel
import torch

def pad_inner_lists_to_length(outer_list, target_length=16):
    for inner_list in outer_list:
        padding_length = target_length - len(inner_list)
        if padding_length > 0:
            inner_list.extend([1] * padding_length)
    return outer_list

if __name__ == "__main__":
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f'Using device: {device}')
    tokenizer = AutoTokenizer.from_pretrained('facebook/esm2_t33_650M_UR50D')
    model = AutoModel.from_pretrained('SkywalkerLu/TransHLA_I', trust_remote_code=True)
    model.to(device)
    peptide_examples = ['EDSAIVTPSR', 'SVWEPAKAKYVFR']
    peptide_encoding = tokenizer(peptide_examples)['input_ids']
    peptide_encoding = pad_inner_lists_to_length(peptide_encoding)
    print(peptide_encoding)
    peptide_encoding = torch.tensor(peptide_encoding)
    outputs, representations = model(peptide_encoding.to(device))
    print(outputs)
    print(representations)

Using the TransHLA_II Model

Similarly, to predict whether a peptide is an epitope using the TransHLA_II model, you would employ the following code:

from transformers import AutoTokenizer
from transformers import AutoModel
import torch

def pad_inner_lists_to_length(outer_list, target_length=23):
    for inner_list in outer_list:
        padding_length = target_length - len(inner_list)
        if padding_length > 0:
            inner_list.extend([1] * padding_length)
    return outer_list

if __name__ == "__main__":
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f'Using device: {device}')
    tokenizer = AutoTokenizer.from_pretrained('facebook/esm2_t33_650M_UR50D')
    model = AutoModel.from_pretrained('SkywalkerLu/TransHLA_II', trust_remote_code=True)
    model.to(device)
    model.eval()
    peptide_examples = ['KMIYSYSSHAASSL', 'ARGDFFRATSRLTTDFG']
    peptide_encoding = tokenizer(peptide_examples)['input_ids']
    peptide_encoding = pad_inner_lists_to_length(peptide_encoding)
    peptide_encoding = torch.tensor(peptide_encoding)
    outputs, representations = model(peptide_encoding.to(device))
    print(outputs)
    print(representations)

Troubleshooting Common Issues

While utilizing TransHLA, you may encounter some hurdles. Here are a few troubleshooting tips to guide you through:

• If you receive an error regarding CUDA, check if CUDA is correctly installed and your GPU drivers are up-to-date.
• Make sure you have installed all the required packages and they are up-to-date to avoid dependency issues.
• For device-related errors, ensure that the devices specified in the code are correctly identified. Use ‘cuda’ whenever available, or ‘cpu’ otherwise.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.